Building your data community through empathy
Today’s data and analytics tools make it easier for organizations to visualize data quickly and easily. At the same time, an influx of data has left organizations unable to fully tap into their data resources. According to a McKinsey Analytics Survey, only 8% of companies achieve analytics at scale. To bridge the gap, organizations need people who have the analysis skills to explore the data as well as the critical thinking skills to add narrative and context to their findings. Additionally, they need to understand the business context beyond their purview.
Looking back on my data journey, I’ve come to realize that the most important foundation of a data team is a thriving community. As a civil engineer earlier in my career, I was eager to solve problems. I knew that having technical expertise was important to understand data, but it was not the sole requirement. As my career progressed, I took on roles where I collaborated with multiple teams, gathering requirements for product enhancements and data solutions. What I have learned is that businesses that value data literacy need to create ways to immerse their people in the language of data. The most effective way to do this is through community engagement.
As organizations become more data-driven they need to understand where the data flows and how it’s being consumed. This exercise requires data empathy—a willingness and a dedication to deeply understand how people use, interpret, and share data. This starts with fostering a community. Community sparks collaboration, which sparks empathy. Data empathy will vary from individual to individual and team to team.
Data empathy also requires a willingness to understand how people feel about data processes. In addition to understanding how the organization consumes data, leaders need to understand how the community feels about the data they’re accessing. For example, do the data sources serve their business needs? Do they have the proper context to interpret the data?
Some questions to ask are:
Data communities give everyone in the organization a space to collaborate on complex business challenges and increase data literacy. While culture refers to the beliefs and behaviors that determine how the company interacts with each other and external customers, community goes a step further. It is the sense of belonging and caring for something larger than ourselves. Growing a community is about people not programs or campaigns.
Let’s walk through the concept of community and map out how different talents within the organization can collaborate and engage with data.
We’ll start by introducing Mocha, a fictitious coffee delivery company, giving local coffeehouses the specialized technology, data insights, and shared services these small businesses need to serve customers.
Having this platform for local coffee shops helps them compete against major corporate chains. Mocha is looking to sign up more local shops in the San Francisco Bay Area. For the past three months, they have been having monthly analytics readouts. During these meetings, key leaders and stakeholders from the business meet with the analytics team to discuss the prior month’s performance and highlight potential coffeehouses to target for membership. At the end of each meeting, stakeholders have shortlisted coffeehouses that they would like their analytics team to explore, with the ultimate goal to develop an acquisition strategy for coffee shops in the Bay Area.
Currently the team has a working dashboard that tracks the location of prospective coffee shops and their distance to coffee shops already signed up with Mocha. They are also reviewing sales made through the Mocha platform in the Bay area to highlight a few interesting data points to discuss during the meetings. This collaborative process building a sales and acquisition dashboard evokes data empathy among the team.
For starters, it exposes them to talents they might not have.
