Fixing the 80/20 Rule for Data Analysts
You’ve probably heard of the 80/20 rule used in many ways, but today I’m most interested in its business efficiency application.
Also known as the Pareto Principle, the 80/20 rule states that 80% of outcomes (or outputs) result from 20% of all causes (or inputs) for any given event. When applied to business efficiency, this means that an organization needs to identify its critical success factors and then put its energy behind driving those forward.
Simply put, the 80/20 rule drives home the importance of knowing your business and focusing efforts on activities that drive impact rather than on “busy” tasks.
Here it is, summed up in 4 steps:
- Identify your organization’s key drivers
- Build actions and initiatives around them
- Work efficiently and with resolve on said initiatives
- Push & pull the levers that move the needle for your business
Seems easy enough, right? Well, not always, especially if you’re a data analyst.
The 80/20 Rule for Data Analysts
A Data Science Survey conducted by CrowdFlower, showed that most data scientists spend much of their day cleaning data, organizing data, and collecting data sets and only minimal time mining the data:
- 60% spent cleaning and organizing data
- 19% spent collecting data sets
- 9% spent mining data for patterns
- 7% spent building training sets and refining algorithms
- 5% spent on other activities
The other key finding, and a jarring one at that, is that 76% of data scientists also said that data preparation is the least enjoyable part of their work. Business Intelligence Analysts didn’t go into their field with the goal of collecting, collating, and scrubbing data, they pursued their career because they have a passion for ad-hoc analysis, custom report and dashboard creation, and enhancing business processes and decision-making via the data.
Here are 3 key steps an organization can take to help their data analyst teams get back to what they were hired to do – mine, analyze, refine, and build.
- Centralize its data: integrating the plethora of data sources into one single source of truth cuts down the time it takes to access the data.
- Build a data dictionary: establishing consistent data definitions across an organization ensures that everyone is speaking about a metric in the same way.
- Democratize the data: making data accessible and nimble allows super users (BI Analysts) to support the wider community (FP&A, Operations, Marketing, Portfolio Managers, etc.) with individual reporting needs.
Let’s give data analysts their time back!
Featured image by Austin Distel on Unsplash.