Ask two data analysts the same business question, and you may get two completely different answers.
Why is there so much dispute about what should be cold, hard facts? Unfortunately, the act of analyzing data is not a simple one. There are so many variables that even if you use the same data set, you can interpret the findings in multiple ways.
If you’re not a professional data analyst, it’s all too easy to get lost in your business’ data and never find the answers you so desperately need. The smallest, most insignificant errors can have huge consequences in your analysis, and you may not even realize it.
How do you ensure that your data analysis isn’t completely bogus? There are a number of concrete steps you can take to improve your analysis, and get the right answers every time.
1. Have a Clear Goal
Like any successful business venture, you have to set a specific end goal in order to be successful. Having unclear goals or no goals at all is going to destroy your data analysis before it even starts. If you don’t set a stopping point, you could end up chasing an unreachable goal and staring at the data for hours. Instead of wasting valuable time, start by setting yourself a narrow, and specific, goal. For example, your goal could be to learn what’s lengthening your team’s sales cycle. With that as a starting point, you can delve into the data, analyzing the sales cycle over time and for each individual. Sometimes you may learn something completely unexpected with your analysis, but that should never change your end goal. Remain focused and don’t be distracted by interesting but insignificant patterns. Keep your eye on the ball at all times, so you can figure out what’s really going on.
2. Ask the Right Questions
Once you have a goal, you have to ask the logical questions that will help you reach that goal. Much of the confusion around data analysis stems from business people asking the wrong questions from the beginning. If you don’t know what you want to know, how can you get the right answers from your data? It’s not a good idea, for example, to go into a data analysis asking generally, “What’s holding back my sales team?” or “Why aren’t we closing more deals?”
Instead of just randomly looking at the data from any angle to try to find answers to these broad questions, start out with a very specific business question you want answered for a specific time period. For example:
- What is your average sales cycle in the past 12 months?
- What is your sales win rate in the past month?
- What is your average deal size in the past quarter?
These questions start your analysis off on the right path, because they are very specific and have set parameters that will help guide you. By answering these questions clearly, you can then begin to understand what’s holding back your team and why you’re not closing more deals.
3. Be Consistent
The best data analysis tracks patterns over time, so you can experiment on your sales team and try new things to improve your business. However, you’ll never be able to track your results successfully if you’re not consistent. If you analyzed your sales win rate by employee over the past month, including upsells and new business, make sure you ask that question the same way next month. If you forget to include upsells in the data analysis in a month, you’ll unsurprisingly have a rough time tracking patterns in your data. Consistency is the key to seeing measurable, actionable information from data analysis.
4. Always Run the Numbers Multiple Ways
Much like your teachers told you to always check your work in school, you should check your data analysis carefully before you make any serious decisions based on that data. You should to always have an eye out for data errors and bogus data that could alter your analysis and skew the numbers.
To check your work, run the analysis from a different angle – considering a different time period or a slightly different data set. Then, triangulate the two reports to find your true answer. Looking at the data from slightly different perspectives will confirm that you’ve found an accurate result.
Data analysis can seem intimidating or even risky to an everyday business user, but the value to your team far outweighs the risks. By analyzing your data consistently, with clear goals, specific questions, and always checking twice, you’ll be able to improve your business results faster than ever before.