Why is Data Management so Important to Data Science? | 7wData

Why is Data Management so Important to Data Science? | 7wData

High data availability may help power Digital Transformation, but Data management systems are needed to keep that data organizaed and make it accessible. Read this article to see why Data management is important to data science.

Data is at the core of all analytics tools and machine learning algorithms. It enables the leaders to get to the bottom of what moves the needle and cuts the mark with the customers. Put simply, data is an asset to any organization when used effectively and smartly. Gone are the days when organizations were data-deprived and did not have enough awareness to leverage its power. Recent times have shown that a lot of organizations have moved beyond the data constraints and have it in abundance to start the analytics drill. 

However, the data availability single-handedly does not resolve one-of-the-many issues organizations face in their Digital Transformation journey. They need data management systems in place that take birth from the marriage of IT and business teams.

So, let us first understand what is data management.  

Data management, as the name suggests, is all things data - right from how the data is ingested, stored, organized, and maintained within an organization. Data management is conventionally owned by IT teams but effective data management is only possible through the cross-collaboration of IT teams with the business users in the loop. Business needs to provide the data requirements to the IT as they have better visibility of the end goal the organization is aiming to achieve.

Besides creating policies and best practices, the data management team is also tasked with a range of activities, as outlined here. Let us understand the scope of what all comes under data management:

Easy data access and self-serve analytics - the core pillars of data democratization, significantly increase the speed to generate actionable insights and business impact in turn. 

Let me elaborate on this a little more. Think of a case where a business analyst presents a report to the business leaders that focuses on solving a particular objective, say customer segmentation. Now, if the business needs to know some additional details that are not captured in the first draft of the analysis, they need to funnel down this request back to the analyst through the entire data cycle and wait for the updated results before they are in a position to take action.

As it must be evident by now, this leads to an uncalled delay in getting enough information on the table to empower all leaders and executives to trust the data and analysis and design the business strategy. Not only does such delay lead to lost business opportunity in terms of competitive edge, but the report along with the data also becomes stale by the time it is exhaustive as per the business needs. 

Great, so we have understood the problem now. Let us shift gears to how we can fill this gap between the business needs and the analysis presented. Now, one issue is clear in the scenario explained above - the current situation where the data is mostly handled and used by the analysts aka the tech users. Well-managed data systems enable non-tech business users (data consumers in general) to simply pull out the analysis of their needs and take timely decisions.

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