Data scientists and IT push the limits of what's possible -- whether that's operating more efficiently, taking advantage of new opportunities, or innovating. Here are 5 ways businesses can boost their effectiveness.
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3. 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.
4.
5. 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.
6.
7. 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.
8.
9. 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.
10.
11. 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.