In building business simulations, we are often called upon to create systems that mimic what humans do manually. Call them business rules, work processes, whatever you like – these are the minute-by-minute decisions and analyses that people perform, often without even thinking very hard about the complexity of their own solutions.
We have recently been working in two projects where the essence of our software simulation is the duplication of a basket of these human cognitive tasks. Our clients were surprised by our seemingly slow and clunky process for creating software code, just to crudely mirror its human counterpart. “Why does it take so long for you guys to write code to do X?” was our client’s frustrated plea.
“Making software that competes with human intellect is hard”, was our less-than-profound response. But its true – the human brain processes an amazing amount of information very fast, and also in parallel. If I showed you, for example, a box of red colored balls with one yellow, the human can spot the yellow in an instant. If I build some code to do the same thing, I effectively have to write a loop that makes sure that it examines each and every ball exactly once, up to the total number of balls. I have to create a logic statement to determine the difference between yellow and red and apply it inside the loop. I have to command it to stop when it finds a yellow…you get the picture.
Of course, once the software is in place, it runs very fast, possibly as fast as or faster than a human. Computers are “brawny” – they execute brute force, single-minded logic very fast once programmed to do so. It is the creation of the logic that is a slow and arduous process that even a child perceives to be primitive.
Sometimes the work that we do is viewed as “magic” by our clients. Our ability to quickly turn a business process into a working simulation using a sophisticated 3D visualization simply amazes people (even me, at times). However, simulation is really hard work, and not for the reasons most people think – most people assume the work is in the technical integration. The “mechanics” of putting software componentry together are actually fairly straightforward. No, the hard work is in compressing the thousands of simple rules that humans readily apply into a finite number of lines of code in software. And these days it seems that fewer and fewer people “write things down” anywhere, making our job even more difficult.
I hear you saying - so what? So computers are dumb? This isn’t exactly a groundbreaking finding, so what is the big deal for me and my job at at ACME Corp.?
The big deal is this: a corporation survives or dies on its ability to make decisions based upon data. Fundamentally, that’s strategy, my friend. And the difference between good strategy and very good strategy is as high as its ever been. There is nothing more important to competitive advantage.
Therefore if decision making is a function of collective institutional knowledge, it serves you well to preserve that knowledge to as high a degree as possible. If that knowledge is hoarded inside of human brains, you’ve got a big problem, because brains are fallible, inconsistent, and portable.
Here’s what to do about it:
1. Encourage people to think deeply about the top 5 key decisions they make every day and commit to writing down (in a picture is best) the structure of that decision and all of the influencing factors.
2. Create an environment where people role-play and game-play with analogs that are appropriate to your industry (if you are in the real estate industry, encourage the game-play of monopoly and then debrief afterward).
3. If you’ve never used business simulation, try it out on one particular business process. Be observant as to how human subject matter experts (SMEs) express how they do what they do.
Now I have to get back to work…it’s a human thing.
Business analytics defined (sort of)
You’ve probably heard a lot about Business Analytics lately – it is a term that is thrown around with other vague notions like added value, ecosystem, and integration. If we keep it vague, firms won’t be able to embrace it, won’t recognize when they’ve got it, and can’t compare it to “not having it”. Yet Business Analytics is one of those things that defy strict definition, even to those of us who build Business Analytics every day.
Business Analytics “feels” different from simple graphs derived from a spreadsheet, even different from a finely tuned executive dashboard using the best available Balanced Scorecard practices. It is different from Six Sigma measures of process performance. But if these aren’t it, what is it?
For the moment I’ll sidestep the challenge of strict definition and try to lend some identifying characteristics to Business Analytics. Think of these as signature features that if present, probably mean that you are somewhere in that space, somewhere near the arena of good practice – a place we like to call a Next Generation Enterprise.
Business Analytics has the feel of search. BA is not static, not a bunch of numbers in a pre-ordered format…good BA starts with nothing but a notion of “I know what I want when I see it”. Therefore you might start with typing a phrase such as “how many blue widgets with the optional thingy did we produce last quarter?” Hmmm…lower than I thought…was it a seasonal thing? I’ll now compare that to different quarters across the years…hmmm…yep…it does appear to be seasonal. I wonder if the red widgets are similarly effected…? If such free-flowing navigation is not on par with your favorite search engine, you probably don’t possess Business Analytics.
Business Analytics allows for user-driven, rule-based automation. If people are thinking about their jobs, and thinking about their firms, they also should be thinking about what vital information might trigger an important action. Let’s say that you’ve noticed that whenever a competitor adds a new product line, unit prices follow a distinctive curve over two quarters. Any BA system worth its salt should let you take that idea, describe it in a non-programmatic way, and have the system automatically support or refute that hypothesis over time.
Business Analytics measures everything. I know a certain software CEO who has measured every hit to the company’s website since 1994. They’ve committed every email archive to a freeform searchable repository. Each year when the Nobel Prizes are awarded, they know within minutes whether the winner owns their software, uses it regularly, and how many interactions they’ve shared. Now this is a bit radical, I know…but the overarching point here is that storing data comes at a near-zero cost, and data is the basic fuel of good analytics. You can’t hope to know in advance what data someone might need to do some innovative study -- why not err on the side of too much data than the more frequent stance of collecting just enough data to get by?
So your homework is this: think of one basic, fundamental question that you could ask about your company – a question that anyone outside the firm might want to know. Then see how much energy, time, and consternation this question generates.
If you are in a car company, you might ask: How many white XLC pickups did we sell in Nebraska in 1987?
If you are in a drug company, you might ask: how many labor-hours went into the development of our latest cholesterol drug?
If you are in a retailer, you might ask: which store has the best ratio of sales to floor space?
These are fairly simple, straightforward questions, wouldn’t you agree? This is data the company should be studying regularly, and therefore should be within someone’s grasp at a moment’s notice. I suspect that you will find that more often than not, this data will be surprisingly difficult and expensive to acquire.
Firms talk a big game these days about innovation – unleashing the intellectual power of its talent to solve tough problems. But if we haven’t given our talent access to the most basic ingredient of innovation, such talk is exactly that.