Guest Post: Are you a good evidence-based decision maker? Take this quick test to find out.

A lot of organizations are facing new mandates to be more “evidence-based” or “data-driven”. Part of the reason is that there’s a lot more data available than ever before, so why not use it? Sophisticated Business Intelligence solutions can process and analyze this data and visualize it on ever more powerful and attractive dashboards.  Artificial Intelligence—Machine Learning in particular — go even further in helping you convert data to value.

Let’s assume you’ve been given some great data, visualized well. How good are you at using it as evidence to make a great decision?

Here’s a chance to test your evidence-based decision-making skills. The test comes from a common scenario in business: determining the price for a product, and deciding how much of it to manufacture. But it’s important for you to know that this is just an example: opportunities for evidence-based decisions are all around us, whether you’re setting a policy to keep people safe from Covid-19, helping a nonprofit to route donations to the most valuable projects, or hundreds more decisions in complex environments.

So even if your job isn’t this particular decision, please try the test below anyway.

Putting your evidence-based decision-making skills to the test

Please imagine that you are responsible for a new product which is ready for its first commercial production run and sale to potential customers.  Specifically, you are faced with three decisions:

  1. How much should you charge for each unit of the product?
  2. How many units should you order from the manufacturer for your first run?
  3. How big an investment should you make in marketing the product?

After laying out the relevant relationships in a spreadsheet, it turns out that you need the following additional pieces of information to calculate the profit:

  • The demand curve (the relationship between the price and the proportion of the available market that will purchase the product at that price).
  • The demand curve (the relationship between the price and the proportion of the available market that will purchase the product at that price).
  • The increase in demand created for each level of marketing spend.
  • The relationship between unit price of production, and the size of the production order.

(Again, these types of relationships are typical of many kinds of decisions, not just this one.)

Fortunately, your organization has a great analytics and data science team and when you ask them for the list above, they very quickly come up with the answers you need and present them on the following dashboard.

Your challenge is simply stated: Using the information provided in the above dashboard, decide on the:

  • price to charge for each unit of the product,
  • number of units to manufacture, and
  • percentage of profits to re-invest in marketing

given that you want to maximize profit.

As a stretch, do that again, but this time with the constraint that the likelihood of making a loss greater than $500K is less than 20%.

The answer isn’t obvious from the dashboard

If you’re finding this to be a difficult task, it is.  While all the information you need is present in the dashboard, it is not aligned with the questions you’re asking.  If you wanted to know “How much will each unit of production cost if I order 125,000 units”, or “What fraction of the available market will buy my product if I charge $11.00 for it”, then the above dashboard is exactly what you need.  But that is not what you are asking and, although the above dashboard does contain the information you need, it does not present it in a way you can use.

The problem is that information presented in this form doesn’t tell you the outcome from various actions. In other words, it doesn’t tell you what to do.

You need decision intelligence to find the answer

This misalignment between available data and what needs to be decided highlights the fact that the “data-to-dashboard” approach that is the mainstay of Business Intelligence (BI) goes only part of the way towards effective evidence-based decision support.  If you have come across the term “Decision Intelligence”, you may be wondering what it means (if you haven’t come across it, chances are you will before long. In its Top Strategic Technology Trends for 2022 Gartner states “By 2023, more than a third of large organizations will have analysts practicing decision intelligence, including decision modeling”).  Gartner goes on to say that,

“Decision Intelligence is an approach to evidence-based decision support whose methodology provides decision makers with the information they need, and does so in a form they can directly use.”

Gartner Group, Top Strategic Technology Trends for 2022

Specifically, rather than focusing on data and how it should be visualized, Decision Intelligence focuses on the actions available to the decision maker and the outcomes they are trying to achieve.  Data and other supporting information are presented in these terms and are therefore immediately interpretable by the decision maker.

So the answer to this test is: a) How many units do I order? 138,000 b) What retail price to charge? $15, and c) How much to re-invest in marketing? 7%

You’re probably wondering where these answers came from! I’ll be writing about that in my next post. And if you’d like to enhance your professional skills in this area, then you should also check out our course, Getting Started with Decision Intelligence.

Mark Zangari
Co-Founder and CEO at | Website

In addition to his duties as CEO, Zangari leads Quantellia LLC's Scalable Solutions division, where he is responsible for financial technology, telecom, and Covid-19 solutions. Zangari is also the architect of the company's World Modeler solution suite.  Before joining Quantellia, Zangari spent 15 years as CTO for a spatial GIS company, where he specialized in providing solutions to utilities and telecoms.

A physicist by training, Zangari's papers on cosmology are still referenced today.

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