Red Insights #1: Lifting the Lid on Data Bloat
The silent killer of your AI success—and how to fight back.
Foreword: Data Stagflation — A Data Trap You Can Avoid!
Are you starting to wonder if your data and tech spend is actually paying off? Spoiler alert: More data doesn’t mean more value. This isn’t about vague “keys to the kingdom” or buzzwords. It’s about hard-hitting, practical methods to get the most out of your data investments, starting at the record level. If you’re looking to cut through the noise and finally get control over your data OpEx and CapEx, you’re in the right place.
Welcome to data stagflation—the dilemma that’s haunting businesses everywhere. Just like economic stagflation (think stagnant growth and rising costs), data stagflation is what happens when your data volumes soar but the insights? Barely a trickle. It’s the situation where your data bills keep climbing, but decision-making isn’t getting any easier.
Data: Not Your Average Resource
Here’s the thing—data isn’t like oil or cash. You can’t just stockpile it and expect value. Data demands extraction, refinement, and deployment to reveal its worth. And yet, many organisations are drowning in data without converting it into anything useful. Tackling this means two things: the right tech and a cultural shift. We’re talking real data governance here—the kind that industry standards like DAMA and various ISO’s lay out.
As we dig in, we’ll keep it clear and actionable. Expect real strategies and examples that show you how to turn data stagflation into data control, backed by best practices and global standards. Ready? Let’s get practical about managing the impacts of data stagflation and building a foundation that works.
Navigating Data Stagflation: Building a Rock-Solid Data Foundation for Businesses
How Startups, SMEs, Enterprises and Emerging Businesses Can Tackle Data Pains and Drive Innovation
Intro
In today’s digital world, data is supposed to be the new oil, right? But for a lot of businesses, it’s looking more like a costly liability. Just like economic stagflation—where growth stalls even as costs rise—data stagflation hits when your data piles up but doesn’t deliver the value to match. Without solid data management, you’re looking at mounting costs with very little return. That’s where standards like DAMA DMBOK and ISO 8000 come in—they’re the playbook for beating this challenge.
Understanding Data Stagflation
📊 Data as a Strategic Asset
Think of data like an economy: it needs a balance between production (collecting it) and consumption (using it) to thrive. Yet, many businesses get it backwards, hoarding data without a game plan for how to turn it into value. Recognized data management standards are there to help—if you’re ready to get serious about making data work for you instead of just weighing you down.
The Pitfalls of Poor Data Strategy
🔒 Siloed Data: Treating data as a private stash for each department? That’s a one-way ticket to fragmentation and inefficiency. You need a unified data strategy that actually aligns with your business goals—otherwise, it’s just noise.
⏳ Weak Information Management: If your data doesn’t have a lifecycle framework, it doesn’t have a future. The best practices for data management? They’re simple: manage it end-to-end, so it’s an asset, not an afterthought.
Understanding Data Production and Consumption
⚙️ Data Production: These are the everyday activities that churn out data—customer interactions, system logs, website analytics. Think of it as the “data factory” side of things.
📈 Data Consumption: This is where data earns its keep—used in reporting, analysis, decision-making, and even powering machine learning models. If your data’s just sitting around, it’s not doing its job.
The trick? Balancing the “factory” with the “value.” Best practices remind us that an efficient balance between production and consumption is what separates data that just exists from data that works.
The Risks of Imbalance
📊 Data Overload: Too much data, not enough use—it’s like owning a warehouse of products no one’s buying. You’re left with data silos, runaway storage costs, and a cluttered mess to sort through. Miss the mark here, and you’re just a step away from the discount retailer losing out because their e-commerce data got lost in the noise.
🛑 Data Underutilization: When data isn’t trusted, it’s ignored. If managers won’t touch the data warehouse because they don’t trust the numbers, that’s a red flag waving. But it’s fixable. Proven standards like ISO 8000 and ISO 9001 are there to help turn your data from a liability to an asset.
Drawing Parallels with Economic Stagflation
💸 Data Stagflation = Rising Costs, Zero Payoff
Think economic stagflation: costs rise, growth stalls, and returns? Hardly worth the effort. Data stagflation is the same deal—more data, more complexity, but not an ounce of extra value. Companies end up throwing cash at fancy infrastructure, only to watch it sit there without delivering. The fix? Stick with proven global standards that keep things efficient and aligned. They’re like a guidebook to making sure your data investments actually pay off instead of just piling up costs.
Impacts on Newer Enterprises
For newer businesses, data stagflation isn’t just annoying—it’s a serious roadblock:
💸 Resource Drain: No clear data strategy? You’re just setting fire to cash on tech that doesn’t deliver. Investing in the right tools without a strategic backbone is a fast track to an empty budget.
💡 Innovation Stall: No insights, no innovation. When data isn’t actively working for you, it’s working against you. Analytics and BI tools aren’t just nice-to-haves; they’re essential for keeping up and adapting in real time.
⚠️ Increased Risk: Poor data management means you’re rolling the dice with compliance. Without solid data governance, your risk level shoots up. Global standards like GDPR and those focused on security practices help build in protections, keeping your data secure and your mistakes (and fines) minimal. It’s the difference between a controlled data ecosystem and a compliance minefield.
Strategies to Beat Data Stagflation
🧭 Develop a Clear Data Strategy: Align your data goals with business goals, plain and simple. Building a strategy that’s actually useful is the difference between data that drives action and data that just sits there. The best practices? Look for the frameworks that help you build a roadmap and keep it on track.
🔒 Implement Strong Data Governance: Accountability isn’t just a buzzword—it’s essential. Building standardised practices across your data ecosystem keeps everyone on the same page and the data flowing smoothly. Think of it as the guardrails that keep your data from becoming chaos.
📏 Focus on Data Quality and Literacy: Accurate, consistent data is a must, not a nice-to-have. Make sure your team not only trusts the data but knows how to put it to work. Building data literacy across the board turns data from a mystery into a tool everyone can use effectively.
🔧 Leverage the Right Technology: Go for tools that simplify, not complicate, your data processes. Your tech stack should add real value, not pile on unnecessary complexity or debt. The right tools follow best practices and keep things efficient—helping you avoid that costly trap of "tech for tech’s sake."
Conclusion
For startups and emerging businesses, tackling data stagflation isn’t just a “nice-to-have.” If you want data to drive growth and spark innovation, it’s essential to build a solid data foundation. With the right approach, your data shifts from being a cost centre to a serious advantage.
Final Thoughts
Sick of paying for data that just sits around collecting dust? It’s time to rethink the way you’re handling it. Data stagflation is the silent killer of ROI, draining your resources while delivering nothing in return. But with a smart strategy that combines solid data practices with the right tools, you can turn that data drain into pure data gain. It’s all about balancing the tech and the culture—take control of your data, and let it start pulling its weight.
Ready to dive into the numbers? Next up, we break down exactly how to measure—and tackle—your data inefficiencies head-on.
Quantifying Data Stagflation: Formulas to Tackle Your Data Inefficiencies
Introduction
📈 So, your data’s piling up, but your insights? Not so much.
That’s data stagflation in a nutshell—sitting on mountains of data without seeing any real value.
💡 The good news? You can start to break down this issue with a few simple formulas. They won’t fix everything, but they’ll give you a solid foundation and a clear direction to begin tackling the problem. Think of them as entry points—stepping stones that lead you towards solving the conundrum.
🔧 Laying the Groundwork: Proven best practices in data quality and governance will help make sure you’re heading down the right path and not getting bogged down again.
Technical Data Stagflation Formula
Variables Defined:
Data Volume (DV): Total data stored, measured in terabytes (TB) or petabytes (PB).
Data Complexity (DC): Data variety and interconnectedness (number of sources, formats).
Insight Generation Rate (IGR): How often you’re actually pulling insights out of your data, measured per month or quarter.
Data Utilisation Efficiency (DUE): How effectively data is used for decision-making (0–100%).
Explanation:
Calculate your Data Stagflation Index (DSI) to see if you’re overloading on data without the payoff. The higher your DSI, the more data you’re hoarding without any real use.
Example Calculation:
If your organisation reports:
DV: 200 TB
DC: 20 data sources
IGR: 15 insights/month
DUE: 60%
A DSI of 444.44 means your data management practices need serious work—more data, more costs, little value. Time to cut the fat.
Business Impact of Data Stagflation Formula
Variables Defined:
Increasing Data Costs (IDC): Your data storage, processing, and management costs.
Operational Inefficiencies (OI): Lost productivity and other costs due to data issues.
Return on Data Investment (RODI): Revenue or cost savings generated from data initiatives.
Explanation:
The Business Impact (BI) Ratio shows whether your data spending is worth it. If BI > 1, you’re spending more than you’re making back—a clear signal that something’s gotta change.
Example Calculation:
If your organisation has:
IDC: $2,000,000
OI: $800,000
RODI: $2,500,000
A BI of 1.12? For every dollar earned, you’re spending $1.12. That’s a one-way ticket to inefficiency unless you make some adjustments.
Aligning with Industry Standards and Best Practices
🔍 Improve Data Quality (ISO 8000):
Action: Get serious about data quality management—it’s the backbone of reliable insights.
Impact: Better data quality means a higher Insight Generation Rate and improved Data Utilisation Efficiency, making your data work harder for you.📘 Adopt Robust Data Management (DAMA DMBOK):
Action: Use the DMBOK framework to establish clear governance and operational practices.
Impact: Streamlined data processes, reduced complexity, and a serious boost to efficiency.🛡️ Enhance Data Governance (ISO 38505-1):
Action: Put governance policies in place and stick to them.
Impact: Improved decision-making and a lot more value squeezed out of your data assets.
Strategies to Reduce Data Stagflation
🧹 Conduct a Data Audit:
Purpose: Identify and cut redundant, obsolete, or trivial (ROT) data.
Benefit: Less clutter, a lower Data Stagflation Index (DSI), and smoother operations.🚀 Invest in Data Analytics Tools:
Purpose: Transform raw data into actionable insights with speed.
Benefit: Boosts your Insight Generation Rate, turning data into decisions.📈 Improve Data Literacy:
Purpose: Train your team to use data effectively, not just stash it away.
Benefit: Higher Data Utilisation Efficiency—getting more value out of each data dollar.⚙️ Optimise Data Infrastructure:
Purpose: Streamline storage and processing for a leaner, meaner data machine.
Benefit: Lower costs, fewer inefficiencies, and better BI performance overall.
Conclusion
📊 By quantifying data stagflation, you’re not just gathering stats—you’re uncovering the exact spots where inefficiencies are holding you back. Aligning your data strategies with proven standards like ISO and DAMA can help you cut the waste, boost efficiency, and finally turn data from an overhead into a valuable asset.
Call to Action
🧪 Evaluate Your Data Practices: Use the formulas to check your data stagflation levels and see where you’re leaking value.
📘 Align with Standards: Implement best practices from ISO 8000, ISO 38505-1, and DAMA DMBOK. They’re the benchmarks that keep data management grounded and effective.
📈 Monitor Progress: Regularly recalculate your DSI and BI to measure what’s working and spot areas that still need improvement.
🔥 Get on top of data stagflation now, and watch as your data shifts from a liability to a genuine powerhouse.
Coming Up Next: Spotting Data Stagflation Before It Drains Your Business
👀 Up Next: We’re diving into the early warning signs—what data stagflation looks like and how to catch it before it starts eating away at your bottom line. If you’ve felt like you’re losing control of your data, the next section is for you.
Spotting Data Stagflation Before It Drains Your Business
Key Warning Signs in Data Management
Introduction
Data stagflation is the silent killer of business efficiency. It sneaks in as your data grows faster than its value, quietly inflating costs and eating away at your competitive edge. Spotting the early signs can save you from a costly spiral. Here’s how to identify the red flags and stay sharp in your data game.
1. Escalating Costs Without Returns
💸 Where’s the Payoff?
Throwing money at premium storage and processing, but seeing no value in return? Effective data management should be giving you something back. If your data’s just racking up bills without insights, it’s a liability. Focus on making data quality a priority—without it, even the best storage setups won’t pay off.
2. Complexity Leading to Bottlenecks
🚧 More Data, More Headaches
New data sources can quickly turn into new problems. Each one adds complexity, and without a cohesive approach to architecture, you’ll be knee-deep in bottlenecks and silos. When departments aren’t on the same page, data stops flowing, and insights dry up. Standardised integration practices are key to keeping data accessible and budgets under control.
3. Disproportionate Compliance Costs
⚖️ Compliance Costs Out of Control
With GDPR, CCPA, and more regulations, compliance can get expensive fast. But if these costs outweigh the data’s value, there’s a problem. Building compliance directly into your data processes can prevent this drain on resources, making governance a proactive feature, not an afterthought.
4. Unclear Data Value Contribution
💼 Hoarding Data Without a Purpose
If you’re collecting data without a clear strategy for using it, you’re just digital hoarding. Many organisations fall into the trap of amassing data with no game plan for turning it into decisions or innovation. Align your data efforts with your business goals—otherwise, it’ll keep costing you without delivering anything in return.
Underlying Causes of Data Stagflation
Let’s get to the root of it: here’s why data stagflation grips so many organisations, with real-world examples to drive the point home.
1. Data Volume Growth Outpacing Capacity
📊 Too Much Data, Not Enough Capacity
Data is pouring in faster than your systems can handle, overwhelming your storage, processing, and analysis capabilities. Scalable, flexible data architectures are essential here, alongside solid data warehousing and lifecycle management to keep everything running smoothly.
Example: An Irish company stumbled upon 35,000 forgotten Access databases during a data sweep—critical information left to gather dust in isolated storage, inaccessible to anyone. This is where data cataloguing and centralised management come in. Without them, valuable data sits unused and invisible.
2. Dependence on Fragmented Legacy Systems
💾 Old Systems, New Problems
Legacy systems aren’t built for today’s data demands. They’re slow, clunky, and don’t play well with others, dragging down your data operation. Modernising your data architecture and adopting integration strategies will make your data work harder and smarter.
Example: A consultancy spent weekends recreating documents for every tender, even though they had a library of 650,000 reusable files buried in a legacy system. Without proper knowledge management, resources go to waste, productivity drops, and frustration rises.
3. Data Silos, Lack of Interoperability, and Inconsistent Governance
🔒 Silos and Scattered Standards
Data silos are the enemy of effective management. When data is scattered across departments with no integration or standardisation, you lose the full picture, which kills analysis and decision-making. Master data and metadata management are crucial for keeping data consistent, accessible, and interoperable across the organisation.
On top of that, inconsistent governance is a recipe for chaos. Clear policies, defined roles, and standardised procedures are non-negotiables to ensure quality, security, and compliance.
Final Takeaway
Data stagflation is a real threat to your growth, but it doesn’t have to be the end of the story. Spot the signs early, get a solid strategy in place, and make your data an asset—not an anchor dragging you down. Addressing these root issues head-on can unlock data’s potential without the headaches.
🚀 Coming Up in Part 2: Tactical Moves to Outwit Data Stagflation!
Get ready to dig deeper with hands-on insights and straightforward tactics.
🎯 Level Up Your Data Maturity
Practical frameworks to advance your data game, one maturity level at a time.
🤖 Turbocharge AI and ML
We’ll break down structuring techniques to make sure your AI and ML projects shine without being buried under data clutter.
💰 Maximise Record-Level Value
Get real about ROI with tips on valuing your data records and making every bit count.
No fluff, no theory—just the tools you need to transform data from an expensive hobby into a competitive edge.
👉 [Subscribe here](https://garyfccronin.substack.com) to catch Part 2(https://open.substack.com/pub/garyfccronin/p/part-2-unlocking-ais-full-potential?r=1ul0kp&utm_campaign=post&utm_medium=web). Don’t miss the next round of actionable insights that’ll keep your data working as hard as you do!
About Me:
Coming Up in the Data Stagflation Series
Ready to tackle data stagflation and turn that mountain of data from a cost center into a competitive edge? We’re just getting started. Here’s what’s lined up in the next parts of this series:
Part 2: Practical Moves to Up Your Data Game
Forget vague strategies—this is where we get hands-on. Think custom frameworks to crank up your data maturity, smarter ways to keep AI/ML projects lean, and tips on getting every penny out of your data at the record level. If you’re looking for impact, this is it.
Part 3: Real Stories, Real Wins
Enough theory—let’s talk about what works. From telecoms to automation, see how real businesses are tackling the data bloat and pulling out some serious value. These case studies show you what’s possible when data stagflation meets smart strategy.
Part 4: Future-Proofing for the Long Haul
This one’s for those who want their data house in order, no matter what regulation or trend hits next. We’ll walk through the essentials of a rock-solid, scalable data foundation that’s built to last. Regulations? Covered. Best practices? You bet.
Don’t miss the rest of this journey—these next parts will take you from data overload to data advantage. Hit subscribe to stay in the loop and see how the pros do it.