5 Ways To Get More From DATA SCIENCE

Company DNA May Have To Adjust

• Highly competitive companies realize that IT can't be an afterthought. It has to be an integral part of the business. The same is true of data. However, some organizations try to bolt a data strategy onto an existing business model, when it may be more advantageous to consider how the business model should evolve to include data. In fact, data should be applied in the first place to determine the business model.

Focus On Business Impact

• Technology for the sake of technology and analysis for the sake of analysis have little or no practical value. The possibilities typically exceed what is practical or advisable, although not everyone might agree on the best course of action. Because different people tend to have different vantage points, there may not be common agreement about what the scope of an initiative should be, what it will cost, the time it will take, what its likely impact will be, and what the priorities should be.

Clean-Up Efforts Should Be Appreciated

• Server sprawl, virtual server sprawl, database sprawl, dirty data … the clutter wastes resources, and the degree of redundancy isn't always obvious. It is estimated that data scientists spend 50% to 80% of their time collecting and preparing data, a fact that is likely not apparent to others in the organization.

Legacy Issues May Be Challenging

• IT teams have to integrate legacy systems and software, and IT and data science teams have to integrate legacy data with new data sources. While integration is necessary to enable greater value and insight, leveraging legacy systems and data can sometimes be a complicated and time-consuming task. It's a situation not everyone understands.

A Talent Acquisition Strategy Is Wise

• Not all businesses require Hadoop developers or data scientists. But what's hot may sell, even though the decision may not be in the best interests of the company or the individual who has been recruited. It is not uncommon for organizations to put less thought than they should into why they need a data scientist in the first place or the questions they should ask a candidate during an interview.

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Source: http://www.informationweek.com/big-data