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5 Ways FP&A Professionals Can Be Better Data Consumers

  • By Bryan Lapidus, FP&A
  • Published: 1/29/2018

We are all both producers and consumers of data, personally and professionally. We ingest data, analyze and interpret it, then apply our business savvy to make it more useful, and then deliver that to the next person. But are we doing this well?

A Harvard Business Review article defined a problem faced by enterprises: “Investments in analytics can be useless, even harmful, unless employees can incorporate that data into complex decision making. [In fact,] there’s an odds-on chance that someone in your organization is making a poor decision on the basis of information that was enormously expensive to collect.”

What can we do to be better consumers of data and develop more informed skeptics?

Think about your role when you interact with data. In formal environments, there are different roles and access granted to maintain integrity. While the formality is not necessary in all instances, knowing where you are in the supply chain of decisions, and what to expect from others, will help you to handle your role and make the others better as well. And if you are playing multiple roles, it is still important to think through what needs to be accomplished: 

  • An administrator prepares and makes the data accessible to others. Data security requires that you protect sensitive information, sharing what is necessary without exposing too much. Also protecting source data, preventing version errors, and maintaining integrity are central to this role.
  • A writer has the authority to reformat, change, and interpret data. In practice, since source data is rarely changed, the writer may create an intermediate copy or version to work from, and this will be the basis of analysis, dashboards, reports, text or presentations.
  • An explorer looks at the data or many different data sets to create insights and connect different dots.
  • An editor will scrutinize the information, try to poke holes in the data or inferences drawn to test its veracity. This is a key data validation step, and you may want to have a peer editor before taking it to the boss-editor.
  • A viewer is often a senior person who is on the senior end of the data (or related information) and is going to take action based on it.


    Give yourself time to understand the data. Slowing down is hard to do, but getting to know your data is like building a solid foundation for your house—everything else rests upon it. If the data is going to be recurring, know (and verify) the common dimensions to ensure integrity and your understanding, such as the level of aggregation/atomization, date and time, frequency of collection, unit, business unit level, etc. Recognize that data systems are often split into systems of record and systems of analysis. Is something lost in translation from one to another? Are your colleagues using different data than you are?

    Check your biases at the door. With so much data out there, the human mind creates rubrics to simplify and understand the world, and we do not look at the data objectively. There are a host examples: we often suffer from a “confirmation bias” of seeking data that supports our existing ideas, a “recency bias” of overweighting recent experience, or any of several other cognitive biases.

    Develop your company’s analytic capabilities. Notice that I did not say self or FP&A team, but your entire company. We will be more effective in our roles and provide more value to the company if we everyone has the common language and skills to talk and share information. Here are some ways to get moving on this goal:

  • Take advantage of formal instruction in data management and data analysis, such as workshops in the office, online courses, or external classes.
  • “Coach people up” during the normal course of business and make it a mission to include data in every decision and discussion so that you set the cultural tone of what is expected.
  • Standardize the decision making process to include data and metrics that leverage existing data libraries and common definitions; be skeptical of newly created data sets and measurements. Enforce the “single source of truth” that comes from using standardized data.
  • When rolling out new data tools, focus on the decisions they will support and not simply the mechanical workings of the tool.

Balance “good enough” versus “perfect” data. One challenge of data is that it can become a goal by itself—the perfected data set, the pretty chart, the purified chart. However, the world is messy, and it moves fast. Think of a cost-benefit analysis: What is the additional cost in time and resources, and what is the expected value? The goal of data is information that will inform actions.

In our roles as consumers of data, what is an informed skeptic? To use and modify a quote from Miles Kington, data may tell you that tomato is a fruit, judgment will tell you not to put it in a fruit salad. May the fruit of your (data) labors taste sweet!

Bryan Lapidus, FP&A, is a contributing consultant and author to the Association for Financial Professionals. Reach him at

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