Insights
Guides for AI and data leaders.
10 articles on building modern data and AI platforms.
Analytics Engineering: The Role Between Data Engineering and BI
For years, modern data teams operated with an awkward, friction-heavy division of labor. On one side stood data engineers, tasked with writing complex Spark jobs, orchestrating distributed streaming i
10/24/2026Analytics & BI EngineeringBuilding a Metrics Layer: Consistent KPIs Across Every Tool
Ask three different department leads for last quarter’s customer churn rate, and you will likely receive three conflicting numbers. Finance queries an ERP export in Excel, Growth pulls an aggregate da
10/24/2026Analytics & BI EngineeringPower BI vs Tableau vs Looker: Enterprise BI Compared
Modern enterprise analytics is rarely constrained by data ingestion or storage. Between modern cloud data warehouses like Snowflake, Databricks, BigQuery, and AWS Redshift, companies have virtually un
10/24/2026Analytics & BI EngineeringSemantic Models: The Foundation of Self-Service Analytics
Self-service analytics has long been the primary objective of data platform investments, yet most enterprises struggle to deliver on its promise. Business stakeholders often encounter conflicting metr
10/24/2026Analytics & BI EngineeringDashboard Design That Executives Actually Use
Most enterprise business intelligence initiatives suffer from a quiet failure: dashboards that cost hundreds of thousands of dollars to build sit completely abandoned by the C-suite. While engineering
10/24/2026Analytics & BI EngineeringEmbedded Analytics: Dashboards Inside Your Own Product
Modern software users no longer tolerate exporting CSV files to build pivot tables in Excel just to understand their usage, ROI, or team performance. Providing reporting within your application has tr
10/24/2026Analytics & BI EngineeringOLAP Cubes in the Modern Stack: Still Relevant?
For over a decade, the death of the multidimensional OLAP cube was declared an inevitable casualty of cloud data warehousing. When massively parallel processing (MPP) engines like Snowflake, Google Bi
10/24/2026Analytics & BI EngineeringDrill-Down, Slice and Dice: Designing Explorable Dashboards
Modern enterprise dashboards often fall into one of two traps: they are either rigid, single-metric status boards that fail to answer follow-up questions, or they are cluttered, unoptimized data dumps
10/24/2026Analytics & BI EngineeringRow-Level Security in BI: Serving One Dashboard to Many Teams
Maintaining separate dashboard copies for regional sales teams, departmental managers, and client-facing tenants is an operational anti-pattern. Beyond the immediate overhead of synchronizing schema c
10/24/2026Analytics & BI EngineeringFrom Spreadsheets to governed BI: A Finance Team's Journey
For most enterprise finance teams, the corporate balance sheet is less of an automated system and more of an intricate house of cards. Late nights before quarter-end close are routinely spent debuggin
10/24/2026