As the shift to digital learning increases and learning analytics becomes more commonplace throughout the L&D world, so has the need for teams dedicated to implementing, managing, and ensuring the quality and consistency of the learning ecosystem and data analytics. In this blog post, we’ll explore the roles each team member plays and the processes needed to keep everyone and everything in check.
Though a strong step in the right direction, simply having software in place doesn’t solve the goal of applying analytics to your learning programs. In other words, you need the right people and processes in place when implementing an enterprise learning analytics solution. Otherwise, you risk frustration, delays, or even failure.
Hey wait—read this first!
Chances are you don't have all these roles on your team quite yet. And that’s ok! You can still run a successful learning analytics and measurement campaign with what you have now, and you can probably do a lot of the work that’s described in these roles. But having a dedicated dream team for deploying enterprise-scale global learning is best for long-term success.
What does an enterprise learning analytics team look like?
For any team to be successful, it needs a combination of people who bring different skill sets that complement the group as a whole. In this scenario, you need the right mix of people to help you “interrogate” the data.
Five roles to consider—which work closely with IT, but are ideally within the L&D organization—are:
- Data & Analytics Lead
- Data Engineer
- Data Analyst
- Data Architect
- Project Manager
As an overall consideration, we don't see learning analytics teams creating roles that specifically require xAPI knowledge, but rather an understanding of event-driven data models and data integration techniques. That’s because the understanding of data models and integration techniques consists more of general skills that will help enable people in these roles to quickly learn xAPI.
These team roles generally function at a global level and serve to enable smaller teams inside and outside of L&D (be they domain, function, or geography specific).
For instance, an L&D Data Analyst can marry sales performance data with learning program data to expose insights to sales leadership. Likewise, to the same example, a Data Engineer would help integrate CRM data with learning data. Some of these roles may also be duplicated at a more micro-level to solve those teams specific needs.
Let’s look a bit deeper at each of these roles.
1) Data & Analytics Lead
The Data & Analytics Lead functions similar to a Product Manager, where the business is its customer. This person collaborates closely with the business and learning teams to define measurement strategy for major learning programs. That means they typically integrate learning analytics with the greater business and analytics strategies.
Why is this role important?
This person reviews existing learning measurement processes, reporting, and leads a cross-functional, global learning analytics team. This role serves two important functions:
- Setting the direction for the learning analytics effort
- Ensuring it supports business strategy and uses storytelling to convince stakeholders of new actions to take based on data
What kind of experience and skills are recommended?
Ideal skills and experience include:
- Leadership
- Project management
- Consultative listening and storytelling
- Prioritization
- Understanding of statistical methodologies
- Data modeling
2) Data Engineer
The Data Engineer focuses on the implementation and maintenance of learning and data architecture. This role is the closest to a software engineering role in that they coordinate closely with the Data Architect to implement designs, and they’ll need to get their hands dirty with code and data.
Why is this role important?
Building a learning analytics strategy requires data from multiple platforms. The Data Engineer is the key resource to integrate the data from different platforms and maintain those integrations.
They provide access to the required data that the rest of the team relies on, ensures that data meets the requirements set out by the data architect and business analyst (see below), and bridges any gaps if that data doesn't work with a provider.
Wait, can’t our IT department do this?
In short, they could do this—but it’s not going to give you the best outcome. Being in control of the data you are consuming is the most important part of implementing good analytics. If you are continually relying on IT, you are relying on their capacity, roadmaps, and skillset to provide you with your most important resource (i.e. good data). Your data engineer will probably be someone who works very closely with IT, but their goals and aims are very different.
What kind of experience and skills are recommended?
Ideal skills include:
- Being able to build solutions to integrate data between different platforms
- Having specific experience with event-based data models preferred and the ETL (Extract, Transform, and Load) Process
- Experience with APIs
- Data wrangling
- Experience with software automation tools
3) Data Analyst
The Data Analyst understands how to represent and visualize data in a way that answers business questions. While this person doesn’t have to be a Data Scientist, they must have an understanding of how to conduct exploratory analyses.
They create ad hoc reporting and analytics, and also create templates to be used by Instructional Designers and Program Managers for analysis. This role also may be supplemented by an admin role for someone who maintains access and permissions to the learning analytics platform, and can aid in more basic reporting functions.
Why is this role important?
The Data Analyst executes the operational analytics strategy, building the visualizations that help tell a story with data. The analyst also is a key supporter of regional and local teams, providing them with programmatic dashboards and ad hoc reporting.
What kind of experience and skills are recommended?
Ideal skills include:
- Basic statistics skills
- Data visualization
- Experience working with SQL, Python, R, and/or Excel Data Analysis
4) Data Architect
The Data Architect designs data architecture and influences overall learning technology architecture, leads data governance design and implementation, and designs data models and framework that supports analytics strategy (i.e. statement design and maintenance).
Why is this role important?
This person makes decisions on technology infrastructure specific to L&D—keeping in mind team requirements and integration considerations—and designs the structure of the data to be collected by the Data Engineer. This has a critical impact on the sustainability and relevance of the analytics.
Wait, can’t our IT team do this?
Same as above—your needs and requirements are likely to be different compared to IT’s needs and requirements, and you will end up clashing, or not being able to deliver what is required if you are beholden to them. Again, this role will end up working closely with the IT team, but it’s important to remember that your team’s goals will not be the same as the IT team’s goals.
What kind of experience and skills are recommended?
Ideal skills include:
- Experience designing and modeling data frameworks
- Knowledge of event-driven architecture
- Knowledge of learning standards and systems
- Knowledge of APIs and integrations (e.g. SSO)
5) Project Manager
The project manager is responsible for planning and leading the delivery of your team's objectives. This role takes your team’s requirements and maps them into deliverables and ensures your team is doing what needs to be done.
Why is this role important?
This person essentially acts as your learning analytics version of air traffic control. Project managers map the project plan with other initiatives top of mind, monitor the process of your initiatives, and know when to switch course when obstacles arise.
Though project management software is an ideal technological tool to ensure you keep track of milestones, having a project manager to facilitate efficiency, repeatability, and agility is key to the team’s continued success.
What kind of experience and skills are recommended?
Ideal skills include:
- Experience in technical project management
- Experience in software development and web technologies
- Excellent communication skills
- Organizational skills including attention to detail and multi-tasking skills
- Additional certification such as PMP or PRINCE II is a bonus
Recommended Reading
For Your Consideration
In addition to the roles listed above, we also recommend considering how these roles will complement your learning analytics team.
Business Analyst
A business analyst analyzes an organization and documents its processes and systems, assessing the business model or its integration with technology. Business Analysts work to identify and document the problems the business needs to solve and help to design the end solution to those problems.
Why is this role important?
When implementing learning analytics, you need to start by identifying how it can solve the problems and challenges in your business. From there, business analysts can help you work out what specific actions and deliveries your team needs to design, build, implement, and prioritize your needs.
Instructional Designer
When implementing xAPI across an organization, there isn’t usually a need for instructional designers to take on new roles or duties. However, they may experience a learning curve that presents an opportunity to understand how to best package and effectively deploy xAPI in newly created content.
Your learning designer(s) is a key partner in getting good data, so keep them in the loop regarding your strategy, goals, and expected outcomes.
Check out our xAPI implementation guide, which goes into some considerations around packaging and launching content.
Processes make the world go ‘round.
Even with an experienced learning analytics team in place, implementing specific processes is critical for consistency and continued success. For example, new learning programs must have:
- an approved learning strategy that’s well defined and documented,
- an accompanying measurement strategy linked to the learning strategy and its intended outcomes, and
- a high-level, easy-to-understand “what will we track and why?” document for sign-off by Learning Management, and agreement of technical feasibility from the Data Architect or Engineer.
The learning analytics team also generally contributes to the evaluation and implementation of new learning technology—assessing whether or not internal or external technologies can provide the data needed and integrate with other learning technology tools in use.
For example, when evaluating a new vendor that does not support xAPI, feedback can be provided similar to "this is the data model we use internally, and in order to be considered for adoption into our ecosystem you must be able to communicate activity/event data in this format."
We'll reiterate that these roles are needed for a global deployment at scale, so the level and method of deployment can certainly change this landscape.
Teamwork makes the dream work.
Remember, an enterprise-scale deployment of a global learning analytics solution performs best when there’s a dedicated L&D team. We hope our list of recommended roles and processes help you create an all-star team. And if you don’t have one yet, let us know and we’ll help you put one together from our list of trusted partners.
About the author
Bill focuses on evangelizing the message of utilizing learning analytics to improve the workforce. He is known for making complex solutions easy to understand and showing how software can create safer and more enjoyable organizations.
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