Building the bigger picture: data engineering in marketing

Chances are that you’ve heard the phrase ‘data-driven marketing’ – but what does it really mean?

Essentially, it’s the process of using the data you’ve gathered to identify patterns, trends and generate insights into marketing performance, customer behaviours and preferences, using these insights to inform more successful strategies.

In this way, data has become the currency for effective marketing, and while it may sound straightforward on the surface, marketers are inundated with vast amounts of information from various channels and tools.

That’s where data engineering comes in. Your data can only do the heavy lifting in your marketing strategy if it’s properly collated, and with data coming in from so many sources, it can be difficult to tap into the bigger picture. Though not usually associated with marketing, data engineering is taking on increased importance as a lever for effectively using data to drive both performance and decision-making.

What is data engineering?

The average business generates large quantities of data from many different sources, whether that’s website activity, customer interactions and transactional information, social media, CRM or Google Ads and analytics. Data engineering works by drawing together these large sets of data from disparate sources and collating them within streamlined, highly customisable dashboards.

This means once-disparate information can be displayed in real-time, pulling together and integrating distinct data sets into a system that makes it easier to analyse and draw meaningful insight. The ability to visualise data more effectively in this way makes it far easier to use this information when making key decisions regarding campaign optimisation, strategy and budget.

When done correctly, data engineering not only allows you to see what is currently happening with your marketing activities, but also lets you draw inferences and predict future customer and market trends.

Effective data collection

Before data can be collated and optimised within a dashboard, it needs to be gathered. There’s a high business cost to marketing decisions that are based on ‘bad data’ – that is, data which is outdated, inaccurate, or otherwise compromised. Salesforce suggests that up to 70% of data held in CRMs, for example, becomes obsolete each year, which can lead to entire campaigns or strategies being built on shaky foundations.

Data engineers are able to develop and maintain the right systems for collecting data from different sources and channels, and enable streamlined processes for structuring, sorting and cleaning up the data gathered. This ensures up-to-date data and secures its integrity, providing a stable foundation for marketing and sales decisions to be built on.

Data warehousing

Employing a data warehouse is one way data engineers can provide a unified view of various data sources. A data warehouse is essentially a cloud storage system that lets brands consolidate their data from their CRM, analytics, marketing and sales tools, as well as other sources such as their website, social media and Google Ads.

Using a data warehouse, such as Google Cloud’s BigQuery, allows you to manipulate that data however you’d like, enabling faster, more streamlined and highly customisable dashboards that far exceed pre-built versions in HubSpot or Looker Studio. This in turn enables complex data analysis, allowing better customer personalisation, segmentation and reporting, that simply would not be possible using pre-built models.

Optimising ad campaigns

The ability to collect, collate and manipulate data in real-time is a crucial element of understanding how your campaigns are performing.

For example, collecting data from Google Ads and your CRM enables you to see specifically which leads clicked on which keywords. This data from Google Ads can be broken down even further into more granular dimensions, with a custom dashboard allowing you to create custom labels or attribution models, which in turn lets you see which marketing activities are generating those impressions, clicks and leads.

This can be taken even further by setting up server-side tracking, resulting in more accurate data collection, and improving customer experiences.

Measuring ROI

Perhaps one of the biggest benefits of data engineering in marketing is that is allows you to concretely quantify the success of your campaigns. Often, brands are running multiple campaigns at once, and it’s important to know which ones are actually providing a return on investment.

With customised dashboards and robust tracking and data collection, you can see at a glance how campaigns are performing against your key metrics and business objectives, and even how underperforming campaigns can be improved and optimised. At a time when every penny of your marketing spend needs to be pulling its weight, these data insights into performance ensure you’re funnelling resources into your most impactful activities.

Final thoughts

For businesses spending large amounts on their marketing and advertising campaigns, data engineering isn’t simply a nice idea – it’s a crucial driver in long-term marketing strategy and decision-making.

The ability to visualise live data and use it to inform real-time decisions is key, particularly as the volume and complexity of marketing data continues to increase.