How To Avoid These Common Big Data Traps?

With huge amounts of data flowing in businesses on a daily basis, the concern today is not how to get more data but how to manage the data you already have. It is often harder to interpret and translate received data into something useful and meaningful. This useful information can be then utilized to take better business decisions, lower operating costs, increase revenue, and improve productivity.

Both startups and large organizations are equally vulnerable to data traps. As the data inflow in your business increases, so does the likelihood of getting trapped in this data.  Then, instead of growing your business and increasing efficiency, you might end up making decisions that lead to negative outcomes.

To circumvent falling in big data traps, avoid these 4 mistakes.

4 Common Big Data Traps to Avoid

1.Data Doubt

In a recent KPMG report, it was revealed that 84% of business owners or CEOs don’t believe in the quality and efficiency of their data. For most of us, no doubt, data quality is the topmost priority. There is no denying to that. However, going too far and losing faith in your data completely leads to lower productivity.


You still need to make decisions which are important for your business. The only difference is that you are not taking help of wide, useful data available.

While worrying about the quality and usefulness of data is important, unmanaged and unfiltered data is still useful. It can provide you guidance and direction in which you should work. For instance, with data analytics, you can keep 10-15% fraud orders or returns at bay. This won’t be possible with the traditional system.

Too often, we aim for perfection, which is not achievable. In an effort to do so, we end up wasting our time, resources, and money.

Instead of assuming that the efficiency or quality of your data is low, use these tips to remove data doubt:

  • Review how frequently you observe data discrepancies.
  • Analyze whether your issues related to data are imaginary or widespread.
  • Constantly collaborate with IT professionals to ensure data errors are kept at acceptable levels.
  • Gain confidence in your data by evaluating the precision and how useful data has been in making decisions.

2.Data Overload

When too much data comes in, many businesses get overwhelmed by the amount of information available to them. It is data overload, which occurs when there are too much data and too many metrics, insights, and analytics to consume.

For instance, businesses usually term every KPI as essential, which means every metric is important. Hence, to make visualizations and dashboards attractive, more information is incorporation. However, the knowledge that should be extracted from the given data remains minimal. While analyzing data, having too many important KPIs lead to noise and confusion.

Further, many times, data overload happens when analysts have little knowledge of interpreting data or they don’t understand the insights, metrics, and methods they are using.

How can you overcome overwhelming data overload?

  • Reduce KPIs to the ones that align with your business’s strategy.
  • Consolidate KPIs and their relevant metrics to improve the reliability of data.
  • Make small data groups make it more understandable.
  • Simplify your dashboards and visualizations.
  • Remove irrelevant information and reduce it to what is necessary.

3.Over-Analyzing Data

Many analysts suffer from over-analyzing of data. They just can’t stop thinking and over-analyzing the given information. Hence, they fail to achieve important insights or outcomes from it.

It is good to keep digging deeper but you should know when to stop. Sometimes, you would just be wasting your time and getting nothing useful out of it in the end.

In many actual instances, analysts suffer from data doubt first, which triggers the over-analyzing of data. This not only wastes resources and valuable time but also nothing useful is extracted from this hard work in the end.

Here’s how you can stop over-analyzing data:

  • Know the limit or threshold.
  • When there is no solution, step back. Start over with a fresh mind.
  • Share your progress with someone else to know what profitable outcomes can be achieved from your research.
  • Refocus on original goal when you get lost.
  • Start from the basics and utilize basic approaches.

4.Useless Metrics

Focusing on data, metrics, and KPIs that don’t matter is also a problem. In fact, this is the most common trap in which many organizations fall. You are unnecessarily wasting your time on data that won’t give you any useful outcome.

To avoid falling in the loop of analyzing useless metrics, here’s what you can do:

  • Know what type of data and insights are useful and valuable for your organization.
  • What data insights will assist you in analyzing the needs of your customers.
  • Stress more on quality of data rather than quantity.
  • Evaluate the purpose of analyzing data in the first place. For most, it is the ability to make better decisions.


It is easy for data analysts to get lost in data, get overwhelmed by the amount of information, and forget the purpose of analyzing it. However, it possible to overcome these traps by gaining proper knowledge, working in collaboration with other analysts, and sticking to the original plan.