As we’ve discussed in the past, there’s value hidden in your business’s data. You’ve undoubtedly experienced this frustration: you know the data exists, but that doesn’t make it inherently easy to access. In this article, we’re going to explore how to uncover trends in your business’s data in order to derive insights and make smarter decisions at your organization.
Uncovering trends in your business’s data
Using analytics software such as Tableau can help businesses find trends in data quickly versus sifting through Excel spreadsheets and trying to differentiate numbers. By utilizing graphs, charts and other visualizations, exploring data becomes more intuitive because analyses can be made efficiently.
Visualizing your data can be helpful, but what should you be looking for in your visualizations? Compare values against each other to find patterns. Patterns to look for include:
- Outliers/anomalies (When one data point is distinctly different from the majority of the others). For example, in a line graph, there will be spikes and dips. If your business notices a large spike one day, take a closer look at what happened that day and seek to understand what happened on that day.
- Clusters (Groups of similar data points). When you see small groups of data points on a graph, dig in further. Are they directly related or is it a coincidence? This can signal that you should put more effort in one area (if you’re seeing positive results) or less effort (for negative results).
- Relationships/correlations. Similar to looking at clusters, dig in deeper when you see data points that could be correlated. Look for opportunities to optimize these and drive more results for your business.
- Frequencies. Typically viewed using distribution charts such as a histogram or a box plot, analyze frequency to determine the magnitude of an event such as how many outbound phone calls a sales team makes within one day.
- Patterns. Use these to recognize relationships, regularities or repetitive occurrences in data. Visualizing patterns can be important in understanding relationships between variables and are relied on for predictive analytics.
- Variances. (The relative difference between data-points or other baselines such as averages.) Analyzing for variances is beneficial for determining the difference between what was planned versus what actually happened.
Most importantly, question why any of the above are appearing. This is the type of insight that businesses should be researching when collecting and analyzing their data.
Your business’s data has immense value and provides the largest business opportunity when formatted properly. Grab this resource to further understand how your data strategy needs to evolve to get the most use from your data.