/Repurpose big data to get more analytics bang for your bucks

Repurpose big data to get more analytics bang for your bucks

By repurposing your company’s big data, you can leverage it to solve new problems and free up time to answer other pressing questions.

 

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Image: iStock/Andrey Popov

  

Researcher
IDG Enterprise released on Jan. 6, 2014 its 2014 big data survey results, and included a prediction that 2014 will see big data in the mainstreams of most large companies. These are some of the results (there were 751 respondents to the survey).

  • 70 percent of enterprises have either deployed or are planning to
    deploy big data-related projects and programs, with a per-company investment
    averaging around $8 million. Seventy-four percent of these companies said that
    big data will be put into use by at least one business unit or department.
  • 37 percent of survey respondents said that it is critical
    to identify the business areas and processes where big data can have the
    greatest impact.
  • 29 percent said that it is also critical to ensure that a
    corporate skills base exists so that value can be effectively extracted from
    big data.

There are two reasons why infusing value into big data is likely to be the next big data push once
initial implementations are over. First, enterprises want to see returns
from substantial big data investment. Second, unless you can
harness your data by producing new value, big data initiatives will falter. If
big data success stories like Cytolon AG are an indication, big data faltering is not a likely
outcome. Cytolon AG uses big data to match stem cells for cancer treatments.

Various uses for repurposed big data

Big data brings big
results, but the next job is architecting an information strategy where big
data continues to deliver value in new ways. This continuous “extraction
of value” is more than just asking the right questions of big data in your analytic; this data must also be repurposed so it can be leveraged to solve new problems.

For instance, if
you collect big data from the Internet of Things (IoT) (monitoring the performance
of devices that you sell for homes and businesses), and your goal is to dispense
your geographically distributed service technicians where repairs must be made
so you can meet your warranty obligations, you have successfully automated a
large part of your service and have improved your ability to respond to customers. If you look to
infuse new value by repurposing the same data, you can move on to answer other questions
that bring in even more results.

The IoT data gathered
from electronic devices installed in homes and businesses can be queried so
your engineering and manufacturing staffs can see which product components are
most likely to fail. This enables them to redesign products that are more fail-proof,
and therefore less costly to maintain.

The same data can
again be queried by your supply chain manager to determine which components are
failing most often, and which suppliers are producing them. A decision could be
made to source the part from someone else, resulting in fewer failures, less waste,
and better profit margins.

The data can be
repurposed again for inventory management, because if you are placing your inventory
in different locations around the world, you only want to stock as much
inventory as you need for support of local customer bases. Parts with high failure
rates will require more parts on hand in inventory, which adds to carrying
costs.

You can also use the same data for predictive modeling. If you sell transformers, for
example, you might see from IoT data that more failures occur in hot, dry
climates. If you know this, you can better position your inventory and
logistics.

Make your big data deliver ongoing value

“The need
to justify the expense of accumulating and managing huge volumes of data has
led many organizations to consider monetizing or productizing their information
assets,” said Doug Laney, Gartner research vice president.

He’s right, which
is why the time is now for data analysts to determine how big data can be
repurposed and reused so it delivers ongoing value. C-level executives
will expect this of their data analytics teams.