We believe that metrics are the shared language between data producers and consumers. Metrics, sometimes called key performance indicators or key results, distill large amounts of information into meaningful and understandable forms. They guide and inform decisions in order to quantify, report, and focus on the organization’s pursuits.
There are a range of tools in the modern data stack that take data in its raw form and serve information about the data to end-users. While it’s easier than ever to deploy these tools, there remains a gap between the many applications for data and the places where data sits cleaned and polished for consumption. There are many technical challenges involved in building and serving metrics, as well as organizational challenges. Still, fundamentally we lack tooling to define, organize, manage, and deliver metrics at the scale and speed that companies require to make confident decisions.
Many analytically-minded people are capable of taking distilled information and turning it into valuable insights. Microsoft Excel proved that data can be organized in a suitable form and consumed by broad audiences long ago. Humans are naturally hungry for information, and modern data organizations are no different. The volume of data is ever-increasing, and the tools we have available continue to match that growth, but do not fluently speak the language of metrics, which is how most humans discuss the pursuits and outcomes they care about.
Unfortunately for less data-savvy consumers of information, much of the value in our data stacks remains inaccessible to the broader audiences because it lacks context and metadata to make it actionable. While learning modern technologies in the data tool space would be immensely valuable for a much broader audience, not everyone will have the time or resources to build the technical acumen required to access data today. Even for the people who choose to invest in understanding these tools, they are met with the equally daunting task of understanding the subtle nuances and caveats needed to use data within their domains.
So what do we do about the gap between the data producers and the less data-savvy consumers?
At many companies, there is a single person, or maybe a small handful of people, who earns a reputation amongst their colleagues as the most helpful data folks. They're known to be kind and patient, but they're also overworked. Despite their best efforts, they can never satiate their coworkers’ demand for data.
They're the first people their co-workers come to because they know the datasets, and they're willing to set aside their work because they believe that data democratization is vital to their organization's future. They have indirectly delivered immense amounts of value to their colleagues by empowering data-informed decisions, and they deserve an improved toolkit to carry out this critical work. In short, these helpful data producers deserve to be empowered.
On the other side of this relationship, there is a data consumer who, despite never having had a formal education about working with data, has seen the impact of using data with context to form an insight, and then make decisions and persuasively argue for a direction. They keep trying to build the datasets they need, they work hard to self-serve before asking the helpful data analyst for support, and when they do ask their questions, they generate even deeper, more insightful questions. At some point, they stop because they feel that they are monopolizing the time from their helpful, kind, patient, overworked co-workers struggling to support the broad audience for data.
Good tooling could help make this relationship more mutually beneficial, and we started Transform because we have seen it work in practice at some of the world’s leading tech companies. These companies were able to dedicate entire teams of software engineers and product managers to purpose-build tooling to bridge the gap between data producers and data consumers. They built tooling to support this data consumption at scale using metrics as the interface for the two groups because, after all, metrics is the language for data,
Transform’s mission is to make data accessible. We believe that by building better interfaces on top of data, we can expose metrics as a more useful and distilled form to a broader range of applications and a larger group of people. When everyone has access to data and the information it contains, people make better decisions, collaborate better, and are better informed about how the work they do impacts the trajectory of the company.
Our new company, Transform, aims to empower data analysts to expose better interfaces to the datasets that they work with and to meet data consumers where they are by providing a context-laden story to inform bold decisions.
