Building a robust self-service analytics strategy is no small feat, but it’s a crucial step for organizations aiming to unlock the full potential of their data. In this article, we’ll explore the Shearwater Framework—a proven approach to enable autonomy, streamline workflows, and foster a data-driven culture.
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Gartner defines Self-Service Analytics as a form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own.
It’s often characterized by simple-to-use BI tools with basic analytic capabilities and an underlying data model that has been simplified or scaled down for ease of understanding and straightforward data access.
We suggest starting with a deeper understanding of 'internal clients.' To achieve a comprehensive overview, we propose conducting the following three complementary analyses:
Questions to guide:
If more than 80% of your backlog is simple tasks (ad-hoc analysis, queries, dashboards modifications) and if you find a clear bottleneck in gathering data for decision-making, you probably need a plan for Self-Service Analytics.
💡Transform most used reports in reusable components
It is very common where people fall into ruts and end up limited using the same report over and over again because they are familiar with it and they think it is consistent. During discovery, we try to understand the real reasons those reports are used so much. This will be a starting point to embed that logic into reusable components on a semantic model and led it to become much more accessible to non-technical users.
Questions to guide:
This is useful to have an objective-panorama of your baseline scenario and for prioritizing where you should focus on.
Questions to guide:
It is common to start with a low-budget BI tool that is not simply integrated with the main data-sources, a very basic or disorganized semantic layer, and the need of advanced SQL-knowledge (or other programming languages) for data analysis. If that's your case, there’s a clear opportunity to facilitate data analysis by reviewing the stack.
Gathering insight from your data specialists is a critical part of building a self-service foundation. It is important that the Data Team act as “enablers” and are incentivized to share their knowledge and empower internal users.
For example, Looker’s entire early go-to-market practice was built around enabling a technical end user to do more with a data model on fast scalable data warehouses, and that person served as a magnet to bring other’s on board. It worked.
Based on the backlog analysis, you can develop a data-product (with the tools you already have available) that answer the most important and relevant questions for the business. The implementation of a new tool, leveraging data expertise, and establishing governance will take more time. You don't need to wait for that to address the most valuable needs for internal stakeholders.
💡Larger companies with more layers in management, for better or for worse, have this requirement to show wins as you build.
Large scale migrations or implementations take a long time for enterprise companies. Therefore, it is important to demonstrate progress to justify the project continuing. Sure it’d be great to start from scratch and take your time to build everything correctly from the ground up, but it’s simply not realistic for large organizations.
We recommend considering Omni, a powerful platform for self-service analytics that allows you to integrate, manipulate, and organize your data without necessarily using SQL.
It is similar to other tools that has a semantic modeling layer that creates safe spaces for self service, but also balances front end flexibility to do, often necessary, one-off analyses without polluting the governed metrics layer.
A data governance strategy needs to be implemented to ensure data access, quality and security.
This includes:
💡 Many times a company’s first take is limit all data access.
While data access is very important, if there isn’t an avenue for end-users to access data of their own volition self-service analytics will never truly be achieved.
On this topic, we recommend to simplify: start with a basic catalog, distributed ownership for specific metrics and centralized for core ones, set minimum rules to ensure quality, compliance and security. If it is necessary, be flexible and iterate.
A training program is important to empower teams to use the new tools effectively.
This involves:
💡 Co-developement is a great hands-on training strategy. It’s efficient because we build out real implementations during these sessions and people always engage more when the data and the result is real.
Are you ready to empower your teams with efficient self-service analytics solutions? At Shearwater, we specialize in helping organizations design and implement data-driven environments that enable autonomy, agility, and better decision-making.
🚀 Whether you’re just starting your journey or looking to optimize your current setup, we’re here to help!
📩 Contact us today to explore how we can partner with you to achieve your analytics goals. Let’s turn your data into a true competitive advantage!