Interactive voice response systems are essential to fully taking advantage of VoIP technology, one that modern businesses use to ensure no customer calls are missed. An IVR system’s ability to help customers resolve an issue promptly ensures customer satisfaction and improves business efficiency.

Defining interactive voice response

IVR systems are automated phone systems companies use to respond to customer calls without talking to an actual person. They use pre-recorded messages, with each message being a separate menu option that a customer can select. Customers communicate with the IVR system using its voice recognition software or touch-tone keypad to assist callers through the available menu options for specific information. IVR systems use Automatic Call Distribution (ACD) to automatically answer and route calls based on pre-configured rules. ACD systems use the IVR-collected data to route the call to the appropriate agent.

VR systems are often used in call centers to help manage initial inbound calls. IVR systems provide self-service options without assistance from a call agent, which can improve the customer experience and satisfaction. More advanced IVR systems allow customers to verbalize their needs on the phone using speech recognition software that the IVR understands and responds to accordingly in real-time.

How IVR works

Interactive voice response (IVR) uses voice or Dual Tone Multi-Frequency (DTMF) that allows callers to communicate and interact with computer-operated phone systems to direct user’s calls to a live agent, an appropriate department, or a caller’s options to an available database. A customer calls the IVR number and is greeted with a pre-recorded message. Then, IVR systems audibly present a menu of options that a customer can select from using the keypad (DTMF) or a verbal response.

The IVR responds accordingly by presenting options to wait until an appropriate agent is available, schedule a call back, or move the call to another channel. h This can be a phone call, an email, a live chat, a text messaging platform, a video chat, a social media customer care option, or a live agent.

Steps involved in an IVR call flow

An IVR system always starts with a welcome greeting that provides basic information about the company and business hours. 

After the greeting is completed, the following steps occur:

• Menu options are presented to the calling customer, allowing the caller to select an option
• The IVR collects information about the caller’s needs using prompts and messages
• Calling routing and transfer options direct the call to the appropriate destination 
• If applicable, self-service options are presented to the caller
• Prompts and messages guide the caller through the process, including an opt-out option for customers wanting to talk to a live agent
• An agent connection is established with a caller
• A live agent is available to handle calls that the IVR system is not designed to answer
• Call metrics like call volume, resolution rates, and customer satisfaction are collected

IVR integration with other business applications

Multiple methods are used to integrate an IVR system with a business application. The pre-integration steps are:

• Map out the workflows and data flows 
• Use Application Programming Interfaces (APIs) and webhooks to sync data-sharing seamlessly
• Verify data mapping will not fail due to mismatched data
• Continually test to validate the accuracy and stability of mapped data
• Create feedback channels to collect user input for improvement
• Monitor performance by watching for errors, bottlenecks, and latency
• Document the integration process for administrators.
• Assign ownership to manage and improve each integration effort

The methods for system integration are:

• APIs are used to connect to other platforms to share directly and sync data
• Webhooks automatically trigger actions and data transfers between systems in real-time
• Flat File Transfer can export or import data between systems to exchange information in standard formats, such as .csv files.
• Direction integration is an out-of-the-box integration for major CRM or ERP platforms
• Database integration using Structured Query Language (SQL) acts as middleware between different platforms using scripts and queries

Key Benefits of IVR

The IVR systems provide tangible benefits for businesses and customers:

• Improved customer experience: An IVR system enhances the customer experience by improving First Call Resolution (FCR), which improves customer satisfaction and business efficiency.
• Cost reduction: Interactive Voice Response systems reduce costs by addressing and resolving users’ calls without talking to a live agent. Plus, the 24/7 availability of the IVR system without using a live agent reduces operational costs.
• Scalability: This allows a business to scale up or down according to business demands. It prevents a company from having dormant hardware when market demand is low and costs remain high due to un-used infrastructure.

Choosing an IVR VoIP solution

An IVR system is an integral component of a VoIP solution for businesses that want 24/7 communication capability with their customers. Here are some VoIP providers that offer IVR systems as part of their VoIP solution:

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RingCentral

RingCentral is a cloud-based communication platform using VoIP technology that allows customers to make calls, text, and fax over the internet without using the Public Switched Telephone Network (PSTN) used by hardwired telephones or landlines in residential homes. The IVR system is integrated into the RingCentral VoIP cloud contact center solution, and the IVR executes an action based on the caller’s selected options. The cost per user is $20 a month, paid annually.

Zoom

Zoom is also a cloud-based VoIP solution that offers IVR services featuring its virtual agent to guide customers to accurate answers quickly without burdening engaged staff involved in other activities that may require hands-on attention. Zoom’s AI chatbot uses machine learning and natural language processing in AI technologies to improve business products and support operations. The 24/7 IVR services include web, mobile, and social channels in multiple languages. Zoom pricing starts at $10 per month per user.

Nextiva

Nextiva can also be cloud-based and offers basic IVR and an advanced IVR version as part of its cloud center solution. Nextiva’s IVR system is easily set up using its visual call flow designer. Nextiva IVR system reduces operational costs while delivering a better customer experience across all channels and workflows. Nextiva also empowers teams to create optimal IVR menu options with automated workflows and chatbots that enhance the customer experience. Nextiva’s base price per user is $18.95 a month.

Two of the most popular VoIP providers that use IVR systems are RingCentral and Nextiva. Look at the pros and cons of each VoIP provider using this comparison article.

Best practices for implementing IVR systems

The key to a well-designed IVR system is simplicity.

Real-world examples and case studies

Erie Insurance and Plum Voice IVR

Erie Insurance offers home, auto, and life insurance products to over six million customers in twelve states. The insurance company is a Fortune 500 company based in Erie, PA, so to remain competitive, Erie’s claim division uses customer surveys to collect feedback using a manual mail-in process. Nearly 5,000 surveys processed annually did not justify the effort when a business has over six million customers.

Erie Insurance tried a web-based solution, but survey responses only increased by 7%, and the web-based solution missed out on customers unfamiliar with computers. Realizing the web-based survey slightly improved survey responses, the contracted company that developed the online survey suggested translating the online poll into a voice channel using the Plum Voice IVR system.

The Erie insurance company received positive customer feedback regarding the IVR-based phone survey. The IVR-based phone survey automatically sends daily data to the company’s web dashboard at night. The IVR phone survey gives the claims division the information needed to adjust quickly and better respond to customers’ issues.

Delta Airlines and Nuance conversational IVR

Delta Airlines’ ten-year-old IVR system continually left customers hearing repeated information or stuck in a menu loop that kept repeating the same information.

Delta Airlines wants a modernized IVR solution and selected Nuance conversational IVR. After implementing the IR solution, Delta Airlines customers’ experience immediately improved by using the modernized IVR system. The results of implementing the Nuance IVR system resulted in callers requesting an agent fell below 10 percent. Other notable metrics were:

• A 15 percent reduction in misdirected calls
• 75 percent of the received calls captured the caller’s intent
• A 10 percent drop in customers listening to repeated information

The positive results from both case studies improved the customer experience and boosted revenues for both companies.

Finding the right IVR solution for your business

The initial implementation of the Interactive Voice Response system is only the beginning of the CPI process for your IVR system. Any business changes or enhancements to your business that may potentially impact the IVR or the ACD must be updated concurrently with any business updates.

A call agent should always be available for any customer who chooses to use the opt-out option, and if a call agent is not available, a call back option from the agent should be part of the opt-out option process.

Ensure the IVR language is simplistic, and if a marketing or sales company has any promotional sales, special discounts, or beneficial information that a customer can act on after the call, share those details while customers wait. 

A weekly or monthly checklist should be created to routinely check on the health of the IVR systems to ensure the IVR is meeting the original goals and objectives of the organization. Eventually, something in the business will change. An IVR system is the primary tool used to communicate with your customer base, so it must always be in alignment with business objectives.

Companies interested in an IVR solution should explore industry-specific IVR solutions for their type of business. Business-specific IVR solutions will have features and functions more aligned to your specific business versus a general IVR solution. Free VoIP phone services can work with an IVR system, which is ideal for a startup or small business.

Frequently Asked Questions (FAQ) 

The post What Is Interactive Voice Response (IVR)? appeared first on TechnologyAdvice.

When comparing Looker vs. Tableau, two of the most popular business intelligence (BI) software tools on the market today, it’s crucial to have the facts laid out as clearly as possible before making a decision.

Business intelligence software executes data preparation and management, data querying, predictive analytics, and many other analytical tools that help managers make better decisions based on BI outputs. We will look at the features of each BI software tool to give readers more insight into which product will best fit their companies.

Looker

Visit Looker

Overall Score

4.16/5

Pricing

3.13/5

General Features & Interface

3.75/5

Core Features

5/5

Advanced Features

4/5

Integration & Compatibility

5/5

UX

3.75/5

Pros

• Powerful data modeling
• Scalable to handle large datasets
• Highly customizable
• Free tier

Cons

• Cost
• Mixes reviews on customer supper
• Limited data security due to cloud hosting

What is Looker?

Looker is a cloud-based Business Intelligence (BI) platform purchased by Google in 2019. Designed to offer insights and analytics, its strength lies in the powerful data modeling layer. This layer emphasizes a “single source of truth” model, ensuring accurate metric readings by consolidating information from various sources onto one dashboard.

Looker’s data modeling allows users to define intricate information and create reusable models. These can be used throughout the platform, ensuring data consistency and accuracy. This approach streamlines data exploration, giving users confidence in the displayed data.

The 23.4 release introduced the Looker Studio Connector and Connected Sheets for all Looker-hosted instances. This enhancement allows users to access and analyze Looker-modeled data, expanding the platform’s capabilities. Another feature, the Performant Field Picker Labs, offers refined search options for large Explore field pickers.

Key features

• Data Exploration and Discovery: Looker allows users to explore and discover data in real-time using an intuitive interface that requires no SQL knowledge.

• Customizable Dashboards: Users can create and share interactive, customizable dashboards that offer a comprehensive view of business metrics.

• Integrated Workflow: Looker integrates seamlessly with other tools, enabling users to embed insights directly into business workflows and applications.

• Data Modeling Layer: The LookML modeling layer enables users to define business logic centrally, ensuring consistency across all analytics.

• Collaborative Data Analytics: Looker supports collaboration by allowing users to share data insights with team members through links, dashboards, or scheduled reports.

• Real-Time Data Insights: Looker provides real-time insights, enabling businesses to act on the most current data available.

• Embedded Analytics: Organizations can embed Looker’s analytics capabilities into their own applications to offer data-driven experiences.

• Robust Security and Compliance: Looker offers enterprise-grade security features and compliance certifications, ensuring that data remains safe and secure.

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Tableau

Visit Tableau

Overall Score

4.34/5

Pricing

3.13/5

General Features & Interface

3.75/5

Core Features

5/5

Advanced Features

4/5

Integration & Compatibility

5/5

UX

3.75/5

Pros

• User-friendly interface
• Excellent data visualization capabilities
• Ability to blend data from multiple sources

Cons

• Slow performance when working with large datasets
• Additional software required for advanced data cleaning

What is Tableau?

Tableau is a data visualization and business intelligence (BI) tool designed to help users interpret and understand their data.

Through its interface, users can create visual representations like dashboards, reports, and charts from raw data. The software allows for data integration from various sources, from databases to cloud services. Over time, Tableau has been adopted by many due to its straightforward features and ability to handle complex data sets. With each release, including the 2023.1 update, Tableau introduces modifications and improvements, reflecting feedback and the changing landscape of data analysis.

Key features

• Data Visualization: Tableau excels in creating powerful, interactive visualizations that help users understand complex data intuitively.
• Drag-and-Drop Interface: The drag-and-drop interface allows users to easily create visualizations and dashboards without the need for advanced technical skills.
• Data Blending: Tableau enables users to combine data from multiple sources into a single view, providing a holistic perspective.
• Real-Time Data Analysis: Users can connect to live data sources and perform real-time analysis to make timely decisions.
• Dashboard Sharing and Collaboration: Tableau allows users to share dashboards and collaborate with others, enhancing teamwork and decision-making.
• Mobile-Friendly Dashboards: Dashboards in Tableau are optimized for mobile devices, ensuring accessibility and usability on the go.
• Advanced Analytics and Calculations: Tableau offers built-in tools for performing advanced analytics, such as trend analysis, forecasting, and statistical modeling.
• Integration with Other Tools: Tableau integrates with a wide range of data sources and tools, including cloud services, databases, and spreadsheets, making it versatile and adaptable.

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Salesforce Tableau integration for clearer data visualization (Source: Salesforce)

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Looker vs. Tableau: A detailed comparison

Business intelligence software has many tools to provide insightful details managers can use. However, to get the most out of one of these tools, managers need to know the company size, the types of charts and graphs needed, and the business analytical requirements.

With that data in hand, one can use this comparison as a tool to help make your final decision the right decision. 

Looker vs. Tableau: Commonalities

Looker and Tableau are both comprehensive, flexible, and scalable solutions that prioritize user accessibility and collaboration. While they each have their unique strengths and weaknesses, these core commonalities make them leading choices in the realm of business intelligence software:

Looker and Tableau both offer an array of BI tools to help businesses make informed decisions using advanced Machine Learning (ML) concepts.

Both platforms are incredibly user-friendly.

You don’t need to be a data scientist to navigate through them; they’re built for everyone from the intern in the marketing department to the CFO. This universal appeal is what makes them so indispensable.

It’s like having a universal remote for all your data sources.

Integration is another area where both stand out. Whether your data lives in the cloud, in SQL databases, or even in good old Excel sheets, Looker and Tableau make it a easy to pull that data in.

Let’s talk about scalability.

Looker and Tableau have engineered their platforms to be highly scalable so they grow alongside your business.

Tableau offers a distributed server architecture that allows you to add more server nodes as your data and user base expand. This means you can handle larger data sets and more concurrent users without sacrificing performance.

Looker leverages an in-database architecture, allowing it to push queries to the database itself. This ensures that as your data grows, you can scale your database resources to maintain high-speed analytics. Both platforms also offer cloud-based solutions, providing the elasticity to quickly scale up or down based on your needs.

Both tools are big on teamwork.

They offer nifty collaboration features that make it easy to share insights, annotate findings, and even distribute reports. It’s like a virtual huddle for your team, where everyone gets to contribute to the game plan.

Also read: Tableau Alternatives

Looker vs. Tableau: Key differences

Looker is fully deployed as a browser-based intelligence software with no desktop installation requirement. In addition, Looker offers a mobile app solution. Tableau is a desktop-based platform with a Tableau license for cloud access but with limited cloud capabilities. Looker uses its version of structured query language called LookML with pre-built Looker Blocks. Tableau uses a spatial file connector to create maps.

While those are substantial differences, when it comes to team-facing features, the two titles serve the same overall purpose.

What are the key features of Looker?

Looker offers several key features that will enhance the ability to view data in real-time, build applications with embedded analytics, and provide proactive insights with instant alerting for quicker decision-making. Here are some of the key features of Looker:

What are the key features of Tableau?

Tableau’s focus is on creating sophisticated visual representations of data. As a result, Tableau allows users to quickly analyze large amounts of data that can be converted into reports. Tableau has a heavy focus on dashboards, but here are some other key features of this BI solution:

Looker vs. Tableau: Pricing

Looker pricing:

• Platform pricing: Looker offers three editions—Standard, Enterprise, and Embed. The Standard edition costs $5,000 per month with a pay-as-you-go model. Enterprise and Embed editions require you to call sales for an annual commitment.
• User licensing: Looker offers three types of user licenses—Viewer ($30/user/month), Standard ($60/user/month), and Developer ($125/user/month). These fees are consistent across all editions.

Tableau pricing:

• License tiers: Tableau offers three tiers—Creator, Explorer, and Viewer. The Creator license is $70/user/month for the cloud version and $35/user/month for on-premise. The Explorer license is $42/user/month for cloud and $20/user/month for on-premise. The Viewer license is $15/user/month for cloud and $10/user/month for on-premise.
• Enterprise package: Tableau also offers an enterprise package with custom pricing.
• Additional Costs: Some users have noted that Tableau may require additional tools for data extraction, adding to the total cost.

Key takeaways

• Looker offers a more customized pricing model, allowing for tailored solutions. Tableau offers fixed pricing tiers, which may or may not suit all organizational needs.
• Both platforms offer tiered user licensing but differ in costs and capabilities.
• Tableau provides upfront pricing, whereas Looker requires you to engage with their sales team for most of their offerings.

Looker vs. Tableau: Feature comparison overview

As you can see, Looker and Tableau have different key features available to users. This section will look at some of the expected features of a business intelligence and data analytics software solution.

Feature	Looker	Tableau	Advantage
Data Visualization	Looker provides a dynamic dashboard filter feature that can filter for data visualization types and specific users or groups. Looker also has a mapping feature to aid in chart creation.	Tableau specializes in visualization dashboards and infographics. Tableau provides pre-made templates and has a wizard feature that helps non-technical users.
Integration	Looker integrates with big data platforms and databases and can execute queries without extracting data	Tableau has over 200 connectors that can connect to multiple sources such as RDBMS, spreadsheets, Presto, SQL Server, Cloudera, Hadoop, Amazon Athena, and Salesforce
Data Modeling	LookML is a data modeling tool that is reusable and flexible. LookML has pre-built modeling called Looker Blocks used to build sophisticated query analytics	Tableau uses snowflakes and dimensional data models that help improve the performance of queries
Reporting	Provides basic reporting templates	Users can build customized reports with extensive connectors to create dashboards and reports
Speed	Looker is slower, and users may need several hours to share a report	Allows a user to create visualization reports in short periods
Advanced Analytics	Looker pre-built code blocks are configurable, and LookLM allows for a deeper analytical review of data	Tableau does not have pre-built code blocks, but it is more configurable than Looker
Security	Looker provides an admin panel to change security settings and 2-factor authentication	Tableau allows a user to protect reports and dashboards, but not with the detail of Looker
Mobile Support	Looker’s mobile app allows you to view data using any browser with an Internet connection. Emails and chat sessions are available too. Users can log in using a QR code or biometrics	Android and iOS devices can use Tableau’s mobile web application
Pricing	For a company, $5,000/per month, but Looker offers a variety of business plans depending on size	Tableau’s creator license cost per user is $70/per month, and for a viewer, it’s $15/per month

Looker vs. Tableau: Which BI software is right for your business

The best BI solution is subjective since every company’s situation differs slightly. For example, a company that uses Salesforce may lean towards Tableau since Salesforce purchased Tableau four years ago. Any company heavily invested in using Google products may lean towards Looker. Just because a company uses Google or Salesforce products should not be the basis for a decision.

Other factors to consider are the existing infrastructure, analytical data needs, and storage preferences, whether cloud or local, are only a subset of reasons why a company may choose one BI product over the other.

How to choose between Tableau vs Looker

A business need for a BI software solution was identified as a requirement. Next, the company generates a list of requirements on why BI software is needed. These requirements vary from company to company, but they are the driving force behind choosing a BI solution. Finally, decision-makers are on track to make the right decision using the key features and the comparison overview along with additional research.

Looking for the latest in Business Intelligence solutions? Check out our Business Intelligence Software Buyer’s Guide.

The post Looker vs. Tableau: An In-Depth Data Analysis Showdown 2024 appeared first on TechnologyAdvice.

Read more: How to Use an API

What is needed to build and execute an API?

Figure one example simply shows what occurs when an API call is successful.

• A client initiates communication by submitting an HTTP method request to the REST server
• The server receives the requests and accesses resources on the server to respond to the valid API request.
• The server locates the content and sends a response in a JSON or XML format.

The figure diagrams below are examples of GET and POST API calls. To build an API call, you need to use the API documentation to help you correctly set up an API call for the website or application you want to communicate with. Good API documentation will describe its purpose, tell you how to get started, answer questions about functionality, display helpful examples, and provide instructions on getting an API key. The API documentation for newsapi.org used in Figure 2 shows users how to build out the request parameters, display the endpoint, and get an API key to access the site. The API documentation also provides a client library for different programming languages and a section to help with errors. The Figure 2 example uses the Curl programming language.

Figure three example is the format and syntax used to send new information to a resource.

• Endpoint – is a Uniform Resource Identifier (URI) that identifies where to find a Unique Resource Location (URL) web address.
• Headers – stores relevant information for both client and server, such as authentication data (API key), the name or IP address of the server, and the information about the response format.
• Body – in this case, the information will be added to the server resource.

The post Common API Errors & How to Fix Them appeared first on TechnologyAdvice.

What sets VoLTE apart from other communication methods is its ability to deliver high-definition voice quality, making it easier to understand the nuances of speech. This is particularly beneficial in noisy environments where clarity is essential. Moreover, VoLTE differs significantly from both cellular and VoIP (Voice over Internet Protocol) technologies, providing a unique blend of reliability and quality that enhances the overall mobile communication experience.

Defining Voice over Long-Term Evolution (VoLTE)?

Voice over Long-Term Evolution allows phone calls over a device’s 4G or 5G LTE data connection. VoLTE uses data packets to transmit calls in a way similar to how websites, emails, and social media content are transmitted over the internet. 

Smartphones, smart watches, Internet of Things (IoT) devices, and data terminals capitalize on VoLTE high-speed wireless communication, allowing users to use voice and data concurrently. 

It uses the IP Multimedia Subsystem (IMS) framework combined with the capabilities of the 2G General Packet Radio Service (GPRS) networks with 3G or 4G LTE networks. The GPRS networks are wireless communication networks that allow mobile networks to send data to external networks like the Internet. 

GPRS networks enable features such as Short Message Service (SMS) and Multimedia Messaging Service (MMS) messaging, advanced phone features, and always-online functions, which allow users to connect to different internet data-based services. General Packet Radio Service enables users to access mobile internet browsing, although GPRS is slower than some newer technologies.

Read more: VoIP vs. Cellular: What’s the Difference?

How does VoLTE work?

VoLTE only works on mobile devices with Global System for Mobile Communications (GSM) gateways (SIM cards) installed. It prioritizes voice calls, and operates by connecting a mobile device to a cellular tower. It works by transmitting voice data packets through the 4G/5G LTE mobile network without using the typical Wi-Fi or broadband Internet connections like VoIP phones.

VoLTE is based on the IMS framework that delivers multimedia services over IP networks, including mobile and fixed networks. The IMS framework is based on the Internet Protocol and uses VoIP. The IMS framework provides VoIP, video conferencing, cloud gaming, instant messaging, virtual reality, and IoT services. 

The IMS framework plays a significant role in transitioning from traditional switched networks to IP networks, and it supports 4G and 5G technologies. The IMS framework is the core network that VoLTE uses. 

When a user calls VoLTE, their device sends a Session Initiation Protocol (SIP) to the IMS network that acts like a proxy server. The IMS network establishes the call using the phone number and routes it over the LTE network using Real-Time Transport Protocol (RTP). 

Read more: Best VoIP Software Buyer’s Guide

VoLTE vs. VoIP vs landline

VoLTE and VoIP use an internet connection to make calls, but these technologies execute those calls differently. It uses the 4G or 5G mobile network, while VoIP must use a broadband or fixed line connection. VoLTE calls are limited to LTE-capable mobile phones, whereas VoIP can be used on any internet-enabled device. 

Data transmission is also different for each of these two communication technologies. VoLTE uses separate streams for each data type, such as voice, email, and audio, while VoIP combines all data into one stream.

VoLTE prioritizes audio quality using a Quality of Service (QoS) that reduces quality degradation problems like packet loss, network jitter, and high latency. VoIP packets are sent in one stream, regardless of data type. VoLTE uses IMS and a separate radio frequency that helps maintain the equality of the transmission. 

Landlines use a telecommunication connection by a cable laid across the land by either telephone poles or underground. VoLTE and VoIP technologies can support services like instant messaging and video calls, making them somewhat similar to conventional landlines.

VoLTE offers many advantages over VoIP and landlines. It provides improved audio quality, extended battery life, and the ability to make calls and use data concurrently. 

Other advantages include:

• Conference calls: allows up to a six-way conference call
• Mobility: allows users to make and receive business calls on personal devices
• Network management: simplifies network management, making it easier for developers to access

On the flip side, there are some limitations to using VoLTE technology. Several older devices may not be compatible with the technology, so to take advantage, users will need to upgrade or purchase a new mobile device. Not all mobile service providers have fully deployed VoLTE networks, which may limit users’ ability to use it in their local area. 

VoLTE can also drain a phone battery due to its complex signaling process requiring more power. Interoperability issues may arise because 2G and 3G use different signaling protocols.

Read more: VoIP vs. Landline: The Pros & Cons for Business

Key Benefits

There are numerous benefits of using VoLTE, and the most notable is the crystal-clear voice quality compared to circuit-switched traditional calls. It’s suitable for mission-critical communications that ensure an improved user experience when conversing. 

Businesses and consumers can reap many benefits from using a VoLTE solution. For small businesses, the phone bill will be lower by making faster calls versus negotiating calls using circuit-switch technology. The technology also removes the need for voice and data networks. Additionally, the technology can be easily integrated with a PBX system that uses VoIP technology, which can be  cost saving for small businesses. 

Consumers or businesses are no longer required to keep a dedicated call plan as voice calls are made using VoLTE. Users also have the option to send text messages at no additional cost. VoLTE technology consumes data only, so bill tracking becomes simple and easy for consumers or businesses.

Requirements for using VoLTE

To enable VoLTE services, service providers must enable voice services on their LTE cellular network and create profiles that must-have information for each service provider. Device manufacturers must update their devices with each operator’s profile. After the manufacturer updates each operator’s profile, the operating system (OS) will automatically enable the customer’s device. 

Customers can enable VoLTE in their device’s settings or check the compatibility of their mobile device using this tool from Videotron.

Enabling on iPhone/iOS devices

• Tap Settings
• Tap Cellular
• Verify that the Cellular Data switch is in the ON position
• Tap Voice and Data

Enabling on an Android

• Tap Settings
• Tap Network & Internet (or Connections)
• Tap Preferred Network Type (or Network Mode)
• Select LTE or 5G

Future of VoLTE and Mobile Communication

As VoLTE technology is becoming more popular and implemented in the business world, security enhancements are increasing dramatically to protect voice calls using robust authentication protocols to safeguard communications over LTE networks. 

Emerging trends like IoT and VoLTE convergence have only heightened the need for strong security protections for LTE networks. It can improve IoT applications and interaction with devices that require two-way communications for medical alert devices and security systems.

Expanding the global roaming features of VoLTE will enable users to make voice calls while traveling globally without using older network technologies. Integration with 5G networks provides a more reliable connection, seamless voice services, and faster data speeds.

Finding the right solution for your business

Only mobile devices with a GSM sim card can use the VoLTE network and resources. Companies or consumers can upgrade a compatible mobile device or purchase a new one with the pre-installed hardware to operate in a VoLTE environment. You can review the URL link in this article for compatible devices.

A VoLTE solution benefits any business, particularly an international or a small business. As technologies continue to emerge, companies can use this technology to improve business operations while generating cost savings. Yeastar is an example of an IP PBX service provider offering an integrated VoLTE solution with their VoIP PBX systems. Several mobile network operators (MNOs) offer VoLTE services, such as Verizon Communications, AT&T Mobility, and T-Mobile.

Frequently Asked Questions (FAQ)

The post What is VoLTE? appeared first on TechnologyAdvice.

Business intelligence (BI) software is a valuable tool that helps BI users make data-driven decisions. The quality of the data will determine the BI results that are used to make data-driven decisions, which means the data executed in BI software must be factual. 

Included in this article are some best practice recommendations to ensure data quality.

What are the most common data quality issues? 

Duplicated data that can be counted more than once or incomplete data are common data quality issues. Inconsistent formats, patterns, or data missing relationship dependencies can significantly impact BI results used to make informed decisions. Inaccurate data can also lead to bad decisions. Poor data quality can financially suffocate a business through lost profits, missed opportunities, compliance violations, and misinformed decisions. 

What is data validation?

The two types of data that need to be validated are structured and unstructured data. Structured data is already stored in a relational database management system (RDBMS) and uses a set of rules to check for format, consistency, uniqueness, and presence of data values. Unstructured data can be text, a Word document, internet clickstream records from a user browsing a business website, sensor data, or system logs from a network device or an application that further complicates the validation process. 

Structured data is easier to validate using built-in features in an RDBMS or a basic artificial intelligence (AI) tool that can scan comment fields. On the other hand, unstructured data requires a more sophisticated AI tool like natural language processing (NLP) that can interpret the meaning of a sentence based on the context.

How to do data validation testing

Initial data validation testing can be configured into a data field when a programmer creates the data fields in a business application. Whether you are developing an application or an Excel spreadsheet with data fields, the validation requirements can be built into the data field to ensure only specific data values are saved in each field. For example, a field type that can only accept numbers will not allow a data value to be saved if the data value has alphabets or special characters in the data value. 

If an area code or zip code field contains all numbers but does not meet the length requirement, an error message will appear stating the data value must have three or five numbers before the data value is saved.

Natural language processing is used to examine unstructured data using grammatical and semantic tools to determine the meaning of a sentence. In addition, advanced textual analysis tools can access social media sites and emails to help discover any popular trends a business can leverage. With unstructured data being 80% of businesses’ data today, successful companies must exploit this data source to help identify customers’ purchasing preferences and patterns.

What are the benefits of data validation testing?

Validating data from different data sources initially eliminates wasted time, manpower and monetary resources, and ensures the data is compatible with the established standards. After the different data sources are validated and saved in the required field format, it’s easy to integrate into a standard database in a data warehouse. Clean data increase business revenues, promotes cost effectiveness, and provides better decision-making that helps businesses exceed their marketing goals.

What are some challenges in data validation?

Unstructured data lacks a predefined data model, so it’s more difficult to process and analyze. Although both have challenges, validating unstructured data is far more challenging than validating structured data. Unstructured data can be very unreliable, especially from humans who exaggerate their information, so filtering out distorted information can be time-consuming. Sorting, managing, and keeping unstructured data organized is very difficult in its native format, and the schema-on-read allows unstructured data to be stored in a database.

Extracting data from a database that has not been validated before the data is saved can be extremely time-consuming if the effort is manual. Even validated data extracted from a database still needs to be validated. Anytime large databases are extracted from multiple sources it can be time-consuming to validate the databases, and the process is compounded when unstructured data is involved. Still, the availability of AI tools makes the validation process easier.

ALSO READ: What is Data Visualization & Why is it Important?

Testing BI data using the Extract Transform and Load (ETL) process

The ETL process is used to ensure structured and unstructured data from multiple systems is verified as valid before it’s moved into a database or data warehouse that will be used by BI analytical software. Data cleaning removes data values that will not be transformed or associated with the database in a data warehouse. The transformation process involves converting the data into a format or schema that the BI software can use to derive deeper insight into the BI results that help management make informed decisions. See Figure one for a pictorial representation of a BI data pipeline.

Figure 1.

Check the data at the source

The first part of the BI data pipeline is when all the validation checks occur. Checking structured data at the source is not a complicated process unless there is a large volume of structured data to check. Once unwanted data is removed from your structured data, RDBMS validation features check data values before it’s extracted from an RDBMS. For example, using the RDBMS structured query language (SQL) or Excel power query, you can easily remove duplicate records and any missing data values by creating a filter to check for blank fields. Checking the accuracy or relevancy of structured data can be done using AI tools, but it will still require human participation to verify the accuracy and relevance of the data. Hevo is one of several data cleaning software tools available on the market that can perform this action in the ETL process. 

Cleaning unstructured data involves pulling data from multiple sources that may cause duplicate data if added to any structured or unstructured data. For example, an email address, a unique username, or a home address in unstructured data can be used to identify and remove duplicate data already in source data. 

Check the data after the transformation

The transformation process aims to convert unstructured data into a usable storage format and merge it with similar data in the data warehouse. Structured data can have its database in a data warehouse or merge with another database in the warehouse. Unstructured data is a more complicated process because unstructured data needs to be converted into a readable format first. Data parsing converts unstructured data into a readable format for computers and saves it in a different format. 

Grammar-driven or data-driven parsing techniques in a data parsing tool like ProxyScape can be used on unstructured data such as scripting languages, communication protocols, and social media sites. However, duplicate data removal, structural error repair, unwanted outliers by filtering, and missing data may still be required. In addition, businesses can use the BI results from this type of unstructured data to improve their network management processes or products and services sold to customers. 

Verify the data load

The clean, extracted, and transformed data is loaded into target sources in the data warehouse for execution. The clean and extract process ensures the data is consistent, accurate, and error-free before execution. You can verify the data is correctly loaded by testing a small sample size of 50 records from each source as long as you know the results before the sample test is run. When you’ve tested four or five sample sizes, and the results are what you expect, you have an indication that the data is loaded correctly and is accurate and true.

Review the BI test results

Business intelligence test results are the basis for companies making smarter business decisions. An error in the BI results can occur anywhere along the BI data pipeline. The BI data pipeline starts with the source data that goes through the ETL process, which puts the validated data in the data warehouse. The data layer is what a user uses to create the BI report or dashboard.  For example, a user generates BI results in a report and compares the results of a known sales revenue report saved in an Excel spreadsheet against the BI results.

How should you address data quality issues?

The recommended method for addressing data quality issues is at the source. Any data saved in an RDBMS can be rejected if not entered into a field in the prescribed format. The format can be numeric, alphanumeric, or alphabets with assigned lengths. Text fields and unstructured data can be challenging, but AI tools are available to validate these character fields.

How important is data quality in Business Intelligence (BI) software?

The quality of your business data is equal to the quality of the business decisions you make. Making a business decision based on flawed data can lead to losing customers and eventually business revenues if bad data is not identified as the reason for the downward spiral. Like the foundation of a house, accurate business data is foundational for a successful business since the data is used to make business decisions. 

As foundational data is the core of a business, organizations need to have established policies enforced through data quality measurements.

The importance of continual data verification

The purpose of verification checks is to make sure the validation process has successfully occurred. Verification occurs after data is migrated or merged into the data warehouse by running a scripted sample test and reviewing the BI results. The verification process is necessary because it checks the validation of all data fields, and sample BI test results can verify the data is validated by producing known results. Therefore, verification is a continual process that should occur throughout the entire BI data pipeline anytime a new data source is added or modified. 

Looking fir the latest data quality software solutions? Check out our Data Quality Software Buyer’s Guide

Frequently asked questions (FAQ)

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Software as a Service (SaaS) provides businesses with several advantages that increase productivity and business income by using a SaaS solution that lowers the total cost of ownership by eliminating upfront hardware costs. Other benefits of a SaaS solution are the ability to access business information from anywhere, scalability, which allows a business to expand quickly, and the flexibility SaaS offers by enabling enterprises to scale up or down based on resources needed.

Conversely, there are some disadvantages decision-makers need to consider before investing in a SaaS solution. This article focuses on the most common disadvantages of using a SaaS solution and how to mitigate these common risks.

Limited Customization

Businesses that use a cloud-based SaaS solution cannot customize the SaaS application to meet  unique business needs. SaaS vendors limit modifications to maintain a standardized application that is easy to learn and use. SaaS software is an Out-of-the-box (OOTB) solution with specific features and functionality. However, an on-premises SaaS solution allows for more customization than a cloud-based one.  Here are some differences between the two SaaS solutions.

So, even though customization is limited for cloud-based solutions, personalization and configuration options are available to enhance the individual user experience. Personalizing users’ experiences with limited configuration options increases user satisfaction and makes users more inclined to continue using the SaaS solution.

If a business decides that a modification is required, the company can expect to pay for the modification and the vendor to maintain the modification, which can get costly. Before deciding to use an OOTB SaaS solution, businesses need to ensure the OOTB solution will meet present-day requirements and any future growth a company may experience.

Internet Connectivity

A degraded or downed internet connection significantly impacts a cloud-based SaaS solution. A bad internet connection causes slow response times, service disruptions, or a total outage. An on-premises SaaS solution can function without an internet connection, as it relies on an organization’s network. A hybrid SaaS solution offers the best benefits of a cloud-based and an on-premises SaaS solution because it is not 100% dependent on an internet connection. 

Businesses wanting to minimize SaaS downtime must establish a comprehensive Service Level Agreement (SLA) with the service provider.

Security

In addition to limited customization, businesses lose control of protecting their data. Cloud-based SaaS systems are exceptional at protecting data and do a much better job than on-premises or hybrid SaaS solutions. Even though security is a strength of cloud-based SaaS solutions, decision-makers must ensure that proprietary business data is protected and determine contingencies to address any data breaches.

An SLA can include regular security audits, a data-at-rest policy, and dispersed redundant storage locations. Data in motion and in use should also be included in an SLA. The prevention of data breaches must include high-grade encryption options, a comprehensive network security policy, firewalls, an incident response plan, and regular risk assessments, which are security topics that can be included in an SLA. 

Additionally, a business may like a SaaS provider’s application, but it is uncomfortable knowing their data is not adequately protected. The company can subscribe to a Security-as-a-service (SECaaS) vendor to protect its data.

Service Level Agreements (SLAs)

The primary goal of an SLA is to instill trust and peace of mind for the using organization and the SaaS service provider.

The SaaS performance a business expects from a SaaS service provider must be captured in an SLA with a level of service section for each Information Technology (IT) service category. A SaaS service provider is not required to meet a business’s expected performance criteria if it’s not listed as a level of service. 

Eventually, this can lead to poor performance and response times and cost a business financially by missing expected revenue goals, including the additional cost of adding the expected performance criteria in the SLA.

An SLA is a foundational document that defines the organization’s performance expectations and the service providers’ responsibilities to meet those performance metrics. A good SLA includes IT solutions and penalties if agreed-upon service levels are not met.

Depending on the type of business, an SLA may contain unique requirements to meet any specific objectives. Using an SLA template will ensure nothing is missed in an SLA. See the example of an SLA template. 

SaaS Performance

A business decides to adopt a SaaS solution to improve business operations. A SaaS solution’s performance helps companies understand their processes, track performance and growth, and improve decision-making. Specific SaaS metrics are used to evaluate a SaaS solution’s performance.

A SaaS solution can only be successful if used, so the most critical SaaS metrics focus on the number of users and the adoption rate, reflecting how many users use the SaaS solution. A continually decreasing churn rate indicates a problem with the product or service. Monthly active users, customer retention rate, and the SaaS quick ratio are quantifiable metrics used to determine if the recurring revenue is persistently higher than the churn rate. A good SaaS quick ratio number is four, which should always be higher than the losses from the churn rate.

The SaaS performance metrics are important numbers for the service provider and the using organization. A business considering going to a SaaS solution can use these performance metrics to determine if a SaaS solution is meeting business and customer needs.

Software Integration

When a decision is made to use a SaaS solution, finding a solution that meets 100% of a business’s intended needs can be challenging. Even if a SaaS application does meet every need a business requires, new business requirements will eventually come about that may require a new feature or function to be added to the SaaS application. Integrating a built-in-house function into the SaaS solution can be time-consuming to implement and maintain. Additionally, you need to be careful not to void the SLA.

Other challenges are the lack of skilled staff, technical compatibility issues, and the complexities of data transformation, which can create productivity issues or lead to data siloing since the original SaaS application may be unable to access this new data. Security risks are also involved if the organization doesn’t have the technical knowledge, advanced tools, and available resources to adequately protect the software and data in the manner a SaaS server provider would do with its around-the-clock monitoring tools. 

 The cost may be prohibitive, but seeking the service provider’s assistance to modify the SaaS solution removes the security burden from the organization. Suppose a business decides to alter a SaaS solution. In that case, a cost-benefit analysis must be completed to determine when and if the generated revenue will offset the cost of adding a software module to an existing SaaS solution.

Other SaaS solution concerns

The loss of control is one of the most notable results when a business moves to a SaaS solution, and if a company is not careful, it can lose transparency, too. Ideally, you want to find a SaaS application that meets all your needs to avoid application sprawl by having multiple SaaS applications to complete a task, which can cause a disjointed and fragmented user experience. A SaaS solution should not create any compliance issues, so whatever compliance regulations you maintain must still be maintained when using the SaaS solution. 

As a business grows, the monthly subscription will likely increase when more seats and data are added.  A SaaS solution is not a guarantee for success, and there are multiple reasons why a SaaS solution fails for a business. Market research, Product Market Fit (PMF), and using a Minimum Viable Product (MVP) are some methods used to mitigate a SaaS solution failure. The PMF and MVP give businesses a quick method to validate that a SaaS solution has the potential to be successful.

What are the two categories of SaaS solutions

A variety of business applications exist as SaaS solutions. The SaaS categories are horizontal and vertical solutions designed to help businesses improve their operations. 

Horizontal SaaS

Horizontal SaaS software solutions are designed for multiple business industries and provide more generic features and functional tools used across different industries. Horizontal SaaS software examples are:

• HubSpot
• Microsoft 365
• Google Workspace

Typically, horizontal SaaS solutions are user-friendly and, therefore, more straightforward to use than vertical SaaS solutions.

Vertical SaaS

Vertical SaaS software is designed to address one specific business industry, like finance, hotel management, marketing, or accounting, including a unique business niche such as dental care. The SaaS features and functions for vertical solutions are created to complete all tasks associated with a specific business niche or function. Examples of vertical SaaS software that focus on a business niche or a function are:

Business Niche SaaS solutions:

• Healthcare – ClearCare
• Car Dealerships – Cox Automotive
• Restaurant Management – Toast
• Dental Practice Management – CARESTACK

Business Function SaaS solutions:

• Human Resources (HR) – People HR and BambooHR
• Enterprise Resource Planning (ERP) – Oracle NetSuite
• Accounting – FreshBooks
• Content Management System (CMS) – WordPress
• Project Management – Microsoft Project
• Communication – Slack

In some cases, SaaS solutions like Slack or HubSpot may show up under both SaaS categories.

What business applications or scenarios are most suitable for a SaaS solution?

This question can be tricky, but any successful business will likely have a horizontal SaaS solution like Microsoft 365 or Google Workspace for typical business communications and daily business transactions. However, if there is a vertical SaaS solution for a business niche, then it’s probably worth exploring to see how it can improve your business. 

Suppose a business does not have this expertise in-house. In that case, you can contract a SaaS implementation specialist to perform a feasibility study, including how the vertical SaaS solution can work with a horizontal SaaS solution to improve overall business operations. A feasibility study can take 3 to 6 months to complete, and it’s a project that should not be rushed.

Ideally, suppose a vertical SaaS solution performs all the actions of a horizontal solution while meeting all the business niche requirements. In that case, your decision is much easier to make. In either case, the feasibility study should not be rushed.

Software recommendations

Google Analytics and Time Doctor are complementary software tools that allow a business to evaluate the productivity of the SaaS application for employees and customers.

Google Analytics

Google Analytics allows businesses to monitor, track, and report on critical Key Performance Indicators (KPIs), such as web page visits, time to complete a form, and website visitors. These types of reports can determine if a form is taking too long to fill out and if changing the form helps complete the form faster. This featured product can generate real-time reports and determine how much activity a web page receives. Overall, Google Analytics can help identify issues quickly, determine a course of action, implement the action, and evaluate the changes in real-time to see if the required change improves the website.

Time Doctor

Time Doctor provides management with a tool that ensures productivity remains at the same level when employees are not in the office and working off-site. Time Doctor can honestly evaluate an employee’s performance after any connectivity issues are resolved at off-site locations, and it can track an employee’s location with GPS tracking. This product can be used for employees in the office, off-site, and for any work outsourced to a third party. Time Doctor can send inactivity alerts, eliminate inefficiencies, and maintain work-life balance by suggesting daily work breaks.

Listed here are five other SaaS tools that help improve business efficiencies.

Is a SaaS solution right for your business?

A SaaS solution is a costly investment. Is a particular business process a pain point in your business environment? Can a SaaS solution significantly improve a business function that dramatically increases revenues? Whatever the reason for considering a SaaS solution, it must be evident to all stakeholders that a potential SaaS solution can potentially address the problem. To validate an actual problem, you can use the churn rate or the MVP concept to test the idea with customers before committing to a SaaS project. 

If the churn rate and the MVP values come back as positive numbers, it’s an indication the SaaS solution can positively impact the company. Now, you can research service providers and generate a list of questions that address concerns like compliance requirements, security practices, customer support, pricing structure, and any other relevant questions for your business.

Frequently Asked Questions (FAQ)

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What is an API, and why do they matter?

APIs serve as a software intermediary, allowing two applications to talk to each other. However, they can also do much more. 

APIs allow two software applications to communicate using pre-established rules and protocols. The API serves as a software interface for the two different applications that need to communicate. The API can pass data back and forth between two applications, access the features or services of other applications, or create an application. 

APIs are essential because they allow developers to easily integrate existing services or features from other applications without actually developing the service or feature. APIs increase development speed by not having to develop code from scratch, and the APIs are reusable for routine processes that are available for authorized users. 

They also allow businesses to communicate with external entities or use third-party software easily, including using various business intelligence tools.

Key components of an API 

Building an API requires a set of programming-based instructions that allow software applications to communicate. An API client initiates an API request that goes to a server. The API retrieves the requested data from an external server or program, which is returned to the client. Besides retrieving data, APIs can also trigger functions, transfer information back to a server for management and storage, or return real-time information, such as pricing or availability. To successfully execute an API, the following components are needed:

API client

A user can be an API client that initiates an API request, or a request is automatically activated by an external event or notification from a service or application. The API client can be triggered by a user clicking on a button, application, or service. The API client makes it easier for a person to use while hiding the complexities of the backend details.

API key

A unique passcode containing letters and numbers that grants access to an API

API requests

An API request is a message sent to an application asking a server for information or a service. The Representational State Transfer (REST) APIs are commonly used, so we’ll discuss what is involved in a REST API request. The sub-components or parameters that make up an API requests are:

Endpoint

An endpoint is a dedicated Uniform Resource Locator (URL) that points to the location of a resource on a server. The API endpoint allows different systems and applications to communicate by sending and receiving information with instructions. See Figure 1.

Request method

The request methods are the specific operations the client wants to perform on the URL resource. REST API uses the HTTP method that can perform the following actions:

• GET – retrieves data from a server; see Figure 2.

• POST – adds new data saved to a URL resource on a server; See Figure 3.

In the Figure 3 example, the good_comment phrase in the Body field will be posted as a new comment in the URL resource.

• PUT – replaces an entire resource with new information
• PATCH – is used to partially update an existing URL resource with additional information
• DELETE – used to remove data from a database 

Parameters 

Parameters are the variables passed to an API endpoint to provide explicit instructions for the API server to process. The parameters can be included as part of the API request in the URL query string or in the request body field, as shown in Figure 3. In Figure 4, notice how the parameters are included in the HTTP endpoint URL sent to an API server on a web server.

Request headers

The request headers provide essential information for a server to process the request, and the header information is in the message body. Headers give the following information:

API server

The API server is software that resides directly on a server. The API server sits between the client and the data source, so web APIs sit between a user application and the web server. Once an API client creates an API request, the request goes to the appropriate endpoint on the API server for processing. The API server handles authentication, validates the inputted data, retrieves or manipulates the data from a database, and returns the appropriate response to the client. See Figure 5

API response

The API server generates the API response that returns to the API client. The API response can respond in multiple ways depending on what was in the API request. An API response provides the following information:

Status code

The status code informs the client of the results of the submitted API request. The codes help the client understand what happened with the request. Code 200 signifies the server successfully returned the requested data, and code 201 indicates the server successfully created a new resource. The code 404, which we have probably all experienced, means Not Found, so no action was taken by the server.

Response headers

Response headers provide additional information about the server’s response. Response headers can provide metadata, instructions, and other information about the response back to a client. A cache-control header lets the client know how long the data can be stored in a cache, and the set-cookie header is a cookie in the browser used for session management or authentication.

Body

The response body is the data that is returned by the API server based on the client’s request. The body typically includes structured data objects representing the requested resources, metadata, or possibly an error message indicating what went wrong if the request was unsuccessful. 

Simple Object Access Protocol (SOAP) API

The SOAP API is another popular API used, and it’s more structured using an Extensible Markup Language (XML) schema messaging format. SOAP can only use the XML format, while REST supports XML, JSON, plain text, and Hypertext Markup Language (HTML). The REST API processes are faster due to the smaller messaging and available caching, while SOAP follows a rigid set of rules and messaging patterns, making it slower than REST. Since SOAP is far more secure than a REST API, it is the preferred API to use in online banking and financial institutions. The SOAP API process is similar to the REST API client call:

The SOAP API prevents unauthorized users from accessing critical data.

Step-by-step guide on how to use an API

To implement an API, the two applications must follow the established rules and protocols so they can communicate with each other. The client-server relationship requires both entities to fulfill their respective responsibilities. The API developed by a company must understand the goal of the API and how customers submit API requests to get the desired response back to the client. 

The endpoint, headers, data format, and any associated parameter values all must be clearly defined in the API documentation. For each HTTP method, the client must correctly submit specific parameters and headers in the API request for the server-side application to respond to the API request successfully. API development starts with API documentation, and the created API is tested multiple ways before it goes into production.

To implement a successful API application, you need to follow a similar step-by-step process:

The goal of monitoring API software is to minimize downtime by addressing issues before they escalate, identifying issues that may impact the API’s performance, and resolving any problematic issues that could affect the customer negatively or cause potential revenue loss.

The full implementation of a successful API involves multiple steps that become the foundation for your API app. Even though the development and deployment processes are undoubtedly critical in building and running an API, you want to ensure that as long as the API is online, it’s performing optimally. Therefore, implementing an API entails constant monitoring and applying practical updates as business processes evolve. The automated API tools that help with API creation and monitoring are essential in ensuring the API developed in the API implementation process constantly meets the intended goals outlined in the API strategy.

Also read: 5 Capabilities an API Management Tool Should Have

Guidelines on how to use an API

Using an API can save you development time if you know an application that can provide the information you require already exists. 

If you are not sure an application already exists, you do a search on GitHub that provides links to all the public APIs available. Once you have found an API that meets your needs, you must review the API documentation. The documentation will provide examples and list the objects, parameters, and endpoints needed to execute an API call successfully, so thoroughly reading the API documentation is necessary. 

The typical steps involved in using an API are:

In this example, a sports fan wants to catch up on all the sporting events during the weekend of November 11^th. The fan must request an API key that will be appended at the end of an HTTP method. Once the fan has an API key he must thoroughly read the API documentation. See Figure 6.

The documentation lets the user know how to select a country and a specific category, like business or sports. After thoroughly reading the API documentation, the sports fan created this API request. See Figure 7.

The results of the Get method return seventy different sports articles in the United States that include possible trades in baseball, National Football League (NFL) game results on November 11^th, and college basketball and football results. See Figure 8.

Once you understand how to use an API, the numerous benefits include saving time and money. Learning about backend data models, API integration, workflow automation, and page builders will require patience for non-programmers. The no-code platforms are designed for non-technical business users to develop applications without doing actual coding. The NoCodeAPI platform is explicitly created for non-technical business users to build API applications.

What does an API do?

Application Programming Interfaces are used every day in a multitude of ways. The API creates a gateway for one application to use the available services of another application without actually doing any coding development. The API user must read and understand the API documentation, including examples of the various API requests available. 

All APIs remove the intricacies of backend logic for the specific application receiving the API request or call, which helps a business become more efficient and productive. An API allows everyday citizens to quickly execute actions, such as making mobile payments or flight reservations, accessing rideshare apps, or retrieving the latest weather information.

Overall, APIs improve businesses’ productivity and our personal lives in numerous ways. 

What are the types of APIs, and how do they work?

The REST and SOAP APIs have been reviewed. The other type of API is GraphQL, which can use one API call that can return data from multiple data sources, and the other is gRPC open-source API, which allows an application to pass data to a function in another program on the Internet. The WebSocket API is another type of API. A WebSocket API is bidirectional in communicating between a user’s browser and a server. A client can send a message to a server, and the service will respond with a message back to the client. The service on a server can send information back to a client without the client making an explicit request.

API software recommendations

There is no shortage of API management software available on the market. Every API solution does something better than its competitors, so you must select an API solution that aligns with your business security posture if that is a priority. Therefore, if you are looking for an API software solution that protects your data, select an API platform focusing on security. 

Along with security, API management software features several capabilities that you may find helpful as you create, update, and manage APIs during an API lifecycle. Other essential capabilities you want in an API Management Tool solution are features such as API lifecycle management, API gateways, and a development portal, which are must-have features included in a comprehensive API software solution.

Also read: Top API Integration Platforms

APIs make the world go ‘round

APIs continue to grow, and the benefits we reap from using them improve the quality of our daily lives, whether in a business environment or using DoorDash after a late night at work. As you become familiar with APIs and understand them better, you can add value to your organization by addressing an underperforming aspect of a business or improving a process, making the organization more efficient. 

FAQs

The post How to Use an API: Just the Basics appeared first on TechnologyAdvice.

Artificial Intelligence (AI) uses a computer to mimic how humans think and perform tasks when using a computer, except AI is far more efficient than a human. It is a set of applications humans create that allows a computer to learn, reason, make decisions, and help solve problems like a human would.

Though Artificial Intelligence is the primary name used to describe AI technology and concepts, mathematical equations, statistical formulas, and computer programming are what make AI applications emulate human thinking — though AI resources work a thousand times faster and more efficiently than a human can. Here, we’ll explore different AI applications and technologies that make AI more efficient than humans.

What is Artificial Intelligence?

Artificial Intelligence is a science that uses computer systems to perform sophisticated and complex tasks that, at one time, only humans could do. Today, AI operates by using advanced algorithms that contain conditional statements, which allow AI technology to react to specific scenarios like a human would. Artificial Intelligence technology uses testing models applied against an executed algorithm to ensure the correct results are captured in a manner that a human would identify or capture an issue.

Though AI technologies and tools require humans to help construct them, the AI resources are highly efficient operationally and designed to adapt and learn when dealing with outlier information.

What are the types of AI?

Artificial Intelligence applications, technologies, and tools fall into two categories. The two types of AI are known as weak AI and strong AI, though strong AI is still in the theoretical stages. 

What is considered a Weak AI and a Strong AI?

Weak AI is considered any AI application designed to do one specific task and no more. Examples of weak AI are image and video processing technology that can detect images and recognize facial features. 

Robotics, Natural Language Processing (NLP), and transportation AI technology are all considered weak AI because they can do one thing well. Robotics uses an algorithm to manipulate movements, like grasping and recognizing a package. Natural Language Processing is designed to allow computers to understand, produce, and manipulate human languages. Transportation AI technology helps improve traffic efficiency, find the shortest route, and improve fuel consumption.

Strong AI aims to understand how humans think and interpret human needs and emotions, including the ability to reason and adapt. Currently, strong AI cannot accurately understand human feelings, emotions, or wants. Still, it has a role in cybersecurity, integration of strong AI in the Internet of Things (IoT), and language translation machines. Strong AI is a work in progress and a hypothetical ideal to match human-level thinking.

Artificial Intelligence (AI) is the broad category referred to when discussing AI. Still, underneath AI, there are additional AI technologies that are subsets of AI and the workhorses that allow a computer to perform tasks more efficiently than humans.

Machine Learning (ML)

Machine Learning is a branch of AI that uses algorithms and data to make recommendations based on evaluating the data processed in an algorithm. Machine Learning applications are taught to look for specific data in test phases. Additionally, ML can translate text from one language to another, detect fraudulent transactions, identify cancer growth by analyzing medical scans, and predict stock market changes once trained on what to look for in the test examples provided.

Natural Language Processing (NLP)

Natural Language Processing is another branch that uses multiple AI techniques (like ML, deep learning, and computational linguistics) using training methods (such as unsupervised, supervised, and reinforcement learning techniques) before computational linguistics becomes effective. Natural Language Processing also uses statistics combined with the other AI methods discussed to analyze processed text and voice data to understand what is written or spoken.

Neural Networks

Neural networks are designed to process data like the human brain processes data and makes decisions. They are trained by processing large sets of labeled and unlabeled data. Neural networks use a process called backpropagation that assigns a weighted value or number to a neural network by analyzing the error rate produced.

Backpropagation works backward from outputs to inputs to figure out how to reduce the number of errors in the produced output. The backpropagation process continues until the error rate is reduced, making the neural network more reliable.

Deep Learning

Deep learning uses the neural network concept for training and reinforcement learning to identify complex pictures, texts, and sound patterns. After deep learning has learned what it’s taught, it can predict accurate insights for better business decision-making. Deep learning is another branch of AI that must compute large amounts of data before it’s thoroughly trained to make accurate decisions. 

Deep learning helps businesses identify trends in customer buying patterns, identify specific preferences and behaviors, improve inventory planning, and optimize supply chain processes to reduce the risk of delayed deliveries. Deep learning helps doctors properly diagnose patients accurately and faster. Deep learning can improve overall business operations in multiple business processes.

Robotics

Robotics are generally physical machinery in production-type environments that perform the same task repeatedly. They are controlled by complex algorithms that process information from cameras and other sensors, such as light, temperature, ultrasound distance sensors, or infrared sensors. The algorithm allows the robotic machinery to execute a movement based on the written code.

Speech Recognition Systems

Speech Recognition Systems are different from NLP. While NLP focuses on understanding the meaning of text data, speech recognition systems aim to convert speech into text. Natural language processing and speech recognition systems are complementary technologies. As speech recognition prepares the data for NLP, the NLP attempts to understand the tone and meaning of the generated text. Speech recognition systems are also known as Automatic Speech Recognition (ASR) systems. 

Speech recognition technology improves customer service by providing quicker responses to customer questions and FAQs. This subset of AI can help businesses with mundane customer service tasks, such as booking appointments and routing calls. 

Are Artificial Intelligence, Machine Learning, and Deep Learning the same?

Machine Learning and Deep Learning are both subsets of AI. Artificial intelligence is the primary category, and ML, DL, neural networks, and NLP all fall under AI as subsets of AI. Artificial Intelligence is generally associated with making computers mimic human intelligence. Still, the subsets of AI, along with mathematics and statistics, help facilitate AI technology to mimic human thinking. 

How are AI technologies and tools trained?

Before AI technology becomes an effective resource, it must be trained using large data sets that can be structured or unstructured data labeled or unlabeled. The three primary types of machine learning methods are supervised, unsupervised, and reinforcement learning.

Supervised learning

Supervised learning occurs when an AI resource is provided with labeled datasets to train an algorithm to recognize patterns and predict future outcomes. These models establish a baseline of the correct results in the generated output. Supervised learning must identify images, predict future behavior, properly categorize customer feedback, and distinguish between spam and non-spam emails.

Unsupervised learning 

Unsupervised learning presents more of a challenge because the algorithm analyzes unlabeled data without human supervision or guidance. The AI resource aims to discover hidden patterns, insights not detected by a human, and data grouping without any human guidance. Unsupervised learning helps detect anomalies — such as security breaches, fraudulent transactions, or faulty equipment — without human intervention. Unsupervised learning is successful when it accurately identifies outlier data points in a dataset.

Reinforcement learning

Reinforcement learning uses the trial-and-error learning process to achieve the most optimal result. It is behavior-based, recognizing correct actions and ignoring incorrect ones. This behavior-based training is continued until the stated results are achieved. For example, Tesla uses reinforcement learning so its vehicles learn how to avoid obstacles and other cars.

What are the Advantages and Disadvantages of AI?

When artificial intelligence is properly handled from its inception to its final implementation, it can benefit businesses across many processes and tasks. However, if an AI subset resource is not correctly vetted for a particular AI project, it can lead to bad decision-making for a business. The same principle can be applied to a dataset used by an AI resource that generates results used for decision-making.

The advantages of AI are:

• Accuracy – When used properly, AI eliminates or reduces human error while increasing precision
• Automation – AI automates repetitive tasks that help streamline processes
• Decision-making – When AI is used correctly, it allows businesses to make faster decisions and data-backed recommendations
• Digital assistance – AI-enables systems are available 24/7 to address specific tasks

The disadvantages of AI are:

• Security risks – AI can expose a business’s proprietary information when mishandled and pose a security risk
• Ethical issues – mishandling AI can lead to manipulation and mistakes, jeopardizing Personally Identifiable Information (PII) or sensitive business information.
• Varying processes and procedures – can lead to compliance violations that can be costly.

Companies to watch

A featured partner is a business that does something unique that promotes the advancement of artificial intelligence. Listed are two companies focusing on some aspect of AI that innovatively improves AI technologies. 

BasicAI

BasicAI is an organization that develops an AI-powered data annotation platform to simplify data labeling for AI and ML models. Its platform lessens the tedious task of data labeling while increasing an algorithm’s accuracy. BasicAI provides data labeling services for all business industries.

Deci AI 

Deci AI recently became a part of NVIDIA. Deci AI’s platform is an end-to-end deep learning acceleration resource developers use to build, optimize, and deploy detailed and precise models for different IT environments, including cloud, network edge, and mobile.

Generative AI uses machine learning to create new content, and the NVIDIA AI platform can accelerate the development process by improving the entire AI workflow process. This allows businesses to reach production faster with improved infrastructure performance that lowers operational costs.

How are businesses using AI technology today?

Organizations like Open AI and Google DeepMind are constantly pushing the AI envelope to enhance AI technologies that businesses can use to their advantage for improved business performance. Any successful business must continually improve its daily operations, and any company not invested in using AI technology risks losing revenues or its competitive edge. Here are some popular AI resources that businesses use today to remain competitive:

ChatGPT

ChatGPT helps marketing organizations generate leads by assisting marketers in creating engaging content for a target audience. ChatGPT creates product descriptions and email newsletters using the style and tone of previously written marketing material. ChatGPT improves customer service by using its natural language processing features to respond to customer requests. This AI technology also processes customer data to understand customer needs and preferences better. OpenAI created ChatGPT.

Gemini

Google DeepMind developed Gemini, and it’s designed to be multimodal, meaning Gemini can process various data types concurrently, such as images, text, videos, numerical data, and speech. Multimodal AI models can take in multiple forms of sensory input, similar to humans. 

Businesses can use Gemini for data analysis to find patterns and trends in large amounts of data, automate tasks, and summarize conversations in Google Chat or documents in Google Docs. Gemini helps write and refine business documents and emails.

Anthropic’s Claude

Anthropic’s Claude is a family of AI models and chatbots that can perform text-based conversations, creative content, and cognitive tasks, such as vision analysis, code generation, and complex analysis. Claude can analyze images, debug code, and create websites using Hyper Text Markup Language (HTML) and Cascading Style Sheets (CSS).

Businesses use Claude as an AI assistant to cancel orders, provide weather updates, and access database information. Security teams use Claude to respond to attacks through automated detection and respond accordingly. Claude can also understand and draft legal documents and analyze pharmaceutical companies’ scientific data for research. Claude can also do vision analysis by transcribing static images, graphs, and photographs.

Meta Llama

Meta Llama is a group of open-source large language models developed by Meta AI. Businesses use Llama to generate educational content, summarize video calls, and provide medical information. Llama is another AI resource for customer service, improving and streamlining internal communications, and employee training and development.

Llama can be used for market research by analyzing customer reviews and social media posts. This Al resource can also improve risk management and monitor online content for potential risks, threats, or negative customer reviews.

Is an AI tool right for my business?

If you own a business, manage a team, manage projects or anything else, odds are an AI tool exists that can make your life easier. It’s not a one-size fits all approach. With a sea of newcomers and constant updates, the best tool for the job can sometimes change by the week.

Stay abreast of news surrounding the subject. Google something like “Which AI tool is best for (your task)?” This should help you locate a quality tool that, with a minimal amount of learning, can help you throughout your day.

FAQs

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Data mining involves using computers, automation technology, and intelligent automation, such as robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML) to extract useful information from large, raw datasets. The extracted information is cataloged, organized, and presented in a data analysis process businesses use to make informed, data-driven decisions.

The internet, personal computers, and mobile devices helped accelerate the digital age that required further advancements in technologies like data mining. Using automated data mining tools became necessary because the large quantities of raw data made it unrealistic for humans to process raw data in a reasonable time. Additionally, using AI tools combined with RPA processes on raw structured, unstructured, and semi-structured data allowed the processing of these data types for 24 hours a day with minimal errors and no breaks. 

The value of data mining and the automated technology used drastically reduces the time-consuming effort while minimizing human errors when processing large sets of raw data. Automated data mining allows businesses to make faster and more accurate decisions using relevant data after data analysis and interpretation. This article focuses on the data mining techniques, applications, and challenges of data mining.

Read more: Business Intelligence vs. Data Analytics: Know the Difference

Understanding data mining

Data mining searches and analyzes large data sets to find patterns, trends, anomalies, and correlations that can help businesses make better decisions, cut costs, increase revenues, reduce risks, or improve customer relationships. It aims to improve various aspects of a business’s operations continuously.

Data mining is a critical component of the data analysis process. It uses advanced analytical methods like artificial intelligence, machine learning, and neural networks combined with statistical methods and association rules (if-then statements) to extract relevant information. Some advanced methods require an algorithm to distinguish different data points and categorize them correctly before any data is analyzed.

The Data Mining Process

The first step in the data mining process is to define business goals and objectives. Once the business goals and objectives are defined, a business needs to select the appropriate data sources that will address the business goals and objectives. After the data sources are selected, the following steps occur:

• Data transformation: Converts raw data into a usable format for analysis and modeling
• Data cleaning: Prepares the data for data mining
• Model creation: Testing the model against a known hypothesis
• Publish the model: For use in a data analysis process

Mining data can also be used in business intelligence and data analysis processes or projects to help businesses improve upon one or more business operations. Mining data is one of the essential phases of the data analysis process.

Techniques in Data Mining

Advanced analytical techniques are critical in extracting relevant information from data analysis methods and techniques. The typical advanced techniques used are the following:

Machine learning and neural networks are AI techniques like descriptive, diagnostic, predictive, and prescriptive analysis used in data mining. Other techniques are anomaly detection, network analysis, and outlier detection.

Data Mining Software Recommendations

Data mining solutions exist for different levels of user experience and different types of business industries. Listed are some recommendations for the different levels of user knowledge and business types:

Data Mining software for beginners

RapidMinder is an ideal data science platform for businesses with employees with different knowledge and skill sets. RapidMinder can perform all the expected actions of a data science platform, such as data preparation, ML, and predictive modeling.

Data Mining software for advanced data mining needs

GoodData provides advanced features like microservice architecture and React, Python, and JavaScript Software Development Kits (SDKs) while still allowing engineers to use their coding skills, data analysts to use their limited coding knowledge, and consumers to use AI-supported tools that require no coding skills.

Oracle Healthcare is a platform that lets healthcare providers seamlessly exchange healthcare records with authorized medical professionals using an Electronic Health Records (EHR) system, making comprehensive medical information available in real-time.

Applications of Data Mining

Data mining can benefit any industry by exploring data sets and extracting meaningful data. It can help businesses improve operations or make better decisions based on analyzed data. Different industries use data mining to meet or exceed specific business goals or objectives. 

Challenges and ethical considerations

Despite the benefits and pros of data mining, businesses need to be aware of the cons. Businesses data mining large quantities of raw data must be mindful of the challenges and ethical concerns when processing data to avoid any security, legality, or compliance violations. 

Legal issues can arise if personally identifiable information (PII) is compromised, including the time-consuming effort of notifying customers while resolving the breach, which costs several thousand, if not millions, dollars. Regulatory compliance protections for intellectual property rights, privacy, security, Payment Card Industry, Data Security Standard (PCI DSS), Health Insurance Portability Accountability (HIPAA), and General Data Protection Regulation (GDPR) are all compliance regulations that must not be violated.

Ethical concerns can be a slippery slope if consent, ownership, and maintaining customer information privacy are violated. Businesses using data mining tools to access user information must inform the customer of the reason for accessing a customer’s personal information. Transparency and the protection of customer’s data are crucial. Other ethical concerns are third-party risks and the convenience versus privacy of customers’ data. Protecting customers’ data and getting consent to collect customer information helps address moral concerns.

Another con associated with data mining is there is no guarantee that whatever business goal you are trying to accomplish may not be successful for many reasons. Failures can be caused by a lack of training or knowledge, inaccurate or inadequate data analysis, and the inability to correctly interpret the processed data, leading to a wrong decision. Data mining can be costly if it doesn’t produce the desired results.

The pros of data mining are beneficial to any business that understands the criticality of data mining, selecting the appropriate technique, and correctly interpreting the analyzed data can reap several benefits from data mining and data analysis. 

Data mining and your business

Regardless of the business industry, data mining and data analysis can improve overall business operations when used correctly. The transformative potential of data mining begins with extracting meaningful and valuable information from large data sets to find patterns and insights leading to better data-driven decisions. Processing accurate and relevant data in the analysis process can lead to increased revenues and optimized business operations when the analyzed data is interpreted correctly.

Clean, analyzed data leads to good decision-making. However, businesses must always be aware of the ethical concerns that can arise and create significant issues. A comprehensive data governance program can highlight any analyzed data that can cause ethical problems before use.

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What is cloud computing?

Cloud computing occurs when IT resources are off-site and remotely accessed using an Internet connection. A cloud service provider (CSP) now hosts the daily IT resources user access. As a result, the IT resources are no longer physically in a local business’s server farm or data center. Instead, the servers, databases, and IT applications are only accessible via the Internet. 

What services can cloud computing provide businesses?

Cloud computing delivers any IT service a user typically uses as if accessing a local business server farm. Performing database updates, application processing, business intelligence (BI) actions, and saving business data on cloud-provided storage are all available using the CSP-provided IT services and resources.

What are the benefits of cloud computing?

When a business fully adopts a cloud computing solution, the two most significant benefits are IT cost savings and access to business data from anywhere. Additionally, over ninety percent of the companies that adopt a cloud computing solution claim to significantly improve their cybersecurity posture and meet any mandated compliance requirements. 

In addition to better cybersecurity posture, businesses benefit from the CSP storing their business data in multiple locations. Having business data stored in multiple locations also enhances a company’s disaster recovery posture. Here are some additional cost savings points and benefits when a company selects a CSP:

• 24/7 monitoring with IT experts – Twenty-four-hour monitoring by IT experts is expensive to duplicate with a company’s IT staff, but the 24/7 monitoring is part of an agreed-upon SLA. 
• Scalability – When a business’s demand increases for its product or services, the cloud service provider will automatically match that demand with increased IT resources and decrease the IT resources when business demand drops. 
• Mobility – smartphones, iPads, and Android tablets can access the cloud like a laptop or desktop.
• Loss Prevention – Cloud-based servers in multiple locations containing your business data minimize the chances of losing any business data.
• Automatic software updates – Cloud service providers immediately update any software as soon as the updates are available, which minimizes any potential zero-day attacks.
• Accessibility at any time – business data is available twenty-four hours a day from any location in the world, providing a reliable Internet connection is available at the location.
• Less downtime – with business data stored in multiple locations, one cloud computing location can be offline for maintenance while the other sites are still online.
• Competitive edge – Businesses that move to a cloud computing solution gain a significant advantage by reducing IT labor and IT resource costs versus businesses that keep everything in a local business server farm with continued IT resource costs.

The cost savings and the myriad of concerns regarding a business’s local data center’s availability are eliminated, provided the IT manager has chosen the right cloud computing and deployment models. Each cloud computing model and deployment strategy are slightly different, which dictates cost and any remaining behind IT staff.

What are cloud computing models?

Businesses can move their entire IT infrastructure and software applications to the cloud or retain only selected portions of their IT systems that they will be responsible for maintaining. Here are the differences between the different cloud computing models.

Infrastructure as a Service (IaaS)

With an IaaS solution, the business is responsible for the operating system, middleware, software applications, and business data. However, the CSP’s responsibility is the physical storage space where the business data is saved. 

Platform as a Service (PaaS)

The CSP manages and maintains the operating system, the middleware, and all the hardware associated with the PaaS platform. The business paying for the CSP services is only responsible for the applications and all business data. As a result, the PaaS cloud solution is popular among organizations involved with application development.

Software as a Service (SaaS)

The CSP is responsible for the entire IT infrastructure and platform, and SaaS applications are generally web-based applications that users can access with an internet browser. A SaaS cloud solution eliminates the need to invest in any IT infrastructure or software, and a business’s entire IT budget can be redirected to implementing a SaaS solution. SaaS solutions typically host only one type of application, like a customer relationship management or human resource application. See the visual representation of each cloud computing model. See figure 1.

Figure 1

Once a cloud computing model is chosen, IT managers must decide on a deployment model. The three deployment models are public, hybrid, and private or on-premises, and each is slightly different.

What are cloud computing deployment models?

Knowing the audience accessing a cloud computing model will help managers select the best deployment model. In addition, advanced cybersecurity requirements that are more than what a CSP can provide or retain a portion of an IT staff are factors IT managers need to consider when deciding on a deployment model.

Public cloud deployment

When a business selects a public cloud deployment solution, they will use the cloud service provider’s IT infrastructure. Whether a company is responsible for the operating systems, middleware, and applications depends on the cloud computing model selected. For example, regarding data, a business may be responsible for creating, modifying, and deleting data, but the CSP is responsible for storing your data in the cloud. 

The benefit worth repeating is that business data will be stored in multiple data centers geographically dispersed in an area, region, or state. Public cloud deployments are reliable, scalable, and available to meet business needs anytime. 

Hybrid cloud deployment

Hybrid cloud architectures provide businesses with on-premises and public cloud services. Companies required to maintain some IT infrastructure on-premises and still take advantage of the public cloud services will select this deployment option. For example, businesses that must maintain a higher level of cybersecurity due to a compliance requirement but still need to provide public access to a specific line of business (LOB) can implement a public cloud deployment to meet specific LOB needs.  

IT managers must understand their organizational requirements to align the hybrid cloud architecture with the correct LOBs properly. Hybrid solutions are used mainly by large enterprise organizations with several LOBs.

Private cloud deployment

Businesses that select this deployment option are responsible for all the hardware and software associated with a private cloud solution unless a third-party provider is contracted to support the entire private cloud infrastructure. Additionally, a private cloud will provide some public cloud advantages, such as automatic resource provisioning and self-service, with the added benefit of a more advanced cybersecurity posture that private clouds offer. 

Private cloud deployments can be hosted in a business’s IT data center or hosted in an external IT data center by a local service provider as a managed service. Companies can have an external, dedicated IT team manage every aspect of the private cloud in an IT data center. However, if a business wants to reduce overall IT costs, this is not ideal as a cost-saving option.

Why is cloud computing cost-effective?

Cloud computing is cost-effective because costs usually directed at purchasing hardware and software are immediately eliminated. Operational, maintenance and upgrade costs are all expenses that disappear. The IT staff that does setup and maintenance is eliminated too. The cost savings include the additional benefits of using cloud resources with an improved cybersecurity and data recovery posture outlined in an SLA.

Choosing a cloud computing model and deployment strategy

To get the most out of a cloud computing model, IT managers must understand an organization’s LOBs and any cybersecurity requirements that a CSP cannot meet, including any compliance requirements. At a minimum, IT managers must understand these three items to make an informed decision. 

Every organization is different, so IT managers must form a project charter covering the intended purpose with clearly understood objectives for implementing a cloud computing solution. Whether an organization is large, midsize, or small, stakeholders from each LOB need to provide feedback for an IT manager to make the best decision for an organization.

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