Impact discussions of the baby boomer generation retiring from the workforce have been going on for a long time. This and the advancement of technology are examples of why executives have been looking to automation for product manufacturing and service capacity help within their firms.

I recently participated in a project where my client wanted to transform their workforce to remain competitive as they confronted the clash of the increasing use of technologies (e.g., artificial intelligence [AI]) to replace repetitive and mundane tasks and a shrinking labor pool.

This background set the stage for an aha moment I had along the journey. It began, as usual, with a few questions:

1. How do you know if your training solution is really moving the needle for the business?
2. Where do training requests originate and under what conditions?
3. How do you design a sustainable impact that shows real results?

My experience is that often a business problem emerges because some business metric doesn’t meet its desired measurement threshold. This is discussed within the business. Frequently, this discussion leads to a request for a learning solution. The learning department develops a training course. Everybody is happy, right? Maybe.

My experience is that training is NOT a “field of dreams.” It is not inherently true that when you build something, people will come. Rather, my experience is that even with the best content, an entire learning and engagement ecosystem is necessary to realize training’s true value to the business.

The Flywheel Model¹ is a simple way to visualize a holistic engagement strategy.

A flywheel keeps an engine turning between sparks when the fuel is ignited and force is applied to run the engine. The flywheel consists of weighted nodes that generate momentum. With each progressive application of force, the flywheel velocity increases. As with an engine, the same principles apply to learning.

Consider Amazon, a company that began with selling books. First, it focused on improving its point-of-sale (POS) systems, its inventory tracking systems, and its supply chain. After it achieved its goals and had confidence in the performance of these critical systems, it added retail. Amazon’s success in retail proved the repeatability of the model. Now, it has groceries. As it added new features and products, it applied “force” to its nodes. As a result, its flywheel spins faster and generates more revenue and market share.

Figure 1. Flywheel Model—Learning Engagement Strategy has five nodes:

At GP Strategies, we have adapted this model to more efficiently and effectively assist our clients on their learning transformation journeys.

1. Provide Meaningful and Relevant Content: This node is where we provide learners access to content that builds foundational knowledge, skills, and experiences.
2. Drive Engagement: This node is where we build content awareness, interest, and engagement.
3. Access Data for Insights Often: We use data to gain new insights and provide the necessary information to inform evidence-based decisions.
4. Prepare the Workforce for the Future: We help associates apply new learnings and insights within their work environment.
5. Enhance Reputation & Branding: Our learners share with their peers the content value and they demonstrate to their business leaders how the learning impacted their performance and mindset.

Each node in our flywheel impacts each of the other nodes. For example, the Access Data for Insights Often node informs:

1. The Provide Meaningful and Relevant Content node, by telling us what content is viewed most valuable as well as when content needs to be refreshed.
2. The Drive Engagement node, by helping us track overall program engagement progress or, when using trackable links, which communications are most effective. Additionally, this node becomes an engagement multiplier when we apply a systematic experimentation approach that identifies best practices and informs evidence-based decisions.
3. The Prepare the Workforce for the Future node, which tells when the learners effectively integrate their new knowledge and application into skills. This node also helps us know how well our training and support products are being integrated into the flow of work.
4. The Enhance Reputation & Branding node helps capture qualitative comments about the content. This information can be used to determine how much internal guerrilla marketing is going on, and which business leaders are really promoting content.

My aha moment came during a discussion with several colleagues while comparing our approaches to training. My colleague’s program was organized and funded to focus only on the top node, building meaningful and relevant content, while the GP Strategies approach integrated a plan to execute the Flywheel Model.

We provide data that demonstrates not only the level of engagement with our content, but could illustrate how applying “force” to multiple nodes at the same time can result in stacked benefits. This approach has led to our ability to show the rate of acceleration over time in high fidelity, bringing lasting change and learner adoption for the organization.

Consider how your organization could benefit from a similar flywheel approach.

1 Adapted from Jim Collin’s book Turning the Flywheel, Harper Business, Feb 2019.

About the Authors

Rocky Ellens

Rocky holds a Master’s degree in Human Resources and Organizational Development (MSHROD). Rocky brings a wealth of expertise in organizational effectiveness to a variety of companies, situations, and environments. Rocky is typically called in to consult on difficult strategic transformational initiatives resulting from technology or process changes. Throughout Rocky’s 20-year consulting career supported many Fortune 100 Companies spanning a range of industries including: Food and Beverage, Finance, Insurance, Aerospace, Manufacturing, Retail e-Commerce, Distribution, Software Development, and Military. Rocky’s contributions in these initiatives included: strategic operational planning, organizational deployment, in-depth needs analysis, root cause analysis, workforce transformation, performance improvement, program / portfolio management, process definition, information alignment and reuse, global change management, innovation and experimentation, metrics and measurement definition and reporting.

Awhile back I was consulting on a project where the client wanted to apply more science to their decision-making. Though my team and I did a great job assisting this client, that request has been bouncing about the recesses of my mind for some time. I spent long hours appreciating and defining the problem to be solved and researching the related landscape. A few questions I am grappling with include:

• Where should we start with data?
• Is it appropriate for a transformational initiative or program to have its own data strategy?
• How would a program data strategy align with an enterprise data strategy?
• What are the critical components of a data strategy?

I read many books on data, including topics ranging from establishing corporate data strategy and the use of analytics to harnessing big data for competitive advantage.

I wanted a concise template that I could put in my consulting toolbox and use to help clients posture their program for success by creating a programmatic approach to data before they jumped into the process without a thoughtful plan. Due to the nature of consulting, in which every company’s needs are different, I asked myself if I was being realistic in my quest for a template where “one size fits all.”

My research uncovered some common data strategy themes. I also came to the conclusion that it is completely appropriate to have a data strategy at the program or initiative level. My position is that when you know you will be working with data, you are well served if you begin with a strategy.

I did not find the simple, concise data strategy tool that I wanted. So I developed my own tool, as shown in Figure 1.

Each component of my tool deserves at least a chapter, its own book, or a lengthy conversation to fully grasp its rationale, nuances, considerations, and approaches as you begin to work through them for your specific company.

This blog is not the place to immerse you in all this information. Suffice to say, Figure 1 is a data strategy template that is scalable from project level to enterprise level. As you consider working with data, use this tool to prepare for and guide your thinking and discussion about a data strategy for your unique situation.

Figure 1. Data Strategy Critical Components

I believe business today is at an inflection point; I see more and more businesses asking for assistance to apply science to their decisions. We are watching the information age unfold before our eyes. Each day, we are experiencing new ways of data collection and learning how data is being used to grow businesses and enrich our lives. We are also seeing examples of when data planning gaps are exposed (collecting the wrong data, data breaches, etc.) and even when data may be used for nefarious purposes (influence elections, etc.).

Embracing a data strategy will not only contribute to a more comprehensive data plan, it can help align your work with corporate business goals, drive efficiencies, and improve decision-making confidence—it is also necessary to help you stay ahead and win in a competitive market.

How does this data strategy template compare to the way you think about data?

About the Authors

Rocky Ellens

Rocky holds a Master’s degree in Human Resources and Organizational Development (MSHROD). Rocky brings a wealth of expertise in organizational effectiveness to a variety of companies, situations, and environments. Rocky is typically called in to consult on difficult strategic transformational initiatives resulting from technology or process changes. Throughout Rocky’s 20-year consulting career supported many Fortune 100 Companies spanning a range of industries including: Food and Beverage, Finance, Insurance, Aerospace, Manufacturing, Retail e-Commerce, Distribution, Software Development, and Military. Rocky’s contributions in these initiatives included: strategic operational planning, organizational deployment, in-depth needs analysis, root cause analysis, workforce transformation, performance improvement, program / portfolio management, process definition, information alignment and reuse, global change management, innovation and experimentation, metrics and measurement definition and reporting.

In my consulting career I find that more and more executives are relying on data versus intuition to make business decisions. This aligns well with my belief that at the end of the day, the sole purpose of data is to inform a decision.

So before collecting any data, I first determine:

• What problems am I trying to solve?
• What are the core decisions that needs to be made?

If you agree with me, you will embrace the notion that data is not the star of the show, rather it is a supporting actor. The star is solving the core problems. But to do that, decisions will need to be made. And to make good decisions, you will need data.

So before you start to collect data, make sure you have clearly identified the problem you need to solve and the associated decisions necessary to take action to resolve the problem. I find it extremely helpful to begin by documenting your answers to the two basic questions listed above. It will force you to ensure you are working on the right problem. It will also require you to actually think through the problem by considering various points of view.

As you work through this process, consider one of these three distinct types of purposeful binary decisions:

1. START – To commit to something new (a venture, a product, a technology, or an approach)

2. STOP – To discontinue something (a venture, a product, a technology, or an approach) or to continue if your decision is not to stop

3. PIVOT – To change directions by deliberately deciding to shift your strategy

Interestingly, the more complex your organizational responsibilities, the more you need this simple approach of thinking through the decisions before launching into data collection.

In conclusion, this data and decision making process will:

• Ensure you are addressing the key problem.
• Help you communicate effectively and efficiently with your staff and customers.
• Focus and prioritize your data collection and analytic requirements.
• Help you and your staff ask the right questions that focus your precious resources.
• Ensure that your data analytic strategy supports the key business issues and goals.
• Provide a written baseline that all stakeholders can refer back to at any time in the process.

About the Authors

Rocky Ellens

Rocky holds a Master’s degree in Human Resources and Organizational Development (MSHROD). Rocky brings a wealth of expertise in organizational effectiveness to a variety of companies, situations, and environments. Rocky is typically called in to consult on difficult strategic transformational initiatives resulting from technology or process changes. Throughout Rocky’s 20-year consulting career supported many Fortune 100 Companies spanning a range of industries including: Food and Beverage, Finance, Insurance, Aerospace, Manufacturing, Retail e-Commerce, Distribution, Software Development, and Military. Rocky’s contributions in these initiatives included: strategic operational planning, organizational deployment, in-depth needs analysis, root cause analysis, workforce transformation, performance improvement, program / portfolio management, process definition, information alignment and reuse, global change management, innovation and experimentation, metrics and measurement definition and reporting.