How do we prioritize our project portfolio according to our business objectives?
Why are our customers buying mountain bikes and not city bikes?
Couldn’t we try to sell more of those top-brand bagels instead of regular sandwiches?
How do we prioritize our project portfolio according to our business objectives?
Why are our customers buying mountain bikes and not city bikes?
Couldn’t we try to sell more of those top-brand bagels instead of regular sandwiches?
What is the best time to alert our customers about our deals on Black Friday to maximize our profits?
When should I buy my flight tickets to Europe to get the best deal?
From the largest company to the individual, we all naturally strive to maximize, optimize, get better returns, reduce turn-over, pay less…
One solution to such problems is to dig into the data that is nowadays available to us, and is often untapped. But there is a plethora of such data, supplied by an ever-growing number of devices: computers, mobile phones, or sensors built into GPS devices, roads, buildings and more. Finding one’s way into such a large amount of data can be a daunting task. Gathering a team of people in a room for a few days to analyze pages and pages of information is simply not possible anymore.
This is why companies have devised, over the years, a number of methodologies and tools to make raw data more palatable. These methodologies and tools were grouped under the general term of “business intelligence,” which, since the late 90’s, is described as a set of “concepts and methods to improve business decision-making […]” (source: Wikipedia).
So the whole idea is to try to make something meaningful out of the gigantic tables of data, in order to reach, ultimately, a number of informed decisions based on that data. Hoping, of course, that those decisions will have the expected impact.
Most of the software tools available today focus on sipping in all that data (whatever its technical source, be it SQL databases, data marts, data warehouses, Excel spreadsheets, et al.), hopefully available in a normalized form (some companies have teams dedicated to making sure that the stored data actually means something) and “regurgitating” it in the form of synthetic dashboards and reports.
These highly-graphical representations of a subset of all of the available data, looked-at from a particular point-of-view (using pivots, or cubes), or resulting from a number of calculations on the source data, can be supplied by means of spreadsheets, Word documents, PDF files, or, more interactively, through some intranet site or mobile application. By condensing the data into a number of charts, graphs, gauges or other means of visualization, these reports and dashboards try to fulfill the ultimate goal: help the business and the decision makers make the right decisions based on past data and projections.
But an analysis of data is usually not sufficient to make a decision! Decision makers intimately know that: the output of decision intelligence practices and tools are essential, but general knowledge from the field, experience from stakeholders, and yes, gut-feel are all part of the decision.
Business Intelligence is not the complete picture for decision making: it provides part of what is necessary to make the right decisions according to the business objectives of the moment.
In addition to the synthetic view provided by business intelligence, we also need heuristics, or techniques based on experience to reach a conclusion, as opposed to the “pure” analysis of data.
This is where Decision Management comes into the picture. Decision management methodologies and tools make it possible to model that knowledge and gut-feeling not visible in historical data or in statistical forecasts.
Decisions Management adds that extra layer of fine-tuning to the analysis of data. Moreover, the simulation capabilities of some of the tools bring the ability to test a number of scenarios on the whole data set, to improve the decision-making even further. By combining continuous analysis of live or near-live data, and maintaining the rules encoded in your decision system, it becomes possible to better understand the impact of this marketing strategy for Black Friday, or that promotion on mountain bikes.
And even better: it becomes possible to automatically provide, in addition to the dashboards and reports offered by business intelligence tools, suggestions on how to improve business performance as it is defined.
John, mountain bikes have sold better last month than city bikes. We have better returns on city bikes and Google News indicates that people start preferring spending their time in the city on week-ends. Should we heavily promote city bikes this coming month?
Decision Management adds the last piece of intelligence missing from Business Intelligence. This is not science-fiction.
