In my many years of experience in the Data Science industry, I’ve noticed that there are two main groups of data scientists:
- Those who focus on building data products.
- Those who 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 so as to solve business challenges. Yes, many people comment, “We are helping these folks, does the responsibility to convince lie with the data scientist?” Well, Data Science is a new function in most organizations. Most organizations have no idea how Data Science really works, and for stakeholders, they have been making decisions based on gut feel and, let’s face it, making decisions from gut feel is much easier. Moreover, a data scientist needs to constantly gain credibility, 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 that I drop you off in a foreign country where they do not speak 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 such insights can inform marketing strategies. For instance, let us compare these two statements :
“Based on our model, for one standard deviation increase in TV advertising dollar, it translates to a revenue increase of 2.78 standard deviation.”
“Based on our model, for every dollar we put into TV advertising, it translates to a revenue increase of 2 dollars.”
It is obvious that the second statement can be more readily understood by many. No doubt, it means more work for the data scientist to do the conversion but it helps a lot in building stronger 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 definitely an opportunity to translate it into something better for the stakeholders.
Tip 2: Show ME the MONEY!
For a data scientist to add value to the organization, remember this:
“SHOW ME THE MONEY!”
It is pure capitalism and economics. If the data scientist could not add value to the business, help the business to earn more money (more than the salary, please!), why would the business owner pay the data scientist? It would 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 in the strategy planning and execution. 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 are able to 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 into the insights generated.
A word of warning though, you have to manage the stakeholders’ expectations. How the potential revenue gained or cost savings achieved should never be plucked from the sky but rather through simulations/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 analyses. In that case, always ask yourself if the assumptions can be supported by data. For instance,
“Taking our insights into consideration, we believed we can earn $400K by spending $100K buying ad space in this list of websites. This $400K was derived by taking the ad response rate from the previous year, which was about 4%, and multiplied by the expenditure of our customers in Group A last year.”
You will notice that throughout the statement made, the numbers were not made up but were supported using past data. In that sense, it will not be easy to dispute those numbers since it is a reflection of the past. But, of course, with the 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 would love to hear more. 🙂
Those were a few tips for presenting your data insights to stakeholders. Depending on what you present, not all tips may be applicable but 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! 🙂