Data On-Ramp: Enabling Product Managers to be more Data Driven
Being a product manager requires looking at user behavior and using data to aide business decisions. Here are my biggest learnings and a framework I’ve created and found to have been very helpful for guiding my projects at MAQE.
According to an IBM study back in 2017, 90% of the data in the world today has been created in the last two years alone, at 2.5 quintillion bytes of data a day.
We all know by now there’s a huge opportunity to leverage data for businesses, but not all companies are adopting data best practices.
Or some companies may know that they need it, but don’t have the actual tools to start.
“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” — Jim Barksdale
A funny quote, but I’m sure a lot of people can relate. This just highlights all the more the need for Data On-Ramp which is a framework I’ve created and followed after helping clients leverage the use of data to aide product decisions.
Why do we need it?
- Clear metrics
- Reduce personal bias
- Get actual user feedback instead of assumptions
- Structured decision-making
Data On-Ramp In a nutshell
STEP 1: Identify
In the data lifecycle, the first step would be to identify what are the data points needed and the objective for the business to retrieve these data.
Normally, businesses would have problems in either of these 3 business areas:
Once we know the outcome the business wants to achieve and have pinpointed the problem areas stopping the business from achieving that outcome, we can then do a Data Maturity Assessment to assess where the business is in the different stages of data maturity, to have better footing at where the organization currently is, and where we want them to be:
This assessment also includes looking at their Data & Tech readiness and more often than not, it also involves an honest Organizational readiness assessment, wherein we check if there is buy-in from the different departments: from staff all the way to the board. Because at the end of the day, tech and data is there to support, but without the necessary behavioral changes within the organization, we won’t be able to pursue the goal successfully.
Once everything is ready and laid out, we can finally look at what are the metrics to focus on, in relation to the business outcomes identified, and which data points need to be collected. Together with this, we can then see where these data are stored, who has access to these and how do we integrate these into a centralized dashboard. A data map or a tracking plan will then be produced.
To sum up, at the end of the “Identify” stage, we should already have a clear picture of the following.
- What is the business outcome they want to achieve
- What are the problem areas that is stopping them to achieve this
- Where are they in the data maturity stage and where would we like them to be
- Are data, tech, and the organization (buy-in) ready to pursue this endeavor
- A clear map of what data points to get, where to get them, and who has access
STEPS 2–5: Assemble, Clean, Analyze, Communicate
Where Step 1 of this framework is concerned mainly on planning and strategy, Steps 2–5 is where we get our hands dirty. This is where we put that plan into action.
Depending on the findings in Step 1, this endeavor’s scope can be as small as a Marketing Data Analytics Project, a Customer Support Analytics, or to a full Operations one or in the startup lingo, complete the entire customer (AARRR) funnel.
You also need to consider at which stage the company is — Startup, Growth or Profitability in order to focus on the right metrics.
This is when the help of Business Data Analysts, Data Scientists and Data Engineers would be needed to connect APIs if needed, automate data retrieval and updating, clean the data to be in a format that can be analyzed, create models, and find the best tool for visualization, or fully customize a dashboard if needed.
The most important thing is to be able to produce a good communication tool or the best visualization of the insights derived from the data identified during the previous stage, and be able to tell a story that all departments can understand to be able to foster collaboration with management, tech and business stakeholders within the organization.
With the help of a good data visualization and analytics dashboard, stakeholders will be able to create action items that will drive value back to the business.
STEP 6: Monitor
Finally, it is important to track improvements from when we first started to a specific period depending on the action items / experiments the company implemented that were derived from the data insights.
We should be able to track changes in the metrics whether increase or decrease, so that we will be guided in the next coarse of action.
It’s also good to refer back quarterly to the Data Maturity Assessment and see improvements to determine the next set of strategic recommendations.
For an example of a small project I led using data analytics to improve a client’s product, read How I Helped Transform How a Company Views User Data.
