What Could Possibly Go Wrong With Big Data?

Big Data, with Machine Learning and Algorithm at its core, is currently at zenith due to high demand and great features. To attain Big Data analytics solution, several companies are demanding expertise in this field. The culture of Big Data is currently dominating the world and has managed to set a standard as companies strive to attain business intelligence based on Predictive Models and Statistical Analysis.

As data is generating at an exponential rate, the demand for cutting-edge technologies like Big Data, IoT and Cloud Computing is escalating. As per experts, these technologies will become an inevitable part of every business in coming future. Considering Big Data in particular, it is in huge demand. They use massive data sets to run complex algorithms and in due course come out with verdicts that propose far reaching consequences. But the real question is can we solely rely on machines to predict and define our future profits and losses?

In this unpredictable economy, companies are struggling with biased market and unreliable statistics. In such a scenario, Big Data allows them to draw conclusions and utilize prescriptive statistics to emerge with intelligent business decisions. Then where could Big Data could possibly go wrong?

At a point when data begins to control business owners and creativity is kept at bay. At a point, when business starts trusting machine-oriented results instead of real-time interaction based results. And at a point, when businesses are controlled by machines rather than people, the power of Big Data for its face value is being acknowledged. Because the information is coming out of a machine, people assume that it should be accurate but unfortunately, they are not.

There are in-built errors and miscalculations in most of the analytical models, whose forecasts eventually collapse in every official system and with Big Data, the possibility of catastrophe is comparatively high. Let’s understand what are the three most common issues with Big Data.

Ghost Data

The data that we generally encounter to formulate our daily decisions comes from huge database that are analyzed via a complex analytical process. You cannot judge those numbers whether they are accurate or not.

Let’s have a brief overview about the process of manufacturing data. In most of the cases, front-line employees insert data into a machine that are subject to human error. Again, cashiers are responsible for entering correct bar codes while stock personnel must count and place stock accurately. These work responsibilities are yet to be incorporated in machines and are currently assigned to humans.

As a result, errors are inevitable giving rise to inconsistencies in numbers and consequently, affecting the purchasing and marketing decisions of consumers as well as suppliers. It is significant to understand the role played by data and therefore, it is essential to control the numbers entering into the system.

Trusting Data Blindly

From evaluating job performance to depending on a fixed paradigm to assess the quality of students, data has now become a part and parcel of our lives. Today, we are so reliant on data in certain circumstances that some functions cannot be performed without them. Data can be easily manipulated before they are punched into a machine and that’s the drawback of trusting it blindly. Also, everyone would love to question the judgement of human but in case of machines, the results of data analytics often go unopposed. It is vital to contemplate whether data set has been altered by any means before direct comparisons are made.

Statistical Overfitting

For your understanding, any business decision is based on statistical inferences derived from past behaviors. However, this process is integrally faulty, especially where the data sets are small and appropriate for a few outliers to twist the outcome significantly.

There is an element of randomness in every data set, which believes that the more accurately a predictive model is customized to past events, lesser is its future accuracy.

There have been inferences, when models with highest complexity failed and gave disastrous results. For instance, models for stock market predictions, where people risk billions every day. There are applications available in the market that claims to give accurate predictions but fail sometimes.

This doesn’t mean we must stop using machines for making decisions and predicting future. All we need to do is keep other sources open while using machines to gather information. To blindly accept numbers is risky and dicey, therefore, it is necessary to consider the process of data collection and how the inferences were drawn. This will enable you to make informed decisions and consequently escape losses.