All of us are producers and consumers of data. We ingest data, analyze and interpret it, apply our business savvy to make it more useful, and then deliver it to the next person.
But are we doing this well? What can we do to be better consumers of data and develop more informed skeptics on our finance team? Here are a few ideas:
1. 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, according to each role:
- An administrator prepares and makes data accessible to others. Data security requires that you protect sensitive information, sharing what is necessary without exposing too much. Protecting source data, preventing version errors, and maintaining integrity also 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. 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 scrutinizes the information and tries to poke holes in the data or draw inferences to test its veracity. This is a key data validation step, and you may want to have a peer review your draft before taking it to the editor.
- A viewer, often a senior person, is on the far end of the data (or related information) and is going to take action based on it.
2. Allow 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 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?
3. 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 of 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.
4. 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 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 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.
5. 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 paraphrase journalist Miles Kington, data tells 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!
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