A self-service analytics tool should allow non-technical team members to explore analytical data, even without prior experience with business intelligence tools or knowledge of the underlying data. It should have an intuitive interface, allowing users to explore and visualize data in various ways to gain relevant insights. By no means should it require assistance from members of the data team after the initial set-up.
Self-Service Analytics for Different Level Users
The level of users may vary – from complete beginner to someone with good analytical skills to an experienced data wizard with in-depth domain knowledge and a fair amount of institutional knowledge – so it is important to facilitate a good experience for all these different types of users. A key feature for the group of non-technical users with lesser knowledge of the dataset at hand is the possibility to search the data catalog in an intuitive way. This could be accomplished in various ways, such as a drag-and-drop interface or simply using natural language supported by language models, giving the user the chance to explain their level and what they are looking for. Another feature to address the segment of non-technical users is a built-in tutor, guiding the users to the insight they are seeking.
The Traditional Analytics Model: Data-Dependent Workflows
Traditionally, exploring analytical data has meant ad-hoc data requests to the data or engineering team. An engineer or data analyst has interpreted the request, compiled a suitable SQL query, executed it and exported the result set into an Excel sheet, and finally delivered it to the person posting the request. This process is both time-consuming and disruptive for the engineer or analyst and also filled with waiting times, making it rather slow. Business intelligence tools have helped answer the most common questions, but there has always been a long list of questions that need manual attention.
How Self-Service Analytics Shifts the Paradigm
Providing the necessary self-service analytics tools directly to stakeholders allows companies to truly become data-driven. New questions surface and actionable insights are made, fostering a data-driven culture.
Key Benefits of Reducing Dependence on Data Teams with Self-Service Analytics
When data teams are relieved of having to deal with being disrupted and spend time on ad-hoc analysis, they can instead focus on deeper analysis or building entirely new data products, providing their customers with even more value. For business users who can get their answers in seconds rather than hours or days, self-service analytics opens up a wide range of opportunities for entirely new workflows. Just imagine sitting in a meeting and being able to get your insight and make an informed decision right then and there, instead of having to postpone the decision to the next meeting when the data is available.
Challenges and Considerations in Implementing Self-Service Analytics
Analytics tools are only as good as the data they reflect. Companies often possess vast amounts of data but rarely all data is in a state that is possible to run analytics on top of. In order to get true value from the data, it often needs to be modeled in certain ways. It makes sense to start with a small scope, selecting a few tables, and make sure they are in a good state. Then, implement self-service analytics on top of those, and once that is in place, it is easy to expand from there. By analyzing what questions stakeholders have, it should be easy to determine which data is in high demand.
The Role of Data Teams in the Self-Service Analytics Ecosystem
For self-service analytics to function properly, an initial effort is needed. This effort consists of documenting the data available as well as the business language and business definitions used within the company. The data teams are the ones that possess the needed knowledge to document the available data, while they can also function as capturers of the business documentation required.
Future Trends Within Self-Service Analytics
There is a clear trend where all types of business intelligence tools incorporate artificial intelligence or, more specifically, generative AI, with plenty of powerful AI-powered self-service analytics tools emerging. These tools not only provide an easier experience for non-technical users but also open the doors for more advanced analysis, which most certainly will evolve rapidly in the coming years. These AI-powered self-service analytics tools will provide access to several different data sources, both proprietary and public data sets, allowing for deeper insights, not just showing what has happened but also why. Ad-hoc analysis is an area where AI-powered self-service analytics will free up plenty of time for data analysts and provide quicker answers with actionable insights to business users.
The more traditional business intelligence tools are also evolving by incorporating artifical intelligence, adding access to natural language interfaces and advanced, interactive visualizations and providing an improved self-service experience.
Another strong trend is the metrics-first approach, where metrics are pushed rather than dashboards. This resonates very strongly with AI-powered self-service analytics tools, since it allows for a more exploratory approach compared to readymade dashboards.
Conclusion
Self-service analytics empowers organizations to become truly data-driven by giving stakeholders direct access to valuable insights without relying on data or engineering teams for every question. While implementation requires an upfront investment in data modeling and documentation, the benefits include faster decision-making, more efficient use of data team resources and, in the long term, a data-driven culture. With the right foundation, self-service analytics can transform the way businesses explore and act on data.