Technically Speaking

The Official Bigstep Blog

5 Reasons Why Your Data Analysis is Inaccurate

Are you getting inconsistent or confusing results from your big data analysis? There are several common mistakes that lead to inaccuracies. The good news is, all of these problems are relatively easy to fix (once you know what to look for), so that you can get your efforts back underway with greater success.

1. Your Data Isn’t Properly Cleansed

Your data is dirty. Clean it up for better results.

The majority of data sets hold errors like redundancies in the data (data that is entered multiple times), incomplete data, data that is outdated, or data that is simply inaccurate. Data cleansing is the process of going through the data to weed out the errors in order to assure that the remainder of the data is pure and accurate. Only by starting with good data can you render accurate analytical results.

2. You Failed to Normalize the Data

The data has to be normalized, or transferred into a consistent format in order to produce accurate results. For instance, if you run one analysis using the monthly income of your group and another analysis using their annual income, the results won’t be compatible. This sounds like a ridiculous mistake to make, but in fact, it’s more common than you’d think. NASA once crashed a $125

  orbiter designed to study mars because one team of scientists was working with metric measurements while the other team was using standard English measurements.

3. Your Algorithms are Inaccurate

Not all algorithms are created equally. Look at Google: they tweak their algorithms continually, always making adjustments and refinements based on what they see in real world application of the product. All data scientists should do the same. Continue to revise and refine your algorithms until you get those right. Then keep tweaking to go from better to best to perfect to even better than that.

4. Your Models are Too Complicated or Too Simplistic

Like algorithms, working the models out correctly takes time and some trial and error. It’s extremely easy to develop models that are entirely too complex, and just as easy to come up with models that are far too simplistic. If the data analytics is not yielding the results that you think make sense, take a look at your models. See if simplifying the model or perhaps asking a little more out of the data might help you get the analytics just right.

5. You are Holding a Bias That Skews Your Interpretations

Most of us are holding some sort of preconceived notion or bias without even realizing it.

Of all the problems on this list, this is the most difficult to identify and even harder to correct. Every researcher wants to believe that they are 100 percent objective. Yet few if any truly are. What biases are you holding that might be causing your interpretations of what the data is telling you to be off? It’s helpful to get a second (or even third, fourth, and fifth) pair of eyes on the data and analytical results to see if you are actually approaching your interpretations with the objectivity a scientist needs to have.

With some effort, your data analytics will render accurate and reliable results in no time.

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