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Author: Nirmal Jain

SAP HANA for Data Analytics

As the boom for Digital Transformation, AI & IoT is growing Multifold, so is the need of effective extraction of meaningful insights. What is clear is that data science is solving problems. Data is everywhere, and the uses we are making out of it (science) are increasing and impacting. Let’s understand what Data Science is all about and how effectively SAP services can be used for the same.

What is Data Science?

Data Science is an interdisciplinary field about processes and systems that enable the extraction of knowledge or insights from data. Data Science employs techniques and theories drawn from a wide range of disciplines such as Information Science, Statistical Learning, Machine Learning etc to build insightful result, trends and aid discussion making.

There are different Data Science solutions available from SAP, let’s take a look at SAP HANA in this blog.

SAP HANA

HANA is the most trusted Predictive Application and performs in-memory data mining and statistical calculations which generate large datasets in quick time for real-time analytics.

In-Memory Database

HANA allows data analysts to query large volumes of data in real-time. HANA’s in-memory database infrastructure frees analysts from having to load or write-back data. HANA’s columnar-based data store is ACID-compliant and supports industry standards such as structured query language (SQL) and multi-dimensional expressions (MDX).

Rage of Algorithms

With wide range of algorithms that are available in HANA to do various Analysis like Association, Classification, Regression, Cluster, Time Series, Probability Distribution, Outlier Detection, Link Prediction, Data Preparation and Statistic Functions, SAP HANA offers to identify unforeseen opportunities, better understand customers, and uncover hidden risks.

Real-time Analytics

HANA also includes a programming component that allows a company’s IT department to create and run customized application programs on top of HANA, as well as a suite of predictive, spatial and text analytics libraries across multiple data sources. Because HANA can run in parallel to a source SAP ERP application, analysts can access real-time operational and transactional data and not have to wait for a daily or weekly report to run. You can integrate R with SAP HANA and standalone.

In our further blogs, we will high-light other capabilities of SAP HANA for your businesses

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What’s the linkage between ERP and BI?

In ERP the entire focus is on automation or efficiency improvement. BI is more about improving performance. The top management has to question the current reality. For example, if revenues/sales person are Rs. 1 Cr., Can I make it 1.25 Cr. And if I want to do it what should I do?

You talk about incentives, training etc. We suggest you give him intelligence and the impact on his her performance could be highest. BI is the case for empowering people to perform better and without the empowerment, training and incentives will produce only partial results.

But there is skepticism about BI. This will go away in near future like it happened with ERP. Today, it is rare to see people who argue about the need for having ERP software. For any new plant that is being put up the boiler, generator and ERP investments are put at par. Unfortunately, in case of BI there is no physical system like ERP. BI starts with a proposition
like – can I bring in a 25% reduction in your inventory. It’s a conceptual thing and often it is relatively difficult to visualize the end outcome. Also, end outcome is dependent upon action taken based on BI. Therefore it is difficult to assign the ‘credit’ to BI.

Data driven decisions or your gut feel?

If you can see the performance of the organization with naked eyes, you don’t need BI. For example, the owner of a south Indian restaurant doesn’t need BI because he is observing the performance in run-time. In restaurants, performance can be dramatically improved by earning more revenue per table/hour or by getting the customer to spend more. The owner is able to see and control the situation in runtime.

Now imagine, if the same guy had seven restaurants across various locations. All his managers at other restaurants may not be equally competent as the owner and that is where BI can play a role. Another daily life example would be a medical shop. A pharmacist once told me he keeps twenty days of inventory. He was using simple billing software. Although, the data could be seen with naked eyes, with four thousand items in inventory, it was almost impossible to really
identify the excess inventory in the shop.

BI is useful where performance or data is not visible with naked eyes (because you are at a distance) or the data is too complex to be analyzed purely in one’s mind. Also, BI is not advocated to replace gut feel; but gut feel decisions can definitely be far more effective after studying the facts rather than without the facts; won’t you agree?

Who needs Business Intelligence (BI) and Analytics?

To draw an analogy from the medical world, BI is not a medicine for a disease. It It’s more like a tonic.
Off course, if there is a disease, meaning a problem, it will help. The cause of disease in any organization cannot be lack of data analysis. Medicine is to remove the disease. Tonic is to remove the weakness induced by the disease.

Challenge the norms!

From a framework perspective, companies which are stagnating or certain departments/people within the company who are stagnating, would do very well with use of BI.

Stagnating, in terms of daily work, can be defined as a situation where people accept some failure rate as normal. For example, if you meet 100 prospects and 10 are converted to customers, you say, these are good results because traditionally you have never done better. Similarly, in a manufacturing environment a particular rejection rate, over a period of time, becomes a norm. But if break-through performance is to be achieved, these norms need to be challenged.

BI will help you to find ways to where you are failing, why you are failing and how to improve the performance.