Dig Into Your Data

Posted on Oct 1, 2018 - By Leesa Love

Data is by far one of the most powerful tools a business has at its disposal. Companies across all different industries have learned to harness all kinds of data and use it to their advantage. This is because examining data in large amounts reveals patterns in customer behavior and company operations, which indicate how a business is performing. Through further analysis of these patterns, it is possible to identify issues, make predictions and build strategies to maximize efficiency and revenue. However, as powerful as data is, it can be a downfall when it is colored by assumptions from outdated analysis.

The real challenge of analyzing extensive collections of data is avoiding assumptions. Over time, it is easy to be lulled into thinking you know the most optimal way to look at your business. Many companies fall into this trap by always relying on the same methods of analysis to interpret data. Focus is often placed on an approach that works, and subsequent data is simply monitored through spot-checking. But spot-checking does not always guarantee the most accurate interpretation of data. Unfortunately, when data is only examined from one perspective, it is more likely that issues will slip through the cracks. As a result, you might be unaware of an ongoing issue costing the company significant time and profits.

It is vital to fight against the tendency to form assumptions about your data. The best way to combat this is through challenging these assumptions head-on. Essentially, dig into your data. Instead of relying on an outdated analysis to tell you when changes are happening, intentionally seek out anomalies in the data, and find out what caused them. Try dissecting your data in different ways, and know how those different approaches impact the results so you are able to spot anomalies. Take notes on your process as you go, as subtle differences like time zone or default browser settings can cause huge discrepancies.

Improving your approach to data analysis will help you make better decisions in regards to the future of your business. By examining the data from multiple perspectives, you can ensure that your business is optimized to its fullest potential. This will help you draw in new customers, increase customer retention and improve marketing procedures, all while increasing revenue. There are several different techniques to challenge the way you look at your data. The approach often depends on the specific industry you are in and the type of data you are working with, but all involve actively searching for anomalies during quality assurance.

  • Break down your assumptions. The first step to challenging your assumptions is to know that they exist. Understand the assumptions you have about your data and where they come from so that you can work around them. This is especially effective for examining areas like customer data. Predicting customer behavior might seem intuitive, but it can surprise you. For example, while you might think that sending out an email right after each transaction is seen as good customer service. However, your data might suggest otherwise.
  • Be your own worst enemy. Many times when you analyze data, you examine it with the expectation that it will agree with your assumptions and trends. However, when you are only focused on the result you want, it is easy to dismiss discrepancies as natural deviations in data. Instead, try going out of your way to find an error. Test and retest your log data using queries with the intention of tripping yourself up. This way, you won’t simply be complicit if the data agrees with you and accidentally miss something.
  • Challenge the trends. The circumstances around which you regularly pull your data matter. The market is constantly changing, and so is your data, so find a way to challenge the trends that emerge. If you always pull data from the same day or time frame, you are likely only getting part of the picture. Shake up your trends by flipping your data-pulling strategy. If you normally pull weekly data ranges from Monday to Sunday, try pulling a different range. When you pull Wednesday to Tuesday, the results might reveal different patterns that bring new issues or trends to light. The same idea goes for times of day and times of year.