In my many years of experience in the Data Science industry, I’ve noticed there are two main groups of data scientists:

  1. Those that focus on building data products
  2. Those that focus on using machine learning to provide actionable insights to stakeholders

If you belong to the 2nd group, this is exactly written for you.

The main objective of any insights presentation is to get stakeholders to adopt the insights presented to solve business challenges. Yes, a lot of people commented, "We are helping these folks, why is the responsibility to convince lies with the data scientist?" Well, Data Science is a new function in most organizations. Most organizations have no idea how Data Science work, and for stakeholders, they have been making decisions based on gut feel, and let's face it, deciding from gut feel is much easier. Moreover, a data scientist needs to gain credibility constantly, to convince stakeholders that data can provide value to their work. Without stakeholder support, the data scientist will have difficulty providing value to the organization.

Here are a few tips for your upcoming presentation.

Tip 1: Talk in their "Language"

A simple analogy is to imagine I drop you off in a foreign country where they do not speak in English, or their understanding of the English language is minimal. There will be a communication problem right? When you prepare your data insights presentation, speak in the audience's (mostly business users) language. If you are doing marketing analytics, translate the insights into suitable marketing terms or how can the insights be translated into a possible marketing strategy. For instance, let us compare these two statements,

"Based on our model, for one standard deviation increase in TV advertising dollar, it can translate to revenue increasing by 2.78 standard deviation."


"Based on our model, for every dollar we put into TV advertising, it can translate to our revenue increase by 2 dollars."

It is obvious that the second statement can be understood by many. No doubt, it means more work for the data scientist to do the conversion but it helps a lot to build strong communication with stakeholders. Remember, look through your presentation and see how you can "speak their language". Every time you realize you have a technical jargon, it is an opportunity to translate into something better for the stakeholders.

Tip 2: Show ME the MONEY!

You really think I am not going to put this in? :)

For a data scientist to add value to the organization, remember this:


Its pure capitalism and economics. If the data scientist cannot add value to the business, helping business to earn more money (more than the salary please!), why would the business owner pay the data scientist? It will be a loss-making move. So to justify the salary drawn (be it high or low), the data scientist has to show the business where revenue opportunities can be gained and cost can be reduced.

Having said that, the next challenge after showcasing the data insights is to convince the stakeholders to use the insights into the strategic planning and execution phase. How to convince? Well, show them the money! After the data insights presentation, you want to show them the potential revenue to be gained or the potential cost-savings. Once you can provide some tangibles in the conclusion of your presentation, stakeholders have a better idea of what they are missing out and can be convinced to undertake initiatives that tap onto the insights generated.

A word of warning though, you have to manage the stakeholder's expectation. How the potential revenue gained or cost savings should never be plucked from the sky but rather through simulation/calculations, which brings me to the next tip.

Tip 3: Convince with Data

During the presentation, there are times where you have to make certain assumptions for your calculations (mentioned above) or analysis. In that case, always asked yourself if the assumptions can be supported by data. For instance,

"Taking our insights into consideration, we believed that we can earn $400K by spending $100K buying ads space in this list of websites. How the $400K was derived is we have taken the ad response rate from the previous year, which is about 4% and multiply by last year's expenditure of our customers in Group A."

You will notice that throughout the statement made, the numbers are not made up but have been supported using past data. In that sense, it is not going be easy to dispute those numbers since its a reflection of the past. But of course with a huge assumption that the future will be like the past. Having said that, is there a better way to get the numbers? If you have, I will love to hear more. :)


Here are a few tips for presenting your data insights to the stakeholders. Depending on what you present, not all tips are applicable but it will be great to keep them in mind so that you, the data scientist, will continue to add value to your employer.

Have fun in your Data Science learning journey! :)

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