Decision Intelligence For Dummies. Pamela Baker

Чтение книги онлайн.

СКАЧАТЬ level of clarity seen only in the rarest of diamonds.

      Experiencing the epiphany

      To date, we humans have all been mining data to sort information into useful pointers — pointers to which products customers might want to buy now or next and which price they might be willing to pay, for example, or pointers on who on the team might have an edge to help win a baseball game or reach a sales quota, or even pointers on which activities might indicate the beginning of a data breach and which are the routine work behaviors of hundreds of staffers now working from home during a pandemic.

Pointers of all kinds — here, there and everywhere. And there’s nothing wrong with any of this. Pointers can, and often do, direct people to a path forward in business, at home, and in various other endeavors.

      However, pointers mark a path (often, one among many), but they don’t decide which path you should take. In other words, pointers aren’t the same thing as decisions. You’re free to make the wrong choice as much as you’re free to make the right one. But that approach falls woefully short of the tall promise in data driven decisions, doesn’t it?

      

“Following the data” was supposed to render some infallible truths and reliable actions. That’s why so many analytics vendors tout actionable outcomes or actionable outputs. Short of automating that action, however, even recommended actions are pointers as well.

      For example, predictive analytics might produce an actionable outcome, saying 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).

Impact is ultimately the answer everyone seeks. When every business and life question essentially boils down to the question of what you should do to achieve this impact, it makes sense to start the decision-making process with the impact you seek and the answer you most need.

      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 СКАЧАТЬ