Why Your Business Needs Data Engineers
Introduction
Looking back at at my career I can say I've worked with companies with significantly different tech stacks and data strategies. Irrespective of the size or industry, technology is often an afterthought. I think I've been biased by my first working experiences when I was lucky enough to join companies that considered technology a natural part of the organization, a way to make things easier and gain advantages.
After years of work my picture has changed: most businesses have a large technical debt, more than they imagine. The cost of not integrating technology with their business is highly underestimated, and I'm not referring to using the latest and greatest tech like AI, many companies don't have a well integrated data warehouse, let alone the ability to provide real time context for AI. Here's my quick take: unless you're running a mom-and-pops shop your organization is losing opportunities at every level by not having a solid data strategy.
I think the journey to the promised data land starts with establishing a solid data engineering practice. With the rise of AI, RAG and MCP servers, having high-quality data has become a priority to make the most out of your technology. If we factor in also regulatory aspects it becomes clear that any modern organization needs a solid data foundation. That's where data engineers come in. They don’t just manage data — they make it usable, reliable, and valuable across the organization.
Problems and Remedies
I'm going to try to make the case for data engineering referring to the above idea: the losses for not having your data in check are high. Trying to understand your business' performance when your data is all over the place—stored in different formats and in various places, becomes next to impossible. In that situation data can even be a liability (see financial regulations). The lack of consistency and reliability makes it nearly impossible to extract valuable insights or even have a clear view of your finances, let alone make sound decisions. Sales teams end up having different numbers from accounting, marketing costs must be measured accurately and campaigns optimized to avoid runaway costs.
When properly managed though, data becomes one of the most valuable assets a company has. Here are four concrete examples I've tangled with personally.
Performance Clarity and Business Valuation: Making Data Count
High-quality, timely data gives management a clear, real-time view of how the business is actually performing. Whether it’s tracking revenue, cost drivers, customer retention, or operational efficiency, this level of visibility allows leaders to spot trends, course-correct quickly, and allocate resources where they’ll have the most impact. Just as importantly, consistent, trustworthy data is essential for accurate business valuation—whether you're preparing for fundraising, M&A activity, or internal strategy reviews. Without reliable data, assessing the true health and value of the company is guesswork at best.
Sales Data: Understanding What Drives Revenue
With reliable sales data, companies can analyze patterns to identify which strategies, reps, and products are most effective. They can also fine-tune their commission structures based on actual performance metrics—aligning incentives with results and boosting overall sales effectiveness.
Marketing Data: Optimizing Campaigns and Attribution
When marketing teams can access clean, well-structured data, they can measure campaign performance across channels, assess ROI, and properly attribute conversions. This allows them to double down on high-performing channels and adjust or cut those that underperform—maximizing budget efficiency and overall impact.
CRM Data: Powering Customer Success
Customer Relationship Management (CRM) data is a goldmine for customer success teams. When properly structured, it helps track customer interactions, flag at-risk accounts, and identify upsell opportunities. This leads to better customer retention, higher satisfaction, and increased lifetime value.
Regulatory Compliance: Meeting Data Privacy Standards
With regulations like GDPR, CCPA, and others becoming stricter, businesses are legally required to know where customer data lives, how it's used, and be able to delete it upon request. Without a unified and well-engineered data system, locating and managing that data across multiple platforms is nearly impossible—putting companies at serious legal and financial risk. Data engineering ensures that data is both traceable and manageable, enabling compliance without operational headaches.
Conclusion
When your data is structured, secure, and accessible, your business can run smoothly and you have the tools in place to make the right decisions. Nowadays, especially with the rise of AI, data engineering is no longer a nice-to-have—it’s a necessity for organizations looking to stay ahead in a competitive landscape. By investing in solid data infrastructure, businesses can unlock new opportunities, make smarter decisions, and gain a lasting competitive edge.