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How to Launch a Successful Data Literacy Program

Introduction

Launching a successful data literacy program is one of the most effective ways to see cost savings, efficiencies, and revenue from your data.

Data literacy initiatives are also some of the most challenging to start.

From finding the right solutions to setting the right strategy and implementing a program to getting your company to adopt it, data literacy projects are bound to run into roadblocks.

Gartner reports that two of the top six roadblocks to data and analytics initiatives are:

  1. Cultural challenges to accept change
  2. Skills and staff shortages

Yet, when organizations adopt change, they see success.

McKinsey found that when organizations ensure employees feel a sense of ownership and take the initiative to drive change, they see a 79% success rate in their initiatives. That’s compared to the approximately 1 in 4 success rate when they don’t.

If you’re going to launch a data literacy program and make it successful, it’s clear you need your employees to own and take the initiative in any changes you enact.

 

Why Data Literacy Programs Fail

To make your data literacy program successful, let’s start with an unconventional question: What would you do if you wanted to launch a data literacy program in the hopes of achieving the worst possible outcomes?

What would you do?

You could:

  • Force your line-of-business leaders to learn how to code
  • Require every employee to take a data science course using their own funds
  • Adopt software and resources that couldn’t handle advanced analytics
  • Set a one-week deadline for everyone to be certified
  • Mandate all of this on December 23

That plan sure sounds destined for failure.

It’s also evident no one in the right state of mind would launch a data literacy program using that format.

But would you launch a data literacy program with the following:

  • A lack clear of objectives and goals
  • Inadequate support from leadership
  • Insufficient communication and awareness
  • A one-size-fits-all approach to learning
  • A lack of practical, hands-on training
  • Insufficient time and resources
  • No follow-up or reinforcement

Your immediate response to that list was probably, “Of course not!”

Yet so many initiatives fail for those very reasons.

It’s not intentional. No one sets out to provide a lack of support and insufficient time and resources.

But it happens.

However, just as you would never accept a plan that required people in your organization to become certified in data science in a week starting on December 23, you should never accept a plan plagued by a lack of support and insufficient time and resources.

How to Launch a Successful Data Literacy Program

As mentioned, nearly 4 out of every 5 change management processes are successful when employees are involved.

However, your employees can’t be involved if you launch a data literacy program using a purely top-down approach.

But you won’t succeed if you take a purely bottom-up approach, either.

Instead, you need to take a blended approach.

With a blended approach, you can bring employees into the change management process while aligning your data literacy program with your organization’s goals.

Not only will your employees feel listened to, but they’ll be more likely to adopt the program when their input is used to guide their learning.

With that said, here’s how you do that.

 

1 – Create or Reassess Your Data Strategy and Roadmap

The first step you should take is to establish your data strategy and roadmap. If you don’t have one, now’s an opportune time to create one. If you do have one, use this time to reassess it.

Data strategy roadmaps should be clear and concise, highlighting what you hope to achieve, how you plan to differentiate your strategy from competitors, and the goals and objectives you hope to accomplish. As you establish goals, you should set KPIs that you can use to measure the effectiveness of your strategy.

For example, suppose your strategy this year is to apply advanced analytics to business decisions to provide better offers to your customers over your competitors. In that case, your roadmap should lay out how to do that.

Things to consider include the following:

  • What is your objective?
  • What resources will people need to accomplish this?
  • How long will this take?
  • What is the expected result?

Each objective or goal should answer who, what, where, when, why, and how as much as possible. Here’s an example of one objective you can set.

Objective

Increase revenue by 10% by applying machine learning to sales forecasting, which will help us improve decision-making and optimize resource allocation across opportunities.

Plan

  • Adopt analytics software/platform with machine learning and data science capabilities
  • Train members of the data team to apply machine learning models and data science to our data and produce predictive and prescriptive insights
  • Educate leaders and people in decision-making roles on what machine learning models are trying to do and data science terminology
  • Create, develop, leverage, or purchase learning materials for both data worker and decision-maker learning tracks

A clear objective with goals helps you guide conversations with people in your organization, demonstrate what you’re trying to accomplish, and collaborate on determining the resources everyone needs to deliver.

 

2 – Communicate Goals to Your Organization and Solicit Feedback

Many organizations often set goals and then do one of three things:

  • Adopt software and services without input, then expect employees to adopt them to meet goals
  • Ask for input, then adopt software and services using some or all that feedback
  • Expect their organization to meet goals without additional support or changes

We don’t have to tell you how low the success rate of that last approach is.

At this stage, we recommend you solicit feedback from your employees and use that to determine the next steps you take.

Using the example objective and plans from the first section, here are a few questions you should ask:

  • What information, services, and resources would your organization need to meet this objective?
  • What obstacles are there currently that prevent you from meeting this objective?
  • What data and insight do they need to meet this objective?
  • What would you need to exceed these expectations? (It never hurts to aim high)

You can get the answers to these questions through a survey, team meetings, or any other format that works best for you. What’s important is that you actively and earnestly use this feedback to guide your data literacy program.

Sample answers might include:

  • We currently perform all analysis ourselves using spreadsheets
  • The analytics team is bogged down with requests and needs to speed up processes
  • We have no one/few people on our team who can perform advanced analytics
  • We need adequate training to apply these techniques to our data
  • We don’t have access to the data we need
  • We have the training, but we don’t have the resources/software to perform this

Use these answers to guide the next steps of your planning and to refine the goals and objectives of your data strategy and roadmap.

You may have to send out follow-up surveys or conduct multiple meetings/conversations. But that’s good. The more transparent you are, the more aligned your organization can be on meeting goals.

 

3 – Run a Skills Gap Analysis

After you communicate your goals and objectives to the rest of the organization and solicit feedback, you can use the results to create and conduct a skills gap analysis.

The results from your survey/meetings will help you determine the training, resources, and skills you need to implement a data literacy program and meet your goals.

The skills gap analysis will help you determine:

  • Which skills your organization needs to meet your goals
  • The types of training you need to provide
  • Who should be required to learn each skill

Note that last bullet. Not everyone will need the same training—even if everyone works in the same department or team.

Data literacy is comprised of many different aspects, and ranges from basic data interpretation and statistical analysis to understanding terminology and explaining results.

For example, you may need to ensure everyone involved in your data literacy program understands basic data interpretation and statistical analysis, but only need to provide machine learning and data science training to the analysts who work on forecasting.

Knowing your goals and objectives will help you establish what needs to be included in your data literacy program. Use this to run a skills gap analysis. After you run your skills gap analysis, review it alongside your objectives, goals, and KPIs, and use this opportunity to amend and update them as needed.

Along with a skills gap analysis, you can also use this

 

4 – Assemble Your Data Literacy Task Force

With goals established and a skills gap analysis performed, it’s time to assemble a data literacy task force.

A data literacy task force aims to provide adequate support, follow-up, and transparency.

Your data literacy task force should include a good mix of internal champions and advocates who can keep your program moving forward.

As you build the team, ask yourself, who would be the best people in the organization to answer specific questions related to this program?

Here’s how to pick a team that can both encourages and energizes your organization along this endeavor:

Select a diverse team of experts and stakeholders, including team advocates.

Remember, a mix of a top-down and bottom-up approach, including employees such as an analyst who can advocate for others, will help ensure your program is successful.

Define roles and responsibilities within the task force.

Include roles that oversee implementation, including analytics software, courses and learning materials, and mentors.

Determine where the funding for initiatives will come from.

While some resources will require spending (such as software), others might be free, such as courses and communities from vendors.

Identify the levels of literacy each individual/team/department needs.

You can use your skills gap analysis as a starting point to determine who should and shouldn’t receive training.

It’s also vital to remember the needs of those who will use the information in their roles. There will be people on your team, especially in leadership, who will use the information they receive to make decisions.

While they won’t need to learn data science or machine learning, they will need to understand specific terms and concepts to use the information to make decisions. So, ensure your data literacy task force includes someone who can focus on training and providing resources for communicating data to other teams.

Use your skills gap analysis during this stage and use the results to work with HR to establish a strategy for training and talent management. This strategy should include:

  • The resources you’ll need to train your teams
  • The number of people you’ll hire vs. upskill
  • The timeframe for learning, adoption, and application

 

5 – Design a Comprehensive Data Literacy Curriculum

One of the main reasons data literacy programs fail is because of a one-size-fits-all approach. As you create your data literacy curriculum, strive to create a tiered curriculum based on individual and group needs.

It’s important to remember your teams’ differences in experience and knowledge. While your analytics team may have a strong understanding of analytical terms, your leadership teams will likely have less exposure to them.

Additionally, if you provide analytics training for your data workers, you should also consider providing training to help them communicate and present ideas and concepts.

Here’s a list of other educational aspects you should cover as you develop a curriculum:

  • Interpreting results and analytical terms
  • Explaining concepts to non-technical and technical audiences
  • Visualizing insights for reports and presentations
  • Determining when and when not to apply machine learning and data science to decision making

 

6 – Choose the Right Training Methods and Tools

Like selecting a curriculum, choosing a training method and tools shouldn’t be one-size-fits-all. For your data literacy program to have the best chance at success, it’s crucial you select suitable training methods, formats, and tools for your teams.

The goal is to empower and energize people to adopt the program and learn. To do that, people need formats that match their learning styles.

Preferred learning formats include:

  • Asynchronous/Synchronous
  • Self-paced or untimed
  • Scheduled or timed
  • Individualized paths
  • Small group paths
  • Large group or community paths
  • Reading lessons and resources
  • Hands-on learning with products

As you consider the learning styles, you should also do the following:

  • Compare in-person, online, and blended learning approaches
  • Evaluate various data literacy training platforms and tools
  • Customize training materials for different roles and departments
  • Establish a timeline and milestones for the program
  • Communicate the program’s goals and expectations to employees
  • Ensure ongoing support and resources for employees during the program

When possible, provide reward systems to keep people engaged.

Gamification is excellent for motivating people to complete tasks—but not everyone. Some enjoy learning and want the freedom to use their creativity to try new things and solve problems. Others prefer to apply their learning to projects, see their work in action, and receive recognition.

This is where the bottom-up approach can pay massive dividends to the success of your program. Before you set up a reward system for progress, discuss it with your employees. Run a survey. Tailor the learning process to the people who will use it.

No matter your learning format and setup, you must account for the current workload of your organization. Your organization may already be stretched thin, and adding a new learning program to their workload may cause friction and fatigue.

It’s also possible that your data literacy program excites everyone, reinvigorating your workforce and boosting morale! Especially if you provide software that can make their lives better.

Here’s a guide you can use to select a cloud analytics platform that caters to the five different types of data functions within an organization.

 

7 – Measure the Impact of Your Data Literacy Program

Whether you meet or surpass the business goals aligned with your data literacy program, you’ll still want other ways to measure your program’s impact and assess its effectiveness.

However, measuring the impact of your data literacy program requires more than a grade and a completion percentage.

Instead, you should establish KPIs that help you evaluate your program’s overall contribution to your organization. Some KPIs can include:

  • Percent of upskilled employees
  • Project completion time
  • Percent of data sources available (before and after)
  • Percent of decisions made using advanced analytics (before and after)
  • Efficiency increases, cost savings, and revenue generated (before and after)

If you aren’t tracking these metrics now, it may be hard to establish a baseline you can use to compare with later. In that case, estimates are fine, but look to establish a baseline as soon as possible.

The key here is to demonstrate the value of your efforts, plus the progress (and hopefully success).

These metrics will also be essential to sustaining and expanding the program.

 

8 – Sustaining and Expanding Your Data Literacy Program

One of the (unfortunately) consistent things in life is that all things trend toward disorder. That includes well-planned data literacy programs.

Your program will always need slight course corrections and adjustments to keep it humming. And no matter how much you involve employees in the change management process, some may veer off course.

Tracking metrics will help with this.

However, the success of your program depends on your ability to integrate it into your organization and instill it as part of your company culture.

Once you have all of the people, data, and materials you need to launch the program, your efforts should turn to sustaining and expanding your program.

To sustain your data literacy program, you should:

  • Emphasize data-driven decision-making across the organization.
    • For every decision where data and insights can improve the results, everyone should ask whether they can back the decision up with numbers.
  • Recognize and reward data literacy achievements.
    • As people progress, learn new skills, and create new features, recognize their efforts and share their success with your organization.
  • Integrate data literacy into employee performance evaluations.
    • If data literacy is important to the organization, learning it should also be tied to promotions and pay.

To expand your program, you should:

  • Keep the program up to date with the latest data trends and technologies.
    • Platforms that provide continuous updates and new features will make this easier for you.
  • Provide ongoing learning opportunities and resources for employees.
    • Conferences, webinars, learning events, and more will help you keep an eye on trends—but taking a bottom-up approach will always help you understand what your employees crave.
  • Scale the program as your company evolves and grows.
    • As your data literacy program reaches milestones and goals, open it up to anyone who initially could not participate but showed interest.

You can see a model that Phillips 66 used to generate over 1,500 use cases with their data analytics program.

 

Conclusion

Launching a successful data literacy program isn’t easy, but it can lead to astonishing results, such as better decisions, greater efficiency, empowerment, professional development, and greater employee satisfaction.

Engaging employees in the change management process and solving the problems that are most likely to cause a program to fail are the two main steps you can take to increase your chances of adoption and success.

No program is perfect. The process is messy. Adjustments will always be needed. Things will trend towards disorder.

But, if you place the people who run your organization first, they’ll also be the ones to see to it that the program is successful.

 

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