Self-Service BI Is a Lie: 3 Problems You Can Resolve Today And Improve It
Enhancing collaboration between data teams and stakeholders
The basic idea behind self-service BI is to put the power of data in the hands of business users. Give them tools to query data, build reports, and explore insights independently.
The promise is faster, more flexible reporting and better data-driven decisions. This will free up time for the data team to work on strategic projects instead of never-ending ad hoc requests. Sounds too good to be true, right?
Right!
I don’t believe in absolute self-service. At least not in the way your stakeholders see it. Way too many issues are leading to poor decisions and loss of trust.
But don't despair! You can still allow your stakeholders to build their own reports. All you need is a bit of tooling, proper processes, and one central body to rule them all.
Here are three of the biggest challenges in self-service analytics and how to tackle them.
Read time: 7 minutes.
😵💫 Problem #1: Inconsistent Metrics and Definitions
Imagine this (real story, by the way): the Sales team reports $10M in revenue for Q1, but Finance shows $8M. Marketing has 10,000 leads, but Sales only sees 5,000. Different teams report conflicting numbers for the same KPIs, leading to confusion and distrust in the data. Sounds familiar?
Here's what happens: without centralised governance, each team creates its own definitions and calculations for metrics in its self-service reporting. Sales includes recurring and services revenue, while Finance only counts recurring. Marketing counts MQLs, while Sales only looks at SQLs. It's like having every department use its own currency. There's no single source of truth!
Over time, executives lose faith in the data. Meetings devolve into arguments over whose numbers are right instead of making decisions. People give up on the data altogether and revert to gut-based decision-making. Not precisely the data-driven organisation you are going for!
The Solution 🤝
Establish a data governance committee. Align on standard definitions and calculations for key metrics. Document these definitions in a shared dictionary and ensure all self-service reporting uses the agreed-upon definitions.
Now, I must say this committee’s job is not easy. Each department believes its definition is the correct one. Setting the data dictionary standards is a long and painful battle. Here are some quick tips to make it easier:
🥇 Discuss metrics one by one
🗓️ Set a clear agenda for each meeting
👂 Make sure you take every team into account
🖐️ Invite a small number of critical people to meetings
📝 Document discussions and decisions in a shared Wiki
It takes some upfront work, but it's essential. Standardise to enable self-service!
🫠 Problem #2: Lack of Scalability
Ever seen a self-service BI environment starting small and simple but growing into a massive tangle of reports?
Self-service reporting often breaks down as organisations grow and data complexity increases. As the number of data sources increases and business logic evolves, maintaining a sprawl of self-service reports becomes untenable.
It’s like hosting a dinner party where every guest insists on cooking their dish in your kitchen. It might be okay with a couple of people, but your kitchen becomes a chaotic mess as the guest list grows. No counter space, dirty dishes piling up, and conflicting flavours.
That's what self-service reporting can feel like at scale without proper governance. An unmanageable mess of conflicting reports, out-of-date and inaccurate. The "single version of the truth" splinters into many competing versions. Confusion reigns, and adoption of self-service BI dwindles.
The Solution 🎯
Adopt a semantic layer to manage business logic. Instead of having users query raw data, create a curated data model with objects, metrics, hierarchies, and friendly naming. Then, empower self-service through this semantic layer.
Here’s what you need to do if you want to replicate our setup:
Leads, accounts, acquisitions and churn? Model everything in dbt.
Add all that to Looker views. Then, sprinkle it with some aggregation metrics.
Model the relations between objects. Keep the number of explores to the minimum.
Build essential dashboards. Think about what everybody in the company wants to know.
Push your number to Salesforce and HubSpot. Make sure everybody uses the same numbers, no matter where.
Users get the flexibility they need while data stays consistent and maintainable. Use Looker, dbt, Cube… I don't care. Simplify to scale self-service!
🤓 Problem #3: Inadequate Data Literacy
Here's a hard truth: self-service reporting is only as good as the data competency of those using it. You can put the best BI tools in place, but it's a recipe for disaster if users lack basic data skills.
Handing a BI tool to business users without proper training is like giving car keys to someone who's never driven. Accidents will happen!
Stakeholders who don't understand fundamental data concepts will misinterpret the data they see. They'll compare incomparable metrics and confuse correlation with causation. They will make decisions based on flawed analysis.
Over time, these self-inflicted wounds from data misuse erode trust in the analytics program. Self-service gets a bad rep, and data-driven decision-making grinds to a halt.
The Solution 🧑🎓
To prevent this, invest in data literacy training for everyone, not power users. Make fundamental data competency part of onboarding and ongoing education.
Teach users how to interpret visualisations, understand common data pitfalls, and know when to ask for help. And here are two things that helped us and you can implement today:
🗣️ Create a central space where everybody can ask and answer data-related questions.
🧑🏫 Organise weekly data office hours. Encourage people to seek advice on how to build reports.
Build a common language around data for your organisation. Educate to enable self-service!
🏁 Summary
You and I covered a lot of ground here, so let's recap. Self-service BI often falls short due to:
Inconsistent metrics and definitions across teams
Lack of scalability as data complexity grows
Inadequate data literacy among users
And here's the hard truth: absolute self-service BI is a lie. It's a myth sold by vendors and bought by wishful thinkers.
Successful self-service requires a solid foundation. You can't give people tools and expect magic to happen. Self-service will collapse under its weight without proper infrastructure, governance, and education.
But here's the good news: focusing on these foundational elements enables a degree of self-service that works. Standardise your KPI definitions to get everyone speaking the same data language. Build a scalable data architecture to manage complexity as you grow. And invest in data literacy to create a culture of data competency.
Self-service BI isn't an all-or-nothing proposition. It's a spectrum, and the degree to which you can enable it depends on the strength of your foundation. So don't chase the fantasy of absolute self-service. Focus on putting the right building blocks in place and fostering a collaborative approach.
By working together and investing in the fundamentals, you can empower users with a level of self-service that drives actual results. It won't be perfect, but it will be pragmatic - and that's what matters.
Embrace the reality that self-service success requires hard work, collaboration, and a solid foundation. Start strengthening these pillars in your organisation today and chart a path to success. You've got this!
📚 Picks of the Week
Since we are talking about KPI definitions. Here’s an article where
and discuss ARR definitions. The number of variations is mindblowing. (link)Continuing with the metric wave. I found the idea of model-first data products folks discussed in
fascinating. (link)Do you live in the AWS ecosystem? Do you wonder how to host dbt core? Just follow this excellent guide from
. (link)
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