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”

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