Gartner’s recently released 2025 Critical Capabilities for Analytics and Business Intelligence Platforms report once again highlights “Metrics Creation” as the top use case for business intelligence software, underscoring its profound importance.
But what is meant by Metrics Creation, why has it become such an important component of BI tools, and how do organizations know their platform of choice addresses it well enough?
Metrics Creation is a term used by Gartner to describe the features and tools offered by BI platforms that allow users to design, define, build and manage key performance indicators and other data metrics used to gauge the health of their business. Today’s data platforms allow experts to consolidate signals from disparate services and domains, but to turn those signals into insights, they first must run through weighted formulas that reveal their meanings.
With powerful Metrics Creation tools, business teams can move beyond serving up simple data visualizations and codify the most significant KPIs that need to be achieved to ensure that the company is trending in the right direction.
The growing emphasis on Metrics Creation illustrates the evolution of BI away from descriptive analytics towards more prescriptive and diagnostic capabilities. BI platforms that facilitate the creation and governance of key metrics can be relied upon for greater consistency in terms of reporting and make it simpler for users across departments to understand their company’s performance and identify actionable insights. In contrast, a BI platform that lacks Metrics Creation tools is not much more than a data repository aggregator, and will struggle to generate usable business intelligence from raw data.
What Goes Into Metrics Creation?
In its latest report, Gartner defines Metrics Creation as a use case that “Enables organizations to connect to data, prepare data and define standardized metrics that can be shared throughout the organization.”
It subsequently lists five essential capabilities needed to support this use case, with the most vital being a comprehensive “metrics layer,” followed by “data preparation,” “natural language query,” “data source connectivity” and “content management.” The metrics layer is core to Metrics Creation, as it’s what enables users to create and define metrics and KPIs as code, integrate governance for those metrics at the source, and deliver downstream analytics, data science and insights based on them.
The other capabilities mentioned by Gartner enhance the core metrics layer. User-friendly data preparation tools such as drag-and-drop interfaces are most welcome, as they make it simple for non-technical users to combine data from multiple sources to create various data pipelines, hierarchies, groups and sets that power analytics models.
BI platforms that support natural language queries help to simplify the understanding of metrics, allowing users to ask questions of their data in plain language, while the importance of data source connectivity is self-evident. By connecting and ingesting information from more sources, metrics become more accurate, improving the quality of insights the platform can deliver. Finally, content management is key for orchestrating the lifecycle and access controls for the data that powers these metrics.
Which BI Vendors Do Metrics Creation Best?
Pyramid Analytics sits at the top of the pile in the 2025 edition of Gartner’s Critical Capabilities for Analytics and Business Intelligence Platforms report – thanks to its powerful metrics layer, its comprehensive data preparation functionality and its NLQ capabilities.
Pyramid’s centralized metrics layer makes it simple for users of all skill levels to build, store and reuse calculations across any data model or report, with no-code and code-based formula creation. Users can create AI-generated formulas simply by describing a metric in business terms, and every one – both AI and human-defined – is stored within an analytics catalog for reuse across projects. In addition, the platform’s metrics layer is enhanced with AI that automatically generates descriptions of each metric.
Gartner also cites Pyramid’s user-friendly data preparation tools, which leverage AI to infer data relationships, types and hierarchies, and its adaptive NLQ capabilities, where responses are continuously refined based on the user’s feedback, as the reasons for its category leadership.
Similarly, Strategy One ranks highly for its “comprehensive metrics layer” and its NLQ capabilities, which allow for more natural interactions with multiple data sources and provide numerous customization options. For instance, Gartner applauds the manner in which users can tailor how the platform automates and dynamically adjusts data narratives down to the level of detail, verbosity and tone. It’s also a strong performer in terms of data source connectivity, offering multiple paths for connecting data to its semantic model from external data analytics tools.
The report also heaps praise on Microsoft’s Power BI tool, highlighting Metrics Creation as its strongest use case. Power BI provides multiple paths for users to develop metrics, including low-code and code-first approaches. In particular, the report highlights the ability of Microsoft’s Copilot to parse through Power BI’s semantic layers and create easy-to-understand summaries of each metric, which are critical for helping business users to distinguish some of the more subtle differences between performance indicators.
Like Pyramid, Power BI also gets credit for its data preparation tools, which facilitate dataset blending through an intuitive graphical user interface and enable users to visualize each step of data pipeline creation.
Which Vendors Struggle?
While Sisense excels in terms of data storytelling, Gartner believes there’s a lot of room for improvement in terms of its Metrics Creation capabilities. In particular, its comparatively barebones metric layer prevents it from being used as a centralized metrics repository, which, in turn, limits its usefulness in terms of developing new metrics. Its NLQ functionality is also lacking with respect to rival BI platforms, with only a basic level of analytical understanding and reasoning skills.
The SAS Viya platform also seems to come up short in Metrics Creation due to the comparative weakness of its NLQ capabilities. Instead of offering a traditional NLQ interface with type-ahead functionality and search bar suggestions, as found on most competing BI platforms, Viya relies on programmatic integrations with third-party large language models. This likely increases the latency and impacts the quality of its responses. On the other hand, Gartner rates the platform highly for its seamless data source connectivity.
Surprisingly, the widely-respected SAP Analytics Cloud platform didn’t impress Gartner much, either. The report notes that the platform’s data preparation and data source connectivity are in urgent need of improvement. For instance, it lacks AI-powered data preparation tools such as AI agents and generative AI chatbots that can help to automate these processes.
In addition, SAP Analytics Cloud’s connectivity is restricted to the company’s proprietary enterprise resource planning and customer relationship management tools, limiting its usefulness for organizations that work with third-party systems. Fortunately, SAP’s recent announcement that it intends to support Google’s Big Query suggests it’s urgently looking to improve on this.
Metrics Creation Matters
For businesses, Metrics Creation is a powerful, next-generation BI capability that can enhance the judgement calls of their employees by providing them with substantial, data-based evidence to inform their decision-making processes. As these capabilities become increasingly accessible, they will reduce the strain on IT teams and data analysts, democratizing access to more powerful analytics and insights.
BI platforms that lack robust Metrics Creation capabilities run the risk of creating a “data vacuum,” where KPIs are defined inconsistently, resulting in conflicting reports and the inability for teams to understand their business’s health. In turn, this hampers effective decision-making, potentially harming the business’s overall performance and leading to missed opportunities.
As such, organizations should prioritize BI tools that support self-serve Metrics Creation. It’s fast becoming a strategic imperative for organizations to increase their agility and responsiveness in today’s data-driven business landscape.
