You Mean Big Data Isn’t Always Right?

There are a set of words which has been trending for the past several years across the business scene:  big data. You know, the use of data to assist the organization make decisions, analyze its customers and employees, and just be all about smarter in the marketplace. In fact, the wave of data being requested and utilized today is far greater than most people can keep up with and it is estimated that 90% of the data available today has been created in the past two years alone.

As I sit back and think about that statistic – 90% of data available today has been created in the past two years alone – it is both shocking and real Databecause it demonstrates how the emergence of technology is continuing the ease of society to analyze everything. What used to take many months or years to compile and analyze, simply takes several hours or a couple of days to compute now. However, with the emergence of data readily available to give us insight, how come it isn’t always right?

Wait, am I really saying that data isn’t always right? YES, I am. Just like data was not always right twenty years ago, doesn’t mean it is always right today because we have better technology that can compute it faster. Let’s analyze some thoughts on why data isn’t always right and how to improve overall performance when it comes to using data.

Over Analyzation

Seeing as every where we turn today there is some sort of someone spouting out this data or statistic, it doesn’t mean it always has meaning. Because technology has made it easier to compute and compile data to use in our analyzing of business, education, etc., humans many times get caught up in the effect of over analyzing data. Meaning the first revelations discovered by way of the data are not good enough and we need to keep analyzing to determine what can be found next.

Although this can sometimes be a good thing, we have to stop and put back into perspective what it is we are wanting to know. Versus, not getting the answer we wanted and let’s dig some more to see if we can get what we were hoping for. Information is only as relative as we make it and need it – therefore, stop using it for the sake of solving all of the problems at hand. Especially when it comes to employee performance.

There is a Story Behind the Number

As someone who enjoys data and analyzing it, I get my kicks out of the detailed reports and insights around employee behavior, market segmentations, etc., but I also know numbers require further perspective. As I always state, there is a story behind a number or spreadsheet. For instance, if you are evaluating your employee retention – just based on a number/spreadsheet – it is easy to make the assumption that because one manager has high retention they are probably a good manager and have a high performing team; whereas the manager with low retention is more likely to be a bad manager and have a less than stellar performing team.

Digging into the data a little deeper could reveal that although the manager with high retention can keep employees, all of his/her employees may not  be engaged and are looking for something better or perhaps performance could be significantly low. The manager with the low retention could reveal a difficulty to finding the right candidate to build tenure in the department – meaning no issue with manager per se, but it could be a recruiting issue or perhaps the manager needs more training in interviewing skills.

As the saying goes:  we know what happens when we assume?

Humans Still Analyze the Information

As long as you have humans analyzing the information, there is always going to be the possibility for error. Formulas could be wrong, a sort in data could be off by one column, or the wrong data could have been pulled in. All situations that are real in today’s world. Therefore, it is necessary we not be experts per se, but we do need to be knowledge seekers and know how to apply critical thinking skills to formulate questions, understand how A equals B to C, and ask appropriate questions to ensure the data is correct. In instances where data is incorrect, it sucks, but we are human. We make errors. Learn from the mistake and move on.

How to Get Smarter

There are many more areas I could discuss on why data is not always right, but I think the three I highlighted are a good starting point and many Smarter with Datatimes the focal point of why data does not always work. To wrap it up all in a pretty package on how we can get smarter with data, simply put, become better at it through knowledge. Many people are scared of data because they do not feel they can analyze it to the degree even your best PhD could, but data does not require you to be a genius to get it. If you just sit down, understand what was initial purpose of the analyzation, what were the means of data to analyze, how did we analyze it, and what were the results, you will be in a better place to get it. The “why” is what is missing a lot of times and without the “why”, it can be hard to connect A with B.

Now if you still find yourself struggling to become smarter on data, do not be afraid to ask for help! Again, just like I enjoy data and feel I am pretty good at grasping what is presented, there are more people out there who can do the same. Do not be afraid to ASK FOR HELP!

You owe it to yourself, employees, and customers if you are using data to make decisions about the organization, employee performance, or customer experience.


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