3 Musts For Building Data LiteracyPiyanka JainBlockedUnblockFollowFollowingJun 26A few months into his new role as Data Analytics leader, Alan and his team clearly saw the lack of data-driven thinking across the organization.
They approached leaders about the need to develop their teams, who were swimming in data but without the skills to do anything with it.
But Alan was met with resistance from leaders and managers who didn’t see analytics and data-driven skills as part of their responsibility.
They believed that analytics and insights should come solely from Alan and his team and saw leadership’s role as the consumer of the insights.
Plus, practically speaking, the organization was on track to hit digital transformation goals and leaders were reluctant to invest time in training now.
Alan and his team would have to wait to pursue their vision.
But the leaders did agree to give teams a taste.
Alan would deliver a short training on the benefits and basics of using data analytics to solve business problems.
Everyone would participate and then have a chance to identify a problem that they would solve using data analytics.
After the training, some employees were interested to learn new analytics skills.
But for most, the training only created a mismatch between the expectations of their leaders and the skills needed to achieve them.
Employees were talking about yet another “time-wasting training”.
Those in L&D know that “awareness training” is often used to get buy-in.
But, truthfully it often just leaves people confused and with a negative perception of training in general.
There is no shortcut to developing organizational capability or performance improvement.
And building your data analytics muscle is no different.
L&D leaders need to guide their organization with the right approach.
Here are the three keys to building your data analytics “people” capability#1 Align the learning strategy with business priorities and find the Big RocksA 2017 Gartner report shows 60% of data analytics projects fail because the projects are not aligned with the business strategy or lack the right talent.
Understand where the business is headed, identify the big rocks, and align the learning outcomes.
Executives expect to see improvement in revenues, products and services, and employee productivity and retention.
Partner with data analytics leaders to assign high priority business problems to teams that can start applying data analytics to solve them.
Within these teams, you will find early adopters who are eager to learn.
You can work with these cohorts to baseline training needs and results and experiment to see what works best before fully committing to a strategy or curriculum.
#2 Design capability-based learningDesign Learning: create an experience in which learners have two goals — demonstrate performance and demonstrate capability.
Research clearly shows “The key to success is the efficient use of competencies” (Sanberg, 2009).
Personal attributes and professional skills are as impactful, if not more so than technical data analytic skills.
(Sandberg and Patil, 2013).
Baseline early training cohorts on data analytics capabilities and trend improvement over time as your training program evolves.
Spaced Learning: Design a learning experience in which people use what they learn and apply it to real problems as they go through training.
Spaced learning gives people time between sessions to experience using a new technique and get feedback.
It also creates space to reinforce knowledge and concepts that are often forgotten or misunderstood.
Proven Framework: There are frameworks for most problem-solving methods.
DMAIC is a good one for process improvement.
For data analytics, BADIR is an excellent and proven framework.
It gives learners a problem-solving roadmap, a common language, and common tools.
It also requires effective teamwork, which is a key personal attribute capability in data analytics.
#3 Make it stick: sustainable with its own momentumSuccess breeds success.
But, every organization is different and there are a few other tactics that can help drive and sustain large change management efforts.
Motivate people: Recognition in the form of badges or certification are good incentives.
But the potential for career advancement is what people really want.
If “Data Analytics experience” is listed as a qualification for a higher level position outside the core data analytics team, employees will take notice and are more likely to be intrinsically motivated to build their capability.
Measure success: A metrics plan should start with the most important goals and iterate from there.
Training completion rates don’t tell the story.
Track performance impact from projects.
Track capability penetration rates by function.
Always give the story behind the training data and encourage people in the organization to contribute their stories.
If you are ready to get started, don’t miss our live Analytics Cafe chat on The L&D Leader’s Playbook for Driving Internal Data Analytics Capabilities.
And the pitfalls to avoid…Don’t focus on tools: “A fool with a tool is still a fool”.
Teaching Tableau and giving a statistics exam is fine.
But to see results people need practice and repetition.
This can only be done through real-world problem-solving.
Don’t do Pinterest: There is a temptation to do something fast like curating some free MOOC classes or getting the internal analytics team to do lunch and learn sessions.
It ends up looking like a Pinterest board.
It lacks a cohesive approach and within 48 hours people forget 70% of what they heard and never put in to practice.
Don’t over-burden internal experts: Using internal experts to conduct on-the-job coaching fails to consider the need for formalizing skills through more rigorous learning.
In addition, it is not scalable.
I have seen internal expertise leave because they spent too much time on failed training efforts and not enough time-solving business problems.
For digital transformation to be successful, L&D/Training leaders need to drive the data analytics learning strategy with capability development at the core.
Partner with your Data Analytics leader to create alignment between training and business strategy.
Design an engaging capability-based training experience and inspire change that will grow and sustain itself.
Start small to go fast to see what works and use your own data analytics to point the way.
Sanberg, J.
2000, Understanding Human Competence At Work, Academy of Management Journal, 2000, 43.
1: 9–25Patil, T.
H.
, & Davenport, D.
J.
(2012).
Data Scientist: The Sexiest Job of the 21st Century.
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