The Decision Model and Notation (DMN) provides a number of ways to supply specific content to a model, i.e. some kind of information that is not directly related to the modeling or the decision implementation, but which can be relevant in your context nonetheless:
- All diagram elements (input data, decision, business knowledge model, knowledge source) can have a description
- Decisions have additional information such as a question that may characterize them and allowed answers, objectives, performance indicators, decision makers, decision owners, BPMN processes and BPMN tasks
- Knowledge sources also have additional information such as a location for the source of knowledge, and the type of that source of knowledge, as well as an owner
Pencil Decision Modeler adds more information to the mix, such as the volume of a decision (how frequently the decision is made), its frequency (how frequently it changes) and its latency (how much time is allowed to make and deploy changes). Finally, glossary categories and entries can also have a description.
While this is great, this may not be sufficient for your own needs: you may need more information to be provided either in the DMN diagram itself, or in decision logic.
In part 1, we talked about a number of ways to formalize knowledge, one being the Decision Model and Notation (brought to us by the OMG). Here we will look at some of the concepts used in DMN, and how they can be used to collaborate around the decisions of an organization. Read more…
Every IT project has a number of stakeholders that need to collaborate to make the project a reality. This is of course also true of projects that have a Decision component, or of projects that are strictly Decision-based. And like any project they risk, depending on the organization, falling into the silo effect, where each group of stakeholders lives in its own little island, and very little communication takes place between the silos. This spells almost certain doom for the success of the project… Read more…
The announcement of the acquisition of SocialCast by VMware caught my eye last week. This is not surprising since we have been very interested in the dynamics of Social and Collaboration for over a year as you know. Let me point you to a very good blog post by Mike Fauscette from IDC that describes the value-add of SocialCast to VMware’s portfolio and I will then share my own thoughts on the subject.
I view Social as several waves of capabilities that are gradually penetrating the Enterprise with increasing value.
Social = Communication
The early Enterprise 2.0 companies / capabilities have focused primarily on the social updates to keep the user’s entourage aware of his / her status. Facebook excels at sharing, tagging and commenting on pictures between friends. Twitter allows small pieces of news to travel the world in record time.
Applying those technologies to the Enterprise required some thinking. When I visited the Enterprise 2.0 conference last year, I was only partially surprised that large companies were still struggling to find the right use case for this technology.
The obvious first step was to leverage it with the current users, the “consumers”, for marketing or public relations. The success stories we kept hearing about back then was about Comcast trying to turn around its image by servicing its customers via Twitter — looking out for angry and loud customers and proactively giving them red carpet treatment –, or Dell creating a new sales channel for refurbished equipment. I must admit that the early mover and creative thinking here gave them extra credits and (more importantly) exposure. It is refreshing to see marketing dollars routed to a value-added service rather than pure advertisement.
Product Management / Marketing also started creating some inbound traffic by allowing their customers to express themselves in their communities and share ideas on what they like / dislike in the current offerings as well as ideas on how to make them better. With voting capabilities, you can filter a lot of the noise that could be generated on mass market products.
Social = Serendipity
The next move with the likes of Moxie or Jive has been to shape serendipity. I do not recall who coined this expression but I love it. By communicating at large to an available audience, you can increase your odd of come across the right information at the right time. In our Encounter with Geoffrey Moore post, he amusingly referred to “the serendipity of the guy with chocolate running into the guy with peanut butter”.
The typical Salesforce example is for Sales automation of course. As a sales guy (or gal), you may be looking for nuggets of information in your ecosystem at the time you need it — which is inevitably minutes before a sales call. You certainly do not care to know about every single call into tech support in real-time, but when you meet this important customer, it is invaluable to know that he/she has a dozen open tickets including 2 critical ones that have been pending for over a week now with very little activity, possibly some angry language was exchanged. If you do not have the time and energy to look for it, you may want to post a quick note asking if anyone has anything to report on that very important customer. The answer, happy or not, may come from tech support or fulfillment or training or professional services or legal or marketing, etc. The beauty of the social platform is that only those who are available will look into it and feel compelled to share what they know and think is relevant. Company-wide emails was the old way of doing it but they tends to be pooh-pooh’d if not ignored by most of the employees.
Social software allows employees to connect and get those conversations going. Employee communication is for me a much greater animal than the Voice-of-the-Customer initiatives I referenced earlier. Having a Product Management background, and a relatively niche market (B2B), I feel quite comfortable about getting the meat of what my customers want. Corporate efficiency is a real challenge though. Optimizing one division is hard enough but breaking the silos between those divisions is extremely complicated. Whatever can be done to improve that situation has the potential to reach very high ROIs with little efforts given where we are starting from.
At Enterprise 2.0, a large insurance company asked a great question though. How do you make those tools effective? Having the ability to engage others is great but you still need some guidance to drive conversations with more value-add than comments on the cafeteria food…
Social = Serendipity on the Job
Granted you can post tweets to let your ecosystem know that your plane is late and serendipitily discover that you are stranded with an old buddy and meetup for a drink but you would get great value-add, at least in the corporate sense, if you could mary the social “icing” to the corporate “cake”. Michael Fauscette points out that the ability to bring those activity streams and collaboration tools in the context of actual applications is critical to the enterprise adoption. This is what we call “serendipity on the job” and I agree that those capabilities will enable Social software to soar throughout the enterprise bringing tremendous value.
The raw capability of exchanging information puts the burden on the users to self-organize and find a sense of purpose. When those capabilities are intrinsically integrated with day-to-day tasks, they have the opportunity to be used without excessive thinking or learning curve by the stakeholders.
When Salesforce released Chatter in the context of the Sales Automation application, it unlocked something big: the ability to work collaboratively, to leverage the collective in the context of day-to-day activities. As the Sales exec, I can look at my portfolio of customers and post information that is targeted to a captive audience. Only service reps in charge of my account or for some other reason interested in this account will subscribe to the status updates and will be notified. This reduces the chatter (no pun intended) that goes around in company-wide emails. It also captures the thoughts and contribution of the involved stakeholders on the spot — eliminating unnecessary follow-up discussions as well as capturing tacit knowledge.
Do not underestimate the value of being in the context of your application. I love Twitter but I don’t have the time to read all the tweets from my friends. Nobody does. It serves a purpose of communication and trend-watching. Integrated Social / Collaborative capabilities serve a different purpose of connecting “doers” for a well-defined purpose.
It is not rare to hear about the “intangible” value of Social Software. I would argue that, when it is clearly applied for a given purpose, its value is much more obvious and measurable in terms of productivity and eventually bottom-line results.
I believe that this acquisition is a brilliant move from VMware and we shall hear about more Social Software acquisitions from the platform players that are building the “next generation”.
Remember a decade ago when we were running SETI@home on our computers all night? I was guilty of lending my off-hours CPU time for this fun Berkeley experiment. My husband and I would join forces to help detect little anomalies… We did not take it seriously of course but we enjoyed being part of the program! It was also technically intriguing as the first large-scale grid deployment we participated in…
Time has passed since then.
Grid architecture turned into Cloud deployments for elasticity. The idea to join forces grew stronger though, manifesting itself in various ways. Collaboration and Social capabilities are revolutionizing the way people can work together. Some success stories involve better teamwork within the enterprise; others are about customer ideation. In this post, I will focus on those crowdsourcing initiatives that push the innovation outside of the enterprise, not for feedback but for actual work.
The Netflix Prize
We have all heard of the Netflix Prize but let me remind you of the premise. Netflix launched a competition in 2006 with an appealing $1M prize for the best predictor. Being a movie rental business, they differentiate from the established brick-and-mortar players by offering the service over the mail as a subscription rather than a per-day rental. The business model was a great way to break into the space quickly but they needed increased differentiation since all players could (and did) adapt to the new way of renting movies. Consumers want to watch movies but they do not necessarily know what is out there… Have you ever stared at the wall of movies at Blockbusters without a clue as to what you will bring home? With a powerful Recommendation Engine, Netflix can predict the list of movies that you are the most likely to love, based on your previous ratings. Improving the precision of this recommendation engine increases Netflix’s value-add and therefore competitiveness.
Academics have had opportunities to research algorithms forever of course but, in this case, Netflix made data freely available to the participants. This enabled a pragmatic effort to take place rather than just theoretical. The business objective was clearly stated in the rules: improve the prediction by 10% or more on the provided quiz sample.
This 3-year journey involved a lot of hard work from many teams around the world. It was impressive to see how close the race got, with another submission reaching the stated goal arriving just 24 minutes after the winning project. What was most impressive was the collaboration that took place. The leading teams realized that they could achieve more by working joining than competing. At that point in time, dramatic improvements were achieved. This is a beautiful lesson learned that testifies of the value of collaboration!
The secret sauce for both BellKor’s Pragmatic Chaos and The Ensemble was collaboration between diverse ideas, and not in some touchy-feely, unquantifiable, “when people work together things are better” sort of way. The top two teams beat the challenge by combining teams and their algorithms into more complex algorithms incorporating everybody’s work. The more people joined, the more the resulting team’s score would increase.
— Eliot Van Buskirk, Wired
This prize was a stroke of genius by Netflix who realized very early on the potential offered by crowdsourcing. Not only could they achieve an incredible performance improvement to their algorithm, which they may not have been able to come up with ever, but they only spent $1M! It may seem like a nice price tag but if you consider that a team of 7 people works for 3 years on it, that is ridiculously cheap… What would have been the odds of hiring the right people, with the right motivation and ideas? It would have cost a lot more I am sure, and would have led to less tangible results.
More crowdsourced projects for recommendation / prediction engines?
With the success of the Netflix project, some new similar projects have bubbled up. The chances of FICO outsourcing their FICO score are pretty slim of course. Companies that use a prediction engine but do not live off of it are more likely to launch those initiatives.
Similar to Netflix, Overstock.com wants to provide better recommendations to their consumers, hoping to increase their sales at the end of the day. They have just started a new competition with the now “standard” $1M prize. Following the Netflix footsteps, they also target a 10% improvement or better.
If you are on the lookout for a bigger prize you can also check out this other competition. Heritage Provider Network is offering a $3M Grand Prize for the best predictive algorithm can identify patients who will be admitted to the hospital within the next year, using historical claims data. In that case, data is obviously provided but no hard-and-fast objective is provided. The team with the best prediction will win the prize at the 2 year mark.
I find this trend very exciting for the Decision Management space. Collaboration can lead to great results with or without the carrot those companies are offering here. It may take a little while for companies to embrace collaboration outside of the boundaries of the enterprise for harvesting and fine-tuning Business Rules but I have hope that we are not talking about decades.
Following up on Carlos’s explanation of Social Logic, I had the pleasure to be interviewed by Michael Lippis as part of his Decision Management campaign for the Outlook Series. Michael drilled me with challenging questions on Social Logic of course.
Decision Management can deliver fantastic ROI but its adoption is limited by a steep learning curve.
Business rules requirements need to be specific, clear and actionable.
By doing so, business rules implementations converge quickly to high quality and accurate business rules, significantly reducing uncertainty and project risk.
Decision Management technologies provide agility; but you can make DM more Performance-Driven by leveraging business data and analytics in context at authoring time.
A wealth of tacit knowledge resides in the heads of Customer Service personnel or Call Center Agents.
The key to gaining a competitive advantage is capturing that tacit knowledge out of the collective.
That tacit knowledge can then support Case Workers making manual decisions, providing insights on the impact of those decisions in real-time.
Furthermore, at decision time, collaboration between skilled workers allows you to crowd-source the most educated decision, keeping track of the reasoning behind that decision for consideration to be eventually automated.
We interview Carole-Ann Matignon to gain Sparkling Logic’s perspective on social logic.
Carole-Ann is CEO and Founder of Sparkling Logic.
… or right-click & save target to download the podcast and listen to it on the go…
or visit the Outlook Series page
This is a big moment for all of us at Sparkling Logic. After a few months of work with customers and prospects, of intense design and implementation work, we are finally announcing the company’s first product: Sparkling Logic SMARTS™. We had the privilege of launching the product at Gartner’s BPM Summit in Baltimore last week, and the satisfaction of a great reception and significant customer and partner interest. Having lived through a number of product launches in this space, this is the one that I am the proudest of – please apologize that I use this post to convey our enthusiasm with this new approach and product.
Sparkling Logic SMARTS is a new kind of Decision Management product. One that, we believe, represents a radical shift from the current way of thinking about Decision Management – something even more momentous than the introduction of the Business Rules Management Systems that we were responsible for with Blaze Advisor circa 1997, bringing the AI rules engines and the Business Rules methodologies together.
The current Decision Management industry is stuck in a no-man’s land. On one hand, Decision Management remains at the core of many mission critical systems, with large implementations that impact our daily lives in many different ways – from what we get marketed to how we get sued. On the other hand, it has not grown up to the expectations we had, and that the value it brings to the table should justify. Even more worrisome, the number of Decision Management projects that fail to deliver on the promised ROI – regardless of the stated reasons – is much higher than it should be: too many projects fail before delivery, or take too long or too much effort to get completed. At Business Rules Forum last year, I did ask the vendors panel the question on why this is the case in their opinion, and what the industry should do to overcome the issue. To their credit, the representatives from Pega Systems and InRule did provide some constructive insight on the issue, but in general terms, very little introspection has effectively taken place lately. The industry remains in that no-man’s land.
Having stated this is just part of the journey to the solution. The next step is to identify the root causes for the situation so that we can envision a way out of it. After leaving our former employer, Carole-Ann and I invested a significant amount of time discussing with industry leaders and enterprise application technical and business leaders, collecting our vision and ideas, and identifying what we believe are the key attributes of a decision management solution that will deliver on its promise to have control of the automated and hybrid decisions with the decision makers.
In a few words: empower all decision maker in whatever role they have – from decision area specialist to case worker – to work on their decisions in the context that they naturally evolve in, using the paradigms that they naturally use.
What does that mean?
First, it’s important to recognize that most complex decisions in enterprise applications end up involving both an automated part and a manual part. In general terms, the automated part deals with most of the cases presented to it, and the manual case deals with the business exceptions mostly, where the flexibility of a human is necessary. For example, the decision to accept or not an electronic payment – the automated system may idenfty that a particular payment presents a level of risk for a customer that needs to be well treated: instead of rejecting the transaction, decision control will be passed to a human which will complete the decision. The human, what he or she does with the information received, the process he/she follows to get to a conclusion, the decision taken: all these are part of the actual business decision. Case workers are part of the decision management system, and they are an essential part of it, dealing with the complex cases, those who in the end may represent where most of the actual risk is taken and/or the most opportunity is possible.
There are thus at least two key roles involved in making, codifying, operationalizing decisions: the traditional business user who knows enough of the business and problem that he/she can work in automating and improving decisions and the case worker whose role on a permanent basis is to participate to the decisions as part of the enterprise application eco-system.
This simple fact has enormous implications on what an effective Decision Management system must be. It must take into account all the roles in the decision, it cannot simply ignore part of then. Traditional BRMS-based Decision Management systems stay away from considering case management as part of the decision, the same way Case Management-based decision support systems stay away from automated decisions. As a result, most large enterprise decision management applications include both BRMS-based and Case Management-based decision systems, and these do not communicate nor share anything but routing logic and operational data schemas at best.
We created Sparkling Logic SMARTS with an approach that allows for decisions to be managed consistently by all roles involved, and thus, achieve full visibility on the decisions and their management through their real complete lifecycle, and not just the part that is automated or the part that is manual.
The challenge for such a product is to create an environment which enable these different roles to be all effective on the same decision logic. This is not simple… Technically savvy business experts convey their view on the decisions in particular forms, frequently resorting to graphical representations such as flows or tree. Business domain experts tend to think in terms of policies. Case workers tend to think in terms of cases.
But they all view their decisions in the context of both data, and objectives supported by metrics. In the process of harvesting rules, it’s typical that time will be spent trying to come up with all the “prototypical cases” to categorize them and create the corresponding abstraction (decision tree, rule template, etc…) to represent the way the cases corresponding to those “prototypical cases” should be treated. The process starts with data – the prototypical cases. It also starts with objectives identified – in the example above we want to reduce the number of fraudulent payments we authorize while minimizing the number of situations in which we do not authorize a legitimate payment, in particular between a good customer and a good network partner… It also starts with metrics supporting these objectives: the number and total amount of fraudulent payments non blocked, the number of legitimate payments denied and the impact on the retention of customers. All business users and case workers will have present both data and objectives supported by metrics concretely as part of their daily decision-making work.
Taking them out of their context in order to put them through a methodology which has nothing to do with the way they do their daily work is actually dangerous. They are being asked to think about all the fine details of their decisions and the way they do it, at the same time as they are being asked to change their vocabulary, adopt new tools, learn new approaches. And they are asked to do that at specific points in time, putting aside the evolving nature of the environment decision-making – more than any other part of the enterprise application – is part of. It is not a surprise that we’ve seen a number of times the disconnect between what the business user thought communicated through the process and what they got back through a traditional implementation. Even metaphor-based (for example rule-tables) or template-based systems face the problem – the approach does give flexibility, but only to the extent it does actually capture the reality of how the business user decides, and only to the extent they continue to do so as the way the way the business user decides evolves.
Sparkling Logic SMARTS is the first Decision Management System that enables the business user to design the decisions by actually making them. Design by doing – a very powerful concept, one that is starting to see the light in the BPM world under names such as Dynamic BPM or Adaptive Case Management. The business user continues working manipulating the concepts they usually manipulate, dealing with business data and with the objectives and the metrics always present and contextually available, focusing on making decisions and capturing the decision logic in the process. Sparkling Logic SMARTS uses patent-pending technology to let business users manipulate the decisions by actually doing them – and in the process enabling the collaboration between all different roles and stakeholders in the management of the decisions through the lifecycle. We have invented a very pragmatic approach to solve one of the key problems in the adoption of Decision Management Systems, and we have implemented it in Sparkling Logic SMARTS.
Furthermore, we recognize the fact that decisions are social
They involve multiple stakeholders – even at the objectives level, different stakeholders have different objectives for a single decision, yet the decision taken is in general only one. In the previous example, accepting or denying the internet payment. So, by nature, the making of the decisions and its codification for repeated making will represent a compromise among multiple stakeholders – a compromise that will not be perfect at any point in time and that will need to evolve.
Similarly, making or improving a single good decision may involve deep expertise which is available within the organization but not directly to each and every one of the decision makers, rule designer or codifiers. The roles cooperate. And they do it today in ad-hoc semi-formal manner – discussions in meetings, email exchanges, and, more and more, ephemeral instant messages.
Enabling that collaboration and gathering information on how the decision is managed in the process extends the reach of the individual business user to the collective, and increases the quality of the decision codification and improvement processes. The Enterprise 2.0 movement has seen it clearly – companies like Moxie Software are the new alternative to the old Knowledge Management systems, and one that is far more effective and adaptive. It’s not a surprise that one of the most popular Salesforce products is Chatter, for the same reasons.
We built Sparkling Logic SMARTS around a social collaboration platform, implementing effective collaborative decision management through a number of social techniques, including some patent-pending ones. Sparkling Logic SMARTS is the first Social Decision Management system that covers the full lifecycle of decisions and their operationalization through both systems and humans.
We call that Social Logic!
Sparkling Logic SMARTS represents a pragmatic revolution in the way Decision Management is approached, and enables business users to design and manage operationalized decisions by making them, making the system accessible to all roles involved in decision-making, resilient to changes in objectives and environment, and adaptive to new conditions, risks and/or opportunities.
Stay tuned for further announcements and discussions. We’ll talk more about the details on how the product delivers on its promises.
And in the mean time, tell us what you think!
As a Blogger and Social Media addict, I decided to get my team to meet with Brian Solis. He is very well-known in Marketing, less so in the Decision Management space so let me paste his bio here just in case:
Brian Solis is globally recognized as one of the most prominent thought leaders and published authors in new media. A digital analyst, sociologist, and futurist, Solis has influenced the effects of emerging media on the convergence of marketing, communications, and publishing. He is principal of FutureWorks, an award-winning New Media and business strategy consultancy in Silicon Valley, and has led interactive and social programs for Fortune 500 companies, notable celebrities, and Web 2.0 startups. BrianSolis.com is among the world’s leading business and marketing online resources.
I will not get much into the Art of Marketing and Engaging with customers in the new digital era, which is the topic of his latest book — I might actually in my Product Management blog.
I would like to share a few thoughts that are very applicable to our ecosystem though.
In the same vein as the Encounter with Kare Anderson last month, Brian talked about the value of collaboration. Collaboration does require to break the silos inside the organization. If you are looking for a greater collaboration outside the boundaries of the organization, you must first establish a culture of collaboration and engagement within your organization – then you can engage with customers, partners and/or others.
I love how Brian phrased the importance of Social Media:
ROI – Return On Ignorance!
Social Media is not a discipline or function within the enterprise, and it cannot simply be managed as something for which an explicit ROI must be established. It is rather an additional channel that is taking a dominant place and that enterprises can simply not afford to ignore, and cannot ignore in any case – it will be present and play a role whether or not the enterprise embraces it or not. Although some companies still restrict access to social sites like YouTube or Facebook, studies have consistently demonstrated that employees are more productive when they can use freely those tools. Brian pointed out rightfully that the same debate on whether technology “toys” might distract employees such as, in reverse chronological order, the Internet, emails, computers, or much earlier… phones!
Yes, I am addicted to those — and they take time — but they also provide tremendous potential. It is incredible how much information you can have access to. If you are reading that blog, you are likely already convinced that real nuggets of information are hanging here and there, awaiting to be uncovered. Sentiment analysis, competitive / market research are certainly important tools for the Marketer… but think about the domain expertise and real-life experience sharing you can find in blogs and communities…
If you are not so shy, you can engage others like you that are passionate about Decision Management, and get some conversations or brainstorming going. We do love that too so do not hesitate to engage us on any terrain:
We love comments too 😉