Summary: Advanced analytics and AI are the fourth great lever available to create organic improvement in corporations. We’ll describe why this one is different from the first three and why the CEO needs the direct help of data scientists to make this happen.
If you’re a CEO or any other flavor of top executive leading a large company (or if you’re a data scientists trying to help one) then the mission to create continuous improvement is obvious. Ultimately you probably think about this in terms of EBITDA or shareholder value or some similar KPI like top line or bottom line growth. The real challenge however is in knowing how to make that happen.
My favorite job description of the CEO is this:
That was told to me by a Fortune 500 CEO who said he was good at two out three. He didn’t say which.
Of course a CEO or other top executive doesn’t actually go out and break legs, or even directly hire, fire, or manage the detail operations of a company. Their job is to be a leader of leaders, to point out and prioritize the types and methods of change that are necessary now, and to provide and allocate sufficient resources in the right places.
Ultimately, if you’re looking for those levers to use to create positive organic change, there are only three places to look: people, process, and technology.
Where Advanced Analytics and AI Fits in This
Where advanced analytics and AI fits in all this can be a little tricky for someone not brought up in the ways of data science.
Is this a particular batch of people with these skills and knowledge we should be hiring for? Or is it more of a process as in the way people should be thinking about problems (analytically based on data). Or is it a set of technologies that we need to acquire and integrate?
Well yes. And that what makes it tricky, that it’s a little bit of all these things. If you’re used to thinking about people, process, and technology as separate levers you may miss the important parts of how advanced analytics and AI is enabling change.
A Little History about the Levers of Change
You can draw a straight line through the four major historical enablers of organic corporate improvement dating from the early 1990’s through today. I know. I was there to see it in my career as a management consultant prior to my pivot to data science.
In the early 90s the major lever was Process Improvement and Process Value Analysis. The core of this was about breaking down the traditional organizational silos between ‘departments’ and charting the path the major elements of work took across those often walled-off groups.
You could save a lot of time that people spent on tasks and also speed up the desired end result significantly. That would save a lot of money and better serve customers, basically doing more with less, being efficient with resources.
By the middle 90s we began to think in terms of Reengineering. Anybody remember Tom Davenport’s seminal book that kicked this off, ‘’? That’s where the phrase came from.
Reengineering was the marriage of Process Improvement and information technology. But we were no longer concerned with making existing processes better. We were set free to imagine how wholly new processes could be created if they were aided by IT.
At the outset, this involved specifying and developing large and elaborate custom software solutions. It was slow and very expensive but could deliver extremely large bottom line improvements and really let you break out from your competitors.
Toward the end of the 90s, the big software firms like Oracle, PeopleSoft, and SAP had created large standardized ERP systems based on our early experiences in custom reengineering. It was still expensive but at least someone else was looking after maintaining standardized software. And these new platforms had most of the best practices baked in. We called this Package-Enabled Reengineering.
