7 Haunting Big Data Analytics Mistakes to Avoid

Data analytics is one of the most essential processes in your business, big or small. It’s because it functions as your pair of glasses to see the actual business reality, beyond scattered numbers in graphs. A solid, reliable analysis allows you to make rational, fact-based decisions, while mistaken data analytics can lead you astray and cause enormous harm.

It’s not enough to have quality data, as reliable as it may be. In order to be able to efficiently use your data, you need to first analyze it correctly.

To help you do that, we’ve assembled 7 very common big data analytics mistakes people make that prevent them from using their gathered data correctly.

1.       Assuming that whatever was, shall therefore always be

We get used to recurring data very easily, thinking that no major changes are expected after an all-too-familiar record of the same stats. But, anomalies do take place, and the sooner you identify them, the sooner you can act upon them accordingly.

2.       Depending on the same KPI’s you’ve always used

Things are always changing and your business is subjected to constant dynamics. As you well know, this is a good thing – you can only prosper if you learn how to adapt to the ever-changing environment. So, don’t hold on to the old performance indicators used to measure your past success. Use newer more suitable tools to really reflect your business’ current performance and find out what really drives your business today.

3.       Throwing away data sources that seemed futile or inaccurate

We’re often very hasty in giving up on data sources that didn’t prove to be accurate or complete in the past. Today, innovative solutions are available to help us clean up and sort unclear aggregated data so that you can leverage it for creating  a coherent, valuable report otherwise unmade.

4.       Expecting everyone at the office to find equal value in the same analysis

Rather than introducing the same analysis to both the marketing and finance departments, try framing it according to each department and role. Personalize the analysis specifically for each position in your business and gain different perspectives, leaving room for flexibility to answer varying needs.

5.       Thinking the best analysis is the most detailed one

Leaving out no loose ends is always important, but don’t let your need for a comprehensive report turn it into an overwhelming document, impossible to deduce anything from. Even more important than a detailed report is the ability to carefully separate the important factors from the unnecessary, even if it means it won’t include every metric imaginable.

6.       Believing performance comparisons to last year’s provides the ultimate insight on business health

Year-Over-Year comparisons are very common but can be misleading. Things change over time and phenomena can cancel each other out, leading you to believe your business is stable while in reality you could have improved sales performance by identifying trend changes as they occur and correcting them. It’s important to look beyond such single-time-point KPIs and analyze facts based on a comprehensive picture of the organization’s dynamics along the way.

7.       Reinventing the wheel every time a new boss or user comes along

It seems that every time a new boss or user comes along we’re required to rethink our previous ways. Although it’s true in some cases, data analytics isn’t necessarily one of them: most business questions are well known and common, so there’s no need to introduce inventive analyses every time a new data analytics request comes along. Instead, leveraging pre-packaged applications tailored for your needs, allows you to be able to answer any analytic requests that may be relevant. That should do the trick!

Helping to avoid these common big data analtyics mistakes is a must when it comes to a valid reliable data analytics solution, which is, no less, the backbone of good business decision-making.