As you set out to model and automate your company’s digital decisions, be aware that it requires a way of working and thinking with which you may be unfamiliar. Improving the agility, transparency and intelligence of your automated business decisions with digital decisions is not something you can accomplish overnight. It’s essential to start with realistic expectations.
Your first decision modelling project must succeed in its business objectives and inform stakeholders about how practices and infrastructure must change to support this new way of working. It’s as much about communication and learning as achieving business goals.
Here are some points to consider as you undergo your first project.
Your first decision modelling project should focus on a small problem that is non-trivial but not business-critical. It should be just big enough to act as a meaningful case study, but not so vital that it attracts political attention. It’s easier to build on small successes than large failures.
Some companies feel that decision modelling is so simple that they can get started without help. They may direct some of their staff to read a few books and internet articles on the subject and then expect them to build successful models and integrate them into their business processes. Such an approach is rarely successful.
Get an experienced decision modeller and a workshop coordinator to help you in your first workshops. Select a vendor to help you make digital decisions a technical reality within your company. You will avoid more pitfalls, be more productive and have the confidence to see a proof of concept through until the end. Furthermore, the success you create will motivate more teams within your company to use the same approach.
Get the Business On-Board
Some companies approach decision modelling as an exclusively IT initiative. Treating it this way will almost certainly lead to failure. Defining policy decision-making is a business-led activity that is supported by IT. It is vital to include business stakeholders throughout any digital decisioning initiative because they are the chief beneficiaries. Digital decisions must capture their policy expertise. IT has little to lose in electing program code as the primary vehicle for implementing automated decisions. They will not suffer from the lack of transparency, agility or accountability such a choice would entail.
Work with What You Have
Decision modelling is a powerful means of understanding your real data needs. It exposes precisely what data is needed to make decisions. It’s crucial to develop decision models in tandem with data provisioning initiatives. That way, you don’t waste time modelling decisions that depend on data you don’t have or provision data you don’t need.
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.