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Yogi Berra, the late baseball great and oft-quoted source of humorous statements about the condition of the world, once said, “It’s tough to make predictions, especially about the future.” Some of his most celebrated remarks were eerily prescient on the subject of using technology to predict the future. As many IT managers today ponder the best way forward with predictive analytics, it might be interesting to think about it from his perspective. Consider predictive analytics in the context of the following classic Yogi-isms


“The Future Ain’t What It Used to Be”
We all know what predictive analytics are supposed to do. You mine deep reservoirs of data and/or fast-moving real time streams of information to help make better business decisions. While there are many examples of successful predictive analytics, many attempts have suffered from two basic problems:

  • It hasn’t always worked as well as we might have hoped - Making decisions sometimes defies the simple, rules-based approaches that have predominated in the past. We’ve all experienced problematic predictions, such as an online bookstore recommending a diet book as a follow-on purchase to a cook book because, after all, you’ve shown an interest in eating.
  • It can be challenging to implement - Getting predictive analytics to work means doing intensive data integration, with many cases spanning systems that go outside the firewalls and comfort zones (and budgets) of the people tasked with pulling all the information together.

“It’s Déjà vu All Over Again”
We’ve been here before, being told that computers will help us run our businesses better with improved forecasting. It can be different today, though, with three new elements changing the way predictive analytics is done:

  • Big data – Big data isn’t just about the volume of data used in predictive analytics, though the amount of data available for analysis has surely grown in recent years. Big data gives predictive analytics the ability to gain novel insights from new kinds of data. In the digital world, everything is data that can be accessed. This includes unstructured data such as PDFs, along with machine data and “sentiment data,” which might include the words contained in millions of social media threads.
  • The ability to access and process data in real-time – Making decisions that affect business right now often means getting information in real-time – not hours or weeks later. For instance, if a credit card transaction looks suspicious, the card issuing institution has little time to decide if it will cancel/hold the card or ignore the warning. Getting there involves hardware, software, and networks – all must work well together and at high speed. In some cases, legacy systems inhibit the capability, having reached the point where the volume of structured data is affecting performance.
  • The ability to turn data into actionable information – Predictive analytics has struggled with the challenge of turning data into information that is useful – and correct – to drive business outcomes. The meaning of a given data set can be quite subjective. Enter the data scientist, now one of the most in-demand people in IT. Data science gives businesses a better ability to predict some events with a high enough degree of accuracy that they can act on the information more successfully.

“Baseball is 90 percent mental. The other half is physical.”
The modern platform for predictive analytics is mostly software and algorithms. The rest is physical, involving technologies such as high-speed servers and solid-state memory for in-memory databases. The collaboration between Red Hat and SAP helps bridge the divide:

  • Red Hat is a leader in High-Performance Computing (HPC), Linux is the operating system in 97 percent of the 500 fastest computers in the world and we are a leader in Linux technologies. Red Hat Enterprise Linux offers greater I/O speed, which is important to the performance of SAP HANA’s powerful in-memory data management, and Red Hat Enterprise Linux for SAP HANA is highly tuned for performance, providing a scalable file system (XFS) that can manage up to 500TB of data.
  • SAP HANA enables efficient predictive analytics – SAP HANA’s in-memory database is low latency, which translates into powerful real-time capabilities. The SAP HANA platform includes advanced analytical capabilities, including a predictive analytic library—saving time and resources in implementing predictive analytics. The platform also offers data integration tools that provide fast, flexible access to Hadoop and other sources of unstructured data. SAP HANA’s platform running on Red Hat Enterprise Linux for SAP HANA offers a platform that can help modern enterprises see the future and win.

“In theory, there is no difference between theory and practice. In practice there is.”
Recent real world use cases involving SAP HANA and related technologies show that predictive analytics is working in practice – expanding the ability of businesses to work better and smarter using data. Examples of SAP HANA enabling effective predictive analytics in business today include:

  • Predictive maintenance – Correlating real time monitoring of equipment with historical data on patterns of mechanical failure to conduct maintenance before a breakdown occurs.
  • Predicting product issues – Analyzing massive repositories and live streams of transaction data to “separate the signal from the noise” and make product purchase suggestions for customers in real-time.
  • Improving customer interactions – Automatically looking up and analyzing customer records as an inbound customer service call is received, making problem-solving suggestions to the customer service agent in real time.

“When you come to a fork in the road, take it.”
Platforms, such as SAP HANA’s platform running on Red Hat Enterprise Linux for SAP HANA, enable enterprises to implement predictive analytics to improve visibility and performance across a range of functions. If you come to the fork in the road where you have the opportunity to use predictive analytics, you may want to take it. But it will be important to utilize the right platform for your enterprise to help make it work.

For more information on Red Hat Enterprise Linux for SAP HANA, visit


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