In the world of Data and Analytics, the term Data Gravity is now almost a decade old. The question is how well you are recognizing the opportunity and trying to maximize it.
For starters, Data Gravity refers to what happens when we move to a Data First philosophy – which anyway has become inevitable today. Data accumulates for the business every single second and it pulls your business to it – infrastructure for storage and management, people for analysis, applications for processing it and making sense of it. As data grows so does its density/mass and its influence on the business.
Increasingly, today, when we speak of Data Gravity, we are referring to the shifting of data to cloud and with it the applications and tools that are used to manage the data and analyze it. Most of the businesses worldwide generate and use as much external data as they generate internal data. In several cases the external data could be much more than internal data. And much of the external data resides in the cloud. For example, a company’s data from social channels is invariably being generated and stored in the cloud. Hence, many of the applications or solutions being built to effectively store, process and leverage that data are becoming cloud-based. After all the location of your analytics has a direct correlation to the time taken to move from raw data to insights.
Coming back to our central question. How do you maximize the opportunity presented by Data Gravity?
When you are faced with a whole range of data sources, types and systems that are generating the data, coupled with so many different users of data with their own unique needs – this is easier said than done. Rather, this is where the crux of today’s Data and Analytics challenge lies. Can you navigate through the teething problems of data ingestion from multiple sources, processing of data of different types, its secure presentation to multiple users and its preparedness for supporting advanced analytics – easily, quickly? This means your teams can focus on what they need most – generating insights.
The answer to this lies in changing the lens on traditional data engineering and analytics. Adopt a platform driven approach to data – multiple sources are linked to this platform, multiple users are connected to the platform, multiple applications run on this platform. Sounds exciting? How about moving into this platform and having it tweaked to your unique needs rather than building one?
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