Stop Collecting Data and Do This

This might seem like a crazy thing to say, but it is rare that not having enough data is the reason a company makes sub-optimal business decisions.

It is, of course, a belief of biblical proportions in our community that more data = smarter decisions. This belief is less true than we would like to accept.

[Sidebar] At the end of this email, I have two small requests for you. Thank you. [/Sidebar]

Despite being blessed with an incredible amount of data, the best systems money can buy, and tens (or more) analysts, companies continue to make incredibly poor strategic and tactical decisions. (And, I’m trying to be sweet by calling them poor decisions. A more deserving word would be atrocious.)

The contributing reasons for this reality include:
Senior Leaders allowed to make big decisions without any justification based on data (or even involvement by Senior Analysts)

An organizational culture that lacks any sort of strategic accountability

Mid-management layers are more focused on keeping up appearances versus moving the team/department/company forward

Shiny – transient – objects are widely praised, rarely connected to bottom-line

The analytics team’s lack of influence in the organization

Analysts lacking skills to close the last-mile gap

And of course a lack of optimal staffing (you know that I am a giant fan of the 10/90 rule, postulated in 2006!)None of these problems can be solved by collecting more data.  (And, it is not enough that these companies will eventually transition to non-existence.) 

These problems require a collection of solutions that focus on psychology, behavioral science, organization design, strategies that make you indispensable, etc. I’ve covered these in the last dozen TMAIs. I hope you’ll reflect on those and activate the guidance.

Today, I want to share a table to help you optimize where your analytics investments are targeted – since a non-balance is a big contributor to data’s existence, yet data’s non-influence.

Stop collecting more and more and more and more data.
Or, at least reduce the emphasis on ever more collection of data, ever more implementation of tools, ever more tag optimizations, and giant projects that integrate “global systems to create a unique competitive advantage.”

The reason you are happy with your analytical work, but your soul hurts due to the lack of data’s influence in the organization, is not primarily a ​​​​lack of data.
I classify analytical work into three clusters.

Data Capture (DC). Everything (people, process, tools) you need to do to collect, merge, clean, store, and more, to allow all other analytics functions to, well, function.

Data Reporting (DR). Everything you have to do to automate data spewing from your tools to individuals and teams who express their needs (regardless of wants), and creating customized data pukes (CDPs – aka dashboards, drillable cubes).

Data Analysis (DA). Everything that requires analytical skills including answering questions rather than providing data, focusing on the delivery of insights rather than just data to illustrate outcomes, building advanced models to identify and expose meta patterns in the data rather than focus on narrow silo analysis, and, surprise, surprise, obsess about closing the last-mile gap (ex: earning influence).

All three are important, but the amount of time/people/money you put into each cluster is not equally important. In fact, in my experience, it should be a lot more right-overloaded rather than left-overloaded.

Here’s my recommendation, by business size…
Optimal balance of Analytics investments.If you are a small business, you’ll be lucky to have part of one person available to focus on data. Hence, my recommendation is for the worst case scenario of 0.5 person. Use free tools, use the functionality and features these tools allow, and use part of that person’s time with the distribution above.

If you are a medium-sized business, your optimal DC | DR | DA balance is 10 | 25 | 65 because you are always in a fight for survival and you have fewer competitive moats protecting you. You are going to have a few dedicated Analysts. If you have three “Analysts,” pay more and hire two real Analysts and ask them for real analysis to ensure you solve for 65.

If you are a large-sized business, you have to solve for organizational and systems complexity which will sap your ability to as much DA as you want – hence a goal of just 50 for you. This is also a reflection of my, reluctant, acceptance that massive companies cannot function without the soothing balm of data puking (hence DR at 35). To get to DC of 15, the highest-paid people in your Analytics team will be in DC – most companies try to solve this with quantity, which is a huge mistake. Pay for quality of people.

Pause. Reflect.

What does your company’s analytics time/people/money investment look like?

Are you close to the optimal recommended above?
If your answer is God, no!, that is OK. 

Persepolis wasn’t built in a day.

It takes knowledge (above), determination (you already have this), and time (you have less than you think).

For a medium-sized business, here’s guidance for evolution that lays out time-based milestones:
Timeline to achieve optimal DC | DR | DA state.You can use Column 1 to assess if you are in the optimal state given the months into this journey metric.

Initially, you can see that there is a bigger investment in DC. Then, there is an aggressive shift away from just connective data to actively investing in influencing company decisions based on data. Solving the problems outlined at the top of this newsletter.

While reflective of medium-sized businesses, you can adjust it for your company size. As an example, for larger companies the time to optimal might be 18 months.

If your current state is unreflective of the spirit above, perhaps a come to jesus session might be warranted.

(A little bonus for you in the last row, for how an Analytics team should be organized.)
Bottom line: You come to work because you want to have an impact. Your soul does not feel satisfied because you created a lot of analytics activity (hint: that is while there might be a hole where happiness should be). Treat your analytics life as if the only reason is to drive analytics outcomes.

Your soul will be happy (your employer too).

Solve for soul.


PS: Having switched to a new email service provider, to build something new, I have two small requests:  Please click on this link to visit my blog Occam’s Razor: Responses to Negative Data: Four Senior Leadership Archetypes. It’ll let the service provider know this email is going to a live person!

If you see anything awry in this email (formatting, delivery, anything), will you please let me know?

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