In part five of a six-part series, Trish Uhl – Founder, Talent & Learning Analytics Leadership Forum – looks at how and why L&D professionals need to adapt their skillset to make learning analytics work.

Implementing an effective workplace learning analytics practice requires the right mindset, the right skillset and the right toolset.

In an earlier article, I discussed the mindset and our shift from delivering fixed instructional products to creating transformative learning experiences that deliver value and drive outcomes.

This article focuses on the learning analytics skillset.

Transforming your skillset does not mean abandoning everything you already know.

You can and should take along the models, tools, techniques and skills that still serve you, plugging them into your new ways of working.

What is changing is the need for learning professionals to develop their own ‘digital dexterity’.

This will help to facilitate the organisation’s digital transformation and allow them to interface with managers and business leaders, as well as to deliver this value through data-enabled insights across all L&D capability areas.

Tackling the skills gap

The case for workplace learning analytics as a worthwhile investment is clear - L&D must transform into a data-enabled function to keep pace with the business.

“We are finally seeing evidence that leveraging people analytics to drive business strategy pays off,” says Patti P. Phillips, president and CEO of the ROI Institute, and chair of i4cp’s People Analytics Board.

As interest grows and budgets increase for data analytics functions, many organisations are grappling with skill and competency gaps.

A new report from the ROI Institute and i4cp is a great resource to help us understand where and how people analytics (and its subset, learning analytics) fits in the modern business organisation.

Also included in the Four Ways to Advance Your People Analytics report is the model below, developed by Intel in partnership with i4cp and the ROI Institute, which maps four distinctly different types of expertise – each requiring diverse tasks and relevant skills - against a seven-step process that typifies an analytics project.

Source: Intel in partnership with i4cp and the ROI Institute.

Successful analytics projects and learning analytics practices require a spectrum of skills and many talents.

Team members, whether in-house or outsourced, must represent a balance of varying expertise, each contributing from their own special strengths.

Shifting from functional roles to skills

One of my favorite resources is the LPI Capability Map from the Learning and Performance Institute.

Regularly updated, the map ‘essential skills for the new age of L&D’ of which data analytics is a part.

No one individual practitioner is expected to possess or to acquire all the capabilities covered in the map but ideally, an L&D team will possess them collectively.

The LPI website offers a free assessment tool that generates a customised competency profile, enabling individuals and teams to identify their strengths and skills gaps.

Source: The Learning and Performance Institute.

The beauty of the LPI Capability Map Competency Profile is the granular view of the technical skills and competencies that pertain to each capability area and how different areas enable each other.

It helps us understand L&D in a holistic way, as a multidisciplinary undertaking.

It also helps us put our analytics practice in context; used in service to underpin and enable all capability areas.

For instance, the strategy and operations area includes manage technology for learning which involves, among other things, "implementing data analytics processes to extract learning and performance impact, efficiency, and effectiveness measures for use in optimising the L&D function and its solutions".

Now, data analytics is a distinct competency in a different area, Performance and Impact.

People responsible for managing technology for learning do not need to be data analytics experts (or responsible for any aspect of Performance and Impact) but they do need a working understanding that allows them to draw on it to support their competency area.

Similarly, the person who is the expert in data analytics needs to "select technologies and tools to be used to collect, analyse, and visualise data in the organisation", which requires understanding and being able to engage with various competencies in other areas.

Moving from siloed to synergistic  

L&D generally, enabled by learning analytics specifically, operates as a synergistic network of skills and competencies, instead of the old array of functionally defined silos.

This is where Gartner’s concept of the citizen data scientist becomes useful.

The citizen data scientist is defined as “a person who creates or generates models that use advance diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics”.

What is the importance of communication skills in workplace learning analytics? It can’t be overemphasised. Communication is critical to everything we do in learning analytics.

"More than 40% of data science tasks will be automated by 2020, resulting in increased productivity and broader usage of data and analytics by citizen data scientists," according to Gartner, Inc.

This means L&D can leverage that automation without having to become data experts or statisticians.

In an earlier article, I referred to the BADIR framework, which allocates 20% of your time to data and analysis and the remaining 80% to articulating the business question and deriving insights and recommendations from the data.

In a nutshell, that is how a citizen data scientist leverages analytics to create value and impact.

Upskilling teams

Helping members of a learning analytics team identify their skills gaps is especially valuable, particularly core and foundational skills, in additional to technical ones.

In fact, according to LinkedIn, effective communication is the #1 needed skill across professions. Historically, L&D is not particularly good at it.

In the Towards Maturity 2019 Annual Research Report, 90% of learning professionals rated it as a priority skill, but only 40% claimed to have it.

It’s research reports like these that informed members of the LPI Capability Map Global Steering Committee and inspired us to include the mandatory skills of marketing and communications as mission-critical L&D skills and behaviours.

What is the importance of communication skills in workplace learning analytics? It can’t be overemphasised.

We are not clear in our purpose and subsequently not clear in our value. We need to think like marketers. What is your value proposition?

Communication is critical to everything we do in learning analytics – from articulating well-defined business questions, to negotiating access to data sources with our stakeholders, to building trust and transparency with our people, through to compelling action based on our data-enabled intelligence.

Once we've generated valuable analytical insights, we’re not done until we’ve successfully socialised and promoted those insights to the organisation and compelled people to action.

Achievement requires advanced communication skills.

Visualise your new value proposition

Managing through data and learning analytics is a new way for us to work - it requires rebranding.

That means we have to start from a position of credibility and re-define our value within the organisation.

“We [in L&D] are not branding ourselves well enough,” argues LPI Global Steering Group member Shannon Tipton.

“We are not clear in our purpose and subsequently not clear in our value. We need to think like marketers. What is your value proposition?”

Now, we are moving from functional silos to flat, cross-functional teams staffed by skill rather than functional role.

When it comes to branding ourselves and promoting analytical insights as calls to action within the organisation, we can (and should) turn to an array of newly important skills including visual communication and storytelling.

Digital transformation has turned technology literacy and data fluency from elective to required capabilities for everyone.

For inspiration here, I’m a fan of Tracey Smith’s work using analytics and visual tools like Tableau to solve business challenges across the spectrum.

Also worth a look is Steve Wexler, a leader in data visualisation and dynamic dashboards for real-world business scenarios.

Transformation

My earlier observation that L&D roles are changing was not a passing comment.

It's a central fact of how L&D has evolved and one reason why we're struggling with the change.

The field of practice has traditionally been set up around functional areas, like most organisations.

Now, we are moving from functional silos to flat, cross-functional teams staffed by skill rather than functional role.

It's a more fluid and agile model that can initially feel less secure but presents opportunities to become more indispensable.

I know how daunting and overwhelming it can be to feel something so familiar as our role – even our identity - in an organisation slipping away.

It’s ok – let go. Take what still serves you with you, and jettison the rest.

Be curious. Embrace and explore this multi-disciplinary approach to modern L&D and discover where you can apply your unique skillset to delivering value right away in your learning analytics practice.