As a leader in your organization, you might not think about organizational analytics planning very often. In fact, you might not think about it at all until you come across a question without an obvious answer.

When you find that question, you might start feeling the pressure to get an answer quickly. At this point, you and your analysts are likely scrambling to learn as fast as you can.

No one likes to be in that position.

Developing an organizational analytics plan is a strategic project. Your strategic goal is to create a foundation of analytic processes and skills that serve the organization into the future. Importantly, you want to align the plan with the evolving needs and capabilities of your business.

This article explores the requirements of an effective analytics plan that meets both current analytical needs and future demands. Let’s dive into the process of building an analytics program that propels your organization forward.

Envisioning Future Analytics

The first step in developing an analytics plan is to think about the types of systems and analyses you’d like to have in place.

No, you don’t need to be an IT guru or data scientist planning out databases and statistical models. Instead, you need to think about the questions you want to be able to answer.

It can be helpful to consider these questions separately across each of your business units individually. For each unit, consider the following questions to help frame the analyses you’ll find most useful:

• What are your key performance indicators that you want to have access to on a daily, weekly, or monthly basis?
• Are there any special events or project timelines for which you need specialized metrics?
• Which questions, if answered, could help you design and execute quality improvement actions?

This forward-looking perspective ensures that your analytics initiatives align with your long-term business strategy and goals. Consider both the immediate benefits and the long-term value of the analytics capabilities you wish to develop.

During this process, I encourage you to be aware of what you need to have versus what would be nice to have. It’s easy to throw everything onto a list when you’re brainstorming.

To avoid coming up with a list that includes everything including the kitchen sink, I have an exercise for you. Review your list with the idea in mind that you must not ignore any results from the analyses you move forward with. This will help reduce the list to the most important analyses.

Assessing Current Analytic Activities

Understanding your current analytics landscape is crucial. This involves identifying the analytics activities currently underway within your organization.

Current activities could range from simple descriptions to more elaborate predictive models. Regardless of their level of simplicity, categorize these analyses into three lists:

• Activities to Discontinue: These are analytics activities that you currently engage in but are no longer necessary. Identifying and stopping these activities can free up resources for more valuable endeavors.
• Activities to Continue: These are existing analytics activities that remain relevant and contribute value to your organization. They align with your future analytics vision and should be continued.
• Activities to Initiate: This category includes analytics activities that you are not currently undertaking but will need in the future. Identifying these gaps early allows for strategic planning to acquire the necessary resources and capabilities.

As with your effort to envision future analytics, I want you to be critical of your current analyses. You should only move forward with the efforts that you know you will use. Analyses that have limited usefulness should be eliminated, or have their most useful components merged with other analyses.

Planning for Future Analytics

For the analytics capabilities you’ve identified as necessary but currently lacking, it’s essential to organize them by increasing complexity.

You want to begin with the simplest analyses because the results from these often will feed into more complex analyses. Additionally, you’ll require fewer analytic resources and skills at the outset if you begin by developing the simplest analyses first.

This step ensures a structured approach to building your analytics capabilities, allowing for a smooth progression in sophistication. Your analysts will have time to develop their skills. Additionally, incrementally building analyses allows your team to develop smaller pieces that add greater value by building on each other.

Skill Development

As you outline your current and future analytic projects, consider the skills required for each. Start with analyses that are close to your current capabilities, gradually moving to more complex projects. This approach will help you map out and manage the learning curve and resource allocation needed to achieve your goals.

You may want to find an analytic expert to help identify the skill sets necessary for your future projects. Additionally, performing a data literacy or analytic skills assessment will help identify gaps to be filled on the team.

Armed with an understanding of the required analytic skills, you can now begin developing those skills in the team. This might involve training existing staff or hiring new talent with the requisite expertise. Learn more about organization-wide analytics training here.

The choice between training existing staff or hiring talent externally is often a tradeoff between speed and cost. External hires can often be found quickly but tend to be more costly than training. Additionally, investing in training existing staff demonstrates long-term support and can produce a better workplace culture. The final choice will depend on your specific organizational needs.

Building the Organizational Analytics Plan

With your analyses prioritized by complexity and the required skills identified, you can begin building your organizational analytics plan. The plan should detail the progression of development in analytics capabilities, aligning with your overall business strategy.

Like any other complex project, you should develop a timeline for implementing the analytics and training/hiring processes you need to execute the plan.

Bear in mind that you cannot implement an organizational analytics plan overnight. You will need to give time for the revision of existing analyses and the development of new ones. Depending on their complexity, analytic projects could take anywhere from a few hours to several months for implementation.

Similarly, you will need to allocate time and resources for any internal training or hiring processes. As with the analyses themselves, the training/hiring process could take a few days or several months, depending on your needs and the available labor market.

Training and Development

An important aspect of building your analytics plan is focusing on the development of your analysts. Training programs and continuous learning opportunities should be designed to help your team progressively tackle more complex analyses. This not only enhances their skills but also keeps them engaged and motivated.

Because turnover is a normal fact of business, your organizational analytics plan should also include a few key pieces of documentation. When your team develops proper documentation, the organization will be better suited to absorbing unexpected absences and turnover.

Your team should develop a complete set of analytic documentation for each project. Analytic documentation allows staff to rapidly move between projects and should include a full accounting of the following elements:

• Project purpose
• Audience/Stakeholders
• Description of final reports
• Data source(s)
• Analytic methodology
• Relevant file names and locations on shared drives
• Timelines
• Task assignments
• Notes on challenges and resolutions and any changes to original analytic plans

Additionally, your team should develop documentation describing all commonly used data sources and analytic methods. These documents do not describe specific projects but provide a rapid onboarding process for new analysts.

These documents take time to develop but are worth the effort when your hand-holding requirements are dramatically reduced.

Timing Your Plan

It’s crucial to align the development of your analytics capabilities with the needs of your business. If complex analyses are required sooner rather than later, consider outsourcing these projects temporarily. Ensure that any external partnerships include knowledge transfer components, so your team can learn and eventually take over these complex analyses.

Additionally, if your analyses include cyclical components, consider developing those analyses with the most rapid cycle times first. This is like building an analytic snowball. Once you have the most frequently repeated analyses developed and automated, you’ll free up time for additional development.

Simplifying Complex Analyses

In situations where complex analyses are needed but are beyond current capabilities, look for ways to simplify these projects. For instance, instead of deploying advanced clustering algorithms for customer segmentation, start with basic segmentation based on known demographic differences or purchasing behavior. This approach allows you to gain insights quickly while building towards more sophisticated analyses over time.

Conclusion

Developing an organizational analytics plan is a dynamic process that requires both strategic foresight and adaptability. By thoughtfully assessing your current analytic activities, planning for future needs, and prioritizing the development of your team’s capabilities, you can create a robust analytics program. This program not only addresses immediate business needs but also positions your organization to leverage data-driven insights for long-term success.