Most business leaders understand, better data and better analytics are going to lead to a better decision. But there’s this chasm between business leaders who don’t have a background in data and data professionals.
Even though data professionals have the capabilities to execute high-quality analytics and business solutions, they’re not speaking the same language as some of the business stakeholders, and they can’t seem to get across this divide to communicate insights. The problem is there’s no translator between these two groups.
The Pragmatic Data Analysis Model (also referred to as the Pragmatic Data Insights Model) addresses this problem in a tangible way by giving teams a process that can lead them to success.
Here are some questions we answered during a recent webinar on how to best implement the model.
How is the Pragmatic Data Analysis Model different than other data models?
There are many models that focus on analyzing and streamlining data. The Pragmatic Data Analysis Model is different because it helps organizations handle communicating, defining and presenting data to stakeholders.
What makes this model uniquely helpful is the third step—refine. It is the reality check that helps teams avoid spending valuable time and resources working on projects that aren’t useful. It’s a different way of thinking about the problem.
This model helps you confirm that what you’re doing when you analyze is actually relevant. There are tons of brilliant data scientists and analysts with impressive projects that go to waste because at the end they weren’t presented in a way that the executive team or management could take action.
When this happens, the data professional didn’t solve a business problem, they solved some other problem. It may be an interesting project, but they didn’t actually move the business forward. This model helps teams connect their ideas and projects to solutions for the business rather than simply providing an efficient way of analyzing the data.
Who owns the “define” and “refine” steps, and does an analyst have the ability to push back?
Analysts have fantastic minds and organizations should be utilizing their full potential, so absolutely, they should have the ability and permission to push back on defining and refining the question.
Getting that buy-in from both the management and the data professionals will always make the end result more productive. A shared, negotiated reality for the scope of the project is critical to success.
The define and refine stage is for you to be honest, and it needs to be implemented in the organization and understood as part of a process that both sides of the chasm are embracing. This needs to be part of the culture. We’re going to define this problem together. As the data professional, it’s my responsibility to quantify your problem and bring it back to you.
How would you promote the Pragmatic Data Analysis Model internally?
Businesses realize that if you ask a well-defined question, you’re going to get a usable answer much faster. Then, that kind of efficiency within an organization gets noticed, and in some ways, it will promote itself.
If your company is working on a vague, imprecise and poorly-defined question, it’s going to be an unusable answer. Once you practice the model, the efficiency with which you can execute it in the business or in the data becomes highly efficient.
You promote it by emphasizing it’s not about slowing down the process, but improving it and making it faster.
What kind of tools work best with this model?
The Pragmatic Data Analysis Model works with any tool. Companies can even solve the business problem by doing a pivot table in Google Sheets.
Whose responsibility is it to make the “present” step understandable?
It is absolutely incumbent upon the person who is responsible for the analysis to be able to explain the results of the analysis.
If the answer to the question is long and difficult to understand by the stakeholders, then it means there is a gap in understanding. It could be an unclear question or ambiguity on how the answer applies to the business. Sometimes, it could be the data is not sufficient to support a clear conclusion.
If there is alignment between the requester and the analyst, then you’re much less likely to get the difficult-to-understand presentations.
Before scheduling a presentation meeting, you should be able to explain the results in a tweet. If you can’t say it concisely, then you didn’t understand the results.
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Want to learn how to connect your data analysis to a business problem by leveraging the Pragmatic Data Analysis Model? Register for Pragmatic Institute’s new course, Business-Driven Data Analysis.