Stefan Sigg is the Senior Vice President and Head of P&I Analytics at SAP Germany. During the upcoming ISC Big Data Conference (Oct 1 – 2) in Heidelberg, he will be talking about “Big Data Scenarios from a Business Point of View.” Visit the conference website for the whole program.
Sverre Jarp, the ISC Big Data Chair, asked Sigg about some of the underlying technologies driving data analytics today and why they are important to users.
Sverre Jarp: We have had things like database warehousing, business intelligence, and analytics for decades. How is “big data” changing these traditional applications?
Stefan Sigg: To date, the processing of information has been limited to the analysis of business data and physical factors such as the number of products a company can produce and sell to its customers. In the era of big data, these physical restrictions have become obsolete because we now have a totally new set of unstructured data available to us, generated worldwide 24/7 by machines, social media, sensors, and people using the internet. With big data, we are transitioning from a physical to a digital, data-driven world, enabling a new way of decision-making based on deeper insights.
Jarp: What do you think are the most important technologies driving the wave of big data applications today?
Sigg: Big data allows a totally new way of predicting business. However, the most important question is: How can companies commercialize big data? From our perspective, in-memory and cloud technologies are the answer. In-memory technology is absolutely necessary to analyze the huge amounts of new data types, providing instant benefits to the end user. Cloud computing can be seen as the proper channel to commercialize a company’s digital assets.
Thus, in the future, companies will not only sell their physical products but will also be able to sell their expertise to their business partners – for example, by delivering mathematical models that can help predict when a mechanical part needs to be changed.
Jarp: With regard to in-memory database technology, what kinds of new applications are these enabling?
Sigg: By analyzing huge amounts of data in a just a fraction of time, in-memory technology not only allows data-based decision-making but also the development of new solutions such as in the fields of predictive demand management and predictive maintenance or industry-specific software.
For instance, SAP is collaborating with the German National Center for Tumor Diseases (NCT). The project named “Medical Insights” is envisioned to use a generic healthcare data model and semantic capabilities to pull patient data from many different sources, such as clinical information systems, tumor registries, biobank systems and even text documents like a physician’s notes. It will then run the advanced data analytics on the underlying SAP HANA platform and provide the results in real time.
This is meant to help increase efficiency among various medical teams so that doctors have the most up-to-date information on their patients allowing for a faster, more accurate diagnosis. Patient cohorts are pictured to be posted and edited collaboratively, exported for further analysis in other software or compared according to different metrics.
Jarp: SAP’s own in-memory offering, HANA, appears to be on a fast growth trajectory. What makes it unique and how do you intend to keep it ahead of competitive products from companies like Oracle, Microsoft and others?
Sigg: At SAP, we have over 40 years of experience in the field of business applications and therefore know from an applications perspective how an end user can best benefit from an in-memory platform. SAP HANA contains significantly more semantics functions than an abstract mathematical database because it allocates tasks between a company’s applications and its databases better. We have shifted the paradigm from “bringing data to the applications” to “bringing the application to the data.” This saves time and resources.
Jarp: There is a general consensus that data is growing faster than memory capacity, at least for the most demanding applications. How will in-memory solutions adapt to this dynamic?
Sigg: In a petabyte environment, say, in-memory solutions will always access data from hard-disks, for example data stored in a Hadoop cluster. Thus, the amount of raw data from Hadoop that can be transferred into the in-memory solution and then analyzed in real-time depends on the latest technological development.
Jarp: In your opinion, what is the appeal of the ISC Big Data conference to an organization like yours?
Sigg: The event delivers an interesting new scientific perspective on big data and high-performance computing which perfectly complements SAP’s business perspective on a data-driven world.