While you are improving the agility, transparency and intelligence of your automated business decisions with digital decisions, consider the simple guidelines below.
Start with Clear Business Goals in Mind
When you model a decision, always start with the business motivation for making a decision, consider:
- Why automate this decision, how, specifically, would business operations be improved and how does it add value?
- Which department makes the decision, and which departments might be influenced by it?
- How would you measure the specific improvements this automation can deliver?
Ensure your business goals are SMART (specific, measurable, attainable, relevant and time-bound). Many digital decisioning platforms can track SMART goals, so you can see how well a decision is serving your business needs.
Put Decisions First
Consider the business view of decision making before any technical concerns. It’s easy to be distracted by or become embroiled in the specifics of technical implementation, but this is counterproductive. Focus on capturing a decision’s business value and definition first. A well-defined decision will sharpen stakeholder discussions and improve meeting productivity.
Define Decisions Then Capture Detail
Ensure that you outline a set of candidate decisions before developing the detail any one of them. This practice will ensure that your scope is sufficiently broad and allow you to identify the appropriate stakeholders for later meetings.
Invite the Right People to Decision Modeling Workshops
Confirm that all stakeholders at the meeting are necessary and sufficient. Having the appropriate (and only the appropriate) personnel present boosts productivity and reduces the need to have follow-up meetings. Above all, keep the meetings small (fewer than seven participants) and brief (not more than two hours).
Be careful with Jargon and Acronyms
Every industry has buzz words and domain-specific jargon. Some of these are specific to a business domain, and some are specific to an individual company. Either way, experts can quickly become blind to how much jargon they are using. It’s proper to include these terms in your decision models. However, always ensure these terms are defined and documented in your model. Documentation is critical when two buzz words are synonyms, or your company uses some phrases or acronyms differently from others in the industry. Embrace domain terms but avoid company-specific ones if you can. This practice avoids confusion, keeps decision models readable to a broader audience of stakeholders and helps newcomers to your company to understand decision-making.
Just Replicate Existing Systems
When companies develop IT systems to support business processes and decisions, they tend to reflect the business processes of that time. For this reason, it can be unwise to use existing systems as a sole source of decision-making practice. By doing so, you may be enshrining outdated processes. Consider existing processes, but don’t be constrained by them. Always search for better ways of doing things that benefit from your business experience.
Rush to Implement Business Rules
When modelling decisions, consider the primary business goal first, then what sub-decisions are needed to support it and, ultimately, what data is required by them. Detailed rule modelling, such as the mechanics of how to calculate a discount or assess risk, should be left to a second stage. It’s essential to understand the shape and scope of your business decisions before the nitty-gritty detail.
Include Every Detail Every Time
Some parts of business logic can include complex calculations. Don’t feel compelled to include the nitty-gritty detail of these in your decision model unless they are subject to constant change. Complex, static functions (e.g., amortisation, option pricing or data adjustment) might best be maintained in a library and reused as needed in decision models.
Complete the Model Then Integrate
Decision modelling can be very immersive and fun. There is often a compulsion to ‘complete’ a model and then try to integrate it. Completionist thinking can be a costly mistake as integration (into your company’s infrastructure and corporate databases) can reveal flaws in your approach. It is wiser to follow an agile approach: build a small, approximate model and integrate it early. This helps identify defects such as data gaps, process inconsistencies, unhandled cases and unintended consequences quickly and cheaply.
Modelling your company’s business decisions as part of your digital transformation, and deploying them as digital decisions, will help it achieve more transparent, agile and accountable business processes that have measurable value. Good luck!
About the Author
Jan Purchase has been working in investment banking for 20 years during which he has worked with nine of the world’s top 40 banks by market capitalization. In the last 13 years he has focused exclusively on helping clients with automated Business Decisions, Decision Modelling (in DMN) and Machine Learning. Dr Purchase specializes in delivering, training and mentoring all of these concepts to financial organizations and improving the integration of predictive analytics and machine learning within compliance-based operational decisions.
Dr Purchase has published a book Real World Decision Modelling with DMN, with James Taylor, which covers their experiences of using decision management and analytics in finance. He also runs a Decision Management Blog www.luxmagi.com/blog, contributes regularly to industry conferences and is currently working on ways to improve the explainability of predictive analytics, machine learning and artificial intelligence using decision modelling.