Whether you’re the founder of a startup or an early stage employee, a collaborative initiative needs to be made by your team to establish a data-driven culture at your firm (long before your first full-time data hire). This process can seem abstract and daunting, but is highly necessary to ensure the success and scalability of your company. Here’s how you begin the process of cultivating a data-driven culture at your startup:
1. Understand Departmental Needs
Your goal is for every employee at your firm to be a data-analyst to some degree. Learning the nuances of your product and startup across various functional departments enables you to understand the context behind your firm’s data, and is the crucial first step towards establishing a strong data-minded culture at your company.
Get to know your coworkers and understand how they value their data analyses so that the firm’s data processes can have tailored output.
For instance, sales and marketing might want “big picture” visuals or spreadsheets that they can use as a client-facing tool, while product might want nitty gritty product metrics. Constructing data-processes for a firm is a bit like being a UX designer in that knowing the desires of your end user allows you to produce more resonant work.
2. Make Data Accessible and Reduce IT Dependence (Self-service)
Your company may or may not have a data analyst on payroll – regardless of this fact, there are individuals at your firm who often end up as gatekeepers to actionable data that would ideally be accessible on a whim by other employees. What you DON’T want is for any single person or team to become the one stop shop for data needs. This is terrible not only for this person’s bandwidth (and quality of life), but also for the scalability of the data organization as a whole. Ideally, you want to set up the data organization 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, but never manually executing and assisting with individual workflows.
Here is a non-exhaustive list of tools we use at Kloudio to empower self-service ability for our team members:
- Google Analytics – For marketing and inbound sales leads, there’s no tool more fundamental or important. Google Analytics, if properly set up and made friendly for non-data-minded viewers, can really turn the gears for your organization’s revenue operations. Additionally, Google Analytics plays really well with most of the Google Suite, which is so important for non-technical employees. Take the time to iron out Goals and Events for some of the most important KPIs like conversion and engagement numbers so that your sales and marketing teams are most effective.
- Mixpanel, Amplitude, Heap – These three have varying strengths and drawbacks, but all enable powerful event-based analytics for your web-app and marketing site, and that’s just scratching the surface of what they can do. For product managers and account managers that need to understand feature adoption and user behavior, tools like these can add almost intangible value. 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.
- Tableau, Looker – When members of your organization need to develop a big picture understanding from the mountain of data that’s starting to develop, 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 self-service BI and data visualizations for non-technical users in easy to use and intuitive fashion.
- Kloudio – Of course we use our own product! Kloudio enables business employees to perform complex and powerful data analysis without leaving the tools they are most comfortable with – namely Excel and Google Sheets. The increase in productivity is immense when end-users, who already work efficiently within a spreadsheet, now have the ability to 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 (week over week contacts, demos, conversion rates) can be refreshed weekly, daily, or even by the minute for a live spreadsheet. Kloudio integrates with any database, web app, or corporate application (see: Salesforce Integration, Connect Excel to MySQL, Facebook Ads Integration, and other data integration tools) and thus 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 (eg. Google Analytics). When a colleague is struggling with a new tool, be patient and meet them at their level. Any time spent helping employees become more self-sufficient will pay off in dividends, especially when they inevitably help on-board 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 make more meaningful data-driven decisions.
3. Establish Data Oriented Decision-Making Protocols
At companies without hundreds of documented best-practices, it’s easy for data related tasks to be delegated repeatedly 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 let “intuition” guide decisions that should be made based on hard data.
This can be a major problem until individuals and departments take ownership of the data processes in their respective domains. To avoid this, your firm must 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 (Asana, Trello, etc) 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.
“Brainstorm blog topics for next months content calendar,”
“Use SEMRush to find blog topics in our domain with a keyword difficulty score under 80%.”
5. Limit Data-Sprawl and Utilize Access Controls
The great thing about self-service data solutions is that if used correctly, they 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/teams, things will 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 that as your firm grows, you 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/visualization of KPIs with the ability to drill down and segment. From there, you can iterate based on feedback.
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.
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