Prediction #1: Business Users Rule!
I believe that Business Users will slowly but surely get ownership of their decisioning logic. As the technologies mature and the business users adopt them more widely, they will rule the decisioning world!
In a very good article on business analytics, Dan Vesset nominated Queen’s I want t all as the theme song for this very phenomenon in the entire industry. What a perfect fit!
Business users need insight at the speed of thought to assist them with their decision-making. They need the data, the insight out of the data and the guidance to make the best decision based on that insight. The traditional model that consist of requesting the reports out of IT or the modeling department is not compatible with their actual needs. This is where I see the most direct impact on the technology evolution. Business Intelligence component will make their way into products *and* business users will get used to it routinely.
Getting the insight is not enough though. I believe that business users will also need to play with their strategies to avoid as much as possible unwanted consequences. This is where simulation and experimental design will become critical. By 2015, the number of companies applying champion challenger regularly will probably be on the low side. This discipline will still be appealing to early adopters and maybe a few in the early majority.
This business empowerment will not happen if we do not change the user interfaces for those products. If they have to go through IT, they will suffer from the backlog issue we are painfully aware of. If they need to get an expensive custom-tailored UI built, only the few high-ROI projects will get funding. Regardless, Darwin’s survival of the fittest theory would select over time the “fittest” which means in our world the “most agile”. As I like to say, agility is not inconstancy: change is not random. Competitive Businesses adapt by changing their decisioning to a strategy they have tested as best they could, that they have estimated as best they could, that they trust. Business users can only do that if they have direct access and control over those strategies. There is a strong desire to do that of course, this is what has allowed the BRMS industry to grow as much as it has in the past decade. the barrier to a commoditization of those practices is the steep learning curve. As an industry, we need to remove this hurdle if we want to allow businesses to embrace Decision Management.
Prediction #2: Analytics for Business
Decision Management aims at bridging the gap between business rules and analytics, combining them together for better decisioning. The vision is appealing. That being said, it is hard to institutionalize. Analytics are owned by the modelers, and business rules by IT or business users in a different group. Getting alignment between those two groups has been difficult without an executive directive to do so.
In the coming years, I believe that we will see those disciplines getting much more synergistic than they are today.
The enabler is DATA. Assuming that my Prediction #1 is on target, business users will need data to run their simulations. Somehow the data problem will get resolved. If data is available, and the world is definitely becoming more data hungry, big data hungry, then analytically savvy business users will be able to play with it and gain additional insight, possibly developing some predictive analytics.
Let me clarify one thing… Heavy predictive analytics will not get out of the PhD-packed modeling group. Those will still work hard on the hard problems. There are insights though that are not that hard or that change too frequently for the modeling team processes. This is where business users will have an opportunity to improve their decisioning. the same way they do not want to go to IT for their routine changes, they will not want to go to modelers for their routine data exploration.
Some applications that work quite well in this paradigm are fraud detection and marketing. In both cases, trends can change rapidly, faster than you can accumulate the volume of data needed for the deep analytics. Having other techniques available for the business can allow them to react and either block the new pattern of fraud they see shaping up, or exploit a new fad for their marketing activities. The idea is to use some “easy” analytics jobs to allow business users to better understand and possibly get a model or some rules to reflect the decisioning they would like to add.
Prediction #3: Back to Decision Support
When we think about decision management, we think about the automated decision services that get embedded into the architecture. When applications are processed by those automated business rules, it might be approved or declined, but the automation stops with a refer decision. For some reason, the manual process is always out of the picture. This is where much has to happen both in terms of technology and in terms of practice.
Decision Support is clearly not a sexy term. It brings us back a few decades ago… But I think the term is more encompassing that our dear decision management term. We probably need someone to come up with a better term now 😉
The idea is that decision-making is a much bigger activity than just the automated part. A lot of the assistance that is given in the rich interfaces for business users at rule authoring time, could be given to case workers at decisioning time. As they are dealing with the exceptions, they would certainly gain by having some simulation and estimation tools to visualize the probable impact of their “instant” decisions. The impact of a single application may not be big enough you might think… But, if we could capture the reasoning behind this one decision and apply it to a portfolio, we could better understand the impact of this decision if it was to become automated. And having it somewhat formalized here would allow a business user committee or process to review the candidate business rules and vet some of them, with or without tweaking, for promotion into the automated system at some point in time.
I think the enabler here will be the new generation. They are used to social media. They were born with mobile devices in their hands more or less. My 4-year-old has mastered a long time ago both our iPhone and iPad, and has figured out how to play the Wii with anonymous web players… When Gen-Y will enter the workforce, they will push those social habits into the enterprise. Reaching out publicly in a Twitter or Facebook fashion, likely in the intranet at first, will be second-nature to them.
There is a tremendous value in getting just-in-time information for decision-making… This fits the “speed of thought” idea that Dan Vesset was referring to earlier. Whether those knowledge workers will be in authoring mode for a traditional decision management project, or in decision mode in a customer service center, they will leverage better than any of us all the social capabilities that are or will be available in the tools.