What SAP HANA Means to the Evolution of Data Analytics

IDC predicts the big data and business analytics market will rise to $203 billion by 2020. Growth in the market is driven by the pivotal role analytics play in making profitable business decisions. Through data analytics, organizations gain insight into which processes are working and which need correcting.

If companies want to benefit from data analytics, they need to build IT environments that take a comprehensive approach to organizing and processing data. SAP HANA provides the answer by combining in-memory computing with data management and built-in analytics tools. Running SAP HANA on IBM POWER ups the ante by meeting the performance demands of the most advanced analytics.

How Analytics Have Evolved

Analytics have come a long way. While descriptive analytics provide a record of the past, predictive analytics forecast future trends so a company can remain competitive. For example, based on past customer or client behavior, organizations can take actions to provide more favorable future offers and services.

Predictive analytics can evolve into prescriptive analytics to provide options for future actions. These options can then be tested to find the best outcomes and course of action. SAP HANA is already equipped with built-in predictive algorithms. With these analytics tools, companies can benefit from analytic insights without having to rely on a data scientist.

SAP recently announced they were ranked as a leader in the Forrester Wave: Prediction Analytics and Machine Learning Solution, Q1 2017. Forrester singled SAP out for their promotion of citizen data scientists and laying the groundwork for artificial intelligence through machine learning.

Going Cognitive With IBM POWER

Running SAP HANA on POWER enables companies to take their analytics capabilities even further. IBM has been at the forefront of analytics innovation since designing IBM POWER for big data. IBM POWER provides the backbone for IBM Watson’s cognitive technology. Cognitive analytics use advances like machine learning and natural language processing to function like the human brain.

Cognitive systems process encyclopedic amounts of information, including business intelligence from every department in a company. This information can then be used to draw complex conclusions. Machine learning identifies patterns and anomalies. Natural language processing helps to draw associations between topics in text files and social media conversations. This type of unstructured data holds the key to understanding the significance behind much of the structured data a business accesses and stores.

SAP HANA Analytics Use Cases

Looking at some of the ways companies have used SAP HANA for analytics illustrates the range of business benefits the solution brings.

Improving Customer Service

SKYLARK, a Japanese restaurant chain, used predictive analytics to mine data produced during customer visits so they could increase traffic. They praised SAP HANA for the accuracy and speed to insight it provided without the need for analytic expertise.

Fighting Fraud

Monext, a European e-payment service provider, enlisted predictive analytics to stop fraud from occurring. They could predict when fraudulent transactions were about to happen and reduce the number of false alarms they received.

With the support of IBM POWER, SAP HANA empowers companies to make crucial decisions in real time. Instead of just detecting fraud, organizations can prevent it. Instead of following up with customer support, companies can respond while a transaction is taking place.

Setting the Stage for Better Analytics

Before companies can analyze data, they need to find faster and more efficient ways to store the vast amounts of data that is used in making business decisions. SAP HANA’s in-memory processing allows organizations to process data more quickly by not going to disk. IBM POWER complements SAP HANA by adding to its agility and flexibility. With IBM POWER, SAP HANA users can scale out and in on demand to meet fluctuating big data workloads.

Scaling with POWER is more efficient because it provides 8 threads per core for 4X the performance of x86 servers. Higher performance means the ability to meet the requirements of even the most sophisticated analytics.

Originally published at convergeone.com on June 9, 2017.