How to Roll Out a Data Culture from Scratch

Data has become a crucial part of every business, organization, and industry. In today's digital age, data is not just an asset, it's the new currency. Most companies recognize the tremendous value that data brings, and have lofty objectives around being “data-driven.” The reason this is so difficult to achieve is that this adjective describes a culture, requiring adoption by everyone across the company. It’s one thing to implement and set up a data stack and its tooling and foundation, but it’s another thing to actually get people to use data religiously, understand its value, and leverage it consistently for decision-making. There’s a lot of influencing, educating, evangelizing, and training that needs to happen to establish a data culture.

What is a data culture?

A data culture is the collective values, attitudes, and behaviors of people who practice, prioritize, and encourage the use of data to drive decision-making. It involves empowering employees to use data to make informed decisions, fostering a data-driven mindset, and more data-backed decisions.

Why is a data culture important?

A data culture is essential for organizations because it helps them to make better decisions, reduce costs, improve efficiency, and increase revenue. Data-driven decision-making enables businesses to identify trends, spot opportunities, and reduce risks, which are crucial for success.

Moreover, a data culture fosters collaboration and accountability. When employees have access to data and insights, they can work together to solve problems, align their goals, and measure their progress. It also promotes transparency and trust among employees, customers, and stakeholders.

On February 15th, 2022, we were delighted to welcome Alban van Rijsewijk, Operations Manager at Fabriq, as a speaker at our webinar on “How to roll out a data culture from scratch.” Fabriq is a Series A SaaS company with a digital shop floor management platform designed to improve industrial performance. We’ve summed up the takeaways of the session below, broken out into two different sections: Implementation & Adoption.

Alban was employee number 4 at Fabriq and started out on the Customer Success team. He realized the need to start implementing a data foundation and culture when he was missing product analytics which would be valuable to his customers. He wasn’t able to extract data without bothering his technical team, which grew tiring and was off-putting for non-technical people like himself. He decided to get to the bottom of it and pioneer all things data at Fabriq by taking the below steps.

Implementation

1. Identify the internal team and use case that has the highest demand for data (asking for regular analysis or company dashboards). Write down the pain to evaluate progress later on.
2. Get C-level or leadership buy-in on the importance of this initiative, and have it added to the company roadmap. To really push the prioritization of data, it will need to be a top-down approach
3. Set up the basics like your ETL tool (BI tools like Whaly may also include ETL) + your cloud-based data warehouse. Don’t be afraid to ask for help if it gets too technical. Refer to these resources below when selecting the right tools:
   - Whaly’s Connector Catalog allows you to search for the connectors you need, and see which vendors provide them. Whaly also comes with a some connectors included with our BI platform.
   - Blog article on how to choose the right data warehouse
4. Implement a self-service BI tool like Whaly for modeling and visualizations. Make sure that the BI platform allows for Explorations by non-technical users, since this will be vital to the adoption step below.

Rolling out and boosting adoption - an ongoing process

Data can’t be useful unless it’s used and valued. Now that you have your first data analysis project, it’s time to start evangelizing it, educating internal teams and stakeholders, and rolling it out. This is an ongoing process, and persistence in encouraging adoption is the only way to foster a company-wide data culture.

1. Start with the team you identified in step 1 of Implementation, with the highest demand. This way, you can more easily onboard all teams and C-suite, and elevate the importance of the initiative
2. Leverage self-service capabilities on a small scope so that people can start accessing data on their own and build trust around data
3. Create light documentation and train people so that they become autonomous (be practical through show & tell!)
4. Set up short weekly/bi-weekly syncs to get feedback on data quality and blockers
5. Be quick to troubleshoot and fix basic data access issues
6. Encourage experimentation, since it allows for continuous improvement and internal teams will feel closer to data
7. However, ensure that you have governance practices in place so that when people experiment and play with the data, they are not breaking the data foundation or introducing inaccuracies
8. Measure your progress around usage and how many teams are leveraging data. If you’re using a BI platform, try setting up a way that allows you to see how often people are viewing dashboards and logging into the platform.
9. Keep going! 🏃🏻‍♂️