Official Blog
business
+7

An Enterprise Guide to Building a Data Literacy Program

How to create a purposeful, scalable data program that balances learning with productivity.

Today’s professionals need to be data literate to succeed, but many organizations don’t know where to start to upskill their teams at scale. Online learning can provide the scalability you need, but how do you design an effective learning program that also delivers the ROI you’re looking for? At our recent DCVirtual conference, Aimee Gott, Head of Skill Assessment Content at DataCamp, spoke to Faye Wakefield, Senior HR Specialist at LumiraDX, about how to roll out an online learning program to promote data literacy at your company.

Why is data literacy important?

LumiraDx is in the business of providing diagnostic-led care to enable better outcomes in health and wellness. The company was founded in 2014 and now has hundreds of employees globally. Their main headquarters are in the UK, but they have offices in the U.S. and a commercial organization in different offices around the world. As an organization, they wanted to focus on how to achieve these outcomes in a data-driven way.

It is absolutely key that you understand why you’re undertaking the learning intervention. The HR team was tasked by our Chief Tech Officer to increase the competency of our employees around data and data analysis.—Faye Wakefield, Senior HR Specialist at LumiraDx

Purpose-driven learning

When assessing solutions for organization-wide training in data skills, LumiraDx was looking for a solution that was user-friendly, cost-effective, flexible, scalable, and trusted.

We needed a product that was flexible. We're not able to take everybody out of the office on all-day training courses, all the time. The real attraction of DataCamp is that it's available. It fits in with people's lifestyles. They can do it in their own time. They can do it in the evenings. They can do it during the day. If you look at the way people like to learn today, they expect it to be just-in-time and tailored. They expect it to look intuitive, like one of the apps on their iPhone. That is extremely important for the learning experience.

They also needed a solution that would help with upskilling in their preferred tool of choice. Every organization, regardless of size, should assess which technologies are needed for the personas or roles across the company.

For LumiraDx, their preferred technology was Python, which is widely used across industries and all fields of business analytics. They needed a trusted provider to rely on for effective Python training. An internal champion at LumiraDx advocated for DataCamp for Business because they enjoyed our learn-by-doing approach to learning data skills.

Our product assurance manager who manages our stats team had used DataCamp to learn R and Python and found it extremely helpful. We needed a product that was off the shelf, best practice, and written by experts. You can get a lot of online training out there, but you have to be assured of the quality of it.

Of course, when deciding on a tool of choice, it’s good practice to assess factors like the preferences of internal stakeholders, existing frameworks, and industry standards. For example, Python and R are both great tools for data science, but factors like employee background, the problems you work on, and the culture of your industry can guide your decision.

Your employees’ professional development also benefits your company

Building a data literacy program requires willingness from employees to learn new technologies and skills. It boils down to installing a company culture that values continuous learning. Companies that value continuous learning understand that professional development is both a company benefit and a personal benefit.

These companies are able to reconcile learning with productivity. Many leaders worry about setting training standards: Should they choose a top-down or bottom-up approach to build their data program? Should the training program be prescriptive or lenient? Larger companies may be able to give their employees dedicated time for learning, but smaller companies often have employees that are stretched very thin. Businesses should be honest about where they stand on professional development and support their employees’ efforts to learn new skills however they’re able.

I try to treat people like grownups and not children. I like relationships to be adult to adult. I say to them, "It's your choice. DataCamp is a great tool, but you can make a choice about how you spend your free time. Do you want to invest some of your free time in upskilling in this area?” I'm there to enable them, motivate them, and support them. I don't monitor them. I'm not the HR police. It's about enabling.

Best practices to increase learning engagement

Online learning is a plug-and-play training solution

When evaluating data training providers, it’s important to consider whether they meet your enterprise needs and can scale appropriately. Some companies choose to build their own in-house training programs to fit their exact needs—but this requires a significant investment in internal resources. There are many benefits to in-person and blended learning, but online learning providers are most cost-effective for companies that want a plug-and-play solution.

DataCamp has been an excellent opportunity for [our employees] to actually upskill while working at home. It's been a godsend because it's a really tangible tool that we can give to our employees, and they can work on their own time. It's actually helped us during the COVID-19 situation.

DataCamp has learning administration tools that scale with your company’s needs and training preferences. Learners can learn from anywhere on their own time from anywhere, and administrators can easily manage their learning program and track progress. We also offer advanced enterprise reporting for companies to understand the value of their teams' progress and usage on DataCamp and easily share adoption and engagement success. Careful monitoring of your learning program can improve engagement and productivity.

I particularly monitor the adoption scores and the engagement scores, so I can demonstrate to my leadership team that our engagement scores are actually excellent. If I see a decline in the engagement scores, I will take action. I will do interventions to understand [the reason for the decline] and then [help our team to] increase them, because there's a direct link between a high level of engagement and productivity.

Create custom tracks for role-based learning

Every role or persona at your company has a different relationship with data. Learning paths should be customized to what each role needs to know to make better decisions with data.

To optimize learning, the DataCamp champion and administrator for LumiraDx took the additional step of creating custom tracks for their data scientists and data analysts. Custom tracks give enterprises the flexibility to craft appropriate learner journeys for each role.

Our product assurance manager created a course track, and he selected the most relevant courses. That's about 49 hours. He took the data analyst track, and he then removed some of the SQL modules because we don't use them in our organization. Then he included extra modules on stats and visuals, because our scientists are often asked to present data visually.

At DataCamp, we’ve identified several data-related personas or roles that our customers typically use: Data Consumer, Leader, Data Analyst, Citizen Data Scientist, Data Scientist, Data Engineer, Database Administrator, Statistician, Machine Learning Scientist, and Programmer. Many of our customers create custom tracks with hands-on coding courses for their data professionals and suggest theory courses for Data Consumers and Leaders, like our new data literacy skill track.

Measuring progress in data literacy

Leveraging data gives you credibility

Faye from LumiraDx appreciates that DataCamp gives her team easy access to adoption and engagement data, which is important for her data-oriented leadership team. Companies that have an HR or training team in place to monitor corporate learning require easy access to data about their learning programs. Quantifying and measuring the impact of learning across functions can give your data program more credibility.

HR is an enabling function. If we're asking an organization to make an investment, if we’re trying to demonstrate if a particular initiative has been effective, if we can quantify or put a number around it—[we are] so much more credible. Often, advice I would give to anyone in HR or any other functions that are non-data is that if you can quantify it and measure it, you will have so much more impact and your voice will be heard.

Encourage employees to apply what they’ve learned at work

Of course, what companies really care about isn’t learning engagement—it’s whether their employees are able to apply what they’ve learned. Nurturing an environment that’s safe for employees to apply learnings in a business context is absolutely essential. This yields employee benefits like retaining knowledge and practicing skills as well as business benefits like improving productivity and outcomes.

Like DataCamp, we do take a learn, practice, and apply approach. Learning on its own is not enough. It's got to be practiced. Then it's got to be applied to your own contextual situation. Once individuals have got an agreed level of competency in Python analysis, we then use some real data [from our company] and do a workshop around how to better apply what you've learned to the real context of our problems.

The data maturity spectrum

Data fluency is on a spectrum. If your company is currently data reactive or data scaling, that means very few people have the ability to analyze, report, and present data confidently. If every team has at least one data fluent employee who can do these things, your company is data progressive. Data fluency is when everyone at your company is capable of making data-driven decisions—from Data Consumers and Leaders to more technical data professionals.

The steps to data fluency

Companies at all stages of the data maturity spectrum will be able to see marked improvement by strategically implementing a data literacy program. This involves:

  1. Establishing a high-level data strategy
  2. Building a strong data foundation
  3. Process re-design and culture change
  4. Building strong executive support
  5. Driving value from use cases

Tracking the business impact of company-wide data fluency

After you take these steps, you’ll want to measure the effectiveness of your data literacy program. On an individual and team level, you’ll likely see that employees are generally more competent: they’re asking better questions, interpreting data meaningfully, and using data-driven experimentation. And because data has been properly fed into decision making, you’ll see your leadership making better business decisions. And most crucially, as a result of these initiatives, your business will likely see greater profit as a result.

Watch our webinar for more about rolling out an online data literacy program.