To build data-driven organization, you need decision makers to use data instead of anything else. So you need to help built a culture where data-driven decision-making thrives — usually this is most efficient if you have executive buy-in. Example: A CEO who is a stats-junkie! Of course, not every company would have this. It could be that you find yourself in an organization where the CEO is known to make huge bets just using “gut” — in cases like this, an organization could have some of the best platform and processes but unfortunately, it won’t do any good.
Now just having people who make data driven decisions is not enough — you (as a data professional) need to deliver “data” to them. To do that you need 1) Processes 2) Platform. So let’s talk about them:
A platform in this context is the data and analytics platform used by the organization to get the data they need, when they need it. If the organization is small (e.g. less than 15 or so) then the platform could be excel and engineers/analyst writing ad-hoc queries but as you grow (= team size expands) then you need better platform to serve the data needs of the organization. Some tools are better than others and you would usually wind up using multiple vendors in your analytics stack — but remember that jut having a great analytics platform is not enough. You need the “people” and the “processes” to go with that. So, with that let’s talk about process:
Process is everything between Platform and People. Let me expand on this. Here are few things where having a defined process is key for building data-driven organizations.
- How to prioritize the analytics request? It will be great to have a process where you/team will work on projects that closely align with the strategic objective of the company
- What does the analytics org-structure look like? Do you have analyst embedded in each team or do you have a centralized team or do you go for a hybrid approach?
- What is the process to justify investment in analytics?
- Which is the “right” metric definition? (There needs to be a process that keeps the metric definition standardized in an organization)
- What is the process to clean data? (Maintaining data integrity is key. You could put this on “Platform” bucket as well)
- How do users get “help”? (Is there a ticketing system that they should use? Is it just another “IT” ticket? Who responds to tickets? What’s the SLA around analytics queue tickets? etc)
- Who owns “analytics”? There needs to be someone on the team owns analytics like analytics manager, VP of analytics and he/she should be reporting to someone on management team (CIO, CFO, COO, Chief of Staff, CEO) who is held responsible as well.
The list goes on…but I hope you get the point. Having a well-defined processes in an organization is important — usually, this stuff gets less attention and org’s/teams tend to focus just on “platform” which might not be the best thing to do.
Having shared the 3 P’s, let me share few tips on
How to go about implementing the framework:
- Identify the “P” that has the best ROI
- It’s an iterative process!
- Refine as needed
On #1. To help you identify the “P” that has the best ROI, your first step could be to create a matrix to help you evaluate where your organizations falls. I have shown an example below:
On #2. Understand that it’s an iterative process. You are never done optimizing any of these P’s! It’s a journey and not a destination.
On #3: Just like with other frameworks, you’ll need to refine and adjust this based on your needs. You may have noticed that I focused on “Org-wide” framework but you could be heading up an analytics function for a department and in that case, not all of the things here would help. “People”, “Process” and “Platform” would still apply on a high level but it might just be that you don’t have “control” over the platform. So, you may need to refine/adjust this as needed.
I hope the framework is a great tool for you to think about building data driven companies!
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