A common mistake when building an analytics team is one size (resource) fits all. All too often recruiters are reaching out with a single job posting that spans multiple skill sets. As we all want to be setup for success, it’s important to understand the key skill areas in analytics and what their roles are.

Analytics Developer
[Accountable for configuring all analytics tracking]

Analytics tag managers require the skills and expertise of a developer. To capture reliable data, at the right moment (trigger), one must have a very good relationship with the technical structure of the digital property (website or mobile app). Persons in this role must be vetted for the accountability of potentially impacting user facing functionality. Because of this, the Analytics Developer tends to be a hybrid role within the development team. The best resource for writing code is someone that is embedded in the development team.

Analytics Architect/Lead
[Accountable for designing an analytics configuration that will produce reports that meets all requirements]

The development of the data model or solution design reference (SDR), needs to follow the analytics requirements. These are defined through discussions with the stakeholders with a review of the user experience (UX). We document the digital objectives (the primary purpose of the website or mobile app) and key business questions (what we want to learn from reporting). From there, reports are defined that will answer the business questions, then we know what data will be needed to build the reports. This role then takes ownership of the implementation, conducts Quality Assurance testing (data capture triggers) and User Acceptance testing (data quality) by structuring the initial reports that prove the value of the data collection.

This top down approach to producing a data model, ensures the most value from the data collection. The biggest mistake in analytics development is to devalue the significance of data planning. All to often teams will just “track everything expecting to figure out how to build the reports later”. This is why the SDR needs to come from an experienced analyst who is considering all aspects of reporting, including; the tools capabilities, future integrations with other data sources, diagnostics methods and scalability.

Related Links:

• Why Your Adobe Analytics Reports are Hard to Understand,
• Understanding Adobe Analytics
• 3 Top Data Strategies for Adobe Analytics

Analytics Practice Lead
[Accountable for supporting stakeholders through data driven decision making]

Once analytics are built, the Lead can them transition the analytics practice into a support model. Starting by providing initial reporting (produced during testing) that includes key performance measures as defined in the analytics requirements. They can then support stakeholders by interpreting report results, conducting diagnostics and performing analysis to inform on opportunities for optimization.

This is the ideal path to establishing both a winning analytics practice and a solid implementation. This never more true than when building an Adobe Analytics capability. The Adobe data structure is highly customizable and can easily be misinterpreted. This is why the solution design must be simple, scalable and well documented. In my role as an Architect/Analytics Lead, I have encountered many misaligned data models. It’s a shame to see such a significant investment not reach full potential. If you need to revisit your analytics implementation, let me know, I can help.