What is your favorite analytical tool? Report templates, graphs, bar charts, weekly reports? These and other “analytical information” are nice to have but, quite frankly, are just buzz words. What is most important in analytics? The actual analysis – or interpretation of the numbers that have been crunched.

Over a career spanning nearly three decades in research, information and business intelligence, I have found that organizations focus a tremendous amount of time and attention on creating reports and summaries of much larger data sets. Often, however, few understand the specific parameters around the creation of the report or, worse yet, how to actually “read” the report. Unfortunately, the least amount of time is focused on understanding the business need for analysis, interpreting the reports, learning from the data, and then taking appropriate action.

So while I can’t make anyone read your reports, here are my six simple steps to make data literally move mountains within your organization.

Allocate your time appropriately

I like to use the following for analysis time allocation:

  • 20% upfront understanding the need/issue, asking questions, exploring potential outcomes, identifying data sets
  • 15% collecting data and structuring it for use
  • 60% analysis – actually interpreting the data and crafting actionable insights (not recaps)
  • 5% analysis delivery – creating and transmitting the deliverable to the requestor

Know the need and understand the problem

  • Are you researching a specific issue, creating a report, a score card, key performance indicators?
  • What is the “need” your analysis is solving for: is this a one-time request, or an ongoing need (e.g. measuring the temperature of the business or tracking early indicators that things are not going well)?
    Analysis starts with understanding the situation, problem or issue, clearly defined.
  • Take the time to explore and articulate the specific issue, problem and potential questions to be addressed in the analysis, yet be open enough to allow the analysis to drive some direction.
  • Always ask questions of the requestor. If you don’t ask questions now, you will likely waste lots of time in the analysis process.

Spend the time to define the data set(s) that should, can be, and would be “nice to have” included in the analysis.

This may be driven by both accessibility and/or cost. Don’t spend $1m on data to address a $1k problem and vice versa.

Collect the data

Although this seems like it should be the largest portion of time allowed, it should only consist of about 15 – 20% of the project timeline.

Prepare the Analysis (time taken to interpret the data collected)

  • Interpretation is not “volume is up 5%” – interpretation is “volume is up 5% due to the increase in customer base by 3 new accounts”.
  • Interpretation is rarely addressed in a single brief answer and should lead to further questions to fully understand the overall picture.
  • Find the balance: do not get stuck in analysis paralysis! There comes a point when an analysis loses track of the initial business issue, need or question(s) and continues to dig into data interpretation because it’s interesting and some associates want to anticipate any possible question a business leader may want to answer.
  • My rule of thumb: go no more than three to five layers below the high-level insight. Beyond that is often information for the sake of information.

You understand the problem, and have pulled together a beautiful analysis report, with interpretative statements. What next?

Make sure your analysis answers the most important question: now what? If your analysis does not provide the answer or several recommendations (including pros/cons of each) for decision makers to select from, then you have not completed the analysis process.

Following the above formula can be ground-breaking for organizations, and I have successfully used it hundreds of times when working with clients and internal associates. Why not give it a try and see if it works for you too? I would love to hear your feedback if you do.

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