A look at this year's Gartner® Hype Cycle for Data Management™ report reveals the importance of self-service data management. How do you actually achieve self-service?
This year's Gartner® Hype Cycle for Data Management™ report mentions self-service data management. It’s a game-changer that gives business users the power to work with data without constantly relying on IT, boosting data quality and making data available for analytics and decision-making.
But what is it, really? How do you achieve self-service?
Let’s take a closer look.
Data management is in flux. How we use data and what we need data to do changes constantly. As concepts like DataOps and Data Mesh gain adoption, they may already be on the way out, overtaken by newer concepts that better reflect current business realities.Recognized as an “innovation trigger” (see Fig. 1 above, left column), self-service data management focuses on data democratization and shifts away from centralized responsibility.
When a decision-maker needs access to new data—be it a new feature, field, or table—they must outline what they want and send that request to a task queue. This task queue determines the workload of the central data team. At some point in the future, depending on the number of other tasks in the queue, a data engineer from the central data team will action the request.
This action typically includes building a data pipeline to move the data through an ELT process and making it available to the requesting decision-maker. On average, this process takes between six weeks and nine months to complete. Then, one of these three things happens:
With the self-service model, data management is decentralized. The end users become more responsible for their own data requests and are empowered to process their own data to support critical decisions. This marks a shift in responsibility: from centralized to domain-oriented.
As organizations strive to become more data-driven, the pressure is mounting, starting at the top of the organization chart. In boardrooms and C-level strategy meetings, decision-makers rely on metrics and key performance indicators (KPIs) derived from enterprise data.
Domain knowledge required for specific use cases is also crucial. Operations, finance, marketing, engineering—all departments—get better results from data analytics when they have control and ownership over their own data.
Passive requests for data don’t come with context. Especially when they come from different business units with different objectives, formats, and focus areas (particularly for long-tail use cases), central data teams are left with an impossible prioritization task and a backlog that’s never going to be fully cleared.
No amount of optimization within the framework of the existing data stack can make this Herculean task manageable.
Less complexity, not more, seems to be the answer. Instead of scaling Data Ops to become even more intricate and costly, organizations are moving toward decentralization. Implementing a self-service data management platform provides governed data access to business users, allowing them to obtain data in the form that best suits their needs—be it a report, a dashboard, or other exports for further analysis.
.png)
With a platform that brings all data sets under one system and handles ETL, transformations, and orchestrations automatically, all that’s left for the user is to leverage their domain expertise and drive value with self-serve analytics.
And the end user doesn’t have to be a data scientist or a data engineer, and they don't need an extensive technical background. A solid understanding of your domain’s own data is enough.
Is it easy? No, unfortunately, it’s not.
As data becomes democratized, data literacy becomes more important than ever. Even though low-code and no-code options exist, to make truly data-driven decisions and deliver value, organizations must ensure that people are ready to handle it. This often means providing the right training and support, upskilling or supporting domains with SQL and Python skills, and fostering a culture that values data.
After all, the best tools in the world won’t do much good if people don’t know how to use them. At the very least, understanding what data means and how it can be used is critical.
Another significant theme is data governance. Without the right checks and balances, you're looking at a potential minefield of inconsistencies, inaccuracies, and security issues. It’s essential to set up some solid ground rules:
And as data stops being an issue relegated to a single siloed team, with self-service analytics, it will become everyone’s responsibility. That means that these elevated standards have to be maintained across the entire organization.
Self-service data management is a paradigm shift. It’s more than migrating from an outdated data warehouse to a better stack or automating a few processes that were previously done via frighteningly complicated Excel sheets.
It’s a new way of thinking—a new data strategy that breaks down data silos and unlocks use cases and capabilities (e.g., machine learning and Gen AI) that were simply impossible before.
To make this move as effortless as possible, start small. Zero in on a particular use case with a pilot project and plan the roadmap, keeping your user’s needs and skills in mind.
User-friendly, automation-rich, and template-driven solutions will take you a long way, but be ready to rely on professional services and qualified partners when needed.
A data platform should also provide a uniform user interface. Infrastructure abstraction makes it easier to interact with a diverse data stack and allows end users to focus on data and what they can achieve with it, instead of worrying about many interacting components.
By embracing self-service data management, you're doing more than just streamlining processes—you're arming your teams with the tools to make the right decisions, spark innovation, and stay ahead.
For a deeper dive into where data management is headed, check out the Gartner Hype Cycle for Data Management 2024. It's packed with insights on how self-service is shaping the future.
GARTNER is a registered trademark and service mark of Gartner and Hype Cycle are a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.
*This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Keboola.
