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 datasciencecertification 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 pin 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.