The world of physics has long operated under the concept of gravity–the larger the mass of an object, the stronger the force pulling it toward Earth’s center.
Those who understand and respect gravity find ways to move objects with less energy. Those who ignore gravity… well, they waste a lot of energy and see their efforts fall flat.
A new concept of gravity is also emerging in the world of technology which could determine who succeeds and who fails. In this case, the concept is “data gravity” –the idea that a certain amount of data has its own mass and force. The larger the mass, the greater the effort required to move it, which usually translates into more time, cost and processing power.
Whether they call this concept data gravity or not, most IT managers and data scientists are becoming well aware of its effects, based on the results of a recent Forrester Research report. Almost all (91 percent) of the data scientists surveyed said they would be interested in using predictive data analytics against real-time operational data if there were no drawbacks. But almost all (94 percent) also reported challenges because the data volume in question was too large, too time consuming to aggregate or too difficult to access – they are still, in effect, fighting gravity by moving the data in order to analyze it.
If companies recognize data gravity, however, they can use the concept in their favor. Instead of working against large masses of data, they can work with the data in place to gain insights more quickly and run their businesses more efficiently.
This means analyzing data where it resides. The mainframe, for example, holds valuable information for many large organizations as their core transaction system. More than two-thirds of all IT workloads are run by the mainframe, according to a Solitaire Interglobal report. Instead of offloading this data for analysis, companies should analyze it in place, delivering insights faster.
Data gravity also means that companies should look for technologies that make it easier to access data no matter where it resides. Apache Spark is one technology helping make this possible by creating a common framework for data analysis, whether it’s on an IBM z Systems mainframe or a server in the public cloud. Companies should use technologies including Apache Spark to make data analysis faster and easier without having to move it. (Check out this video that shows how Apache Spark on z Systems can help a financial institution grow its business through targeted real-time offers for clients.)
As the amount of data and priority on real-time insights increases, data gravity will become even more important. A few years ago companies were satisfied with understanding transactions after they happened. Now, they want to understand transactions as they happen (utilizing real-time analytics). As we move into the cognitive era, companies will need to understand transactions before they happen. They will need to anticipate market needs and demands before consumers even know what they want.
Companies that respect and understand the concept of data gravity will have an advantage over their competitors. They’ll gain insights faster, have a deeper understanding of their customers and move more quickly to roll out new services that matter. Companies that don’t acknowledge data gravity will ultimately see their businesses fall flat–receiving insights too late to be used.
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