Most companies don’t have a “data problem.”
They have a decision problem that shows up wearing a data costume, holding a clipboard, and insisting it’s “totally fine.”
You can tell because the symptoms look like this:
- The CFO asks, “What’s our forecast?” and three dashboards answer, “Yes.”
- Sales insists pipeline is up, operations insists demand is down, finance insists everyone is wrong.
- KPI meetings become a courtroom drama where the defendant is a number and nobody brought evidence.
Gartner puts a price tag on this chaos: poor data quality cost…s organizations at least $12.9M per year on average [2]. Gartner
They also call out two classic root causes: inconsistency across sources and lack of ownership [2]. Gartner
IHG Hotels & Resorts didn’t solve that by commissioning a pret…hts that IHG hit a first value milestone in 10 weeks [4]. Reltio
The exact internal play-by-play isn’t fully detailed publicly,…s organizations. That’s what this article is about: the process.
The IHG Story Arc: What Broke and Why It Showed Up as “Data”
1) The problem: what was broken day-to-day
When you operate at scale across brands, regions, and systems, you get a familiar mess:
- the same customer (or guest) represented multiple ways
- systems disagreeing about what’s true
- leaders slowing down decisions because nobody trusts the numbers
IHG’s case study positions “real-time trusted data across glo…ied or trusted enough to support where they wanted to go. Reltio
2) How it was identified: the signals executives actually feel
Data issues rarely show up as an error message that says:
“Congratulations, your master data is melting.”
They show up as business friction:
- Forecasts bounce around because inputs aren’t consistent
- KPI decks need manual reconciliation every cycle
- Leaders start asking for “the spreadsheet version” because they don’t trust the dashboard version
Gartner’s callouts match what most teams experience: inconsistency across sources plus lack of ownership [2]. Gartner
3) What they did: move toward trusted data, fast enough to keep momentum
IHG’s case study lists why the platform choice made sense for them: cloud-native SaaS, scalability/performance, global presence/data residency, and ease of migration [4]. Reltio
That’s the technology headline.
The process underneath is what actually makes the change stick:
- agree on what “trusted” means
- standardize definitions
- create guardrails for what gets ingested
- assign ownership so the same problems don’t respawn next quarter
4) Outcome: measurable progress (not “we’re done forever”)
The case study notes IHG achieved its first value milestone at just 10 weeks from the start date [4]. Reltio
That’s not “everything was solved in 10 weeks.” That’s “we delivered meaningful value fast enough to prove the approach and keep moving.”
5) Takeaway: don’t copy the tools, copy the thinking
IHG’s win wasn’t “buy platform, pray harder.”
It was treating data as an operational system that needs:
- ownership
- standards
- intake rules
- and trust signals
In other words: data efficiency.
Data Management Isn’t a Dashboard Project. It’s a Supply Chain for Decisions.
Think of your data as a supply chain:
- Inputs arrive from many places.
- They get transformed.
- They get validated.
- They get packaged into decisions.
If that supply chain is sloppy, your decisions are late, inconsistent, and expensive.
And if you’re trying to forecast or compare performance across KPIs while your systems disagree with each other, you’re not forecasting. You’re guessing with better fonts.
This is where most organizations accidentally stall: they focus on “storage” or “reporting” and skip the part where data becomes trustworthy.
Data Lake vs Data Farm (and why executives should care)
A data lake is where data goes to live freely. Sometimes it grows up to be useful. Sometimes it becomes a swamp with a nice name.
A data farm is where data goes to become productive. It has:
- boundaries
- labels
- rules
- and a harvest plan
Call it a warehouse, lakehouse, hub, platform, whatever your org’s favorite word is this quarter. The concept that matters is this:
Storage is not the achievement. Reliable flow is the achievement.
If leadership wants forecasting and KPI confidence, you need a process that moves data through stages on purpose.
The Stages of Data (The part most companies skip, then regret)
Here’s the clean path from chaos to clarity:
Stage A: Raw data
This is the “as-is” world:
- exported files
- system extracts
- API pulls
- event streams
- spreadsheets people swear are “temporary”
Raw data is valuable, but it is not trustworthy by default.
Stage B: Standardized format data
This is where different systems start speaking the same language:
- consistent timestamps
- consistent identifiers
- consistent naming
- consistent definitions for the same business concepts
This stage is boring. That’s why it works.
Stage C: Master data (single source of truth)
This is the backbone: customer lists, product codes, locations, vendors. If master data is inconsistent, everything downstream is unstable.
Microsoft Fabric and Power BI endorsement features are useful as a concept: items can be promoted or certified, and “master data” is treated as a distinct badge applied by authorized reviewers [6]. Microsoft Learn+1
Translation: you need an obvious way to tell the organization, “Use this. Not that.”
Stage D: Trusted analytics outputs (dashboards that stop lying)
Dashboards should be the last step, not the first.
If you build dashboards on top of messy definitions, they will look professional while quietly being wrong. Which is the worst kind of wrong, because it comes with confidence.
The DECG Data Efficiency Road Map (How trusted data actually gets built)
At DECG, we focus on the same principle you see in strong case studies like IHG’s: data efficiency is a repeatable process, not a one-time cleanup.
Here’s the roadmap, step-by-step.
- Decision alignment (what decisions are we improving?): Before touching systems, leadership defines outcomes: forecasting accuracy, KPI consistency, customer visibility, margin clarity, operational performance comparisons across teams/sites. If you can’t name the decision, you’ll build a beautiful data machine that produces… decorative output.
- System inventory and KPI map (where “truth” is currently hiding): Most companies run on multiple systems, which is normal. The problem is when each system insists it is the only adult in the room.
Common sources:
- ERP (SAP, Oracle, Dynamics)
- CRM (Salesforce)
- finance systems (NetSuite, Workday)
- ops systems (WMS/TMS/MES/POS/PMS)
- plus “shadow IT” (spreadsheets, email, Teams messages, vibes)
The deliverable here is simple: a map showing which systems feed which KPIs, and where contradictions happen.
- Ingestion guardrails (stop letting bad inputs poison everything): Garbage in, garbage everywhere, then everyone blames the dashboard. OWASP’s file upload guidance is a great model for data intake guardrails: allowlisting, validating file type (not trusting content-type), safe naming, size limits, and secure storage [3]. OWASP Cheat Sheet Series. You’re not building a security thesis here. You’re adopting the mindset: validate inputs and constrain damage.
- Standardization (make systems speak one language): This is where definitions stop drifting: one definition per KPI, one calculation logic, documented sources, and a named owner. Gartner’s “lack of ownership” point is critical here. If nobody owns the number, everyone argues about the number [2]. Gartner
- Master data foundation (the backbone gets cleaned and governed): Master data needs clear ownership, controls for changes, and a trust label the organization can see. Again, endorsement concepts like “Promoted,” “Certified,” and “Master data” help explain how organizations signal trust and quality to users [6]. Microsoft Learn+1
- Integrity controls (because humans are creative, and not in a good way): If definitions can be changed casually, your dashboards become fiction. NIST SP 800-53 exists to provide a catalog of controls to protect organizations from threats and risks including hostile attacks and human errors [5]. NIST CSRC. You don’t need to implement a federal framework to learn the lesson: protect integrity, or your reporting will degrade over time.
- Dashboards and forecasting (now the output is worth believing): Only after the upstream work is stable do you build dashboards that leaders can use without second-guessing. This is the real goal: fewer arguments, faster decisions, better forecasts.
- Continuous monitoring (because entropy always wins if you ignore it): If you declare victory and walk away, the environment decays: new systems arrive, definitions drift, spreadsheets creep back in, and “quick fixes” become permanent. Data efficiency is maintained, not achieved.
Small-company translation: how you spot this without a giant budget
You do not need an enterprise program to confirm whether your KPIs are trustworthy. You need a structured look.
Watch for:
- Same KPI, different answer depending on system or team
- Forecast meetings that start with “Which report is right?”
- Manual CSV merges as a core business process
- “We don’t trust the dashboard” being said out loud and repeatedly
If that’s your world, you don’t just have messy data. You have a decision bottleneck.
The DECG Free 2-Week Starter Plan (You get the map. You find the “wait…what?” moments.)
If your KPIs are playing whack-a-mole across systems, you don’t need a 6-month transformation program just to confirm reality. You need a simple, structured plan that helps your team follow the trail from “this number looks weird” to “here’s exactly where it breaks.”
That’s what this is.
Reach out and we’ll send you our no-cost, 2-week starter plan, built so your team can run it with the tools you already have and quickly surface where forecasting and KPI reporting get distorted between systems. Think of it like a guided tour through your data reality, except the tour guide is a checklist and the souvenir is clarity.
What you’ll get:
- KPI definition + ownership worksheets (so metrics stop changing depending on who’s talking)
- a system-to-KPI mapping template (so the contradictions show up fast)
- a lightweight trust labeling approach (what’s approved vs questionable vs do-not-use)
- a simple governance checklist (so fixes don’t evaporate next quarter)
- an executive-ready recap format (so leadership gets clarity, not a 40-tab spreadsheet)
You run it internally, spot the issues, and end up with something rare and valuable: an actual shared understanding of what’s going on. And if you decide you want help after that, at least the conversation starts with facts instead of feelings.
The Ending (aka: the part where we stop pretending this isn’t urgent)
Bad data is like a slow leak in a tire: it doesn’t feel urgent until you’re on the freeway, late, and suddenly negotiating with physics. With a few days of effort and virtually no new resources, you can figure out where your KPIs and forecasts are being bent out of shape between systems.
Worst case? You learn it’s worse than you imagined and you need help. Annoying, sure. But it beats discovering it during a missed forecast, an audit scramble, or the moment a customer problem becomes a headline. If you want the free DECG 2-week starter plan, reach out and we’ll send it. You run it internally, get clarity fast, and then decide if you keep going solo or bring in backup.
CTA: Want the free DECG 2-week starter plan? Reach out and we’ll send it so your team can quickly map where KPI and forecasting numbers go sideways, then decide what to fix next.
Footnotes
- [2] Organization: Gartner. Author: Not listed. Title: “Data Quality: Why It Matters and How to Achieve It.” Date: n.d. (page references Gartner research from 2020). URL: https://www.gartner.com/en/data-analytics/topics/data-quality. Accessed: January 01, 2026. Notes: States poor data quality costs organizations at least $12.9M/year on average (Gartner research, 2020). Identifies inconsistency across sources and lack of ownership as common challenges.
- [3] Organization: OWASP Cheat Sheet Series. Author: Not listed (community-maintained). Title: “File Upload Cheat Sheet.” Date: n.d. URL: https://cheatsheetseries.owasp.org/cheatsheets/File_Upload_Cheat_Sheet.html. Accessed: January 01, 2026. Notes: Recommends defense-in-depth controls for file uploads including allowlisting extensions, validating file type (not trusting Content-Type), safe filename generation, file size limits, restricted upload permissions, and secure storage. Used here as guidance for file-drop ingestion guardrails.
- [4] Organization: Reltio. Author: Not listed. Title: “IHG Hotels & Resorts scales real-time trusted data across global brands.” Date: n.d. URL: https://www.reltio.com/resources/case-studies/ihg/. Accessed: January 01, 2026. Notes: Case study landing page describing unified data as a critical foundation for IHG; lists selection reasons (cloud-native SaaS, scalability/performance, global presence/data residency, ease of migration). States first value milestone achieved at just 10 weeks from start date.
- [5] Organization: National Institute of Standards and Technology (NIST). Author: Joint Task Force. Title: “Security and Privacy Controls for Information Systems and Organizations (NIST SP 800-53 Rev. 5, Update 1).” Date: 2020. URL: https://csrc.nist.gov/pubs/sp/800/53/r5/upd1/final. Accessed: January 01, 2026. Notes: Control catalog intended to protect organizations from diverse threats and risks including hostile attacks and human errors. Relevant integrity themes include access control, audit/accountability, and system/information integrity controls.
- [6] Organization: Microsoft Learn. Author: Not listed. Title: “Endorse Fabric and Power BI items.” Date: 2025-01-26. URL: https://learn.microsoft.com/en-us/fabric/fundamentals/endorsement-promote-certify. Accessed: January 01, 2026. Notes: Describes endorsement types (Promoted, Certified, Master data) and defines master data as core, single-source-of-truth data (e.g., customer lists, product codes).