What is embedded analytics and how does it benefit BI?

Integrated analytics is a paradigm for integrating analytics directly into business applications, mobile apps, equipment, consumer electronics, and even beehives. The basic idea arose out of the frustrations of traditional BI development cycles that limited the usefulness and scope of analysis.

Kurt Schlegel, then research director at Gartner, introduced the term Embedded BI in 2007. He observed that organizations that presented BI within business processes were better placed to take effective action. Since then, the industry has extended this basic idea to support a variety of analytical capabilities.

Closer to the user

“Integrated analytics brings user data closer together by incorporating tables, charts and dashboards into the applications that decision makers use every day,” said Chris Bergh, CEO of the consulting firm and platform. DataOps DataKitchen form.

But care must be taken to bring out the information that users find valuable. With traditional BI, an expert can provide advice and insight into how predictions can suffer. Careful planning is required to extend this knowledge to integrated analytics to predict how bad data might affect users. If your car predicts your future mileage based on road conditions, you may run out of gas when stuck in city traffic. The problem lies in the first place in bringing the right data into the analysis.

Chris Bergh

“When discussing integrated analytics, people focus on what the user sees,” Bergh said.

What is less pronounced, and perhaps just as important, is the system built around embedded analytics. A better mileage graph could be improved by adding map data that reflects where the user is likely to drive.

But, since a midsize to large enterprise can integrate thousands of data sources, care must be taken to ensure that integrated analytics deliver accurate and actionable insights to business users.

Pushing analytics to the limit

The growth of IoT capabilities dramatically increases the amount of data that could theoretically improve business decisions. This includes things like tracking data from RFID tags in pallets, product data in stores, and diagnostic data from devices used to move products. Analytics managers are looking for ways to bring insights closer to frontline employees so they can act immediately when needed.

“There is simply too much sensor data being generated for analysis to be performed centrally and regularly deployed to all source locations in a timely manner,” said Robby Powell, AI and Analytics Product Manager at SAS.

Pushing analytics to the edge enables tailor-made metrics to be taken at the point where data is collected. As decisions are pushed to the limit, centralized assessment of model performance and decision impact is essential to ensure decision accuracy.

Thus, the embedded analytics lifecycle must consider how to improve the front-line person’s workflow. Equally important is empowering BI analysts and data scientists to assess the accuracy of models and decisions through key metrics visualized both in aggregate and in detail.

It is also essential to understand that many business workflows use Microsoft 365 applications. Incorporating the insights gained through integrated analysis into Microsoft 365 documents, such as Excel and PowerPoint, reinforces the focus on these efforts.

How does embedded analytics benefit BI?

Integrated analytics often complements or extends the scope of BI rather than replacing it. The primary benefit that built-in analytics offers to traditional BI workflows is the improved user experience that helps build buy-in across the organization.

It can also extend the expertise of BI experts and data scientists, as well as their workflows, to support users beyond the organization.

Building better beehives

SAS worked with Beefutures and Amesto NextBridge to apply advanced analytics to read bee movements and identify the optimal location for beekeepers to place hives to help preserve bees. Beekeepers can access an interactive map on their mobile phones and easily track hives, bees and their food sources throughout the day using integrated information and visual analytics.

By leveraging the built-in scans, the map also identifies where beekeepers might move hives to optimize conditions or where to plant new food sources depending on the time of year. Using maps embedded in a mobile app gives beekeepers who may or may not have experience with analytics the information needed to create a better set of conditions for bees to thrive.

The collaboration plans to enter, monitor and publish various decisions and results in a central system. Model tournaments will be continuously held to build better decision models which can be taken to extremes.

“Their goal of preserving bee populations also inspires social innovation, an area that will only grow in the future,” said Powell.

David MarianiDavid Mariani

Outsourcing of inventory management

“Integrated analytics is ideal for sharing data outside of corporate walls, as security and access to data can be tightly controlled with custom applications,” said David Mariani, CTO and co-founder of ‘AtScale, a provider of analysis tools.

AtScale worked with a retailer on an application that allows suppliers to query and download real-time data to manage inventory at 3,500 stores. This helped the retailer improve product availability during the COVID-19 pandemic, resulting in explosive revenue and sales when other retailers struggled. Essentially, the retailer has outsourced their inventory management to their suppliers, whose best interest is to make sure they have products available for purchase.

The real value is that users don’t have to wait for someone else to extract data or use some other tool to create reports that present the results.

Keep it in real time

Dan Simion, vice president of AI and analytics at Capgemini Americas, said the most compelling use cases for integrated analytics are tracking processes, activities, or anything other than a company wants to monitor.

The real value is that users don’t have to wait for someone else to extract data or use some other tool to create reports that present the results. It starts with accessing real-time data to understand business needs and match them with what brings value to the organization.

“Don’t just create reports for the sake of reporting, but make sure the data captured is going to deliver valuable results,” Simion said.

Beware of prejudices

Companies are weaving advanced AI and analytics models into a wide variety of algorithms to automate decisions. Despite the best intentions and efforts to mitigate bias in these algorithms, sometimes it does slip through. For example, a hiring algorithm may ignore factors such as gender when making a hiring decision, but end up selecting more male applicants for a position. The built-in scan could provide some kind of engine warning light for these algorithms before it becomes apparent to business users.

Marc PalmerMarc Palmer

“Algorithmic decision making is biased because the humans who design it are biased,” said Mark Palmer, senior vice president of analytics, data science and data products at Tibco. “By incorporating analyzes formed to identify common patterns of use of factors that are indicators of bias, it can be more easily identified and reduced. “

BI has traditionally focused on descriptive and diagnostic analysis. Integrating predictive and prescriptive analytics can help you take action when it matters. This includes optimizing, simulating, and predicting what’s going to happen, rather than looking in the mirror to figure out what has already happened.

Lance B. Holton