The systems for data and analytics that most companies have were not built to handle the amount and variety of data now available. As a result, for many years, the tendency has been to solve one problem at a time. But in many companies, the one problem at a time approach, which we call the application centric approach, has led to massive duplication of data and separation of data into many silos. You may not notice the pain from the application centric approach immediately because the cost of all these separate solutions is spread over many budgets. What becomes clear at a global level is that costs keep rising and staff still seems frustrated about getting the answers they need.
The way out of this situation is simple in theory. When trying to satisfy an organization’s thirst for data and analytics, don’t just solve the problem at hand but consider how you can solve it and create a system that stores the data so that analytics can be applied easily today and in the future. The goal is that all data can be accessed at any time and all the relevant analytics can be applied to any of the data. This is the data and analytic centric approach. But applying the data and analytics centric approach requires new thinking to master the increased complexity of big data.
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