Our Prescriptive Analytics and Decision Management blog aims to provide educational materials to practitioners from business, data and IT. We share best practices, new trends and thought leadership pieces.

This blog is brought to you by the Sparkling Logic Team:


Co-founder and CPO

Carlos Serrano-Morales

Co-founder and CTO


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VP User Experience

Can you Override that Rule?

Following up on my post from a few weeks ago, I would like to drill down into the technique I recommend for handling exceptions in your business logic.  You can also watch the recording of the webinar we hosted on that very topic, and more types of exception too.

Cascading DecisionsBefore I explain the solution, I want to summarize the challenge at hands.  Let’s say that you have global policies that you want to apply consistently across all segments, where segments can represent geographies, customer segments, client configurations, etc.  For simplicity, I would assume that segments are tied to geography.  Each US State might impose different regulations, that cause the business rules in the global policies to vary from state to state.  In some cases, business rules might need to be changed slightly.  In some cases, business rules might not be compliant with the local regulation and would need to be excluded.  In some cases, additional business rules might need to be added.


Are the Rules the Same for Everyone?

DifferentIt might sound like a philosophical question, but there is a very practical aspect to it that we might not fully realize, when making decisions.

Business Rules execute consistently by nature.  We like that.  We want that.

There are exceptions though, when we would like to treat a segment of the population differently.  We would like to always apply a conservative or aggressive strategy, but we might interpret that differently in different regions of the world, or for VIPs compared to occasional shoppers.  Our decision logic is not so black & white after all.


The Convergence of Data Analysts and Business Analysts

ConnectionsDecision Management has been a discipline fro Business Analysts for decades now.  Data Scientists have been historically avid users of Analytic Workbenches.  The divide between these two crowds has been crossed by sending predictive model specifications across, from the latter group to the former.  These specifications could be in the form of paper, stating the formula to implement, or in electronic format that could be seamlessly imported.  This is why PMML (Predictive Model Markup Language) has proven to be a useful standard in our industry.

The fact is that the divide that was artificially created between these two groups is not as deep as we originally thought.  There has been reasons to cross the divide, and both groups have seen significant benefits in doing so.

In this post, I will highlight a couple of use cases that illustrate my point.


What is Lifecycle Management for a Business Rule?

RecycleBusiness rules free companies from the traditional software development life cycle (SDLC), but it does not mean that they get created and deployed without any governance.  In my experience, business rules are actually tracked with a finer grain of control.  The traceability of these decision logic changes is a huge benefit in the long run.  Let me illustrate a few ways that Decision Logic Lifecycle Management is actually used and leveraged in real life.


From Decision Management to Prescriptive Analytics

compassA number of organizations have adopted the idea of making use of the Decision Management approach and technologies to problems such as risk, fraud, eligibility, maximizing and more. If you read this blog, you probably already know what Decision Management brings to the table.

Decision Management is all about automating repeatable decisions in a maintainable way so that they can be optimized in a continuous fashion.

Decision systems can use Business Rules Management Systems (BRMS), but they do not need to restrict themselves to just that: they can also be built on Predictive Analytics technology; or they can even consist of a combination of both. The increasing availability of data that can be used to test, optimize decisions, or extract insights from, makes it possible for decision-centric applications to combine expertise and data to levels not seen in previous generations of applications.

In this post, we’ll outline the evolution from pure Business Rules Systems to Prescriptive Analytics platforms for decision-centric applications.


Talking about decisions (part 1)

talk_decisions_1Every 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…


Making informed decisions (part 1)

Funny roadsign

We spend our lives, both personal and professional, making decisions, all day long; some without consequences, and some with long-lasting and even perhaps game-changing ones.

Should I eat some Thai food for lunch, or some Japanese food?

Do we make targeted offers to customers that have been with us for more than 2 years, or to those that have been with us for more than 5?

How do we reduce the time it takes us to fix defective devices?

Although sometimes not making a decision is worse than making the wrong one, we all strive to make the best decisions possible. And to make the best decisions, we rely on experience and whatever information is at hand. With experience in the subject matter, decisions can be made very quickly; when the matter is new or information is scarce, we usually require more time to evaluate a number of possibilities, to make a few computations, to balance the pros and cons.

All this is part of our daily lives. But when a large number of decisions need to be made in a short amount of time, or when the data available to us is limited, or on the other hand enormous, automation can come to the rescue. But how can we make informed decisions at a large scale?