In a world where every digital interaction leaves a data trail, it's no surprise that data has become the lifeblood of modern business strategy. From guiding decisions to driving operational efficiency, data is everywhere.
But here’s the catch: raw data on its own doesn’t do much. It’s messy, unstructured, and often incomplete. The real value lies in what you do with it - and that’s where data engineering steps in.
Over the course of this series, we’ve explored the biggest challenges businesses face when trying to become more data-driven. In this final article, we’ll unpack why data engineering is the often overlooked partner to analytics implementation, and why bringing both together is the key to unlocking long-term value from your data.
Let’s start with a quick definition
What is Data Engineering?
Data engineering is the practice of designing, building and maintaining the systems that handle large volumes of data. These systems ensure that your data is:
It includes everything from data pipelines, ETL processes, and data lakes/warehouses, to working with big data technologies and cloud platforms.
What is Analytics Implementation?
Analytics implementation is about turning that engineered data into insights. It involves:
Analytics gets a lot of attention-and rightly so. But it often focuses on isolated tools (think Google Analytics or Looker Studio) rather than looking holistically at the systems feeding those tools with usable data.
To scale, that has to change.
Laddering Up: From Raw Data to Insight
As your business grows and your data needs mature, you'll reach a tipping point where plug-and-play dashboards just don’t cut it anymore. You need a more engineered approach - one that scales, adapts, and supports governance, compliance and deeper analytics.
Think of this journey as a ladder: each rung is a stage that builds on the last, leading to better, more reliable business insight.
Step 1: Data Collection and Ingestion
This is the base of everything.
Here, your focus is on capturing raw data from multiple source: website clickstreams, transactional systems, IoT devices, CRMs, you name it.
Data engineers are responsible for building pipelines that pull this data into a central repository - in real time or batches - like a data lake. This ensures that everything is safely captured and stored, ready for what comes next.
Step 2: Data Storage and Organisation
Once you’ve captured your data, you need to store it in a way that makes sense. Raw data tends to be:
Engineers help shape this chaos into usable form by designing effective data architecture using:
This stage involves defining schemas, setting up indexing, and ensuring compliance with governance requirements so your analytics team can work efficiently.
Step 3: Data Transformation and Cleaning
Next comes the ETL (Extract, Transform, Load) process. Here’s where raw data is cleaned, normalised, and structured for analysis. Examples might include:
Engineers do this using scripts or ETL tools, ensuring the cleaned data lands in the right storage environment. Whether that’s a warehouse or direct to your BI tool.
Step 4: Data Governance and Security
Once your data’s cleaned and stored, it needs to be protected. Engineers implement protocols to ensure:
Without solid governance, data can quickly become a liability. This can be through breaches, non-compliance, or simple misuse.
Step 5: Building Analytics Models
Now we’re into the domain of data analysts and data scientists. With clean, accessible data in place, these teams can build:
For example, a retailer might forecast next season’s sales using past performance and external signals. But none of this is possible without a stable data engineering foundation.
Crucially, this is where collaboration between engineers and analysts is at its peak. Engineers ensure the infrastructure supports complex modelling. Analysts build the models and extract insights.
Step 6: Reporting and Visualisation
Here’s where insights become actionable.
Tools like Tableau, Power BI, and Looker Studio visualise the story your data is telling-highlighting trends, spotting anomalies, and giving stakeholders real-time access to performance metrics.
Behind the scenes, engineers ensure the data feeding those dashboards is:
That means no more weekly Excel exports. No more manual refreshes. Just consistent, reliable insight.
Step 7: Actionable Insight and Decision-Making
This is the top of the ladder.
The entire process - from collection to transformation to modelling - exists to enable smarter decisions. Whether you're:
…insights are only as good as the data powering them. And that’s why engineering matters.
This laddered journey isn’t linear. It’s iterative. New platforms get added. New KPIs emerge. Data sources change. Regulatory expectations shift. That’s why your infrastructure needs to be built with evolution in mind.
When data engineering and analytics work in sync, you can iterate quickly-adding new capabilities without breaking what's already working.
How Cloud & Engineering Are Changing Analytics
More and more, we're seeing analytics implementation lean heavily into cloud-based engineering.
Here are two examples:
1. Server-Side Tracking
Moving tracking server-side (instead of relying on browser-based JavaScript) brings:
These are powerful capabilities, but they require cloud-native architecture and engineering know-how to implement effectively.
Bringing It All Together
Not every organisation needs separate teams for data engineering and analytics. In smaller businesses, one person may wear both hats.
But as you scale - especially into enterprise territory - specialisation becomes critical. Your engineers need to focus on the infrastructure. Your analysts need to focus on the insight.
Each discipline is distinct. But neither works well without the other.