In our last two blog posts in this series we discussed decision engine performance and how performance is impacted by deployment architecture choices. In addition to those considerations, you should also focus on business decision performance, the topic of this post.
Central to SMARTS’s approach to decision design is the idea that you need to have a strong focus on the expected business performance of your decision. The business performance of the decision is measured by multiple KPIs defined by the different business stake holders and characterize how the decision is contributing to the business.
Decision Analytics for Simulations
SMARTS provides you with fully integrated decision analytics, including aggregates, reports and dashboards that you can configure to track those KPIs. As you are implementing and optimizing the decision logic, you can run simulations to assess the impact your change have on the decision, and take appropriate action.This allows you to ensure that the business performance of your decision is actually what you want before you deploy it in production.
Real-time Decision Metrics
SMARTS also provides you streaming decision analytics, allowing you to monitor the same KPIs on the live decisions as they are deployed, and to specify alerts that trigger if those KPIs deviate from limits you can set.This gives you the peace of mind that you are always kept up to date on how well the deployed decision is behaving and that you can take early action to update it should the situation need it.
There are also cases where it is not possible to necessarily know in advance the impact of a change. You may be exploring with new decision options you had never attempted before. SMARTS allows you to deploy your decision in an experimental mode – where part of your invocations will be routed to the new “experimental” version, and the rest to the proven one, and where you will be monitoring the relative performance to identify whether your “experimental” version is doing better than the proven one. In many financial services areas, this is called Champion-Challenger, in marketing or design, this is called A/B testing. With this approach, you can gradually and safely introduce decision optimizations that lead to better and better business performance.
In summary, when considering performance of decision management systems it is critical to consider the topic from a business perspective as well as a technical perspective. We hope this series has helped clarify performance related issues pertaining to decision management.
Naturally, the decision management community demands constantly more best practices. We delivered tips for writing and organizing business rules, and topics of that nature. However, success often depends on these crucial early choices. So, let’s take a step back, and discuss how to start a decision management project.
Join our upcoming webinar to explore what initial steps will ensure success. In particular, we will focus on data and data model, business rules requirements, and business performance.
Anyone with interest in decision management will benefit from this conversation. As a newcomer, you will hear practical advice for this first project you have to tackle. As an experienced practitioner, you will enjoy our design tips.
Why should you care about Data Validation? Actually, your decisions can only be as good as the data they apply to. Consequently, by improving the quality of the data you apply your decisions to, you will improve the quality of your decisions. There is inherently a strong bond between data and decisions. Our previous post highlights the importance of data in Decision Management. In this post, we will focus on strategies to improve Data Validation.
As a matter of fact, there are many forms of bad data: incorrect, missing, fraudulent, etc. Data Validation needs to address all these forms of problems you might encounter. Let’s take a deeper look at those.
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For decades, writing rules has been an abstract exercise. Business Analysts review requirements. They write the corresponding logic. If they are lucky, there is a testing infrastructure they can push the rules to. Often, they have together code for test cases, or wait for QA to catch possible issues. There is a better way that involves bringing data in early on.
The primary objective of a data-centric approach is to provide immediate feedback. As you look at one transaction at the time, you can see what decision and intermediate decisions are made. For example, an insurance application might be too aggressively turned down due to a somewhat-poor driving record. Reviewing this result, you can fine-tune your rule right-away. After adding the proper safe-guards, that same application might end up approved with higher premium, rightfully-so. This quick turn-around is key to quality rules in your decision management projects.
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Business rules provide the flexibility and agility that systems need. By definition, they enable business analysts to adjust the system’s decision logic to ever-evolving business direction. Due to competitive pressure, business regulations, or executive direction, business rules keep adapting. The art of capturing rules requirements dictates the success of the rules implementation to follow.
Rules Requirements are Requirements
As a Product Manager at heart, I value a nicely written PRD (Product Requirement Document). Many articles provide guidance on requirements gathering, and the golden rules to adopt for success. In particular, I like Duncan Haughey’s concise take on the subject. Above all, I cannot stress enough the value of focusing on the problem rather than the solution. Engaging stake-holders or subject matter expert from the start is paramount. Kuddos to him for putting it into writing.
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A little while ago, we hosted a webinar about “why Decision Management?“. However, including the business case presentation alone seemed somewhat incomplete. Indeed, we also needed to focus on ‘how’. As a result, we created a presentation that covered both aspects.
In this blog post, I will focus on the key takeaways of the presentation. However, I encourage you to watch the video, especially if you are a manager, new to decision management technology.
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Integration with data is key to a successful decision application: Decision Management Systems (DMS) benefit from leveraging data to develop, test and optimize high value decisions.
This blog post focuses on the usage of data by the DMS for the development, testing and optimization of automated decisions.
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ETCIO (an initiative of The Economic Times) has interviewed KM Nanaiah, country manager at Equifax. The article highlights the details of the tooling that is now available to financial institutions. They will see dramatic improvements in customer acquisition and loan decisioning.