Decision Management: The Elevator Pitch
Blog: Lux Magi - Decision Management for Finance
Caught in an elevator with a brain surgeon and a lawyer and they asked me what my company does and its value proposition, here is my answer.
So, what does your business do?
We show businesses how to manage their important business decisions, so they can be better understood, verified, improved and automated.
Business decisions? You mean decisions like mergers and acquisitions, product development, hiring, IPOs?
No, we focus on operational decisions. These are decisions larger companies are making thousands of times a day as they conduct their business: whether to approve a loan, whether to investigate an insurance claim or pay it, what products to offer a new or existing customer to promote a better relationship with them, how to price a product given the client risk, determining if a given transaction is compliant with the regulations and so on. In other words, repeatable decisions that impact their day-to-day relationship with their customers.
Well surely, for larger companies, these decisions are automated already, in their operational systems?
True, but in many companies this automation requires that the policy be expressed in a technical format (i.e., a programming language, complex spreadsheet or some other representation). This makes the underlying business policy hard to understand by non technical staff—it’s ‘lost in the code’. So only coders really understand how the decisions are made. Over time, some of these coders may leave and companies’ understanding of the behaviour of their automated systems is lost or compromised.
Companies that don’t automate their key decisions may be in an equally bad place: decisions executed manually, by people, are frequently not performed very consistently, are difficult and costly to scale and are only understood by the few people that perform them. When these key people leave these companies, their knowledge goes with them.
When decision know-how is buried like this, it’s hard for most business experts to understand how automated decisions are made. So, it’s hard for them to verify that decisions are being made correctly, improve them or adapt them to changes in the market. It can also be hard for these businesses to understand how effective these decisions are in business terms and to justify the outcome of individual cases.
We need a separate representation of business decisions, outside the code, that all business stakeholders can understand. These are called decision models.
How does having yet another, paper-based representation of the decision logic help – surely it’s more shelf ware?
Decision models are not paper-based representations of business decisions, they are executable. You can build automated systems around them. But most importantly, decision models can be understood by business subject matter experts (SMEs).
You can ‘kick the tyres’ of a decision model: give it some business scenarios and it will tell you what decisions it would make and why. Also, because decision models often become the basis for decision automation, they are never out of date.
With code or spreadsheets the explanation of what is being done (and why) is very much a side effect of execution, with a decision model it’s the other way around: accurate execution is an outcome of an explicit explanation of business logic and its value.
Perhaps. However, do we really need yet another representation? Most business subject matter experts just get familiar with the code. Some of the analysts at the companies I’ve worked with are real devas with Excel, SQL and Java.
Some may be. But this can foster key man dependencies – what happens when these gurus leave the company? Also, code just isn’t a very effective way of representing how a company makes these decisions and it can’t be understood by important stakeholders. Separating business policies from code and technical infrastructure and representing it as a decision model makes it clearer to more people in the company, including business stakeholders, how their decision-making works, whether it is correct and how effective it is. Then, they can take control of its evolution.
What do you mean?
When you capture the logic of a business decision in a decision model it’s now not only easier to understand but it’s executable. Subject matter experts can change it (in a test area) and see what impact that would have on your company’s performance. You can even compare the performance of established decision-making with new proposals. It also means you are in a better position to justify your decisions after the fact.
Justify your decisions? What do you mean?
By justify your decision, I mean explain, in a specific case, why a particular outcome was reached: why did you refuse that client a loan, why did you recommend that product, how did you calculate that offer?
Why should you have to do that?
Well let’s say your automated systems have made a decision (e.g., refused a client a loan or decided to investigate their insurance claim) that appears to be the wrong one. Firstly, a decision model can help you to understand if you made a mistake in a given case and, if so, how to fix it and make your automated decision even better in future. It can help you explain why the decision you made was actually correct according to the business policy you’ve currently agreed, despite the fact that the outcome was unexpected.
Decision models are connected to your business policy documents, so they can explain all outcomes in terms of agreed policy documentation. Lastly, decision models can be used justify your company’s behaviour to a third party.
A third party? Why is that important?
Compliance regulations are changing so that companies have to be able to explain to regulatory bodies and their clients why certain decisions had the outcome that they did. Some regulations, like the GDPR, insist that you can explain to clients in simple terms why you made the decisions you did.
Surely, you’re not going to explain your business decisions to an outsider? Isn’t that giving away your intellectual capital?
No, not at all. Decision models are very good at representing complex business logic in a format that’s both easy to understand by a business SME and that is executable in a business system. But they are also good at representing simplified views of decision logic, aimed at specific stakeholders – including the customer. You can choose exactly how much detail to share.
So, decision management is about making business decisions more visible to everyone (according to their needs) whilst being detailed enough to be executed as part of an automated system?
That’s right. The rigour of decision management has a number of other benefits. It can…
- Help you to understand your data needs more comprehensively when embarking on a new project
- Help you to embed AI more effectively into your business and
- Help newcomers to the company to understand how the company works more quickly (and more accurately) that if they were being trained from out-of-date policy manuals or code reviews.
How come companies have managed well enough up till now without decision management?
Well many of them have already adopted it and others are investigating its benefits. Business policies are more complex and changing faster today that at any other time in business history, so the need to manage them and the benefits of doing so have never been higher.
There are also more techniques available today to assist with decision-making: business rules, artificial intelligence, optimization and quantum computing. Decision modeling allows you to integrate all of these techniques into a collaborative whole.