I’ll just say it now, I’m not much of a sports guy. I played t-ball, baseball and basketball as a kid. Up until about 5th grade that is. I’m tall, and so they thought I’d be good at basketball – being a foot taller than the other kids does have advantages when you’re eight. However, in my case it was more of a disadvantage because I was horribly coordinated which means I got hit with the ball a lot, dropped the ball a lot, and helped out the other team at least as much as I “helped” my team.
Despite my lack of prowess on the court, however, I do deeply understand the importance of playing well as a team. It’s brutal out there, so the companies that innovate the most, move the fastest, and have the best teams with the best data and best insights will win.
Digital Analytics Team Model
Ideal team structure is paramount to organizational success. I’ve organized the parts of digital analytics into a diagram that I believe expresses the four key areas of operation for a functioning analytics capability. The diagram below shows my “team model.”
TWO PLUS TWO EQUALS… FOUR QUADRANTS
- data input
- data output
and two areas of expertise:
- business expertise
- technical expertise
I overlay these areas together in a grid fashion, with business on top, technical on the bottom, data output on the left and data input on the right. The result is a grid with four quadrants: the business side of data input, the technical side of data input, the technical side of data output, and the business side of data output.
The business input quadrant is where strategy and planning live. In my strategic planning model, the functions of goal discovery, question discovery, and data model planning reside in the business input quadrant. The skillset here is primarily business, not technical. The focus needs to be on business strategy and function, not the nuts and bolts of how it will happen technically. This is a highly creative quadrant, but with creativity focused around business-minded solutions. And, it focuses on the first pillar of my digital analytics framework.
To put it another way, the skillset in the business input quadrant needs to be concerned with aspects of how the data will help the business, what data is going to be most insightful, and what questions will be answered with that data. It won’t worry about how the data will get collected, how it will be later extracted, reported, and interpreted.
Key activities of the business input quadrant include:
- Strategic review
- Business goal definitions
- Key question discovery
- Data model design
- Data dictionary definition
- Implementation requirements
The technical input quadrant is where the business requirements and technology meet. In this field, the focus is on collecting the data dictated by the strategy and planning process and the data model requirements that it delivers. The technical side of input is all about implementation of tools and integration of systems. It requires specialized technical knowledge of the digital analytics platforms used and knowledge of the systems and environments where the analytics technology are being implemented.
Key activities of the technical input quadrant include:
- Implementation requirements (coordinated with business input)
- Data model/data layer implementation (the data layer is where key information that will be used by the analytics tool is defined – for example, a data layer would define the name of the page, the type of content, and the registered/anonymous status of the visitor)
- Core tag implementation (placement of the analytics vendor tags directly into the web or mobile application, or placement of tag management technology through which the tool’s tags will be setup)
- Interaction bindings (this deals with the technical coding required to measure interactions within a page for things such as tabs, widgets, download files, flash objects, video players, forms, and more)
The technical output quadrant focuses on extracting data to make use of it. In the past, this skillset has required keen Google Analytics reporting skills, but I predict that the shift to “big data” analysis will increasingly require this quadrant to have more traditional database analyst skills like writing awesome SQL queries. Specifically, the BigQuery integration for Google Analytics Premium is a great example of where this skillset is so needed.
Key activities of the technical output quadrant include:
- Advanced reporting and extraction of data within your digital analytics platform
- Software Engineering to access large datasets programmatically
- Database administration and querying
- Web development to create custom dashboards and reporting mashups
- Specific software experience – Tableau would be an example
The fourth and final quadrant closes the loop. We started the team with business and we end it here as well. If you have rock stars for the other three quadrants but don’t do this well, you’ll fail to realize value from your investment in Digital Analytics. To be honest, this is the area where I see the greatest need within organizations.
Key activities of the technical output quadrant include:
- Reporting and analysis of digital analytics data
- Finding insights and answers within the data
- Communicating findings to colleagues, stakeholders, management, executives
- Recommending action(s) to take based on data and insights
Winning with data comes down to people: the right people on your team. Finding talent in digital analytics and data science is hard, and expensive. One thing I’ve found in 8+ years of focus on Google Analytics is that you can build talent when you have the right foundation. Hopefully this framework helps you to understand your team’s current capabilities, strengths, and weaknesses and a way to identify the right foundation for the future talent you’ll need on your winning team.
Give me your feedback!