The views expressed in my articles are based on my personal experience and understanding, and do not represent official recommendations from Databricks. Peer reviews and feedback are always welcome.

Self-Service BI empowers business users to access and analyze data on their own, without constant help from technical teams. It lets individuals explore questions, gain insights, and make fast, informed decisions. This speeds up decision-making, boosts agility, frees technical teams for strategic tasks, and promotes a data-driven culture.

Why Not Traditional BI Only?

In today’s fast-paced world, Self-BI is key for agile, data-driven companies. Direct access to data helps users validate ideas, spot opportunities, and adapt quickly. This drives innovation and gives a competitive edge, turning data-rich organizations into insight-driven ones.

To make sound business decisions, teams also need information, ideally acquired through real-time digital data access. Just as digital transformations require agile delivery, so agile transformations require next-generation digital solutions to enable transparency and collaboration.

Giants can dance: Agile organizations in asset-heavy industries (McKinsey & Co. — May 2, 2019)

Traditional BI still plays a role but struggles to keep up with today’s pace. It relies on centralized reports, fixed models, and IT-led processes that introduce delays. When new questions arise or conditions change, existing dashboards often lag behind, leaving users dependent on overburdened data teams. While useful for governed reporting, traditional BI lacks the speed and flexibility needed for rapid exploration and problem solving.

The Problem

Ask any data or enterprise architect about self-BI, and they’ll likely call it one of the messiest areas in the data landscape. Instead of a clean, user-friendly layer, it often turns into a maze of siloed data, ad-hoc pipelines, and unmanaged dashboards. To understand why this space creates so much confusion and risk, let’s break down a typical self-BI architecture (Figure 1) and highlight its common weak points.

Figure 1: Typical Self-BI Architecture

Figure 1: Typical Self-BI Architecture

When I started exploring this topic, I built a challenges tree (Figure 2) based on real issues I've seen while implementing and advising on similar production systems. These challenges fall into functional and non-functional categories, as outlined in Figure X.

Figure 2: Self-Service BI Challenges Tree

Figure 2: Self-Service BI Challenges Tree

Functional challenges

  1. Unreliable “source of truth.” Siloed data that isn’t reconciled and verified leads to questionable analyses, risky decisions based on incomplete or incorrect insights.
  2. Delayed time-to-insight. Even with data in lakes or warehouses, it still needs cleansing and modeling—adding days or weeks of delay and making real-time action impossible.
  3. Collaboration bottlenecks. Teams must hand off data instead of working together, leading to coordination issues, missing data contracts, and friction that blocks true cross-functional agility.

Non-functional challenges

  1. Escalating cost structure. Self-BI tools, licenses, and add-ons come with high recurring costs. On top of that, connected systems often bring hidden usage and egress fees.
  2. Governance and audit blind spots. There’s no central way to track data access or manage compliance leading to either patchy oversight or unmanageable custom ACLs.
  3. Change resistance and innovation stifling. With complex, fragile setups tied to key processes, even small changes are risky. Teams avoid touching the system, accepting disorder to avoid disruption—causing the platform to stagnate.

These challenges don't just cause friction; they undermine trust, slow decisions, increase costs, and lock the company into outdated practices. For businesses needing agility and innovation, this architecture is a constraint, not a solution.

Beware that they may not apply to your company. I recommend creating your own challenges tree based on your situation and comparing it to find overlaps. Starting with the problems often leads to simpler, more tailored solutions.

Next, I’ll show how Databricks can help address these challenges. However, success depends on how you implement and evolve the platform over time.

The Solution

The challenges above can't be solved with just a technical solution. A combination of a transformation process and the right technical platform is needed to address these issues and build a future-proof solution.

The Technical Platform

Databricks provides several components that are highly valuable for self-BI, built on top of top-tier compute capabilities and unified governance. The proposed solution involves securely getting data into the Lakehouse—whether by ingestion or federation—and enabling self-BI through no-code and low-code tools like Dashboards and Queries.