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Bridget Shea
15 Mar 2023

Organizations have been using the term “self-service analytics” for nearly a decade now, but for many companies, it’s not a source of value generation. But businesses cannot afford to waste time or money-spinning up or supporting a self-service analytics program that people aren’t actually using or that’s not generating tangible value. Self-service analytics in the age of AI needs to be about truly enabling people to ask infinite questions of their data and then empowering them to find or build trusted answers on their own.

Imagine the smoothest self-service analytics experience possible at your organization today. It probably goes something like this:

  • A business user looks at someone else’s data product (such as a dashboard) built to answer some specific set of questions.
  • Business user asks a question that is within the realm of possibility to answer with this data product (e.g., “How did sales perform last quarter?” or “How did my marketing campaign perform?”).
  • The business user gets a trusted answer without having to ask anyone in the middle, such as IT or a data team, for help.

The future of self-service analytics is about empowering people

But what happens if this business user wants to ask new questions that are outside the realm of what that dashboard was built for? For example, the marketing manager sees her campaign did not perform well and wants to understand who she should target for her next campaign, potentially even with a score predicting who will be most likely to open her emails. Or a supply-chain manager has identified a pattern of shortages but doesn’t have the tools to dig in and get more visibility to address the problem. Most likely, every new question or business challenge is a new ask to a team to build a data product (dashboard) that provides answers from which they can self-service.

It’s easy to say that the future of self-service analytics is about moving from descriptive to predictive (and even prescriptive) analytics. But it’s more than that. It’s about empowering people, especially business users, to ask questions about their data and find or build trusted answers on their own, whether that means building a dashboard or a machine-learning model for themselves. This is where the term “citizen data scientists” comes into play and why, in the future, the concepts of self-service analytics and citizen data scientist will become somewhat intertwined.

Ultimately, putting the full power of data in the hands of the people involved in the day-to-day business (we call this Everyday AI) is what will move self-service analytics from providing answers to providing impact and, with it, value. Yet, with great power comes great responsibility, so the key in the coming years will be for leaders to provide the framework that allows for this fundamental transformation.

Building self-service analytics for impact

A world in which data is accessible and anyone can build data or AI projects and solutions to answer business questions might sound scary. To be honest, without the right tools, technology, and processes, it is scary and can devolve quickly into data chaos.

Seeing that risk, it’s critical in this new world of self-service analytics that the initiative:

  • Doesn’t exist in a vacuum. When businesspeople have the data, their questions for IT come up a level and can be more impactful. For example, how can I automate what I’ve built so I don’t have to update it every week with new data?
  • Is built on trust. Leaders need to trust employees’ ability to use data in a self-service context. Business users working on self-service analytics need to trust the data that they’re working with. Managers and executives alike need to trust the insights delivered from self-service analytics projects. If just one of these layers is missing, it doesn’t work.
  • Has the proper governance built-in, complete with appropriate guardrails. This can be as simple as proper permissions management at the dataset or the project level, but it goes all the way up to the macro level. How are data and models being used? Who is monitoring this to lower the overall risk to the organization?

For example, Dataiku customer GE Aviation implemented its own version of a self-service system that allows it to use real-time data at scale to make better and faster decisions throughout the organization. Engineering uses data from these tools to redesign parts and build jet engines more efficiently, the supply chain team uses it to get better data insights into its shop floors and streamline supply-chain processes, finance uses it to understand key metrics and more.

At its core, its self-service program equips everyone (with proper access rights) with the ability to discover and use data, prepare that data, and create a data product, including developing predictive models within Dataiku. At the same time, it also ensures projects pass a set of checks, balances, and governance measures.

Next-generation self-service analytics technology

There are people in the business who have the ambition to go on their own data journey and will do it if the points of friction are reduced and they are enabled to do so. This is the essence of the next generation of self-service analytics and, as previously discussed, of citizen data science.

The idea behind the next generation of self-service analytics isn’t that individuals can do and build whatever they want with data (which would lead to data chaos). It’s about empowering people, and choosing the right technology is an important milestone. The right technology should connect doers with data by bringing people of diverse skill sets together to work with data in a common ground.

Ultimately, it should be second nature for anyone in the business to produce new insights and to work with data in a way that is easily reusable. Individuals should benefit from the expertise of the many as timely new data products are created and maintained across the whole enterprise. That’s where the value lies.

“Self-service program equips everyone with the ability to discover and use data, prepare that data, and create a data product, including developing predictive models.”



The future of self-service analytics in the age of AI is intertwined with the idea of citizen data science – both are about truly empowering people.


Business people must be able to ask questions about their data and find or build trusted answers on their own.


The right tools and technology are critical to enabling people while also maintaining the appropriate level of governance and control.

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 5 features 19 such articles crafted by leading Capgemini and partner experts, about looking beyond the usual surroundings and be inspired by new ways to elevate data & AI. Explore the articles on serendipity, data like poker, circular economy, or data mesh. In addition, several articles are in collaboration with key technology partners such AWS, Denodo, Databricks and DataikuFind all previous Waves here.


Bridget Shea

Chief Customer Officer, Dataiku
Bridget Shea, based in New York City, has proved to be a long-time fixture of the city’s tech scene. She has worked closely with the Dataiku leadership team for several years in an advisory role, bringing her deep data science and analytics expertise.