How Embedded Analytics Is Helping to Democratize Big Data: Everything you Should Know

What Is Embedded Analytics

The answer lies in embedded analytics, developed to be more pervasive and autonomous for users. Embedded analytics software delivers self-service analytics (including AI and machine learning), interactive visualization, and reporting directly into an enterprise application, with easy access to the end user through an app interface. 

Unlike traditional business intelligence (BI) applications where users must leave their regular workflow to look at data and insights from a separate set of tools, embedded analytics can be easily accessed right in the application. Users are able to see data from an integrated dashboard, work with the data in depth, and view it from different perspectives in an intuitive fashion. Embedded analytics puts the power data back into the hands of employees and customers to help them make better business decisions. 

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Benefits of Embedded Analytics

With embedded analytics, businesses now have the ability to “democratize” data and analysis, and make it quickly available to the people that need it. Some key benefits include:

• Removing Guesswork: Accurate data is always accessible for any analysis, so users don’t need to apply a best guess to solve a problem.
• Saving Time: Results are instantaneous, choices can be made in real time, and reports can be generated anytime, anywhere. 
• Flexibility: Models can be adjusted to meet the needs of special requests, such as investigating cross-sell opportunities or targeting certain customer segments. 
• Seamless User Experience: Users can blend analytics into their own web portal or application, making it easier to adopt new capabilities and improve the user experience. 

Use Cases for Embedded Analytics

Supply Chain Management

Managing supply chains is an especially complex task in today’s volatile global business environment. Embedded analytics are well-suited to helping companies navigate supply chain complexity and unpredictability by providing customized insights into each type of business application. Users gain real-time insights into live and historical supply chain data to make better in-the-moment decisions. 

For example, it can help them gain better visibility into supply chain availability in the event of a natural disaster like a hurricane. Schedulers can assess the ability of suppliers and partners to deliver goods on time when a disaster occurs. Embedded analytics combines a supplier’s current capacity with historical performance data (such as on-time delivery under tight deadlines) to help choose the right vendor. By streamlining the decision process on the fly, the company is able to recover from a disaster and more effectively meet customer expectations. 

Embedded analytics can also assist with supply chain planning, helping to detect signals from point-of-sales (POS) systems, social platforms, and other public information sources. When orders are placed, the analytics can provide more accurate estimates of delivery times based on real-time supply network metrics. 

Sales

Sales teams can benefit tremendously from embedded analytics. When sales execs wants to run a deep analysis on customer buying behavior, for example, they usually have to go to their data science teams to run the analysis, or they have to be a power user of the software. Embedded capabilities allow the sales execs to look at different data quickly and easily run their own queries without having to create spreadsheets or their own pivot tables. It’s a lot like consumers using travel sights to filter flights and hotels the way they want without having to wade through complex analytics. 

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A Future Look at Embedded Analytics

According to Forrester, there are five trends driving the acceptance of a new generation of analytics. Solutions are becoming:

1. More Pervasive: For every user, data is embedded in an enterprise application they work with (such as ERP, CRM, or productivity applications), putting insights right at their fingertips.
2. Actionable: Users should be able to make insights actionable and effective without having to leave the application they have open.
3. Interactive: Ideally, embedded analytics can be more conversational and interactive through natural language processing, which can also be adapted to analytics platforms.
4. Deeper: Analytics capabilities can be expanded with machine learning and NoSQL technologies that offer search and graph analysis to deliver deeper insights.
5. Anticipatory: Augmented analytics that use AI and machine learning are able to uncover insights you didn’t know about and push them directly to the end user.  

It now falls to data architects, business analysts, and data scientists to empower their organizations with solutions like embedded analytics. By doing so, they can expand the power of data to everyone in the organization, further democratizing the use of data as a precious corporate asset. 

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