Why Businesses Ignore Data Engineering (and How to Fix It)
Most companies underfund data engineering—until it's too late. Here are three key reasons why it happens and how you can turn things around.
Greetings, Data Engineers,
Companies rely on data for decisions, but many don’t invest in the infrastructure that makes it reliable. Executives expect dashboards and AI-powered insights without considering how data moves, transforms, and stays consistent. They assume reports appear magically.
You might have experienced this firsthand. You work on data pipelines, optimise performance, and ensure data quality, yet leadership only notices data engineering when something breaks.
The good news? You can shift this mindset. Let’s break down why businesses overlook data engineering, what happens when they do, and how you can make leadership care.
🏗️ Lack of Awareness About Data Engineering's Value
Many executives don’t understand what data engineers do. They see dashboards with numbers, trust their business intelligence tools, and assume the data is simply “there.”
They don’t realise that behind every report, every insight, and every AI model lies a complex web of data pipelines, transformations, and infrastructure.
The problem is that good data engineering is invisible. When pipelines work perfectly, no one notices. But when something breaks data engineers suddenly become the people to blame for all problems of the company.
Leadership often assumes data engineering is just an IT function, something that “keeps the lights on.”
They rarely associate it with business growth, revenue impact, or strategic advantage. This misconception means data engineering struggles for funding, while analytics and AI teams get the spotlight.
⏳ Focus on Short-Term Wins Over Long-Term Investment
Business leaders are run on results, and they want them fast. Data analytics and AI promise immediate insights and competitive advantages, while data engineering is seen as a background effort—important but not urgent.
Short-term thinking leads to reactive rather than proactive decision-making. Instead of building a scalable data infrastructure, leadership opts for quick fixes:
🪃 Hiring more data analysts to manually clean messy data.
🪃 Investing in AI models before ensuring the underlying data is reliable.
🪃 Patching broken pipelines instead of rebuilding them properly.
Why does this happen? Because short-term wins are easier to sell. An executive can show a new AI feature or an improved dashboard at a board meeting. But saying, “We improved our data pipeline reliability by 30%” doesn’t sound as exciting.
This mindset leads to technical debt. The longer companies delay investing in data engineering, the more fragile their data systems become.
By the time they realise the problem, fixing it is far more expensive than if they had built things right from the start.
💰 Cost-Cutting Culture
For many companies, data engineering looks like a cost, not an investment. Unlike sales or marketing, where every dollar spent is expected to generate revenue, data engineering’s impact is harder to measure.
Leadership often asks, “How does hiring more data engineers make us money?” They understand the value of dashboards, AI models, and analytics because they directly influence business decisions.
But pipelines? Storage? Data transformation logic? These are harder to quantify in revenue terms.
This leads to cost-cutting decisions like:
🪃 Freezing hiring for data engineers while expanding the analytics team.
🪃 Delaying infrastructure upgrades, leading to performance bottlenecks.
🪃 Outsourcing pipeline work to save money, creating long-term dependencies.
At first, these cuts seem harmless. Reports still get generated. The business still runs.
But behind the scenes, inefficiencies pile up. Pipelines slow down. Data inconsistencies grow. Cloud costs spiral out of control.
And when the company finally decides to fix it, they realise they’ve created a far bigger and more expensive problem.
⚠️ What Happens When Companies Underinvest in Data Engineering?
The consequences of neglecting data engineering don’t appear overnight. Instead, they build up slowly, creating a hidden crisis.
📉 Slow, Unreliable Pipelines
At first, things seem fine. Reports take a bit longer to generate, but leadership doesn’t notice. Then, as data volumes grow, pipelines become overloaded.
Suddenly, dashboards are lagging by hours or even days. By the time executives see the data, it’s already outdated.
🔄 Data Silos & Inconsistency
Each department builds its own workarounds to deal with missing or messy data. Marketing pulls numbers from one system, finance from another.
The revenue reported in one dashboard doesn’t match the numbers in another. Teams waste time debating which version of the truth to trust.
💸 High Cloud & Compute Costs
Without proper engineering, pipelines run inefficiently. Instead of optimising workloads, companies spend more and more on cloud resources.
What started as a minor inefficiency turns into millions in unnecessary cloud spend over time.
😡 Frustrated Data Teams
Data engineers spend their days firefighting. Instead of building new features, they’re constantly fixing broken pipelines and responding to urgent requests.
Analysts waste time cleaning data manually, instead of doing actual analysis. Burnout increases. Some engineers quit. Others disengage.
🚫 Missed Business Opportunities
Bad data leads to bad decisions. An AI model trained on inconsistent data makes incorrect predictions, causing a failed marketing campaign.
A delayed sales report results in missed revenue opportunities. Business leaders start distrusting their data, and suddenly, all the investments in AI and analytics mean nothing.
Here’s how small gaps in data engineering snowball into bigger problems:
🔧 How to Fix This
If leadership isn’t investing in data engineering, you need to make them care. Here’s how you can shift the conversation, prove value, and secure the resources needed to build a solid data foundation.
1️⃣ Make Data Engineering Visible
Executives don’t think in terms of pipelines, transformations, or data warehouses—they think in terms of business outcomes. Your job is to bridge that gap.
How to Do It
✅ Speak in business terms, not technical jargon. Instead of saying, "Our ETL jobs are unreliable due to schema drift," say, "Our sales reports contain inconsistent revenue numbers, leading to bad forecasts."
✅ Tie data engineering directly to revenue. Show how improving data infrastructure can increase sales, reduce costs, or unlock new business opportunities.
✅ Create simple, executive-friendly visuals. Leadership won’t read long technical documents. Use one-slide dashboards that highlight key risks, inefficiencies, and opportunities in plain English.
Action Steps
🔹 Find a specific business problem caused by poor data (e.g., reporting delays, inaccurate forecasts).
🔹 Quantify the impact (e.g., "Inconsistent data caused a 10% forecasting error, leading to a $1M inventory misallocation.")
🔹 Schedule a 15-minute meeting with leadership to present the problem in their language. Keep it short, clear, and outcome-driven.
A couple of years ago, my manager, drew a big org chart to demonstrate how big organisation my team needs to support. This secured hiring additional people a few times now!
Want to learn how I translate tech jargon to business stakeholders? Check this article out!
2️⃣ Show the Cost of Inefficiency
Leaders don’t act unless they feel financial pain. Show them how poor data engineering is costing the company money—not just in cloud expenses, but in wasted time, slow decision-making, and missed opportunities.
How to Do It
✅ Track wasted time. Calculate how many hours analysts spend cleaning data instead of doing their actual job. Multiply that by their salary cost.
✅ Show unnecessary cloud spend. Inefficient pipelines mean wasted compute and storage costs. Break it down into dollars lost per month.
✅ Compare against industry best practices. Show how companies with strong data engineering outperform those without it.
Action Steps
🔹 Run a survey or interview analysts to find out how much time they spend fixing data issues.
🔹 Audit cloud and compute costs—identify unnecessary data processing and storage.
🔹 Build a simple business case showing how investing in data engineering reduces waste and improves efficiency.
🔹 Build a dashboard and show how much you spend per stakeholder. This approach worked like a charm for my team.
3️⃣ Start Small, Prove Value, Then Scale
Executives don’t like big, risky projects. Instead of asking for a major investment, start with quick wins that prove the value of data engineering.
How to Do It
✅ Fix a small but painful data issue first. Pick something that impacts decision-makers—like speeding up a key report or eliminating a recurring data inconsistency.
✅ Measure the before-and-after impact. Show how fixing this small issue led to faster decisions, better insights, or cost savings.
✅ Use that success to justify bigger investments. Once leadership sees the benefit, it’s easier to ask for more resources.
Action Steps
🔹 Identify one high-impact data problem that can be fixed in weeks, not months.
🔹 Fix it quietly, track the improvement, then present the results to leadership.
🔹 Use that success to push for bigger data engineering investments.
If you read my stuff, you know how much I like iterating over MVPs.
4️⃣ Let Things Break (Strategically)
If leadership refuses to listen, sometimes the best strategy is to let them experience failure firsthand. Choose a non-critical but visible data issue, stop fixing it manually, and let leadership see the consequences.
How to Do It
✅ Pick an issue they care about. It could be a revenue report delay, an AI model failing, or a dashboard showing incorrect numbers.
✅ Don’t warn them in advance. If you always manually fix broken pipelines, leadership will never feel the pain.
✅ Be ready with a solution. When they ask why things aren’t working, show how proper data engineering could have prevented it.
Action Steps
🔹 Identify a data issue that regularly breaks but is always patched manually.
🔹 Stop fixing it quietly—let leadership see the impact.
🔹 When leadership asks what happened, present a long-term solution (with a budget request).
I call this The Sandcastle Tactic.
💭 Final Thoughts
Many companies don’t realise they need data engineering until it’s too late. The best way to get investment is to connect data engineering to business success and make its impact visible before things go wrong.
Now with the raise AI, some companies started seeing the value in data engineering, but this job is still far from being as sexy as analysts.
It’s your job to explain the importance of your work. You need to be your biggest supporter. If you don’t believe in your value, nobody will.
Want more investment in data engineering? Make leadership care.
Until next time,
🚀 What’s Next?
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This is Amazing. Very clear with actionable tips.