In this post, we present how to deal with the problem of noise, which is both a source of errors and biases in digital decision-making in organizations, through explicit decision rules, dashboards, and analytics. To illustrate our point, we use the example of the Sparkling Logic SMARTS decision management platform.
Noise in organizations’ decisioning and what to do about itIn an interview with McKinsey, Olivier Sibony, one of the renowned experts in decisioning, recommends algorithms, rules, or artificial intelligence to solve the problem of noise, a generator of errors and biases in decisioning in organizations. This recommendation resonates with our vision of automating decisioning — not all of the decisioning but the operational decisions that organizations make by thousands and sometimes millions per day. Think credit origination, claim processing, fraud detection, emergency routing, and so on.
In our vision, one of the best ways to reduce noise, and therefore errors and biases, is to make decisions explicit (like the rules of laws) so that those who define the decisions can test them out, one at a time or in groups, and visualize. The consequences of these choices on the organization before putting them into production. In particular, decisions should be kept separate from the rest of the system calling those decisions — the CRM, the loan origination system, the credit risk management platform, etc.
Noise reduction with explicit decision rules, dashboards, and analyticsOur SMARTS decisioning platform helps organizations make their operational decisions explicit, so that they can be tested and simulated before implementation, reducing biases that could be a failure to comply with industry regulations, a deviation from organizational policies, or a source of an applicant disqualification. The consequences of biases could be high in terms of image or fees, and even tremendous for certain sensitive industries such as financial, insurance, and healthcare services.
In SMARTS, business users (credit analysts, underwriters, call center professionals, fraud specialists, product marketers, etc.) express decisions in the form of business rules, decision trees, decision tables, decision flows, lookup models, and other intuitive representations that make decisioning self-explainable so that they can test decisions individually as well as collectively. So, at any time, they can check potential noise, errors, and biases before they translate into harmful consequences for the organization.
In addition to making development of decisioning explicit, SMARTS also comes with built-in dashboards to assess alternative decision strategies and measure the quality of performance at all stages of the lifecycle of decisions. By design, SMARTS focuses the decision automation effort on tangible objectives, measured by Key Performance Indicators (KPIs). Users define multiple KPIs through graphical interactions and simple, yet powerful formulas. As they capture decision logic, simply dragging and dropping any attribute into the dashboard pane automatically creates reports. Moreover, they can customize these distributions, aggregations, and/or rule metrics, as well as the charts to view the results in the dashboard.
During the testing phase, the users have access to SMARTS’ built-in map-reduce-based simulation capability to measure these metrics against large samples of data and transactions. Doing so, they can estimate the KPIs for impact analysis before the actual deployment. And all of this testing work does not require IT to code these metrics, because they are transparently translated by SMARTS.
And once the decisioning application is deployed, the users have access to SMARTS’ real-time decision analytics, a kind of cockpit for them to monitor the application, make the necessary changes, without stopping the decisioning application. SMARTS platform automatically displays KPI metrics over time or in a time window. The platform also generates notifications and alerts when some of the thresholds users have defined are crossed or certain patterns are detected. Notifications and alerts can be pushed by email, SMS, or generate a ticket in the organization’s incident management system.
Rather than being a blackbox, SMARTS makes decisioning explicit so that the users who developed it can easily explain it to those who will operate it. Moreover, the latter can adjust the decision making so that biases can be quickly detected and corrected, without putting the organization at risk for violating legal constraints, eligibility criteria, or consumer rights.
So, if you are planning to build a noise-free, error-free, and bias-free decisioning application, SMARTS can help. The Sparkling Logic team enjoys nothing more than helping customers implement their most demanding business requirements and technical specifications. Our obsession is not only to have them satisfied, but also proud of the system they build. We helped companies to build flaw-proof, data-tested, and scalable applications for loan origination, claims processing, credit risk assessment, or even fraud detection and response. So dare to give us a challenge, and we will solve it for you in days, not weeks, or months. Just email us or request a free trial.
AboutSparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful digital decisioning platform, accessible to business analysts and ‘citizen developers’. Sparkling Logic’s customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.
Sparkling Logic SMARTSTM (SMARTS for short) is a cloud-based, low-code, AI-powered business decision management platform that unifies authoring, testing, deployment and maintenance of operational decisions. SMARTS combines the highly scalable Rete-NT inference engine, with predictive analytics and machine learning models, and low-code functionality to create intelligent decisioning systems.
Our Best Practices Series has focused, so far, on authoring and lifecycle management aspects of managing decisions. This post will start introducing what you should consider when promoting your decision applications to Production.
Make sure you always use release management for your decision
Carole-Ann has already covered why you should always package your decisions in releases when you have reached important milestones in the lifecycle of your decisions: see Best practices: Use Release Management. This is so important that I will repeat her key points here stressing its importance in the production phase.
You want to be 100% certain that you have in production is exactly what you tested, and that it will not change by side effect. This happens more frequently than you would think: a user may decide to test variations of the decision logic in what she or he thinks is a sandbox and that may in fact be the production environment.
You also want to have complete traceability, and at any point in time, total visibility on what the state of the decision logic was for any decision rendered you may need to review.
Everything they contributes to the decision logic should be part of the release: flows, rules, predictive and lookup models, etc. If your decision logic also includes assets the decision management system does not manage, you open the door to potential execution and traceability issues. We, of course, recommend managing your decision logic fully within the decision management system.
Only use Decision Management Systems that allow you to manage releases, and always deploy decisions that are part of a release.
Make sure the decision application fits your technical environments and requirements
Now that you have the decision you will use in production in the form of a release, you still have a number of considerations to take into account.
It must fit into the overall architecture
Typically, you will encounter one or more of the following situations
• The decision application is provided as a SaaS and invoked through REST or similar protocols (loose coupling)
• The environment is message or event driven (loose coupling)
• It relies mostly on micro-services, using an orchestration tool and a loose coupling invocation mechanism.
• It requires tight coupling between one (or more) application components at the programmatic API level
Your decision application will need to simply fit within these architectural choices with a very low architectural impact.
One additional thing to be careful about is that organizations and applications evolve. We’ve seen many customers deploy the same decision application in multiple such environments, typically interactive and batch. You need to be able to do multi-environment deployments a low cost.
It must account for availability and scalability requirements
In a loosely coupled environments, your decision application service or micro-service with need to cope with your high availability and scalability requirements. In general, this means configuring micro-services in such a way that:
• There is no single point of failure
○ replicate your repositories
○ have more than one instance available for invocation transparently
• Scaling up and down is easy
Ideally, the Decision Management System product you use has support for this directly out of the box.
It must account for security requirements
Your decision application may need to be protected. This includes
• protection against unwanted access of the decision application in production (MIM attacks, etc.)
• protection against unwanted access to the artifacts used by the decision application in production (typically repository access)
Make sure the decision applications are deployed the most appropriate way given the technical environment and the corresponding requirements. Ideally you have strong support from your Decision Management System for achieving this.
Leverage the invocation mechanisms that make sense for your use case
You will need to figure out how your code invokes the decision application once in production. Typically, you may invoke the decision application
• separately for each “transaction” (interactive)
• for a group of “transactions” (batch)
• for stream of “transactions” (streaming or batch)
Choosing the right invocation mechanism for your case can have a significant impact on the performance of your decision application.
Manage the update of your decision application in production according to the requirements of the business
One key value of Decision Management Systems is that with them business analysts can implement, test and optimize the decision logic directly.
Ideally, this expands into the deployment of decision updates to the production. As the business analysts have updated, tested and optimized the decision, they will frequently request that it be deployed “immediately”.
Traditional products require going through IT phases, code conversion, code generation and uploads. With them, you deal with delays and the potential for new problems. Modern systems such as SMARTS do provide support for this kind of deployment.
There are some key aspects to take into account when dealing with old and new versions of the decision logic:
• updating should be a one-click atomic operation, and a one-API call atomic operation
• updating should be safe (if the newer one fails to work satisfactorily, it should not enter production or should be easily rolled back)
• the system should allow you to run old and new versions of the decision concurrently
In all cases, this remains an area where you want to strike the right balance between the business requirements and the IT constraints.
For example, it is possible that all changes are batched in one deployment a day because they are coordinated with other IT-centric system changes.
Make sure that you can update the decisions in Production in the most diligent way to satisfy the business requirement.
Track the business performance of your decision in production
Once you have your process to put decisions in the form of releases in production following the guidelines above, you still need to monitor its business performance.
Products like SMARTS let you characterize, analyze and optimize the business performance of the decision before it is put in production. It will important that you continue with the same analysis once the decision is in production. Conditions may change. Your decisions, while effective when they were first deployed, may no longer be as effective after the changes. By tracking the business performances of the decisions in production you can identify this situation early, analyze the reasons and adjust the decision.
In a later installment on this series, we’ll tackle how to approach the issue of decision execution performance as opposed to decision business performance.
Automating decisions is mostly valuable when you can change the underlying decision logic as fast as your business changes. It might be due to regulatory changes, competitive pressure, or simply business opportunities. Changing rhymes with testing… It would be foolish to change a part of your business model without making sure that it is implemented correctly of course. However, testing is not always sufficient. It is needed obviously, but it has its limitations. How can you test your decision logic when many unknowns are out of your control? What we need in terms of testing is sometimes more akin to a race between different strategies. I will discuss a technique pioneered a few decades ago, and yet not widely adopted outside of a few niches. This technique is called Champion / Challenger.
Why Champion / Challenger
Have you ever experimented with Champion / Challenger? Or maybe you have heard of it as A/B testing… The main objective is to compare a given strategy (your champion) with one or more alternatives (the challengers). This has been used over and over again with website design. The objective could be about highlighting call-to-actions in different ways, or even changing drastically the wording on several pages alternatives, and measuring which version yields the best results. While it is a norm in web design, it is not as widely applied in decisioning. Why, may you ask? My hunch is that many companies are not comfortable with how to set it up. I have actually seen companies that used this technique, and still tainted their experiment with a careless setup. I would welcome comments from you all to see which other industries are making strides in Champion / Challenger experimentation.
Let me explain briefly the basic concept as it applies to decision management. Like web design, decision management experiments aim at comparing different alternatives in a live environment. The rationale is that testing and simulation in a sandbox can estimate the actual business performance of a decision (approving a credit line for example), but it cannot predict how people will react over time. Simulation would only tell you how many people in your historical sample would be accepted versus declines. You can approve a population segment, and then discover over time that this segment performs poorly because of high delinquency. Live experimentation allows you to make actual decisions and then measure over time the business performance of this sample.
How Champion / Challenger works
Technically, two or more decision services can be actually deployed in production. Since your system cannot approve and decline at the same time, you need the infrastructure to route transactions randomly to a strategy, and mark the transaction for monitoring. The keyword here is ‘randomly’. It is critical that your setup distributes transactions without any bias. That being said, it is common to exclude entire segments because of their strategic value (VIP customers for example), or because of regulations (to avoid adverse actions on the elderly for example, which could result in fines). Your setup will determine what volume of transactions will go to the champion strategy, let’s say 50%, and how many will go to the challengers, let’s say 25% for each of 2 challengers.
It becomes trickier to setup when you need to test multiple parts of your decisions. It is not my objective to describe this issue in details here. I might do that in a follow up post. I just want to raise the importance of experimentation integrity as a possible reason for the perceived complexity.
Once the strategies are deployed, you need to wait a week, a month, or whatever time period, before you can conclude that one of the strategies is ‘winning’, meaning that is outperforms the others. At that point in time, you can promote that strategy as the established champion, and possibly start a new experimentation.
It’s a Number’s Game
As you process transactions day in and day out, you will allocate a percentage to each strategy. In our earlier example, we have 50% going to champion and 25% going to challengers 1 & 2. In order for the performance indicator to be statistically relevant, you will need ‘enough data’. If your system processes a dozen transactions a day, it will take a long time before you have enough transactions going to each of the challengers. This becomes increasingly problematic if you want to test out even more challengers at once. And, on the other hand, systems that process millions of transactions per day will get results faster.
So, basically, you end up with 3 dimensions you can play with:
- Number of transactions per day
- Number of strategies to consider
- Amount of time you run the experiment
As long as the volumes along these 3 dimensions are sufficient, you will be able to learn from your experimentation.
Is that enough? Not quite. While you can learn from any experiment, you, the expert, is the one making sense of these numbers. If you run an experimentation in retail for the whole month of December, it is not clear that the best performing strategy is also applicable outside of the holidays. If your delinquency typically starts after 2 or 3 months of the account being open, a shorter experimentation will not give you this insight. While the concept of testing several strategies in parallel is fairly simple, it is a good idea to get expert advice on these parameters, and use your common sense on what needs to prove that a strategy is actually better than the alternatives. Once you are familiar with the technique, your business performance will soar. Champion / Challenger is a very powerful tool.
First time in my life… I heard that it happened to lots of people but it never happened to me before… It was bound to happen: my dashboard failed me!
Does it really matter whether you are at your desk or behind the wheel? The feeling is the same: somewhat frustrated at the technology, but mostly embarrassed that you did not anticipate the problem.
When it happened to me I was in San Jose, driving back from a pretty good class I attended downtown. I was heading for my usual gas station when, always seeking patterns, I felt the car choking, I pulled over and sure enough, I did not make it to the station.
It happens to smart people; it happens to dumb people. I won’t comment much on that!
The point is that dashboards are fantastic tools and they assist us in a very powerful way everyday for big and small tasks. But having a mechanism to monitor your performance does not mean that you are not accountable any longer… It is your responsibility to make sure that everything is still working fine… Because you may get the warning only too late… Or because you may not get the warning at all (like my failing light that never turned on)…
Tools cannot replace human expertise when something unexpected happens. Granted, I know that it is theoretically possible to run out of gas, but I was not expecting that it would happen to me as I am very vigilant of getting gas as soon as the light turns on (or before). But it can happen, and when it does, having some creative processes can help: ask a nearby gas station to loan you a tank or call a friend (or spouse) to come and help, or get AAA assistance. If you don’t know how to handle it, call people that do. This is the power of social media, although I did not go as far as Tweeting for help!
This little incident was hardly a bother in the day, and my husband was very gracious about coming to my rescue (thanks to mood-lifting NPR reporting of the Goldman-Sachs hearing)… But it made me think some more about Pattern-Based Strategy — When do I not think about it? — I felt stranded in this right part of the spider diagram: unexpected exception, creative “ad-hoc” and collaborative process. I felt good about managing it though.
Jim Sinur (again) wrote a great post a while ago when the Toyota incidents started. In short, he stressed the value of knowing how to control the car, rather than relying on the recall to remove the problem.
Now, I can personally relate… People and ad-hoc processes, leveraging people’s expertise potentially in a collaborative manner, is what can help us when technology is failing us.
Social Media has been soaring in the workplace during the tough years of the recession and keeps getting momentum. With reduced budget for travel and less personnel, providers and end-users have been creative in finding ways to leverage the social media platforms. Many ideas have popped up here and there to leverage Twitter or Facebook, some better than others of course.
I found pretty interesting this enthusiasm for true relationship, even if partially digital.
During one of my discussions with Betsy Burton from Gartner, we talked about what may have caused it.
The Chicken and The Egg (again)
I know… I do like this analogy… For a biologist by trade, it is a fundamental question of course. But let’s not digress… Back to Social Media and Relationships…
Gartner explained in an early Pattern-Based Strategy presentation that people turned slowly into numbers. Think about it: we are our social security number, we are our credit card number… If this was not the case, identity theft would not thrive as much as it does. People are getting more aware of that unfortunate reality and as a result value “human relationship” more than they have in the past: it differentiates them from their digital existence.
Social Media was there at the right time. Extensively used by teenagers, suddenly it became exciting to the workforce as well. Services like Twitter allowed professionals to interact with thousands or millions of tweeps around the world. Talking about your everyday life on Twitter may have sounded unproductive initially but it fulfilled the human aspect: being personal, sometimes intimate… People had finally an opportunity to be something else than a risk score. Of course over time more and more companies are finding ways to make use of that service, 140 characters at the time.
With a wider array of social details available and the development of Socialitics (analytics applied to social networks data), a fantastic source of information is becoming readily available. When you explore professional connections within LinkedIn, you can infer “something” about any professional. Digging deeper into the larger mesh of social networks, you can discover more about what they read, what they watch, who they spend time with, etc. Despite the threat of identity theft, people are sharing more details than ever because they find value in it. They can reach out to people that share the same interests or the same goals, professionally or personally, and develop a relationship that fulfills a real need.
I questioned Betsy as to whether Transparency, as one of the 4 pillars of Pattern-Based Strategy, was suddenly valued and appreciated as a reaction to our “digitalization” alone or whether it was also due to the fact that Social Media suddenly made it available? Transparency may have been a value all along but only truly exposed with the explosion of social media.
Some pretty dark practices happen all the time in business unfortunately. People abuse companies, companies abuse people, people abuse people or companies abuse companies. Whether they use power or deception or anything else, the result is the same. Over the past couple of decades, more people and companies have been increasing more conscious. As a result some initiatives, going against pure greed, such as Sustainability have been nevertheless successful. It is clear that one person alone cannot do it all. The new power comes from collaboration across boundaries — companies, countries, etc.
Transparency is also like fuel for Social Media. You get the full benefit of your participation when you actually participate. If you are true to the network, if you are transparent, then you will be able to connect with people of the same vein and reach a higher level of exchange, more practical advice, more comfort or greater motivation. Granted Deception could make its way into the system. It reminds me of the Population Dynamics theories I learned in school… The fact is that Social Media seems to be pretty effective at policing itself.
- First, there is a fairly significant investment for a true participation. “Faking it” seems like an unreasonable investment for pitiful results, not worth it.
- Second, it is hard work to maintain a fake identity. The risk of being uncovered is not small, so eventually those individual disappear. Social Networks also actively develop mechanism to eject those people — Twitter launched verified accounts for celebrities mostly…
- Lastly, social networks are dynamically shaping themselves: poor content and/or pure sales pitches without a little human value-added get quickly rated as such and attendance quickly drops
Transparency, wits, content have naturally become prime currencies on those Social Networks.
So… Is the need for Transparency a consequence of Social Media dynamics? Or is Social Media’s success a consequence of the need for Transparency?
1 + 1 = N
Being a Product Manager in my heart, I am always wondering what people truly want… Some companies have been successful at deploying a community of users. I actively participated in designing one for a previous employer but found true limitation in the technology or configuration that was chosen. What I was quite happy to see though is that some users got involved. A handful of passionate users took ownership of some technical questions. I believe there is a huge potential for even more sparkling exchanges. I am playing with this idea so stay tuned!
I have been impressed with the Transparency of companies that were open to share a lot of design details even to their competitors as part of industry user groups. As I was told in “Ecole Preparatoire” (an elitist process to select students in France), the likelihood of a fellow student to take your spot are tiny, keep in mind that you will gain much more by supporting each other. That quote from my teacher stuck with me (while students in other schools were busy stealing each other’s notebooks!!!). I love to see that companies are adopting the same philosophy. I love to see that Insurance companies that are direct competitors are willing to teach each other how to use Decision Management technologies because they will not compete on whether they design their Business Rules services this way or that way, they will compete on the strategies that the business rules execute. Helping each other will increase each participating company’s skills at effectively leveraging the technology so that they can focus on content. It is beautiful to see it happening in the real world.
This is not completely new as organizations like OMG have promoted competitors’ collaboration in designing standards for many years. Social Media act as an amazing catalyst for more of that. There is so much more we can accomplish when we get together…
Since last August, Gartner has been releasing several dozen presentations on Pattern-Based Strategy. I found it absolutely brilliant. You could probably tell from my tweets but also from the many venues I presented at on this very topic, including my old blog.
Without sounding too arrogant I hope, let me refer to one presentation. I had the opportunity to present at ITxpo, the Gartner Symposium in Orlando last October. It is a huge show, with gold star presenters like Eric Schmidt, Chairman and CEO Google, and Mark Hurd, Chairman and CEO HP. I have been there many times but the show in 2009 was by far the best I have been to. I had a concurrent session on “5 steps to Pattern-Based Strategies”. I was amazed by the fact the room was so packed that we had to turn people away, and some had to sit on the floor inside the room! Keep in mind that the audience is about 30% CIO… sitting on the floor… Must have been a topic they really cared about!
While I surveyed the audience at all of my presentations, I realized that very few people actually knew about Pattern-Based Strategy. I must admit that the name is not very telling. When I attended some roundtable sessions at BPM Summit 2010, I also realized how confusing it was for the new comers:
- First, there are already tons of reports on the subject – where to start?
- And second, it is often presented using different metaphors, different metrics or dimensions…
It is indeed easy to get confused or underwhelmed when you can’t get to the core of it.
My challenge this week is to make it a little bit much more accessible to you all and sparkle some thoughts about the role decision management plays in this new approach.
A new approach… No?
Well, actually, it is not really a new approach. Many of the concepts are fairly common, very practical… It is mostly about common sense. This is the brilliant part! Pattern-Based Strategy (I will refer to it as PBS although it is not a conventional acronym) taps into core principles but lays them out in a very down-to-Earth way that makes it palatable. The roadmap to competitiveness can be spelled the same way regardless of your area of focus.
As I joked with a friend, it is amazing how this simple approach is very applicable to building applications, as well as deploying larger systems (think ERP, CRM or HR for example), as well as your personal goals!
One observation though is that PBS is very much in line with Decision Management. I would certainly not claim that any problem is a Decision Management problem. But when thinking about decisions that you may need to make as part of your processes, a Decision Management approach “by-the-book” would be compliant with the PBS guidelines. That may be why I found PBS so brilliant: it resonated 100% with the vision I created for Decision Management. In French, we have this expression “Les Grands Esprits se Rencontrent” which translates into “Great Minds Meet” or (less literally) “Great Minds Think Alike”. This expression is not as pompous as it may sound, it is mostly a way to say “we came to the same conclusion”.
Let’s look at the many different ways Gartner has been describing PBS in their presentations and reports.
Those 4 pillars are the disciplines, techniques or technologies that are critical for the implementation of PBS.
- Seeking Patterns: We are surrounded by data streams… It could be datapoints like your individual characteristics (height, weight, education, etc.), transactions like your credit card purchases, or trends like stock market tickers… We often pay attention to some data elements more than others: how well our kids are doing in Mathematics, how high unemployment rate is, how often customers buy from you… But by large there are some patterns that we consciously or not ignore. Nassim Nicholas Taleb did a great job describing how black swans (very improbable events) can strike and have a far larger impact than expected modeled events. Monitoring a larger array of data streams, tuning to more varieties of patterns would create a huge advantage as companies would be better educated about their environment and therefore in a better position to make a sound business decision.
- Operational Tempo Advantage: This is an interesting one. I always thought that the faster you change the way you do business the better. This is Business Rules 101. And this is part of the point that Gartner is making here. The recommend that companies equip themselves with the ability to tune their business practices “at the right speed”. In many cases, it would mean as fast as possible indeed. In other cases, it means enabling a slower pace of change. A good example here is anything HR-related. If you have been interested in the workforce architecture discussions lately, you might be already aware of the recent progress in Talent Management. The idea is to plot your workforce needs today and in the future, and derive a roadmap to enable your resources to evolve with the company, providing continuing value — which is a 2-way street of course. Some companies believe that you can turn a switch and make one person or one entire team change role and responsibilities overnight. This is a significant source of job dissatisfaction, poor performance or, at worst, turn-over. Finding the right pace, the right technique, can actually have the exact opposite effect, increasing significantly your personnel’s commitment.
- Performance-Driven Culture: With Business Activity Monitoring (BAM) being a recurrent subject, it sounds pretty obvious. In order to increase your overall performance, you need to create an environment that allows each contributor (personnel or system) to track its individual performance. This may be a bigger culture shift than you might think as more often than not performance is tracked at the contributor’s level, losing sight of the overall performance. Let me illustrate with the never-ending story of sales-versus-marketing. In all the companies I have worked for, I witnessed the finger-pointing game. Marketing claims that, although they generate plenty of leads, Sales does not follow-up on them. Sales claims that they do but the leads are very poor, totally unqualified. This blaming game has made Sales Automation and Customer Relation Management (CRM) systems so successful in the past decade as it provides visibility into the Marketing / Sales hand-off, empowering Chief Operations Officers (COO) or Chief Marketing Officers (CMO) to make decisions about which Marketing expenses were justified. The idea is that those opportunities exist throughout the organization.
- Transparency: I love that one. It has multiple facets.
- Being focused for so long mostly on Financial Services, I was initially thinking compliance, compliance, compliance. But there is a lot more to it than the visibility into accounting practices, etc.
- When you make the extra effort of describing your practices, your business rules or your business processes, you finally have something to share with other groups within the organization and get in alignment. I have seen in the past very sound decisions made by different groups in the same company but when you get a chance to compare them, you see how dysfunctional that company may be. A familiar example was an Insurance company that targeted very specific demographics in their ad campaigns. The Underwriting group was not aware of the strategic direction that was committed to by the Marketing team and either gave those applicants very high premiums or declined them! With a concerted effort to be more transparent about your objectives you can improve communication and as a result the overall coordination across organizational silos.
- The new digital era has redefined how people trust, share and engage. As a result, more transparency is expected from different angles, system themselves are more open for integration — so many services expose their APIs to let people mash them up –, companies are more open — they share information about their products, such as features and price lists of course but more and more companies crowd-source their innovation process too –, and people are also more open — blogs and social media sites like Twitter or Facebook let individuals share more “intimate” details about their thoughts, their aspirations, their frustrations. There is a risk associated with it of course: the more open you are about what you are, what you do and what you think, the less uncertainty about your true brain power. As an IDC analyst stated in an IDC Directions presentation this year “Never underestimate the power of stupid people in large groups”. Regardless of the consequences, it is inevitable that companies will be more engaged with social activities and more truthful, as companies with little integrity or smarts will identify themselves. Gartner encourages companies to increase their transparency to remain or become more competitive.
Those 4 pillars are quite generic on purpose. Gartner does not prescribe how you should so each one of them as it may vary with your industry and your role. The key take-away here is really to practice those disciplines in a way that makes sense to you and your company as a whole. It may be frustrating to some. One person told me “I am a practical guy, I need concrete guidance”. But when you think about it, it is utopia to think that there can be a one size fits all strategy for anything and everything in that world.
I suspect that Gartner drilled down into that slightly different messaging around PBS to answer the “what to do” question they got back. They framed those 4 pillars into a single more actionable roadmap that makes a lot of sense.
- Seek: This refers of course to the pattern seeking discipline we just talked about. You need to start your adoption of PBS by building a framework that allows you to detect changes in trends, regardless of whether they are internal to your company or external.
- In terms of Decision Management techniques, this would refer to the ability to analyze data of course. Think Business Intelligence or Predictive Modeling. Changes in your performance might also be analyzed with Business Activity Monitoring technology.
- Model: With this knowledge, you are now able to model your business in accordance with your business objectives.
- Decision Modeling, Simulation, Decision Optimization could be steps involved in validating your business strategies prior to deployment.
- Adapt: Build flexibility in your runtime systems for change over time as your company evolves, as your competitors become more aggressive in some markets, as your customers become more sophisticated or more demanding.
- Business Agility is typically derived from technologies such as Business Process Management (BPM) or Business Rules Management Systems (BRMS). Techniques like A-B testing or Champion-Challenger as well as adaptive models allow to model and adapt potentially at once.
This is a very simple roadmap that mirrors in reverse what happens in real life in terms of adaptation. Species evolve to reach an optimum level of capabilities for a given environment. That principle applies to companies and systems the same way it applies to Nature, with the caveat that we have more control on what strategies we intend to deploy rather than pure randomness. Instead of randomly changing until the current optimum gets statistically selected, this approach empowers companies to direct the evolution.
The pillars and the roadmap describe very well what PBS is all about. Gartner created yet another tool to help companies understand the needed transformation. This spider diagram allows companies to plot where they are today and where they want to be, based on the world they live in.
- Defined: There are typically defined processes that companies operate under. They maybe for example your underwriting process, your claims processing process, your payroll process, etc. Those are often well-defined and already automated to some degrees, ideally with a business process but sometimes hard-coded into applications.
- Anticipated Exceptions: Flows may account for a number of known exceptions that may occur. During the course of a mortgage, you contract your mortgage and pay it off over time according to a known schedule. Pre-payments or Property sale may be known exceptions. Foreclosures may be other known exceptions.
- Unanticipated Exceptions: There are some exceptions that your systems may not be anticipating like the recession or natural catastrophe like the many earthquakes we witnessed around the Globe. For those, you may know that they could exist but your system may not know how to deal with them. If they did, they would be anticipated exceptions…
- Creative: This category is somewhat interesting as the name changed a few times. This refers to “new things” that we have never seen and that are revolutionizing the industry or simply changing the way you do business. One now not-so-new example is Netflix. Blockbusters and other enjoyed a fairly well-defined business of renting movies. Netflix showed up with a revolutionary business model and pretty much killed the previous generation. Another good example might be the Green initiative, with Smart Cities changing how communities think about development, transportation, energy, etc.
- Collective: Collective refers to social media transformation. Comcast is a good example of a company that started leveraging Twitter to improve its image via red-carpet treatment for the most vocal tweople. It created a lot of publicity. Those are obviously new processes that are changing the traditional approach.
This diagram can help companies understand where to apply a PBS approach in a way that will be effective for their strategic needs. The spider diagram make people think about their priorities and the potential threats in a way that I find more productive that the typical SWOT analysis. It does not replace it but it offers a fresh perspective.
In conclusion, PBS is not that complicated a beast to look at and there can be some very tangible take-aways you can apply today. This is really brilliant and very applicable to Decision Management, so please do not ignore it. Take the time to think about it. If you have questions, I would be happy to answer them! As you can tell I have become very quickly a passionate advocate of this approach!
When Bing came out last year they presented it as a Decision Engine. Microsoft caused a strong reaction in the Decision Management space.
“What are they talking about? No way! Non sense!!! Have they gone mad?”
Well, of course, it took a few minutes, days, weeks, months to get our minds out of the trenches (if at all) and welcome the new perspective. Okay, it requires being incredibly open-minded for a devout decision management expert but, once you make the jump, there is a fascinating world to discover and embrace. Really.
Is it true? Yeah!
Since this old announcement, and with no correlation I must add, I have boosted my social media presence and experimentations, fine-tuning what to share with whom. In the process I ran into some very interesting blogs and presentations. I was inspired and impressed by many. For instance, I was just listening to the excellent webinar by Brian Solis on Hubspot on “SEO is the new Social Media Optimization”. I liked and tweeted one of his quotes:
“Social Media affects every step of the decision making process”
— Brian Solis
May sound like a blanket statement to some but please think about it… In every decision we make day in and day out, we start our decisioning process with some thinking. We ask friends and family, we research specialized websites, we mine our past (personal or corporate), etc. It often starts with a search of some sort. So from that perspective search is a decision engine! Well, a decision support tool…
Decisioning is not all about decision automation like we, in the industry, tend to think. The world of decisions is not limited to those transactions you process in high volume. There are manual, sometimes isolated decisions that can benefit from decision support. All those decisions you make at home about buying a house, where to go on vacation, which movie to watch, what school to attend or what to cook tonight. Do not think those point decisions only happen at home. Corporate life is made of many similar decisions: when to open a new store, who should be promoted, when to sunset a product, etc.
Steve Hendrick put together a great chart at one IDC breakfast I attended earlier this year. I am sure this will be presented again as it does illustrate very well the different nature of tactical decisions (High volume, Low individual impact) and strategic decisions (Low volume, High impact). Obviously BRMS plays in the first category. Decision Management as it is defined today plays in the first category. The larger, more strategic Decision Management has not been invented yet as the traditional Business Intelligence tools are barely connected to the decision automation technology.
How does Search affect Decision Management today and tomorrow?
Social Media is actually the key element of change here. Granted you can search for product comparison or heated reviews and stuff like that online and claim that the web helped you make a decision but this is only step one. Traditional search helps quite a bit here and this is where Google and possibly Bing get their initial start. This is nothing compared to the potential though.
Social Media brings tons of knowledge into the equation. I was intrigued by sites like Hunch that collect tribal knowledge on popular one-of decisions and turn that into decision trees that guide visitors. Again, this is only the tip of the iceberg… There is more.
I see a wonderful opportunity with social media data enrichment for decisions. The opportunity created by the ability to search, the ability to understand, the ability to rank, and the ability to discover the many facets of the subject of your decision is humongous. This obviously goes beyond traditional search. Brian Solis listed real-time and social searches as two key ingredients that complement traditional search. Social searches outperform by far traditional searches like Google to access Press information. People trust people they know and they share. This is a wonderful source of information: just knowing who recommended it, who is connected to whom. I am eager to see the evolution of Reputation in the industry.
If I recall, SAS created a Fraud Management application that incorporated social search so that fraud case workers could better understand relationships extracted out of those social networks. If you befriend fraudsters, it may increase you likelihood of being a fraudster. Sound like patterns could emerge from the real-time data that can be acquired out of those social networks and be leveraged very effectively. This is brilliant and yet so simple. In a few years all systems will have those social aspects incorporated and we will not think twice about it. How exciting to be watching the transformation!
The Chicken and the Egg…
So Social Media Searches could very well start as the decision support tool, but it may also very well provide the necessary data that will feed into decision management solutions tomorrow.
As Google changes its PageRank algorithm to account for the needs of humans — pulling more real-time social results like tweets in the pages but in reasonable amounts — it begs the question “How would decision algorithms need to evolve?”
In a lecture at Stanford, I grew quite interested in the topic of business innovation. Roger Martin’s book “The Design of Business” was featured as a great theory defining the mechanism at play in the process of designing strategies. The key here for me was to take off my usual Decision Management glasses and embrace a different perspective on the very same topic of growing a competitive advantage.
When you think about it, the evolution from Mystery to Heuristics to Algorithms defined in the Knowledge Funnel resembles the Decision Management discipline in a somewhat different order. A fresh new take on concepts we know quite intimately is always intellectually exciting.
If you allow my over simplification of the core concept here, Roger Martin in his book describes an evolution of understanding of the business:
The level of sophistication of the strategy greatly depends on the level of understanding of the problem and the mechanisms governing the business. This sounds like a no-brainer but as highlighted very appropriately this is not really how the market always operates. Although a Heuristic leading to great growth may sound intuitively good, the public market only values the predictability of Algorithms — being able to anticipate to the penny a company earnings is what matters. This is mind-boggling though since Innovation comes from the exploration of Mysteries and a first-mover advantage in that space might create a major disruption of the market. This is an interesting conundrum, isn’t it?
Granted, Decision Management discipline often addresses the problem in a slightly different way. The purpose of such an implementation is to automate part of the business rules. So the Mystery phase is often only looked at as part of the requirement gathering phase. In many projects the starting point is a known problem though. The Heuristic ends up being a partial implementation aiming at reducing the level of manual processing so that skilled personnel can focus on those “difficult” cases. A true Algorithm implementation would like involve predictive models and a fair amount of testing and/or simulation. Another way or looking at it might be to say that BRMS technology is typically applied to Algorithm-type of problems. If we know how to make decisions, BPM and BRMS technologies can be deployed to implement such algorithms.
I expect that going forward disciplines or approaches such as Pattern Based Strategies as described by Gartner might change somehow the way we apply Decision Management technologies. Clearly, there are a number of problems that are in the Quest category (we know the desired outcome but not necessarily the way to get there) where Decision Management technologies could be leveraged. The current methodologies and possibly technologies do not yet lend themselves to this usage but I sincerely believe that the need will create the opportunity here. Gartner is on the right track if you ask me.