The Big Reasons Your Data Analytics Is Consistently Wrong

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These days, businesses run on Big Data. Data can provide outstanding insights that allow for radically better decision-making, allowing organizations to effortlessly deliver on consumer demand and easily out-compete their opponents in their marketplaces. As more businesses undergo digital transformations, more will understand and leverage the power of data analytics.

Unfortunately, this means that even more companies will get Big Data wrong. The usefulness of the insights provided by Big Data depends entirely on the accuracy of the analytics used to understand that data.

Here are a few common mistakes that businesses make in analyzing the data they capture and what you can do to avoid these errors going forward.

You Didn’t Cleanse Your Data

Every data set — or at least the vast majority of data sets — contains errors that will impact the accuracy of any effort at analysis. Errors like redundancies, typos, irregular naming, and more as well as incomplete data and outdated data will all cause data analytics to stray from the truth. Before an organization can benefit from data analytics, they need to make an effort to cleanse the data sets of such erroneous information.

Data cleansing can be a laborious manual task that involves combing through spreadsheets to identify duplicate data, spelling mistakes, and the like. Alternatively, business leaders can invest in augmented analytics, which harness the power of machine learning to speed up and improve the accuracy of every step of the data analysis process. In either case, businesses cannot skip data cleansing, as it is a fundamental step in ensuring data accuracy.

You Didn’t Normalize Your Data

Normalization is the process of transferring data into a consistent format to allow for comparable and compatible analysis. For example, if one data set shows the monthly income and another shows the annual income, the results of the analysis will be incomprehensible. This mistake is much more common than one might suspect, especially given its simplicity and the ease with which it can be rectified. A smart practice is to maintain a standard format for analysis and to normalize data to that format as soon as data is collected.

You Rely on Inaccurate Algorithms

Few algorithms are truly perfect, but many algorithms are critically flawed. Flawed algorithms do not behave as intended; they might ignore vitally important data or place too much weight on data that has little import. The most prominent tech firms are constantly reviewing and tweaking their algorithms to ensure that their programs are accomplishing their stated goals. 

Likewise, data scientists within any organization should prioritize updating their data analysis algorithms. It might be necessary to create an updated schedule, which could keep analytics teams accountable for the consistent maintenance of their algorithms. However, an even more optimal strategy might be relying on AI- or machine learning–driven algorithms, which might have some capacity for updating themselves.

Your Models Are Bad

Many business leaders do not realize that there is a significant difference between algorithms, which are the methods used to analyze data, and models, which are computations created using the results of algorithms. Algorithms might crunch the data, but to gain usable insights, the output of algorithms must be put through models that test the resulting analysis in various ways.

Unfortunately, bad models quickly ruin the results of even the most perfect algorithms. Business leaders need to work with data analytics teams to ensure that their models are not overly simplistic or too complex. Depending on the amount and type of data available to business leaders, models can provide different insights, and it might be necessary to experiment with different models to find the right fit.

You Are Falling Victim to Bias

Bias is one of the most pervasive issues impacting the accuracy of data analytics — and it is often the most difficult issue to identify. There are dozens of types of biases, from biases that impact what type of data is collected to biases that skew leader interpretation. Executives may want to enroll in courses focused on biases in data science to understand and eliminate any biases that may be affecting the accuracy of their analytics.

The power of data is immense — but when that power is wielded improperly, it can cause business leaders to make major mistakes. Leaders should do everything they can to make their data analytics as accurate as possible, which means looking for some of the worst errors listed above.

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