Analytics

5 steps to get actionable insights from raw data

12th Nov `14, 12:14 PM in Analytics

Companies are heavily investing in acquiring and developing talent, technology and business processes aimed at collecting and analyzing…

BDMS
Guest Contributor
 

Companies are heavily investing in acquiring and developing talent, technology and business processes aimed at collecting and analyzing massive amounts of data, so that they can develop actionable business insights aimed at bolstering customer value. The key driver for digital business transformation is improving the ability to convert data to knowledge and understanding that leads to meaningful and timely action. Yet, a gap exists between what businesses need and what big data technology enables businesses to do, which is still largely at the infrastructure layer where it is stored to be searched and retrieved. In order to move beyond simply collecting data, into a pretty visual story that merely summarizes data, to truly analyzing the data so it can be used to gain the insights that businesses really want, often requires the experience of a data scientist. For marketing organization, a data scientist may be the most important hire.

But what does a data scientist actually do? Who better to ask then Chief Data Scientist for Lithium Technologies, Dr. Michael Wu. Dr. Wu spends his days crunching numbers, testing and building models to try to understand social customer behavior on different social channels to predict customer behavior and its effect on the business. Wu helps us to understand how companies can get the raw data into the information insights that they actually want to make better decisions. Today, marketing and sales organizations are working hard to conquer the data deluge.

5 Steps to Get from Raw Data to Actionable Insights:

1. Start with determining the business problem you’re trying to solve – As a starting point in dealing with all of this data, Wu recommends that businesses begin with a business problem. Collecting all the data is important because you don’t know what problem you may have in the future, but in order to use the data to take business action, you need to start with a problem.

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