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When we first heard about "digital twins" in manufacturing, we immediately recognized the powerful role data plays in enabling this concept within modern organizations. This article explores this analogy in depth. https://lnkd.in/ejfacX9A

?? Thoughtful take on how high functioning operational cultures blend analytical rigor:

As we go to market, it is becoming strikingly clear that metric trees represent the final and most impactful step in data modeling, transforming data assets into directly actionable business assets. Read more about this here: https://lnkd.in/e8qtmuUz

Building on the prior post about Analytics vs Software development, user personas pose the biggest challenge, particularly when discussing data consumption. This post explores the evolving needs of decision-makers versus technical analysts, highlighting their struggle with both limited views of data and, ironically, information overload. https://lnkd.in/em9k7vEf

If you compare analytics to software development, the diversity of users and skills makes analytics a uniquely challenging domain. This article explores the key differences we see when comparing the Software Development Lifecycle (SDLC) to the Analytics Development Lifecycle (ADLC). This piece was inspired by an article from Tristan Handy, and a big congratulations to him and dbt Labs for reaching $100M in ARR and 5,000 customers! ?? ?? https://lnkd.in/e8-igZax

We've seen rapid progress in tools for data ingestion (Fivetran or Airbyte) and modeling (dbt Labs), but the consumption layer has stayed unchanged: SQL queries -> Tables/Views of Metrics -> Charts. How do we operationalize data consumption, enabling true democratization and driving operational rigor across the enterprise? The answer lies in modeling metrics as first-class concepts and building workflows around them—there’s no shortcut to this transformation. https://lnkd.in/eG86Fgfs

With the growing excitement around AI, it's worth asking: what does automating end-to-end analysis really entail? To us, this is equivalent to achieving AGI - though we believe key steps in the process can be automated through the power of metrics modeling. https://lnkd.in/eqPaEef7

The future of analytics is a metrics-first operating system, driven by three key macro trends: 1) advances in data modeling tools and standards 2) repetitive, manually expensive patterns in analytics work 3) challenges in scaling data teams and organizational consumption These trends collectively make a metrics-first ecosystem on top of data platforms not just a possibility, but an inevitable evolution in the analytics landscape. https://lnkd.in/g_GDWJTN

Third and final recap of our popular posts from 2024. Theme #3: Challenges with dashboards and data consumption today 1. ?? Exploring Jevon’s paradox applied to data production and consumption https://lnkd.in/eQeBqrQX 2. ???Why proliferation of dashboards and data assets is in of itself a challenge https://lnkd.in/eEq9zN2b 3. ???Despite good faith efforts to borrow from the field of software engineering, there are fundamental differences that make analytics lifecycle development uniquely challenging https://lnkd.in/e2TgftCU 4. ???Are we truly leveraging our data platforms and associated investments? https://lnkd.in/eXihkTpi 5. ????Today’s BI tools are glorified interfaces for exporting data out! https://lnkd.in/eCjGg8PJ

?? Continuing the recap of popular posts from 2024. Theme #2: The Link Between Metric/Semantic Layers and Metric Trees 1. ???When should one consider a “Metrics Layer” solution as part of your infrastructure? https://lnkd.in/eaRMHNnJ 2. ???While you certainly don’t need a metrics or semantic layer to generate dashboards, adopting a "metrics-as-code" framework has the potential to revolutionize data production and consumption workflows. https://lnkd.in/e234VgPM 3. ???Why Metric Trees are the final, and most crucial step in data modeling building on top of lower-level data models including metrics: https://lnkd.in/ewDUJPTF