MILESTONES WITH DATA

DATA INTEGRATION
There are very few and limited industries today that given the resources and the capital to invest have
not yet or not started the procedure of resorting to and making use of the power and potential of data
and analytics for accomplishing tasks that are beyond the scope of human manpower and the accuracy
of computers and machines in order to derive value and make informed decisions that are only going to
work out in the betterment of the organization in the long run. You ask why? The answer is fairly simple.
Data is nothing but cold and hard facts that have been derived from the long journey and the history of
a particular thing. All this information is stored digitally and hence is a pure reflection upon the truth of
things.

THE HURDLES
Even with the adoption of analytics to give a boost to their business/organization/community, there still
exist a multitude of problems associated with analytics that industries face on a daily basis. So, let us
pen down some of these milestones that analytics faces and witnesses on a daily basis-
1. Finding the right data
The world is a messy place, absolutely no doubts about it. Even messier though, is the data that
it produces on a daily basis. Being extremely unstructured and totally heterogeneous in its
format, filtering through it in order to happen upon the correct and relevant parts and aspects
of such huge datasets is a whole other ball game in itself. With such large volumes of data
flowing here and there constantly, it is really hard to make sense of the entire data chunks and
then sieve through them even with the sophisticated algorithms and the immensely powerful
computers and machines at our disposal.
2. Impractical models
The very motive of data analytics, in the end, is to come up with comprehensive models that can
solve the problem and all related problems of the future in real-time. As easy as it may sound, it
needs really high levels of problem-solving capabilities and what we call as “thinking on the
feet”. Lack of these traits very often lead to a model that is supposed to go north but is instead
going south.

3. Lack of clear mandates
Data analytics is a relatively new field with a vast variety of tasks and tools at their disposal.
With more and more businesses and industries affiliating themselves with data analytics, it is
not rare to find clients who themselves do not have a clear vision and mandate of their own

requirements. Unable to put their needs in clear words topped with even more unclear
exchange of ideas and communication during the project development more often than not
leads to the scientists and developers coming up with something that is not even remotely
related to the needs of the client.
4. Data security and integrity
The data set used during analytics is the personal data of some or the other person in this big fat
world. And after dealing with such large volumes of data, companies just sell it to the highest
bidder. As such ensuring that essential information of people does not go large and becomes a
thing to just buy off is highly critical and important.

Resource Box
So many challenges posed in a field that is the fastest growing domain of technology in the modern
world is always on the lookout to get their hands on people who can solve these problems for them
while paying them lucrative and ridiculous amounts of money at the same time. Want to be that next
individual? Join a professional data analytics training in Bangalore program ASAP.