These days, business leaders and employees realize the promise of data to optimize marketing campaigns, drive product adoption, and gain a competitive advantage. Many companies have made huge investments in data collection, storage, and pipelines that will bring that promise to bear. In short: no one disputes the immense value that data can bring to organizations.
Data culture is the organizational mindset and practice of using data to improve operational understanding and make better business decisions. While the collective recognition that data-driven thinking is good for business is important, that on its own won’t magically result in a strong data culture. Putting data culture into practice takes effort, education, and communication to help everyone at an organization understand how data can improve outcomes of their work.
Data analysts are often the people spending the most time in the weeds with company data on a day-to-day basis, making them particularly well positioned to influence how teams work with and make decisions using data.
Here, we share practical ways that data analysts can help build a strong data culture within their organization by increasing the visibility of data work, including collaborators in analyses, and sharing knowledge as they learn.
Increase the visibility of data analyses
People are busy, often juggling multiple projects and deadlines at the same time. With so much going on, data can quickly fall out-of-sight and out-of-mind. When that happens, dashboards begin to rot, data fades from the spotlight, and teams slowly revert to less data-driven means of decision-making.
Unless, of course, someone is making sure that data stays front-of-mind. Data analysts are often the right someone to keep data on everyone’s radar for several reasons.
Data analysts are often the ones building and sharing data analyses, so they also know where a lot of analyses live. Sounds trivial, right? But you might be surprised how often people don’t return to a useful dashboard simply because they never bookmarked the page, have forgotten how to find it, and are now too embarrassed to ask.
Data analysts can often quickly point teammates to the right section of a dashboard, or page in a report, to get the answers they need.
Second, since data analysts tend to keep a close eye on the data, they may be the first to realize and flag when notable changes or unexpected patterns emerge. By flagging data changes, analysts can provide both a timely heads up about a specific discovery, and a more general-purpose reminder that the data is there and ready to be used. At most organizations, however, data analysts are stretched thin, so manually sending reminders about where to find data, or how it’s changing, isn’t always realistic.
Here are some efficient ways that analysts can help keep data front-of-mind, and easily findable, for their coworkers:
Pin or bookmark relevant content. Keep useful analyses right at their fingertips by pinning key data products to relevant internal channels
Automate reminders and notifications. Never underestimate the capacity for teammates to forget about the analysis you’ve built at their request. Consider setting up automated reminders or notifications to help your teammates revisit data at a cadence that works for them, or when an important threshold is reached.
Document your data analysis catalogue. Analyses quickly start showing up in different places like dashboards, reports, slide decks, and computational notebooks. When analyses live across locations, they can be hard to keep track of. Help your teammates help themselves by compiling information about where to find things. Start simple: a list of links in a Google doc, or added to your company handbook, might be all you need.
A strong data culture requires a level of consistent immersion in company data so that when people have a question, their first thought is how they can use data to answer it. Analysts can help to ensure that useful data work isn’t overlooked in company decisions by increasing the discoverability of data analyses.
Practice collaborative analytics
When data analysis is done in a black box, coworkers outside of the data team can feel detached from the data and skeptical of the findings, which decreases the value they place on data analysis and makes them less likely to look to data first in the future.
For coworkers to stay invested in data work, they need to be meaningfully involved in the analysis process. That doesn’t mean that everyone will become a data analyst. It does mean that data analysts should practice collaborative analytics to include teammates, who can share valuable experience and expertise, throughout the course of a project.
Collaborative analytics improves data analyses by incorporating diverse feedback from start to finish. A good side-effect is that your collaborators build a greater sense of investment in the work, and trust in the findings, when they participate directly in data analyses.
“All together now!” is a great slogan, but how can data analysts actually put collaborative analytics into practice at their organization? Here are a few ways to start:
Use tools that let everyone participate. When possible, use tools that let your colleagues interact with and participate directly in the analysis by interacting with charts to explore different scenarios, leaving comments, or adding annotations right where you’re working.
Show your work. Try to give collaborators a clear view into your analyses, for example by using pervasive visuals that help them understand data transformations without reading code. Working transparently helps colleagues and stakeholders follow your work from database to final decision, which builds understanding and trust.
Welcoming your coworkers into the data fold with collaborative tools and transparency helps to build greater value and trust in data analyses.
Share as you learn
A core piece of strong data culture is data literacy. For data to be used in companywide decision-making, employees need a baseline familiarity with data so they can make sense of analyses.
Data analysts typically have a strong foundation in data wrangling, analysis, data visualization, and interpretation. That puts them in a unique position to be a hub of knowledge growth and continued learning around data.
Sharing knowledge doesn’t have to be a huge added lift. Small, frequent actions that incrementally build a team’s data literacy can make a big difference long-term. Here are a few lightweight ways a data analyst can contribute to a stronger data culture at their company:
Repost insightful articles and blog posts to team channels
Share effective data visualizations (and explain what makes them work well)
Host or facilitate a lunch-and-learn session on a relevant data topic
Hold occasional “office hours” where coworkers can bring their data-related questions
There are more involved ways a data analyst can grow data literacy across their organization, like leading workshops or giving internal talks. But when it comes to data literacy, every improvement counts. And, you never know when the thing you share will be exactly what a coworker needs to get over their next data hurdle.
Learn more
Data analysts have an important role to play in building a strong data culture at their organization. By bringing visibility to analysis, engaging colleagues in the work, and creating learning opportunities that improve data literacy companywide, analysts can help everyone confidently interpret and work with data for more informed business decisions.
Want to see how Observable helps teams get more from their data using modern tools for fast, collaborative analysis and visualization? Sign up for Observable Canvases today.