Skip to Content

Sponsored

SAP Makes Big Data Real– And Real-Time

Provided bySAP

The following View from the Marketplace was provided by SAP, the sponsor of our Big Data Gets Personal Business Report.

The theme of MIT Technology Review’s Business Report—“Big Data Gets Personal”—fits perfectly with SAP’s view of this important new direction in technology. We regard Big Data as nothing less than enabling people to reimagine the world by tracking signals that have previously been impossible to track.

We’re already using Big Data at SAP to change how we work, how we play, and how we literally stay alive. We’re helping major-league sports teams tap Big Data to reshape the game experience, letting fans use their mobile phones to follow their favorite players and plays, whether they’re in their homes or at the ballpark. At Burberry’s, when an established customer with a loyalty card walks in, a sales associate will know their ordering history and be able to escort them on a shopping tour personalized to their tastes. And soon, cancer patients will routinely be able to have their DNA tested to help determine the specific treatment that will be most effective for their condition, with the fewest side effects.

Many people, when they hear “Big Data,” think “big business.” Not SAP. Via our Startup Focus program, we are actively working with young companies, helping them take advantage of our revolutionary HANA in-memory Big Data platform. We’re discovering that some of the most adroit users of Big Data can be five-person startups.

And of course many large companies are also launching their own Big Data projects. But when we ask Fortune 1000 CIOs about Big Data, we often hear the same thing: “Well, we’ve got a lot of data, but we haven’t figured out a way to translate it into real results.”

The reason? Many people have an extremely skewed view of Big Data. They associate it exclusively with Hadoop, and they assume that “doing Big Data” is a simple matter of setting up a Hadoop cluster.

Of course, we at SAP are big believers in Hadoop, but it addresses only one portion of a Big Data strategy: storing data in a low-cost archive, often one that can only be accessed in batch jobs that might take all night to run. The problem is that Big Data isn’t just about storing information—it’s also about extracting useful insights from it in real time. And for that, you need much more than Hadoop.

In fact, at the big Internet companies most associated with Big Data—Google, Facebook, and the like—IT budgets and innovation are mostly dedicated not to Hadoop, but to a more comprehensive platform that aquires, accelerates and analyzes insights to meet their business needs. They don’t just use Big Data to generate insights—they also connect these insights directly into their business processes in real time.

SAP HANA was the brainchild of Dr. Hasso Plattner, co-founder of SAP AG and the Chairman of the Supervisory Board, and Dr. Vishal Sikka, member of Executive Board, Technology and Innovation. In his many public appearances, Dr. Sikka has often stressed that SAP needs to go outside its own walls and harness the collective imagination of all of us to realize the full potential of real time and big data.

SAP’s commitment to taking Big Data beyond the simple commodity of Hadoop is exemplified by our HANA platform, which takes advantage of a new generation of columnar databases running on multicore processors. The entire system—both application and data—is in RAM memory, and the resulting speed increases are startling. HANA users routinely tell us that data queries that used to take days can now be executed in a few seconds.

HANA is changing the way companies do business. Our enterprise customers don’t have to separate data anymore into two silos, with servers for real-time OLTP and data warehouses for longer-term OLAP. With HANA, accounting can store receivable information while business units seek out customer patterns, all on the same platform.

Having instant, actionable information is crucial for companies competing in a global marketplace, and SAP’s enterprise customers have enthusiastically embraced HANA. But the platform is sufficiently adaptable that even small startups not normally associated with SAP are finding that HANA can easily handle data-processing tasks that would make traditional systems choke and sputter.

One example is Feedzai, a Silicon Valley company using HANA to perform real-time analytics on credit card transactions, to spot and block fraudulent transactions as they are occurring. That’s a big improvement over most current analytic products, which only flag problematic transactions for follow-up by a human help desk.

EasyAsk, a Massachusetts startup, is using HANA to allow enterprise customers to ask questions about ERP data verbally, in plain language, and get an immediate answer—in a cheerful voice.

NextPrinciples, a next-generation Silicon Valley social media startup, uses HANA to help companies monitor the effectiveness and reach of their social media campaigns. Companies can learn in real time what is working and what isn’t, and shift strategies accordingly.

And we at SAP are especially proud of our work with MIBS, an Indian company working with point-of-sale data from thousands of pharmacies to provide early warnings of disease outbreaks in India—and thus help public health officials better prepare for them.

Improving the health of an entire country: Is there any better use of Big Data? It’s one of countless examples of how SAP is making Big Data a reality for companies both small and large. Real data, with real insights, in real time for your real customers. That’s as personal as it gets.

This article was produced by SAP, not by the editors of MIT Technology Review.

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

How scientists traced a mysterious covid case back to six toilets

When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.