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Noise reduction in digital decisioning with Sparkling Logic SMARTS


noise-digital-decisioning-explicit-decisions-dashboards-analyticsIn 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 it

In 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 analytics

Our 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.

About

Sparkling 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.

Hassan Lâasri is a data strategy consultant, now leading marketing for Sparkling Logic. You can reach him at hlaasri@sparklinglogic.com.

Capturing Business Rules for Medical Treatment Recommendation


Our friend Jacob contributed a sample of medical rules to the DMN (Decision Model Notation) standardization effort at OMG.  He encouraged me to show how those business rules would look like in our Sparkling Logic SMARTS Decision Management tool.

In this demo, you will see how I created the entire project from scratch, in a business-user-friendly environment, leveraging heavily the use cases he gave me.

[youtube=http://www.youtube.com/watch?v=wJW3sxFo35M]

RedPenTM is a patent-pending approach to capturing and updating business rules in the context of data.  Graffiti on the use cases turn into business rules automagically!

Thanks again Jacob for allowing us to demo your project!

BBC 2011 – Business Rules for Cancer


healthcareAlthough Healthcare is a perfect fit for Business Rules technology, the providers have been late to adopt the technology.  We have seen many healthcare insurance companies use rules for eligibility or claims processing, but providers are lagging.  I was delighted to attend a session at BBC on Cancer Care.

Michael Katz, BS/MBA at International Myeloma Foundation, gave us a heartbreaking overview of the disease…  incurable but treatable.

The project focused on providing timely information to diagnosed patients healthcare providers.  The solution is available on the internet, with an iPad client available.

Michael shared a demo of the rules they created to detect anemia or bone issues for example.

The iPad application interprets the input data (gender, serotonin level, etc.) by invoking the rules running in a web service.

What’s next?

Nurses have written a 60-page survivor care plan, which they are translating into business rules now.

Similarly, they are translating a 10-step guide for the newly diagnosed (get the correct diagnosis, tests you really need, initial treatment options, supportive care…).

How Kaiser Innovates


A little while ago, I enjoyed reading the trip reports that both Jim Sinur and Elise Olding published on the Gartner blog about innovation at Kaiser Permanente.  Institutionalizing innovation?  This is just the kind of thing that picks my interest.  I could not pass on the opportunity to do that tour as well.  I signed Carlos and I up for a visit of the Sidney R. Garfield Health Care Innovation Center.

Kaiser Permanente - Garfield Healthcare Innovation CenterI also recommend the tour if you happen to be in California but plan ahead as they fill up very quickly.  Our guide was very knowledgeable as well and could give us some “techie” nuggets knowing we were on the IT side.  They combine 3 main aspects: Facilities, Patient Care and IT.  So you could request a guide with any of those specialties.  Isn’t that cool?

Jim described the process innovation that Kaiser implemented very successfully to reduce drug administration errors.  It is so ridiculously simple it is impressive.  It is often the case that the best solutions are actually very simple.  It was interesting that the environment allowed them to think outside the box and focus on the key issues to address.  With the in-situ brainstorming involving all stakeholders, they have been able to come up with an innovative solution to the problem and to test it.  They literally tested out several sashes once they settled on the idea.

I was intrigued by their robotic initiatives.  Having robots deliver bedding throughout the hospital feels a like Start-Trek-ish and cool.  The future is being tested over there though.  They finish the tour with the room of the future.  The trend to simplification continues there of course, bringing tablet-like devices at reach in replacement for many devices they use today.  With the Cloud and all the modern technology, it becomes less of a stretch to consider digital information exchange ver seamlessly from iPad to television to support interactive discussions between the healthcare provider and the patient.  Information will have to flow seamlessly to the home too, for better support and integration of wellness programs that lower “Back to the ER” statistics.

We only talked about Gamification in the context of nurse training, to simulate birth delivery, but you can tell from their website that Gamification is one important focus they experiment with.

My personal take-away was the impressive results they achieved by testing out their new processes or solutions in “reality”.  They have rooms that are designed exactly as real hospital rooms (although they felt much bigger than the room I was in for my son’s delivery).  They try, try and try again.  They iterate in the context of a real-life situation.  They observe; they measure; they assess.  With this use-case-driven approach, they have been able to correct or abandon ideas that looked perfect on paper — they call them their “successful failures”.  It saved them millions of dollars or more.  Our guide pointed out to a few architecture issues that may have been overlooked without the simulation.

That is an interesting parallel with the use-case-driven approach we are evangelizing.  In Decision Management, eliciting and refining business rules in the context of data leads to similar outcomes:

  • Detection of “successful failures”: decisioning that sounds good on paper but that leads to poor business results
  • Collaborative refinement: talking in the abstract can lead to misunderstanding and misalignment; when the discussion is concrete about a specific case, stakeholders can be more precise on their comments and contributions, accelerating the path to the “best” solution

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