that a part in a machine will last ten more days and should be replaced on the ninth day. This action gets the most use out of that part with no interruption in the machine’s performance. However, a decision still needs to be made. Company leaders may decide to order maintenance crews to follow all such directives and replace parts on the days indicated. Or, management may ignore the directive in favor of upgrading or replacing the machine instead.
That’s okay for as far as it goes. But you need more from data analytics if the aim is to make correct or best decisions of a more complex and demanding nature — especially if they are grave decisions that can carry serious consequences.
For example, take a hard look at the COVID-19 pandemic and the many survival questions that sprang forth from it. Data and analytics can tell us how the virus spread, which virus variations exist, and who was most at risk. This was and is important information. However, data and advanced analytics — even the much-ballyhooed AI — cannot tell us whether it’s safe to send children back to school. The information also shed no light on when, where, or how to safely return people to workspaces or to indoor dining and entertainment venues to help save the economy. In turn, leading public health authorities such as Dr. Fauci and others were unsure which actions to recommend.
In other words, all the data and all the analytics could not tell people what we most needed to know. And this is the recurring lesson that taught some data scientists to look for another way — perhaps by evolving current methods in analyzing data — so that outputs would consist of decisions and not mere information. In the wake of recognizing that today’s data idol has feet of clay, we have come to this powerful epiphany:
Information is useful, but knowledge is power.
If you’re thinking that we humans are rediscovering an epiphany first perceived many years ago, you are correct. Similarly, decision intelligence is not a new idea but rather a renewed one.
Embracing the new, not-so-new idea
The term decision intelligence has been in use for at least the past 20 to 25 years — one of its earliest mentions is in the scholarly paper “Knowledge Management + Business Intelligence = Decision Intelligence,” by Uwe Hannig, a German academic specializing in information and performance management. The meaning of the term decision intelligence continues to change somewhat as vendors try to fit the term to their own products or purposes. Meta S. Brown, the author of Data Mining For Dummies (Wiley) and president of A4A Brown Inc., a consultancy specializing in guidance for launching and expanding analytics projects, says of decision intelligence that “solution vendors associate it with enterprise software, for example, though practitioners not tied to products see it as a broad set of disciplines brought together for decision making.”
It’s still very much a buyer-beware field, in other words. Products will be marketed as decision intelligence tools that aren’t — or perhaps are but are also good for other purposes. Just as a clawhammer can be sold as a house-building tool and a nail-puller, so, too, can many techniques, tactics and tools be used for decision intelligence as well as for other analytical tasks.
Good analytics practice in general, regardless of labeling such as AI, data science, and data mining, are found in an existing open standard for data mining process called CRISP-DM. (Check out Data Mining For Dummies if you’re curious about this standard.)
Many of the details in CRISP-DM are used by data analysts, business managers, IT leaders and others in a variety of business roles. Decision Intelligence extends on that idea.
But the distinction between the two concepts — data mining and decision intelligence — is perhaps best understood in their respective outputs:
Data science tells you what is knowable in any given universe of information. It can do so by answering your questions (data queries) or by automating pattern detection (advanced analytics or AI).
Decision intelligence, by comparison, integrates what’s known into a decision process. It’s the difference between knowing how COVID-19 spreads (data science) and using that and other information in a structured process to decide whether to allow people to return to work (decision intelligence).
Avoiding thought boxes and data query borders
Traditional data mining tends to box in your thinking and put borders around the questions you ask by simply focusing the work on the data in question.
For example, if you’re mining marketing and sales data for insights, your thinking predictably shifts to a more or less standardized list of questions. That’s natural because it’s how humans organize information as well as their thinking about information.
In other words, people label data to organize it, but those same labels also influence how they think about the information so labeled. It’s weird how that works, isn’t it?
Nonetheless, labels are helpful to a large degree, and you can hardly function in using data without them. They’re so helpful that Google’s head of decision intelligence said that if data scientists and statisticians were left to their own devices, they would have named machine learning “the labeling of things” because these professionals prefer names that label what the thing actually does.
If you feel that this discussion is going in circles now, you’re right. And that’s the best illustration I can think to offer you on how traditional data mining limits everyone’s thinking and querying.
Care to go around again? No? Well, people do continue to repeat many of their efforts in analyzing data anyway. Most often folks do that by refreshing the data and repeating the query — over and over again.
Models, algorithms, and queries are shaped accordingly. Machine learning, also known as AI in common use, is trained on this or similar data where it learns the most often asked questions and the common outputs. Querying is often automated, which results in a list of preselected questions. Even drill-downs in data represented by interactive visualizations are preset.
Those parameters form the box analytics software users find themselves in, the bordering that places restraints on querying, and the reasoning behind the repetitions in actions.
DATA ANALYSIS RUTS AND ROAMS
Traditional data mining processes usually look something like this:
1 Prep data from existing sources — such as systems used for routine business operations, streaming data, or data centers — for use in analytics.
2 Mine the data with a variety of tools, including analytics and machine learning (known commonly as AI).
3 Visualize the outputs — make graphic representations of the insights gleaned from analyzing the data, in other words.
4 Decide what action to take based on these insights.
5 Refresh the data and then rinse and repeat.
Unfortunately, data miners and data scientists often hand over the results of their hard work only to find that nothing came of it because there was no realistic business plan to use them in the first place. By comparison, decision intelligence ensures a plan is in place from the outset.
Still, there’s no reason to throw out the traditional data mining process when you make your move to decision intelligence. Indeed, you’d be nuts to do that. This process works quite beautifully in many use cases — most notably, those that are unmuddied by nuance, gray areas, and language confusions. For example, is the color yellow meant to signify Caution or Coward in any given data set? That may depend