![]() ![]() This is when tech companies like Airbnb started conceptualizing the idea of a metrics repository for reporting and prepping metrics for analysis. After 2010, interactive dashboards took the industry by storm, making the way for more collaboration between data teams and business teams. Worse, most companies had more than one BI tool because end-users preferred different interfaces requiring duplication of logic. ![]() But this also led to complications, since analysts had limited time and couldn’t work through the “data breadlines” as fast as new requests came in. The idea of “dashboards-as-a-service” emerged as interactive reports became the primary output of data analyst teams to help business-stakeholders consume their own data. Self-service BI tools emerge (early 2010s)Īfter 2010, interactive dashboards took the industry by storm, making the way for more collaboration between data teams and business teams. Before self-service BI, it was expensive to store large quantities of data, so teams had to be careful about what they extracted, run careful transformations, and then load small datasets for consumption. Orchestrating a procession of manual Excel sheet edits for manual data entry was another challenge faced by the early BI practitioners and IT teams were called in to help build technical steps and processes to keep things in sync. This took lots of care to manage correctly so that data cubes could be useful for driving business decisions. It was also expensive to store large quantities of data, so folks had to be careful about what they extracted, run careful transformations, and then load small datasets for consumption. The tools available to most BI engineers were built around cron jobs and SQL statements, which made it challenging to orchestrate data pipelines. So how did we get here? The early days of data modeling (1996 - 2010)īefore self-service BI, companies relied heavily on people and processes to manage datasets for consumption. It doesn’t take long before your metric logic is scattered all over the place-a data analyst’s worst nightmare. You’ve heard it time and time again, but as the demand for more data grows, so does the complexity. The idea for a metrics store came from a common problem, messy data. Photo by Joel Muniz on Unsplash Origins of the metrics store: A timeline of events Anyone seen Doc Brown? We’re taking it all the way back to the year 2000. Now, let’s take a journey to the past and the future (*cue the DeLorean*). ![]() In this article, we’ll discuss the origins of the metrics store concept, how companies have approached this technology in the past, and the future of the metrics store. Data and analytics projects only work when people trust the data and everyone is aligned on how you’re reporting numbers across the company, within small teams all the way up to the C-level. These companies found that in order to understand their business, conduct experiments, and share insights, they needed a centralized location for metrics definitions, governance, and context. I’ve even talked about my experience at Airbnb in a past blog post. Large technology companies like Airbnb, Uber, and LinkedIn are some examples of companies that saw value in a metrics layer before it was cool. And “buzzy” things are usually new right? Actually, much like business intelligence (BI), the concept of a metrics store has been around longer than you think. The term “metrics store” is creating some buzz in the data community. How an in-house data product became the next hot data tool ![]()
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