Data is ruling the business industry today. Stats reveal that International Data Corporation (IDC) – global market intelligence firm is spending a significant amount on data and analytics to achieve the estimated growth of $274.3 billion by the end of 2022. However, it is unfortunate to know that most of this money is not being spent in the right manner. Some analysts even say that almost 85% of the big data projects fail just because of the wrong processing and analytics strategies.

It is observed that big industries pull data from plenty of resources, and it is further analyzed using complex software tools. But they rarely explore the actual source of data. Some of these data elements are modified unnecessarily and may not even fit for the purpose. The fact is that to get the right outcomes from data; we cannot just rely on its face value. It is essential to do an in-depth analysis of sources and optimize the operations to leverage the best possibilities.

One should start data analysis by asking these four critical questions:

How was data sourced?

We obtain data from plenty of sources, but we are rarely concerned about this data quality. Studies reveal that big firms lose almost $15 million every year just because of poor data quality. It is sad to know that the quality of most of the data is degraded due to human error, sometimes due to unmotivated clerks and inadequate inventory checks. Even if automated methods are followed for data collection, few significant sources of error still exist, such as mistakes in clearing or repeated losses due to cellular towers. Hence, before accessing any data, it is vital to understand how it is obtained and maintained.

How was the data analyzed?

Even if you obtain data from well-maintained and trusted sources, the quality of models used for analytics may vary—most of the time, these models obtained from some open-source platforms and repurposed for specific goals. We rarely know how models are designed and trained. In some cases, the analytics are disturbed due to overfitting, and many times, excess data causes leakage or disturbance in model performance. Experts advise following trusted models for data analytics so that they can meet the actual purpose.

What is missing in the data?

Data is mostly affected by judgments, and it can affect decision making by a considerable level. Moreover, sometimes data you don’t have can leave a significant impact on the overall analysis. For example, while handling a person’s financial details, the missing transaction details may indicate some risks of low credit scores.

How can data be used to redesign business models?

We all know that data has a significant impact on how the business industry works. When data is used efficiently, it can help business owners make better decisions about machine maintenance, process automation, and customer service routines. Without any doubt, data is an essential part of the product and can help to redesign brand value in the market.

When data is used and processed adequately, it can take your business to a whole new level.