Many companies have started to invest in their data analytics teams and processes to make their team more data-driven and self-sufficient. Hiring the best data analysts, scientists and engineers can help drive the efforts forward, especially when processes are lacking, but many companies believe the effort towards self-service analytics ends there. Oftentimes, we hear about data analysts and scientists who are met with unrealistic expectations of creating artificial intelligence or machine learning systems on day one. As most data folks know, this is the wrong way to think about the goals associated with data, and many companies should rethink the purpose of their data teams before hiring people to try to improve your companies decision-making.
Analytics teams can bring a unique, inquisitive perspective to any business if given realistic goals and expectations. Instead of thinking about the exciting new artificial intelligence model, companies should seek out small data analytics projects every quarter. Of course, with the right data and over time, the value of these projects will increase. By giving data teams time to ramp up, data teams can put together a foundation that can help teams drive self-service in the long term. With the right framework in place, teams can make smarter and faster decisions without the assistance of the data team. This gives data teams the ability to spend more of their time on complex analyses. This article will highlight the ways that companies should think about democratizing data analytics to enable self-service.
1. Begin with tangible, short-term problems with the highest level of strategic benefit
Many companies who hire a data team will traditionally start by pointing that team to the place in the business where there is the most data collected. This seems logical, as it offers data analysts the most leverage as they begin looking for insights. Instead, companies should initially focus data teams on tasks that offer long-term strategic value, while also providing short-term strategic benefits.
Some of these tasks may include standardizing the KPIs across the organization or creating a standard dashboard for every employee to access regarding important KPIs. Although these short-term tasks don't provide value and may not create the largest business impact, they create an important foundation for the organization's ability to utilize data in its decision-making in the future. Oftentimes companies that don't invest in standardizing their data early on can incur large costs down the line.
Secondly, teams should consider the probability of success in a given project. Many times, executives believe that by throwing “data” at a problem, it will be solved. Instead, companies should consider that each problem has a probability of success. As the amount of data and complexity of the problem increases, the probability of success decreases. We recommend that teams starting to drive towards making data a part of their decision-making should start with projects that have a high short-term impact as well as a high probability of success. Many data scientists will tell you that developing the insight or algorithm is often the easy step of the processes. The hard part is collecting, understanding, and validating the vast amount of company data that exists across many different siloed. When data teams prioritize projects that are both quick to complete and have a high probability of correctness, data teams can set themselves up to work on larger, more ambitious problems down the line.
2. Make data visible and accessible to all employees
The second way that companies should think about democratizing their data is by making it accessible to all employees in one place. In too many cases, data is dispersed across many warehouses, BI tools, and SaaS applications. Adopting a central data catalog to document and centralize the tool is a great way to enable self-service across the organization. One problem with many data catalogs is that they are built for the technical team, and are therefore difficult for all employees to use. We believe that the data catalog should be easily accessible, regardless of the department or role of an employee.
Additionally, many teams look towards a data catalog as a way to reduce the amount of chaos in their data warehouse once it’s very large. We believe that teams should be considering adopting a data catalog that helps them clean and organize data as soon as they begin to store it. Secoda’s data catalog is built for modern teams that want to stay ahead of poor data discovery from an early stage.
3. Invest in internal training for analytics, statistics, SQL, and reporting
Another important pillar that teams should consider taking towards data analytics and self-service is investing in SQL, analytics, and statistics training for all employees. This type of training would empower all employees to make data-driven decisions, regardless of their role or department.
Airbnb is a company that has invested in an internal data university to help employees learn about data-driven decision-making. Their vision with the Data University is to give every employee the ability to make data-informed decisions. While many organizations focus on technical employees, Airbnb believes that education should be built for every person, therefore, the program is meant to be more accessible and relevant to non-technical employees.
4. Develop and communicate a broad vision of data analytics
Lastly, it’s important to motivate employees with a broad vision of where data analytics can take the business. This is the step that most teams do exceptionally well today. It’s easy to think about the possibilities of using data to make better decisions across different departments, the problem is that many organizations don’t take the proper steps to get there. Without following some of the previous steps, the grand vision can seem daunting and unrealistic. Teams that make sure that they understand that the long-term vision for data analytics is not an overnight expectation tend to execute better in the immediate term. By using this vision to motivate employees, managers can help data teams drive short and long-term improvements to data.
By following these steps teams can start to enable self-service and democratize data while supporting the data team. After talking to many data teams, one common problem that came up was their difficulty communicating with people outside of the data organization. We realized that this is because more managers focus on the big picture, but struggle to break down the steps that can help data teams get there as well as a reasonable timeline to achieve that vision. By empowering data analysts, engineers, and scientists with a data-savvy team, teams will begin to see enormous benefits from their data investment.