Tools

The tools we have available to us change the ease at which we do anything. If you want to cook something, a hammer is pretty useless, but a frying pan and a stove top are game changers. The same is true of the data tools provided to teams. Keep in mind that while teams within an organization are all ultimately operating towards the same goals, the actions they take to achieve those goals can be completely different, so the tools they need are different. 

For example, let’s say at a B2B SaaS company, the goal is to increase renewals of subscription products from 70% to 80%. Every part of the company is involved in this goal but each has different responsibilities. Support is responding to calls and concerns in a timely manner, product is writing specifications to meet the needs of the customer, design is making sure the solution is easy to use, engineering needs to deliver a solution that works and is bug free, customer success is making sure the customer uses the solution, and sales is working to close the renewal. Each team needs different data to help them achieve their goals so the data tools you create for them should be tailored to their needs and their level of data skill. 

Data Product Managers can be extremely helpful as they specialize in understanding the needs of the team, and developing the right tools to help them with their roles. I believe these tools should be treated as their own products, taking the same approach as agile development:  tools that iterate over time and adjust as the needs of the customer (in this case the teams) change. Learn more about the Data Product Manager role in a Data Culture on my website. 

Different ways to use data

I want to take a minute here and outline ways that data can be presented to clarify which are useful for different circumstances. 

When you are thinking of building tools that would be helpful for various teams, consider what would be useful to their day-to-day. The engineering team may need a tool that monitors the site uptime. This is operational data because it helps ensure that operations keep functioning. However, the product team may need an analysis completed to understand the different characteristics between customers that renew or churn. The findings from that analysis data may be turned into a model that helps the customer success team flag customers whose behavior may indicate that they are likely to churn so that the team can intervene. And the customer support team might want to monitor ongoing customer satisfaction scores to determine how well they are providing their services. There is no one size fits all data tool for all teams. 

Processes

Your team is made up of different processes and protocols. But is it clear how data should be used within these processes? Which decisions are made in these processes and is data a factor in the decision making? Are there alerts that fire based on data thresholds? These are some of the questions you should ask yourself when working through the different protocols for your team. 

I highly recommend you get prescriptive on how and when data should be used in departmental processes. Using the different ways data can be used described above, consider how each of your team’s processes should be enhanced with data. Here are just some of the ideas of how data can enhance processes:

• Operational - Data can be used to help alert yourself to unexpected or undesired behavior that might require a team member to do some additional follow-up. 

• Performance - Team leaders can monitor performance metrics of individual team members to identify which ones need additional support or identify high performers to understand tactics that are working well for them. 

• Analysis - There is a process to request or conduct analysis to identify underlying causes when specific metrics change significantly. 

• Modeling - Models are built to help plan for the future. These models are updated regularly with new data, comparing actuals to previous forecasts to improve the accuracy of future forecasting. The output is then used to update decisions on things like investment or resource allocation. 

I have found process mapping to be a helpful tool when thinking through this principle. A process map makes clear what data is being collected and identifies the decisions that have to be made. Team leaders can specify which data should be consulted to ultimately make those decisions. By identifying what data is being collected, teams can also find opportunities to use that data to make their processes more efficient. 

Community

Finally, at the departmental layer, it is important to be able to have the entire community engaged with data. That means fellow team members are comfortable discussing the data being used with each other and the team leaders. Often when we think of communicating with data, it is data being shared from the Data Team or between managers and employees. But when team members start using data to communicate with each other on the day-to-day actions they’re taking, it can strengthen the language and communication of the team. 

Out of my belief in community, the examples here shouldn’t come from just me. Instead, look out for a future post where I invite members of my community to share how data has helped them connect with their team. Let me know if you’d like to contribute your story at cindy@cindylin.consulting!

Finishing Up

Implementing the departmental layer of the Data Culture Framework can be tough as some team leaders may be more hesitant to buy into the Framework than others. A future post will cover implementation recommendations for ensuring a successful Data Culture at the departmental layer. Until next time, where we will cover the Individual layer of the Data Culture Framework. 

By Cindy Lin

Edited by Jason Rubinstein