Whether you’re an early-stage employee or the founder of a startup, your team should set a collaborative priority to pursue a data-driven culture … long before your first full-time data hire.
This process can seem abstract and daunting, but it’s highly necessary to ensure the success and scalability of your company. Here’s how to create a data-driven culture that closes the data literacy gap at your company.
Data-Driven for Data Literacy
1. Understand departmental needs.
Your goal should be for every employee at your firm to be a data analyst (to some degree). Every team member should learn the nuances of your product across various functional departments as this enables them to understand the context of your firm’s data. This is the crucial first step towards establishing a strong data-minded culture at your company.
To do this, understand how your team values and navigates their data so that your company’s data stack can be customized to their needs.
For instance, Sales and Marketing might want “big picture” visuals or spreadsheets to use as a client-facing tool, while Product might want nitty-gritty product metrics.
Constructing data processes for a firm is like being a UX designer in that knowing the desires of your end-user allows you to produce better, more relevant work.
2. Make data accessible and reduce IT dependence.
Your company may or may not have a data analyst on your payroll—regardless, there are individuals at your firm who often end up as gatekeepers to actionable data needed by other teams.
What you don’t want is for any single person or team to become the one-stop-shop for data needs.
This negatively affects that person’s bandwidth (and quality of life), and it’s bad for the scalability of your data organization as a whole.
Ideally, your data organization would be set up so that your resident data experts are the “last resort” for data needs. The highest level data engineer at your firm (who could be the CTO or a software engineer) should be monitoring and optimizing the efficiency of data processes as a whole, never manually executing and assisting with individual workflows.
This is often called self-service data or self-service business intelligence (BI).
Here is a non-exhaustive list of tools we use at Kloudio to empower self-service ability for our team members.
Google Analytics (GA), if properly set up and made accessible for non-data-minded viewers, can affect the bottom line of your organization’s revenue operations. Additionally, Google Analytics meshes most of the Google Suite, which is important for non-technical employees.
Once in GA, decide on your most important KPIs (like conversion and engagement numbers) so that Sales and Marketing are most effective.
Mixpanel, Amplitude, and Heap have varying strengths and drawbacks, but all enable powerful event-based analytics for your web app and marketing site.
These tools are also fantastic for product managers and account managers looking to understand feature adoption and user behavior. Understand which works best for you and your company, and evangelize their usage so that those departments can take their work to the next level.
When members of your organization need to develop a “big picture” understanding of your data, they’ll often look to others to provide reports and accompanying visualizations. Tableau and Looker are now crucial tools for any startup because they enable easy self-service BI and data visualizations for non-technical users.
Kloudio enables business users to perform complex and powerful data analysis without leaving their favorite spreadsheet tools (like Excel or Google Sheets).
Your team’s productivity levels will increase immensely when end-users—who already work efficiently within a spreadsheet—can automatically populate any sheet with relevant data for ad-hoc analysis.
Furthermore, Kloudio has a robust suite of automation features that allow reports to be scheduled to run at any cadence, so a master sales spreadsheet that measures crucial KPIs (e.g., week over week contacts, demos, and conversion rates) can be refreshed weekly, daily, or even by the minute for a live spreadsheet.
Kloudio integrates with almost any database or data source (see: Salesforce, MySQL, Facebook Ads, and other data integration tools) and can be used across all functional departments in your organization.
Self-service data tools are designed to be end-user friendly; however, user-facing platforms can still have steep learning curves (like Google Analytics). When a colleague is struggling with a new tool, be patient. Any time spent helping employees become more self-sufficient will pay off in dividends, especially when they inevitably help onboard new hires on their own.
When employees figure out the “how” when it comes to a self-service data tool, the “why” will become very apparent as they find themselves empowered to close the data literacy gap and make more meaningful data-driven decisions.
3. Establish data-oriented decision-making protocols.
It’s easy for data-related tasks to be repeatedly delegated or even skipped, as data analysis offers actionable insights that are useful but often not paramount to the completion of a deliverable.
This issue is most prevalent when it comes to marketing tasks, where it is easy to follow gut instinct over data-driven decisions.
This can be a major problem until individuals and departments take ownership of the data processes in their respective domains. To avoid this, set proper communication precedents and structures around decision-making that emphasize data and metrics. These precedents should be bendable but not breakable, and establishing these precedents might require collaborative introspection over the course of multiple meetings.
A good way to start establishing these precedents is to use your firm’s task management software (like Asana or Trello) to ensure data-driven work when it comes to routine workflows. Tasks should be written and phrased in ways that explicitly require data analysis and call the specific data engineering tool necessary for the analysis.
Instead of: “Brainstorm blog topics for next month’s content calendar,” try “Use SEMRush to find blog topics in our domain with a keyword difficulty score under 80%.”
4. Limit data sprawl and utilize access controls.
When used correctly, self-service data solutions empower different teams to be self-sufficient and proactive in their use of data while also limiting data sprawl and overly complicated processes. While the goal is to democratize data access for every member of your organization, not every user needs the ability to write SQL and query your data warehouse.
If your organization does not personalize data access for different users and teams, things can get unnecessarily complex and data sprawl will become an issue. In addition, security and regulatory concerns will compound as your company grows, and loose ends will become harder to tie up as more time passes. This is why it is imperative to establish and iterate on a data-governance protocol built around security and efficiency.
Like we mentioned at the beginning of this post, it is crucial that you understand the types of analyses that users across different departments value. If teams aren’t exactly sure what level of access and functionality they want in their data solution, start with a basic dashboard or visualization of KPIs with the ability to drill down and segment. From there, you can iterate based on feedback.
Over to You
Closing the data literacy gap is an ongoing commitment for all companies, especially growing startups. As you establish best practices and procedures for data-minded work across different functional departments, make sure you continue to monitor, evaluate, and improve these practices throughout your company’s growth. Close the data literacy gap with Kloudio—create a free Kloudio account to get started.