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Brands are living in a world where data is everything, but it’s not valued enough. So many conversations surround “taking control of your data and putting in place better data management” — yet before we can broach this topic in earnest, we need to talk about data layers.

A data layer is more specific to brands who utilise a Tag Management System (TMS), and with Google Tag Manager (GTM) being a widely used tool for digital marketers and brands, this article will hopefully be a helpful guide to understanding the importance of these variables in your data management set-up.

Why focus on the data layer?

The data layer is a JavaScript object that is used to pass information from your website to your Tag Manager container, e.g. product or transaction data, and as such it should be seen as the foundation of any data strategy.

The data layer needs to be designed with activation, segmentation, measurement and reporting requirements in mind. Although the set-up needs upfront investment in terms of time and cost, the maintenance of the implementation is far less time consuming in the long run, since the agility and flexibility it can provide makes tag management configuration much easier.

Balancing efficiency vs value

The time efficiency vs financial value of implementing a data layer is often misunderstood. The project usually falls down when developer time and the cost to implement and maintain the data layer scares marketers away.

Trust in analytics data has been questionable, which is why brands are not willing to invest. A well-designed data layer can provide reliable and accurate data, meaning the analytics can be trusted. Guaranteeing the data can be used to make short and long-term decisions that will positively impact the business.

Over a period of a year implementing a data layer should help with cost and time efficiency. Implementing a data layer can save 30% budget and time.

To model the benefits of utilising the data layer in your digital data management set-up, I’ve put together some examples but please note: there are many variables involved e.g. sector of the business, website infrastructure, skillset and capabilities within a brands/its agency of record and how they are applied.

The value of data integrity and the richness of data are difficult to quantify, especially as their value is different for each business, but it’s important to note that not knowing what these are impacts on the ability of a business to operate effectively.

Roles and Responsibilities

When working on a data layer you need minimum of 3 people — a Developer, Analyst and Reporting Analyst. For this scenario I am working on the assumption that all 3 roles are outsourced to an agency, a fixed hourly rate is established, and the project is planned to run for a full 12 months.

At the highest level possible, the 6 tasks are broken down into the 4 phases as follows:

Justification for the Different Phases

1st Phase

Data layer (DL) Design — this is the step required if a data layer is being implemented. This accounts for 9% of the project’s total hours.

2nd Phase

These two tasks are performed as a one-off, the % of time allocated is split as follows:

· DL Implementation task there is a 100% increase in Developer time when a data layer is being implemented

· TMS Configuration task 50% less time has been allocated to the analyst when there is no data layer

· 13% of the total project hours are required to implement a data layer verses 9% of total hours to set-up with no data layer

3rd Phase

These two tasks are on-going maintenance:

· The DL Maintenance task requires a 200% increase in Developer time when a datalayer is being implemented

· The TMS Maintenance task an 80% decrease in time for the Analyst when there is no data layer

There is a significant shift in the number of required hours between the Developer and Analyst if the data layer is being implemented vs no data layer to set-up. The Developer has 3 times the hours allocated when the data layer is being implemented whereas the Analyst hours are reduced by 4 times.

· 52% of total project hours is required to implement a data layer versus 55% of total hours to set-up with no data layer

4th Phase

· The dashboard maintenance task is on-going and has a 50% decrease in total hours required when a data layer is being implemented

· 26% of total project hours is taken when implementing a data layer versus 36% of total hours to set-up with no data layer


The time allocated for the Developer and Analyst time is 30% more without a data layer showing the time and financial efficiency required when looking to invest in a data layer implementation.

The bulk of the hours are within Phase 3 and Phase 4 which can be seen under the 3 tasks (DL, TMS and Dashboarding Maintenance). For the TMS and Dashboarding tasks the amount of work involved when there is no data layer accounts to nearly 10 weeks of extra work which is 2 and half months.

Another way of looking at the value of having a data layer is that when looking at the volume of hours split between Analyst and Developer, the split is pretty even. In stark contrast to when there is no data layer, the majority of hours is put against the Analyst here, which it’s not productive as there will be limited impact to the business.

Total working hours consumption over 12 months

With the Developer and Analyst, the key to the success (the number of working hours over 12 months equates to 2,080, 40-hour week x 52 weeks) over the course of 12 months the Analyst time will decrease by 16% to 12% from 28% when implementing a data layer.

For the Developer time will increase by 7% to 11% from 4% when implementing a datalayer.

Between the Analyst and Developer, when implementing a data layer they will consume in total 22% of total working hours during a 12-month period verses 32% without a data layer.


The value of a data layer from a time and financial point of view is huge and it also means working on projects that are more exciting then wasting time within a TMS or continually fixing and building a dashboard.

The main headline stat is that upto 30% budget can be saved by implementing a data layer and that budget should be re-invested to gather insights, analysis, optimisations and building out segments for activation. This will allow the business to see the full value invested in the data layer to be repaid back to the business in the long term.