Data proliferates at a much faster pace today than it did just a few years ago. Add to that the impact of social media and we now have data proliferation that is also rich in variety. Intense competitive pressures demand that businesses become more agile than ever before –and this translates into the need for being able to adapt business models and business processes much faster. This means that decisions must be based on rich granular information, covering a wide variety of sources, and often in real-time.
In my conversations with Fortune 500 CXOs and others, it is clear that they see these trends strengthening in the future. In fact, they want to be able to predict outcomes better using real-time data in order to buy them the agility to stay ahead of the competition and stay relevant for the customer. Thus, there is a growing need for possessing the ability to always select the most appropriate action dynamically – often in real-time – to address the business question. This is the central issue that enterprises would like to have addressed.
Let us take a look at the case of Bigpoint, a retailer of online gaming. They have demonstrated how their use of a real-time data platform allows them to process more than 5,000 events per second and make targeted offers to gamers (their customers) based on historical and real-time data. In order to deliver these personalized offers to individually targeted gamers while they are online, the solution leverages a real-time predictive modeling system in addition to comprehensive in-memory processing. Bigpoint now projects a significant increase in revenue by applying this solution to its business model. This is an example of how businesses are increasingly looking at getting between the end-customer and the cash register in dynamic ways.
Moving to a More Digital Enterprise
“Most C-level executives say the three key trends in digital business—namely, big data and analytics, digital marketing and social-media tools, and the use of new delivery platforms such as cloud computing and mobility—are strategic priorities at their companies” according to a recent McKinsey Quarterly article.
The challenge before enterprises is to take advantage of these trends—any organization that succeeds in this will have moved closer to being a more digital enterprise. This is where a true in-memory data platform can make a difference. Ideally, it should be a platform that enables the organization to go deep within their data sets to ask complex and interactive questions, and at the same time be able to work with enormous data sets that are of different types and from different sources. Such a platform should be able to work with recent data, without any data preparation such as pre-aggregation or tuning, and preferably in real-time.
This is not a trivial undertaking. Many database management systems are good at transactional workloads, or analytical workloads, but not both. When transactional DBMS products are used for analytical workloads, they require you to separate your workloads into different databases (OLAP and OLTP), and expend significant effort in creating and maintaining tuning structures such as aggregates and indexes to provide even moderate performance.
A system that processes transactional and analytical workloads fully in-memory can transcend this problem. There are differences among in-memory systems, and an important consideration is whether a system requires a business to prepare the data to be processed—which can take a lot of work—or not. There are software vendors today that claim to do some in-memory processing, but deliver on this front only in a limited way. Some of them deliver on speed by finding ways to pre-fabricate the data and speed up the data-crunching – this often runs the risk of missing some key element that might become necessary to decision-makers, while also killing any chance of working with real-time data. One CTO I met recently put it succinctly: There should be a resident ability to report live against transactions as they happen, such that cost-saving or revenue enhancing steps can be taken in real time.
In my conversations with various customers, it is clear that they can’t wait for the day when they can run their entire landscapes on this new type of data platform as opposed to traditional database systems. Such a real-time platform has the potential to bring dramatic process benefits to an enterprise.
Puneet Suppal is a member of SAP’s Database and Technology Platform Adoption team focused on the SAP HANA in-memory computing platform. Follow on Twitter @puneetsuppal and connect to him at LinkedIn. Bigpoint is an SAP HANA customer.