Embedded Analytics for Real-Time Insight: 3 Features to Look For

Blog Posts | November 28, 2017

Embedded AnalyticsThe traditional approach of using separate databases for transactions and analytics (OLTP vs. OLAP) has made it too complicated to embed real-time analytics functionality in ERP solutions. But the advent of in-memory computing has upended this thinking with the ability to run a single data model for transactions and analysis of ERP data. This goes beyond simply speeding up OLAP with in-memory technology. Instead, both functions operate from one copy of the data, eliminating the need for redundant operational data stores.

Enable self-service reporting

Self-service reporting has long been promised. With a single in-memory model, it can now be delivered. This will dramatically change the way that IT operates. Most IT departments must devote significant resources to running reports for business users. But what if you could provide embedded analytics in a next-generation ERP solution that enables them to do that on their own?

What to look for in a solution

Consider these three concepts:

  1. Single point of entry – Today, users typically need to use numerous applications to find information and analytical functionality to support operational decision-making. If that fails, they resort to exporting data to spreadsheets. As a result, they are unable to see the full picture, or get bogged down in a time-consuming effort to produce a comprehensive report. Next-generation ERP solutions enable users from across the business to access relevant information using a single point of entry. With everything in the same place, they can gain insights faster and make better-informed decisions.
  2. Contextual suggestions – Next-generation ERP solutions provide embedded analytics that function based on the user context and specific role. For example, supply chain managers in planning (MRP) can see at a glance their current stock on hand and if it will be enough to meet production goals. The software can then suggest which vendors are best able to alleviate any shortages, and even how to load trucks most efficiently to reduce shipping costs.
  3. Flexibility to explore the data – With in-memory computing, embedded analytics allow business users to drill down to a transactional level from within the ERP solution itself. This means they have the flexibility to explore at will and gain in-depth insights without depending on IT or waiting for data to load overnight.

Free up IT resources

By choosing a next-generation ERP solution based on in-memory computing, IT can better support business users in their day-to-day tasks. Embedded analytics functionality provides insights easily and rapidly to help them make sound operational decisions.

In addition, with self-service operational analytics in place, IT resources are freed up for other important tasks. IT staff no longer need to create thousands of reports (which are often used only once), maintaining indices and redundant aggregate data, and running ETL jobs.

Does this spell the end of data warehousing?

Traditional data warehouses are still a crucial part of any company’s analytical arsenal, with an important role in helping organizations to carry out longer-term and more complex historical analysis. However, if you’re looking to provide business users with real-time support for operational decision-making, embedded analytics functionality within a next-generation ERP solution is the way forward.

Find out more

To learn more about next-generation ERP and how embedded analytics can deliver business value, see the ERP value advisor tool – then contact GROM to discuss your digital journey. Our experts can help!

Originally published on Nov 27, 2017 by Carl Dubler in The D!gitalist Magazine online.  Carl Dubler is a senior director of Product Marketing for SAP S/4HANA.