3 Common Mistakes In Data-Driven Decision-Making

As a leader in the business, typically, you’re expected to utilize data to make a well-informed decision regardless of whether your title includes”data. “data.” Everything from how much money to set aside for a marketing campaign and how many headcounts to be able to approve to what your projection of sales ought to be. But making decisions based on data isn’t just a catchy slogan but an instrument based on best practices. There are three mistakes that business leaders make when using data to make their decisions.

3 Common Mistakes In Data-Driven Decision-Making
3 Common Mistakes In Data-Driven Decision-Making

Skipping Data Validation

If you are presented with a short timeframe (as we frequently are) and an information set, it’s tempting to begin analyzing your data. But, your conclusions will only be as valuable and insightful in the context of the base data. It is vital to take enough time and effort to verify the quality of your data set.

In the case of data validation, begin by keeping an empathetic view of the data. Put on your detective hat and attempt to discover the errors within the information. Use your business knowledge to write the following sentence: If the notification is accurate, then ______. Use SQL or Excel to verify your assumptions before beginning an actual study.

Underestimating The Impact Of Low-Probability Events

The less likely events may significantly influence your striving to reach objectives. For instance, although the incidence of pandemics is rare, a small number of companies around the globe have yet to have a significant impact from Covid-19 over the last few years. As a leader in business you cannot anticipate all the events with low probability that might occur, yet you’re often forced to make a choice. What should you do?

Another option is to explicitly ask yourself: Given the length of the data, What could the data have not “seen?” For instance, if your data includes two years’ worth of sales information, it is possible to conclude that any unusual occasions that occur only once a year are likely to have been recorded in your report. This means that the events don’t require particular attention to account for your research. However, suppose you only have one or two months of data. In that case, you need to consult with your team members to devise scenarios that could only occur every year (seasonality is one of them) and apply your business sense to complement the data’s conclusions. The presentation of a list of less-probable and high-impact incidents alongside your data analysis will assist your employees in making more informed decision-making.

Overlooking The Power-User Effect In Your Analysis

You’re the owner of a gym and trying to figure out how much time your members spend exercising in your gym. An “easy” way to work this is to sit near the entrance to the gym and ask those 20 members who pass by how often they’ve been to the gym over the past month, then determine the average of those 20 responses. Be careful. The standard you get using this method will only represent part of your membership. Why? Because a regular gym user is more likely to be monitored by you than a gym member who goes to the gym monthly.

In analyzing the usage of your product, it is essential to carefully consider whether the approach used results in results bias towards your power users. It’s not to say that you should ignore the findings you discover this way. However, it does suggest that you exercise caution.

It is not a stretch to update that many of our lives are centered around data. Business decision-makers should treat data analysis as a potent tool, but it also comes with errors, traps, and profound ways to harm. By combining information with sense and continuously questioning our methodology and methods, we can increase the benefits of data analytics.

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Samatha Vale
Samatha a senior writer for HC's entertainment team. She is an entreprenuer, mother and an excellent writer. She's also an avid reader, music enthusiast and all around inquisitive person - which is just a nice way of saying she's nosy.

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