How Reliable Is Big Data in Today’s Business World

A recent report by The Economic Times mentioned that BMW developed a data hub with AWS to boost efficiency. The report says, "around 5000 BMW employees will be trained to use AWS technologies to make better use of data." The advent of technology has brought visible changes in the process of doing business. Big data and predictive analytics has had a great impact on industries.

Gartner's definition says that big data is high-volume, high-velocity, and a high variety of information assets, the 3 Vs. These large volumes of complex data cannot be handled in traditional ways. Big data is used to get insights, detect threats, predict trends that enable optimal production.

The human brain has never had the privilege of being error-free. Isn't that why we are more inclined towards technology – a platform that provides immaculate results. Well, the truth differs. Everything comes with its own risk. So does big data. To get clarity, let us see what problems are associated with big data.

Accuracy

Many believe that the more the data, the better it is in terms of accuracy. This is not really true. The large chunks of data arrive from various sources that are not perfect. This may lead to unorganized, inaccurate data, or insights. When the values are mere approximations, we lose precision. All companies are not equipped to process large amounts of data in real-time. Hence, they use sampling to analyze data. This process uses small samples of data from the cloud and tries to gain insights. This leads to inaccurate conclusions and decisions.

Is Your Data Consistent?

The data need to be consistent in order to get the right insights. Data is never static; it keeps changing. Since the collection of data hails from multiple sources, it isn't easy to maintain consistency. Users might get misinformed if the data is inconsistent. Getting different answers for the same query can lead to such inconsistency.

Biases in Data Algorithms

Since these chunks of data come from multiple sources, it is not always credible. These data are not far away from biases. As the human brain is involved in making it, these are not objective values or information. A strain of data might contain the biases and faulty values inherited from its source.

Data processing using algorithms can also lead to biases. These biases in data algorithms are not an open book. They are still considered a black box, which disables us from understanding their roots and purpose. This can lead to misinterpretation. For example, one can interpret social media language in various ways. If the algorithms are designed to understand it in a sexist or racist way, it will result in faulty insights. This is sure to impact the users and, in other cases, your business's success.

How to Make Things Better?

All these biases cannot make big data disappear. Big data will remain a significant aspect of efficient business management. Hence, it needs to be set right.

• Data quality and organisation should be improved. To ensure this, a company should understand its data requirements and define relevant data. These data should be categorised and stored in a manageable way to get efficient results.
• Everything needs cleaning once in a while. Data should be cleaned to get rid of dirty data that are far away from being integral. This will enable us to create a data lake that is complete and relevant.
• Better governance can address data flows and security concerns easily. For example, SAP Data Hub ensure maximum integration and governance of your database to produce efficient business strategies.
• To increase the trust in technology, one must ensure maximum transparency to the users. A better understanding of sources, biases, and errors involved will impact the customers positively. Less manipulation and more statistical evidence can help gain trust.