Guest Post: The Backwards Conversation (part 1 of a series)
Most conversations between decision makers and data professionals begin back-to-front
If you’re a decision maker who wants to make the best use of data and other information resources, such as artificial intelligence (AI), to support your decisions, chances are that conversations between you and your data analysts is happening back-to-front.
Data analysts should encourage us to explain,
“Here’s what I need you to do for me…”
Instead, so often they begin with:
“Here’s what we have for you…”.
The solutions that the analysts produce offer insights that might be relevant. But they do not actually give the decision maker the information they need to make their decision with a level of justification they can be confident in. And there is no audit trail to show how the facts support the conclusion that taking certain actions will lead to the desired outcomes. And the whole process is likely to take too long and require a lot of unnecessary work.
There is a better way; it begins with starting the conversation in the right direction.
Consider a closely analogous situation. When an IT organization creates software to solve a problem, step one is understanding the customer’s requirements. The interaction between data science and analytics teams and their customers should begin the same way. So why is there such a difference between data projects, where the tail seems to be wagging the dog, and software projects where what is to be delivered is defined by those who have the problems that the software is intended to solve? The reason is that software engineering spent years—if not decades—recognizing that software was only successful if it met the needs of the end users. Years of painful failures led to formal and informal methodologies for eliciting requirements from users and representing them in ways both understandable by non-technical users and precise enough that software developers can build systems that meet users’ needs.
Enabling decision makers to fully reap the benefits of the data and analytics assets developed by data science requires a set of methodologies and tools similar to those that software engineers developed to meet customers’ needs.
The need for these tools, along with the articulation of the benefits they stand to offer, is increasingly being called Decision Intelligence (DI). Gartner includes DI in its Top Strategic Technology Trends for 2022, and says “Gartner expects that by 2023 more than a third of large organizations will have analysts practicing decision intelligence, including the sort of decision modeling that is essential to gaining a competitive edge”.
Despite a growing consensus that DI can deliver great value to decision makers and their organizations, there has been little practical guidance on how to implement DI, or specification of software tools to assist in this. This post is the first in a series to fill this gap and provide:
- A practical methodology that any organization can use to reap early benefits from Decision Intelligence, and
- A set of software applications that automate and assist in this.
Specifically, I will explore the two key problems for data-driven decision making:
- How can decision makers identify the data they need to support their decisions?
- How can we use data assets to show how decision outcomes depend on the decision-maker’s actions?
I’ll also introduce new tools to support answers to these questions. More details in the next post.