If data is the new currency of the digital economy, then the companies that succeed will be those that manage their data most effectively. What’s needed is a comprehensive data management strategy supported by a platform designed for simplicity, performance, and openness – all with an eye toward maximizing the value of your data.
Complex data landscapes impede performance and scalability. With data coming in from everywhere, it’s more important than ever to bring it all together in a single source of truth. A data platform for the digital economy needs to combine all data sets coming into the enterprise – structured and unstructured – into one data universe.
The drive toward a single source of truth means that old data management architectures that isolate analytics on separate business intelligence environments are on the way out. Instead, companies are moving toward data management platforms that can hold data from live systems together with data from analytical systems without sacrificing performance (more on this below). This is what enables the real-time analytics that result in quick insight and improved responsiveness.
Simplification doesn’t end with data consolidation. Look for a platform that also integrates both analytics and business processes into the platform itself – based on the understanding that your overriding objective is to extract value from your data and optimize the customer experience. Can your platform, for example, pull machine sensor data into your financial system to drive pay-as-you-go scenarios? Can it use machine learning to detect patterns that help you continuously improve – and employ predictive analytics to see what’s coming next? Such scenarios are only possible with a data management platform that dramatically simplifies your landscape.
Today’s business applications run on live data in real-time. Events trigger actions, analysis is done on the fly, and decisions are made based on insight from that analysis. In this environment, speed of insight – and the ability to quickly use that insight to drive better customer experiences – is what sets companies apart.
One proven way to speed data processing is to move to an in-memory data platform. Rather than making calls to disk for needed data, you simply hold all of your data in active memory where processing speeds are orders of magnitude faster. If you then combine in-memory speed with the simplicity of a single source of truth, now there’s no need to move data between systems using large-batch transfers. Now analytics can indeed be executed in the moment. Now you’re on the road to becoming an intelligent enterprise.
All the speed and simplicity in the world won’t help if your data management platform impedes your ability to work with the wide variety of data sources coming into the enterprise today. Platforms need to be source-agnostic. They need to support cloud portability – allowing you to work with AWS, Google Cloud, Azure, or any other provider. They also need to work across on-premises and cloud solutions, because hybrid environments are still the norm.
Openness also means an open model: one that enables you to integrate with multiple open source compute engines. It means support for all of the languages, libraries, and software that drive intelligent technologies, including TensorFlow, Spark ML, Python, R, Jupyter Notebooks, and more. And ultimately, openness means flexibility – which is another way of saying that your platform won’t stand in your way as you go after new opportunities to realize value from your data.
Let’s talk more about data
The simplicity of a single source of truth, the performance to achieve real-time situational awareness of your business, and the openness to flexibly work with any type data out there – this is what drives data management success in a digital economy.
Learn more about digital data management platforms.
And please listen to the replay of our “Pathways to the Intelligent Enterprise” Webinar, featuring Phil Carter, chief analyst at IDC, and SAP’s Dan Kearnan and Ginger Gatling.