Data Decoded: Calling out some of the traps we see in data programmes.
The last blog in my ‘data decoded’ series looks at the common issues we see when data platforms are built and automatic ROI is expected.
Familiar Data Platform Challenges & Downfalls
- Data Silos
Department A builds a platform, Department B builds their own. Two platforms that don’t talk = fragmented view, no enterprise coherence.
Explore this further in: Should You Build or Buy Your Data Platform?
- Technology First Mindset
“Let’s build a lakehouse then figure out what we’ll do with it.” Wrong order.
- Lack of Business Alignment
If business users aren’t engaged, platforms sit idle.
- Ignoring Usage / Adoption
You may have dashboards, but no one uses them.
- No Clear ROI Tracking
If you cannot measure value, you cannot grow investment.
- Over-Engineering Early
Streaming, ML, fancy stuff before you’ve nailed decision-making and adoption.
Avoid these data challenges by starting small, focusing on decision-making, involving business users, measuring value, and building iteratively.
The Role of the Data Platform Consultant
As a data platform consultant, you’re not just building technology – you’re helping your client (or your business) become decision-driven. Your role is to:
- Ask ‘why?’ early and often: Why are we building this? What decision will it support?
- Translate business outcomes into data product features: What dataset, what transform, what serve layer?
- Build lean-first: Minimum viable platform that supports decisions, then scale.
- Embed metrics & governance: Usage analytics, data quality, cost control.
- Communicate value: Demonstrate ROI, show wins, secure funding for next phases.
In short, you’re a bridge between the business (who must decide) and the engineers (who build) – the ultimate solver of data challenges.
A Realistic Journey
Reporting → Streaming → ML
Let me walk you through a journey:
Phase 1: Reporting Store
Build a data warehouse or lakehouse. Ingest key business systems (CRM, ERP, contact centre data). Transform and serve basic dashboards (how many products, how much spend, where are we today).
Value: decision boundary-based actions; time saved.
Phase 2: Domain Expansion & Streaming
Add live data (fleet sensors, IoT, user-behaviour logs). Introduce real-time alerts. Business can act quicker (if latency > threshold, then reroute).
Value: faster reaction time, cost avoided.
Phase 3: Machine Learning / Advanced Analytics
Build models: churn prediction, pricing optimisation, recommendation engines. Embed decision-making logic into the platform (if predicted churn > X, trigger incentive).
Value: new opportunity, revenue gained, risk reduced.
Few companies make it to the full “data enterprise platform” stage. If you think you have, ask yourself: Are we still measuring decision outcomes? Are people using it daily? Are we still leaning on dashboards or spreadsheets?
How to Talk About Data Engineering ROI and Value to Executives
When you’re presenting to the board or senior leadership about the data programme, use their language: time, money, opportunity. Avoid tech-speak. Frame it in business outcomes.
Example one: “By centralising data into a single platform, we’re reducing reporting cycle time by 3 days, which frees 120 person-hours per quarter, equivalent to £X in cost savings.”
Example two: “By implementing real-time pricing analytics, we expect an uplift of Y% on margin, which translates to £Z additional annual revenue.”
Use frameworks like the ones from industry (ROI framework, ROI pyramid) to back-up your case. Learn more by reading: How to Deliver a Successful Data Strategy Presentation to the Board.
Three Big Takeaways from Data Decoded
So, wrapping up the hattrick of my data decoded articles and you should now understand that:
- Decisions are everything. The only reason you need data is to make better decisions. Without decision-boundaries, you cannot declare you are data-driven.
- Platform isn’t value. Building data infrastructure is necessary, but on its own it doesn’t yield ROI. You need business usage, adoption, measurable impact.
- Value needs tracking relentlessly. Always link your data work to time saved, money made, or opportunity unlocked. Use simple frameworks, speak the business language, and secure buy-in.
Watch me talk about how to determine the value in a data platform here: Value of Data Platforms. And if you missed the first two articles in this Data Decoded series, catch up below.
Oakland’s Advice for Starting A New Data Programme
- Hold a workshop to list key decisions your organisation makes every day. For each decision, ask: what data, what threshold, what action?
- Build your platform in phases: ruling out the “acquire everything” approach.
- Engage business users early. Set metrics for adoption, usage, decision-impact.
- Celebrate wins, and communicate them. E.g. dashboards that work, models that deliver, cost savings, revenue gains.
And if you’re consulting or leading this work:
- Ensure every build has a why.
- Be the voice of value, not just tech.
- Don’t fall into the trap of “we built it, they’ll come”.
- Measure, iterate, expand.
The Final Word
In the world of data, everyone wants to talk about “big data platforms”, “machine learning”, “AI”, “data lakes”, “lakehouses”, “real-time streaming”. But none of that matters if what you build doesn’t change decisions or help you overcome data challenges. Because at the end of the day, you make decisions. Your organisation makes decisions. Your business lives or dies by them. Being data-driven means replacing guesswork with evidence, replacing intuition with insight and decision boundaries, and doing so consistently.
So when you hear the phrase “Data Decoded”, think less about the dazzling technology and more about the decoded decision. Think: what decision are you enabling today, with the data platform you have or will build? How will you know you’ve improved that decision? What value will result – in time saved, money made or opportunities unlocked? And when you bring in consultants or build your data team, make sure they understand: the platform is the enabler, the business decision is the destination.
That’s how you:
- Turn “we have a data platform” into “we are a data-driven business”
- See ROI in data programmes
- Get value from a data platform
And that’s how data consulting really pays off.
To speak to me further about any of the challenges we’ve touched on here – or for anything else to do with data platforms – please get in touch.
