I have talked about the business analytics maturity model in terms of skydiving and preparing to step out into the “wild blue yonder” and so far I feel like my analogy has worked out well. Now it’s time to move on to the second phase of the course: becoming a lean fit, skydiving machine.

In the previous post we outlined a few ground school tactics. If you are following those initial tactics, then you will have used the process of finding outliers, engaged in positive business tactics to resolve issues early, and protected your brand. All the while, you have been building up an understanding of the analytics process and learning what works and what does not with your particular niche. Can you dust off your britches and call things done? No. You’re not even close to being done.

I mentioned in my initial skydiving post that during the fitness phase of your training you will become more sleek and streamlined. Now is the time to begin doing the things that make some analysts more efficient than others. Here are four tips to help you on your way:

Reading Is Knowledge

Expanding your knowledge of who is doing what and best practices in the field of analytics involves some research and reading. There are a lot of good reading resources available on the Internet and at the local bookstore that can help an analyst keep up to date on the latest and greatest in analytics practice. There are so many resources available to read through, just figuring out where to start this portion of your fitness training can be a daunting task. To start, I recommend Competing in Analytics: The New Science of Winning and Keeping up with the Quants: Your Guide to Understanding and Using Analytics, both by Tom Davenport. Even those with advanced degrees keep their fingers on the pulse of the analytics field, and Tom’s stripped down method of conveying complex process is an excellent starting point in research.

Knowledge Is Power

Getting to know your data is incredibly important. I cannot recount the number of times I have consulted with customers who didn’t understand all the data they had on hand. For some, Big Data is like a jar full of pocket change that needs to be sorted. Many people fully expect to dump their jar of Big Data into a sorting machine that will automatically sort their change for them, and the end result will be the machine spitting out a receipt that they cash in.

This is just not true. Like coins, not all data is equal. In your “jar of coins” (data) there can often be a lot of refuse that will gum up your well-oiled machine and leave you with much less solid data than you had originally thought you’d garner for your effort. Before you dump your change into the coin sorting machine, you would have to separate out the items that will not fit into the machine’s mechanism. Foreign currency, buttons, corn chips, and even gold bullion might turn up in your jar. Some of these finds might be big, others might be small, but one has to determine their relevance before continuing on if an analyst is going to cash in on the wealth of data available to them. Sort your data in the same way; identify and evaluate each set of data. Once you have done so, your “sorting machine” will run much more efficiently.

Apply Your New Found Power

Finding efficiencies is equally important. By finding tedious tasks and automating them, you leverage your time and create a lot of your own efficiencies. Some tasks have low value ad should be automated. A good example of this would be an invoice number attached to a certain sale.

I am a big fan of not getting eaten by sharks, so let’s use a shark repellent manufacturer as an example. Each sale made by Jaws-B-Gone could have a sales order number attached to it. Do I need to know what each arbitrary apha-numberic code is? Absolutely not. I might need an aggregate number of canisters sold during Shark Week, however. Perhaps the CEO wants to know the ratio of government sales to private sector sales for the month of December. Creating metadata saves an analysts a lot of headaches and is much more efficient than dealing with ungroomed data. Knowing this means putting automation into place that will extrapolate the data you are going to need from the sales orders, not sifting through a lot of cross-referenced data. Adding this step to your workout regime increases your ability to become leaner, faster.

Find New Power in “Old” Places

Many of those using Adobe Analytics Premium are not using the program to its full potential. Simple outlier detection, segmenting, and clustering is not enough. Using tools built within the software you already have is the key to unlocking all your data’s potential. For example, if you have read my blog, you know that I am a huge proponent of visualizing data. Being able to say “One of these things is not like the other” at a glance saves a lot of time. When graphed, mathematical data can often take on visual properties that are easily identified by the human eye. Using Anscombe’s Quartet as a tool for becoming more analytically fit is a great way to accomplish this task. Having this tool readily available and operating within a large framework, such as Adobe Analytics Premium, is a big win for analysts. Often though, this and many other built-in tools go unnoticed and unused. Not using them is a lot like only using second and fourth gear in your car, you might still get to your destination, but the ride will not be smooth for anyone.

Cooling Down After the Workout

Here is where my analogy diverges from the reality of being fit. Normally, one cools down after a workout, but in analytics, there is no cool down. Once you have these four steps down, keep doing them over and over again. Analytics is a dynamic field and requires constant study and activity. Keeping up with the new trends, new processes, and new software as it becomes available is paramount for success. An analyst should take time from their many duties to stay fit through study and practice.

In the next post I will be discussing how to put into practice what you’ve learned in such a way that you can “show off” your stuff. Look for “Analytics Reporting: Live from the Tube”