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Customize Your Analytics So They Are Relevant and Actionable to Each Stakeholder

by Arun Narayanan | SAPinsider, Volume 15, Issue 2

April 1, 2014

Learn about the practical importance of real-time analytics, and the importance of determining what data is relevant and what isn’t. Insights at the operational level can be achieved by personalized dashboards that help make data consumable across the organization.

1. What business indicators or signals should trigger an SAP customer to start leveraging real-time reporting capabilities?


In today’s fast-changing business environment, it is common to see unexpected indicators that could impact the business. Is working capital unusually low this month? Does the warehouse hold more product than its capacity? Are competitors pricing similar products in a better way?

If you don’t measure in real time, you won’t be able to spot the trends that define the way your business is run. If you can’t measure, you can’t improve, and if you are not improving, then you are in trouble — because your competition is improving constantly. Real-time reporting is vital to gain insight on current problems or to be proactive and optimize the business.

2. With the variety of data companies possess, how can they make analytics consumable throughout the enterprise?


With systems in virtually every facet of the business, the amount of data generated is huge. Analytics can help make sense of the data and give insights about the different aspects of running the business. However, the combination of structured and unstructured data from various sources and the need to analyze multi-year data create a challenge in determining what data is relevant and what isn’t.

Each department needs to have analytics that are meaningful, actionable, and relevant. Personalized dashboards are one piece of the puzzle. While top management dashboards give very high-level insights, operational groups need to have dashboards that provide information at a more granular level. To make data consumable across the enterprise, you need to be able to access the relevant data in any user interface (such as mobile, web, or Microsoft Office-embedded applications) and then make it actionable.

3. What is your overall take on the pace of innovation in the BI space and its impact on business?


Even though business intelligence (BI) systems have grown significantly in the past five years, data modeling at the enterprise level has some way to go. Enterprises have multiple systems overseeing single or multiple areas, and the basic master data components, such as customer, product, and vendor, could be totally different across these systems. This is why corporations spend huge amounts of money on extraction, transformation, and loading (ETL) to extract data from different sources and port it to a common enterprise warehouse. This results in several disadvantages:

  • Data is stored twice — one version on the original system and another version in the warehouse.
  • ETLs have to be constantly changed as the original system undergoes changes.
  • New systems sometimes disrupt the warehouse data model and require lots of adjustments.

The IT industry has yet to reach a mature stage where each product, while retaining its own data model, enables its data to plug in directly to any enterprise data warehouse, from which any BI system can directly provide insightful information. Business intelligence is most advantageous when organizations are running ERP systems to their full potential, thereby to a great extent solving the three points above.

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Arun Narayanan
Arun Narayanan

Practice Director
Analytics and Data
SEAL Consulting Inc.

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