Big Data – is it overhyped?

Big Data is a term that is gaining popularity in the business sector these days. It is the collection of large volumes of structured and unstructured data, extracted from multiple sources. Such data are diverse in nature and are based on real-time analytics. Big Data is a field that deals with ways of analyzing – semantically retrieving information or otherwise handling arrays of data that are too large or complex to handle by traditional data processing software. Data that provides multiple information on a single metric (rows) offers better statistical accuracy. While data with higher complexity (more metrics/attributes or columns) can lead to wrong end results. Big Data has found application in a number of beneficial ways. But the field has some issues that obstruct smooth implementation. Some experts say that the only way of overcoming these defects is by changing its functioning in the long run.

When we’re talking about big data, it turns out that the term describes everything related to data. That way, depending on who you ask, large datasets can be technical infrastructure, unstructured data (without SQL databases), or combining data from more than two sources. So, until standard definition emerges, big data means…nothing.

Even though it’s been talked about large data sets for a long time, the acceptance rate has not yet reached a critical mass. People need to understand the experience of owning data, because although everyone in our society produces huge amounts of data, individuals rarely see or interact with it. When the tools for storing, visualizing and exploring data are available, there will be an understanding of the value and usefulness of this information. This better understanding of Big Data can lead to better ways of solving important problems, such as responding to disasters, diagnosing cancer quickly and accurately, or analyzing the spread of disease.

Here are some reasons, for which Big Data is being criticized as overhyped:

  • Lack of standard model – While Data Scientists can create observations on the basis of results, generated by this technology, one needs an entire operating model to apply and put to use the collected data and the analytics in a repeatable manner. A possible solution could be to embed AI (Artificial Intelligence) with Big Data Analytics, in its application. Another way, in which Big Data Analytics could be implemented effectively, is applying experience and knowledge into insights.
  • Another reason why Big Data is being overhyped could be the claim that by harvesting more data will be created more value. This is absolutely not true. Data with significant amount of history is always more beneficial than large, freshly-acquired data sets. Less data with more history will prove beneficial, instead of mining more data, which can ultimately prove useless in its application to produce desired results.
  • It is a great thing that a merchant can optimize his work with 270 million records from 30 hours for processing to only 2 hours. Time saving is important, but what is expected to happen, is to enable the company to analyze different scenarios with these records. Only then will Big Data be relevant to this merchant.

Despite tis pitfalls, Big Data is a technology that has found implementation in various sectors and one that will witness solid, predictable growth in the years to come. It has not only changed the way the technical world functions, but laid its stamp on the fundamental functioning of businesses. There are concerns that Big Data technology could replace the human factor and therefore may not require certain job positions. This is unjustified as experts interpret the available data and ask the right questions that the system needs to answer. In fact, the system helps to make better informed and personalized decisions for the next best step in the process.