Robert Woods has spent years working with organizations on collaborative lean development, Agile testing techniques, requirements analysis, project envisioning, relationship management, Agile within ITSM and Agile leadership. Robert is the creator of the CLEAR (Collaborative, Lean, Evolving, Adaptable, Reportable) Portfolio Management concept and has developed an entire Agile adoption curriculum. Robert’s passion is helping organizations achieve Business and IT Alignment through creating visibility and collaboration across the enterprise, focusing on delivery of real business value, and creating great teams focused on innovation, communication and trust.
Predictive Analytics is Killing Agility
Can too much of a good thing still be a good thing?
I understand why everyone desires to be what we have termed “data-driven”. Business Intelligence, Big Data, Data Science and Detailed Analytics are the latest buzzwords for many organizational decision-making efforts. I get it...we don’t want to guess; we want to be informed. We made way too many bad decisions using our proverbial “gut”.
But, something ironic about all of this hit me recently.
The Problem With Data
For many companies, one of the core initiatives that will help us achieve long-term innovation and market success is our ability to adapt and flex when needed; and do it better than the next guy. We’ve all been scared stiff from horror movies like “Blockbuster the 13th” and “Nightmare on Kodak St.” So, we’ve placed an enormous effort into making sure we don’t just course correct to market and customer changes but also deliver on the innovations we feel will disrupt our market.
We want to be agile to change and disruption. It’s the right thing to do if we want to succeed long term.
But here is what made me chuckle the other day.
I see large, medium and small companies spend a great deal of time and money on what we call predictive analytics. According to Wikipedia, this is defined as, “a variety of statistical techniques from data mining, predictive modeling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.”
We are trying to use a mix of historical data and what we know as of today, to predict what will happen in the future. I thought we tried to stop predicting the future and started focusing on responding to change instead! I know, I get it. The data at least helps us be more accurate than we were before. But what I am seeing is fewer and fewer companies keeping their focus on being highly adaptable and instead getting seduced by the Corporate Moneyball scenario and tricked into thinking they can predict change.
Here is a scenario to prove the point...
How many times have you gotten up in the morning, walked to the stairs, placed one foot in front of the other and navigated those stairs successfully? Let's say hundreds of times. Historical data seems to indicate you can navigate your stairs successfully at an extremely high rate providing a great deal of confidence. Current data suggests no reason to feel any different. So you confidently walk to the stairs and begin making your way down only to step on the helmet to a Batman Lego! You don’t even have kids! You jump grabbing your foot and realize you’ve lost confidence in your ability to remain on those stairs successfully.
I hope you display great agility…
The data wasn’t bad data. We simply were not prepared for the unknown.
One of the big weapons of the Borg (check out Star Trek if you’re not getting the reference) was their ability to adapt to being fired at, correct? It was not their ability to predict the future, as hard as they tried.
Too much attention to our data analytics can potentially place us into a mental lull, almost an overconfidence in what we expect the future to hold and we go back to being shocked when we encounter the unexpected. Haven’t we been saying for years now that getting good at dealing with the unexpected is where we want to be? Then, when I navigate the stairs, I’m not as concerned if there is something on them because I've got the agility to keep from falling. Then we learn, keep your eyes open going down the stairs. That's good data.
Blockbuster probably felt they had a pretty good idea of what the future was for renting movies...until they didn’t. Profits were good, people walked into stores, movies were still getting made. And then they stepped on a Lego. Was it bad historical and current data or the inability to adapt that sent them tumbling head over rear? Make no mistake…
- No amount of predictive data can prep us to deal with something we have simply never seen before.
- More than ever we are seeing things we have never seen before.
- Responding to change is still more important than following a plan, regardless of how good the data backing the plan is.
Don’t get me wrong, I’m not anti-data. We want to be as thoughtful as we can, just not at the expense of achieving and maintaining adaptability. I would add to that the ability to create our own “things no one has seen before”.
Don’t get seduced by the charts, graphs and predictive analytics that tell us the stairs should be fine to walk down. Instead, use them to help you consider better ways to walk down the stairs and don’t be surprised if, along the way, you step on an unexpected Lego!