Have you ever had a “if only I had known…” moment? Of course you have. We all do. From minor details like forgetting to bring an umbrella, to more significant tragedies, like that big family vacation you planned back for March of 2020. There’s a lot of uncertainty in life, and that can lead to some unhappy surprises.
Most of us console ourselves with self-inflicted platitudes like “hindsight is 20/20,” but that doesn’t usually prevent us from wishing we could turn that hindsight into foresight. We put up with the uncertainty because we don’t have any viable, effective alternative.
But that’s not the way everyone does it. Astronauts don’t launch with their fingers’ crossed, for example (“That’s not how we deal with risk,” Chris Hadfield once put it). And the adage “No strategy survives first contact with the enemy” never stops military operations from attempting to prepare for, and manipulate, the outcome of every engagement.
Which begs a few questions. Namely, are we “guessing” more than we need to? Is there a way to anticipate future outcomes better than we currently do? And, if improved forecast accuracy is possible, how do we achieve it?
Dear reader, welcome to the wonderful world of predictive analytics.
Sit-Rep on Predictive Analytics
First, an answer in brief. An overview for all you “Too Long; Didn’t Read” enthusiasts, if you will.
Your business generates a veritable dragon’s hoard of data on a daily basis. This includes data from customer interactions, market engagement, internal operations, revenue and financial metrics, and a whole lot more. And, if you operate in data complex environments such as media, events, marketing and business information sectors, multiply your data asset by three.
This data can do more than just visualize the ebb and flow of recent bookings, billings, cash flow and profitability numbers.
It can highlight historical trends, identify less obvious points of success or failure, and—most importantly—help analysts develop predictive insights. Such “leading indicators” are the ultimate goal: allowing businesses to anticipate outcomes based on past and current circumstances.
In short: predictive analytics can do for your business what a medical examination can do for a patient. By taking “vitals” and comparing them to both category averages and personal history, future diagnoses can be foreseen and prepared for. For both examples, this can lead to increased longevity, better health, and optimized performance when implemented correctly.
The key is knowing how to go from “scheduling the physical” to “clean bill of health.”
How “Blind Fire” and “Firing Solutions” Relate to Business Intelligence
Ok, we’ve established that data can drive effective decisions that properly address risk factors and capitalize on opportunities. However, raw data doesn’t, by itself, directly translate into winning business strategies. Just because you have a spreadsheet full of revenue data doesn’t mean you’ll be hitting every target from this point forward.
Even in military contexts, hitting a target isn’t always as simple as taking aim and pulling the trigger. In fact, military terminology distinguishes the use of weapons and munitions based on what you’re firing at, and whether you can see it:
- Blind fire—firing inaccurately in the general direction of a target, usually from behind cover
- Direct fire—firing directly at a target within line-of-sight
- Indirect fire—firing directly at a target using targeting information from an external source
Why are we throwing around military terms in an article about predictive business analytics? Because, like poorly aimed artillery, businesses that make decisions without accurate data are effectively launching large chunks of budget and resources at the wrong targets, often overlooking crucial but unseen objectives.
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The Analytics “Fog of War”
Now, while militaries across the globe have been developing and refining their targeting systems for decades, businesses have only just started to benefit from the analytical equivalent of satellite imagery and laser-guided targeting. Digital technologies have made certain realities possible before professionals knew exactly how to use them for those purposes.
Business analytics implementation happens on a bit of a spectrum. At one end, teams may be so in the dark about their own data that they don’t even know where to find it all, let alone start collecting it. As organizations become more data-aware, they may seek to aggregate their information, perhaps even standardize it into a Single Source of Truth (SSOT).
Virtually every business faces issues with data integrity, though. Inaccuracies, redundancies, incompatibilities, and formatting issues all prevent the data from being properly organized and utilized. That leaves a large gap between brands that are data-aware and those that are data-driven, and crossing it can prove prohibitively difficult.
Again, this is something that militaries have dealt with for ages. The term “fog of war” refers to the obscuring uncertainty created by the inherent chaos of the battlefield. Without reliable intel, the fog of war prevents commanders from knowing where resources and threats are positioned, where they’re going, and how to properly direct efforts to turn circumstances to their advantage.
What’s true for military leaders is true for senior business management: it’s virtually impossible to manage or prepare for what you can’t see.
Gaining data awareness is the first step toward converting analytics from a diversion to a secret weapon, but it’s not the only step. You can’t simply print off a satellite photo and hand it to an artillery crew as a “firing solution.” And you likewise can’t hand a database full of yet-to-be-scrubbed information and expect your analytics team to turn it into prophetic industry forecasts.
On the other hand, processing the data by hand to normalize it is a fool’s errand, especially if your end goal is real-time insights.
To get what you want out of the data and your staff, you need something to amplify the power and impact of both. You need a force multiplier.
Conclusion
Here is the final caveat; “the rub,” as they say. Different organizations and different industries have different requirements for their analytics tools.
Some teams can be served perfectly well by the major players in the BI space, and more or less use the solution right out of the box. But some businesses have data environments too complex for these providers to adequately serve.
Media companies, event organizations, marketing service providers, and similar enterprises with complex data environments typically need a number of functions that standard business intelligence platforms don’t offer.
For teams that need to accommodate two-sided business models, complex client databases that require external-facing access, or even simply reporting tools that can turn 30 different marketing and sales enablement channels into a single revenue report, more specialized tools are needed.
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