Huge (but Common) Mistakes in Data Analytics
Data analysis is not particularly useful if it is riddled with mistakes. Business leaders need to be guided by real, accurate insights, which will allow them to make better decisions to steer their organizations toward success. Yet, erroneous data analysis is a rampant problem — and one that costs the U.S. economy an estimated $3.1 trillion every year.
Fortunately, some of the most common mistakes in data analytics are also some of the easiest to overcome. This guide to massive data analytics mistakes should help data professionals and business leaders alike move beyond inaccuracies and develop a stronger analytics system for all.
Failing to Define the Problem
Many organizations collect as much data as they can, but such a chaotic approach to data analysis is usually not fruitful. Big data is most useful when data scientists understand what problem they are striving to answer from the get-go; then, they can direct their collection and analytics processes to deliver appropriate insights.
Without a thorough definition of the problem, neither business leaders nor data scientists will be satisfied by the solutions provided through data analysis.
Focusing on the Wrong Metrics
Some types of data are easier to collect than others, and some types of data are better at demonstrating organizational success. However, by cherry-picking certain metrics and ignoring all others, leaders are not receiving realistic visibility of the problems facing their business.
Executives must work alongside data scientists to identify the metrics most likely to reveal useful truths and continuously track those metrics to allow for more effective decision-making.
Believing Correlation Is Causation
Correlation occurs when two variables appear to have a relationship. Causation is determined when there is evidence that one variable is directly responsible for changes to another. Too often, business leaders assume that correlation and causation are the same — that because two variables seem related that one must cause the other.
For example, skydiving statistics that indicate skydivers are more likely to suffer higher mortality rates do not necessarily state that skydiving is causing premature deaths. However, this is a significant mistake that can result in supremely poor decision-making. Data science teams and executives need to work to overcome this belief.
Skipping Qualitative Data
Many data scientists feel more comfortable working with numbers, and executives can fall victim to believing that quantitative data is more important because it is more difficult to parse.
However, qualitative data, or data based on language, can be just as valuable, and in some instances, it is more informative than numerical data. Often, qualitative data will answer questions like “why,” which can help guide business decision-making in a positive direction.
Neglecting to Cleanse and Normalize Data
Data in its raw form is essentially unusable. Before data scientists can run data through algorithms and models, they must refine their data, eliminating any errors that will produce inaccuracies in the final insights.
Cleansing involves deleting errors like redundancies and typos as well as identifying incomplete and outdated data that might skew results. Normalizing data is the process of converting data into a consistent form, like all time measurements into hours rather than having time measured across minutes and days.
Advanced analytics tools that utilize machine learning capabilities can perform these duties automatically, but without these tools, business leaders need to understand how to cleanse and normalize their data by hand.
Selecting the Wrong Visualization
There are dozens of variations of data visualizations, and each one offers unique benefits. Data scientists should choose a visualization based on what they want the visualization to accomplish, such as displaying change over time, offering a view of data distribution, comparisons of values between groups, and more. The wrong visualization will highlight the wrong variables in a data set, resulting in less effective insights and less valuable decisions.
Falling Victim to Various Biases
Bias is almost impossible to fully eliminate from data science — as to have bias is to be human. Again, relying more heavily on machine-driven data science tools can help mitigate some forms of bias, but both data scientists and business leaders need to be cognizant of how their biases might impact data collection and interpretation.
Understanding the most common types of bias, like confirmation bias and historical bias, is the first step to overcoming bias.
Major mistakes in data analytics set organizations back. By investing energy into eliminating errors in the data analysis process, businesses can not only survive but thrive with big data.