You are not the only one interested in using the right data in a better way. The same is true for everybody you contact about your organization’s data. When it comes to the data for which your organization needs to make decisions, and the decisions that have to be made using those data, you will probably have to involve other people. You will need to reach out to them and ask them what their opinions are on your initiative, and whether they can be persuaded. For the record, this is not necessarily a bad thing. The fact that even those that are unlikely to be particularly convinced by what you are proposing will still help you achieve your goal through data.
However, it is important to remember that the people you are trying to influence are usually not people that you know, or know well. So, you might end up going “off the rails” in the process. You will have to get the right people on your side. For example, you can’t convince a CEO of a company you haven’t worked for 20 years.
In order to do this well, and more importantly, to do this effectively, you have to get better information about the people you need to inform. Not only do you need to be better informed yourself, but you also need a better understanding of the people you intend to influence, and what they find most helpful when you engage them in their process.
The reason this matters, for what is called the “key indicators” of success of your enterprise data initiatives, is that they will make the difference between “good enough” and “better than good enough” – that is, between what is successful and what is not.
To sum up, whatever tools you use, remember these basic data science communication tips and you’ll be more likely to deliver the following presentation:
- Start with a problem. Is this a problem your audience already knows? If not, you should start by clearly establishing the problem. Investigation and preparation are key.
- Show empathy for your audience and present them with the information they want in a format and language they understand.
- Use data visualizations to illustrate your conclusions, but let your own explanations—not graphs—drive your presentation.
- Keep it simple, and leave the unnecessary details in each of your explanations and diagrams.
- Finally, keep practising! You got this!