Many analytics projects, while well intentioned, fail to reach their potential. The reasons are numerous - wrong time for the business to act, poor communication, lack of buy-in, distrust in the process, and more.

Several of these challenges can be mitigated by spending the time upfront to effectively scope a project. This helps businesses maximize the impact of the analytics work to their business or, conversely, to determine that the time isn’t yet right to take on the project.

Below are three tips to maximize the value of an analytics project:

• Make sure the business is ready to act on the results

• Define a clear strategic question

• Know the available information to solve the question

Business Timing: Is the business ready to act on the results?

Some business have seasonal patterns. Some adapt to change faster than others. Some strategic initiatives are further out than others.

An analytics project is only as valuable as its impact to the business. If the business isn’t ready to onboard the results and see it through to action, then it probably isn’t the best time to take on the project.

Consider a couple examples:

1. A retail business is interested in understanding shopper patterns and mining information from their Point of Sale system. Like many retailers, their busiest season is November-December. A project that delivers in mid-December is ill-timed for the business and the same project might have better impact delivering in mid-February.

2. A business is considering expansion but doesn’t have a defined timeline. Kicking off a project to identify high-potential markets or opportunities will collect dust on the shelf until the business is ready. Integrating the work into a broader strategic planning with defined periods of action will ensure the project delivers when it has the highest impact.

The business needs to be ready to ingest and act on the results. Otherwise, the project can lose momentum and fail to reach its value.

Clear Strategic Question: What is the business trying to solve?

This is the most critical part of an analytics project - defining the right question. A misguided or ambiguous question delivers an off-target answer. A good analytics projects gets to the root of the problem before starting, not during or after.

Getting to the root question might look something like this:

“We think analytics can help us in our business” Why?

“We’re growing and can’t keep track of the broader trends in the business“ What type of information would help you?

“We need better visibility into customer behavior and patterns” What problems is this causing?

“We’re nearing capacity with our current equipment and aren’t sure where to invest for growth”

The growing pains of the business is actually a question around trying to solve for how to allocate their limited capital for growth, which involves understanding current utilization, forecasting revenue growth, and assessing the capital & equipment needs based on that growth.

Along with this, it’s important to finish the following sentence: With this information we will…

• …be able to regularly track metrics important to the success of our business and act on changes

• …make the decision about where to invest for growth in the business

• …develop a sales & marketing strategy based on customer behavior & profiles

If the information doesn’t spur change or impact, it’s “nice to have” and most businesses - especially early in their journey - are better off targeting questions and projects that are important, strategic, and impactful in the near term.

Leaving room for exploration is important - it might take the project in a direction that wasn’t previously known - but it’s equally important to have an anchor point to start and have intent behind the project.

Know the Available Information: Is there data to solve the problem?

The impact of an analytics project is only as good as the data that is available to answer it. A well formed question without data isn’t an analytics project.

Data typically takes two forms - internal and external (third-party). Internal data sits within the business - point of sale, CRM, etc. External, or third-party, data is anything outside the business (US Census, industry-level data, competitor locations, etc.).

A couple questions to ask at this stage are:

• Do we have the data available internally to solve this question? Can it be extracted from systems and is it in a form that can be analyzed?

• Are there any noteworthy gaps, constraints, or caveats in the data that we have available?

• Is there external information that will be needed? If yes, is it available for free or will we need to buy it? If paid, is that dollar value worth it to us?

Data might be available but be cumbersome to extract. There might be gaps that render it unusable for analysis. External data might be needed but too expensive relative to the value of the problem being solved.

Assessing the available data on the front end ensures that the information needed to underpin the analysis & findings exists, is accessible, and is accurate.

Many times, we find that the timing isn’t right for a business to take on an analytics project - the question or need isn’t well defined yet, the timing doesn’t make sense, or the available information isn’t right to solve the problem.

We’d rather identify that upfront by working through the steps to scope an effective project and tell a business “not yet” than take on a project when it can’t be well scoped and might not lead to the best outcome for the business when all is said and done.

Interested in this topic? Get in touch with me here or by email at jordan@jordanbean.com.