Data Science and Effort Modeling: Charting a New Path Forward
2 min read
(This is part two of our effort-model discussion. Please see part one here.)
Effort is the amount of time a worker actively applies to their set of tasks, measured in hours or oftentimes FTEs (full-time equivalents). It is neither simply their available hours, nor is it their total throughput in unit volume. While both of these concepts are essential to calculating productivity metrics, effort is its own component that can vary independently of someone’s capacity and throughput.
The core idea of effort is worker engagement – the time spent actually doing the work correctly. In order to make impactful recommendations, we also want to measure the required effort that was not undertaken and misspent effort, i.e. effort exerted on non-productive activities. These are not personal judgments, but rather transparent metrics that can help clarify a company’s overall time-management dilemma.
CKM’s effort model adds value by combining our extensive experience in helping clients with the rigorous application of statistical and machine-learning techniques. It handles anomalies, outliers, and missing values, clearing away noise and laying out millions of segments of effort properly so they are ready for analysis. It is akin to laying out the ingredients before baking a cake. With each data ingredient laid bare before you, you can be sure that your measurements are correct and predict whether or not the cake will rise and whether or not the outcome will be a success or failure.
Effort Meets Analysis: Eliminating Human Bias to Foster Favorable Outcomes
The effort model enriches low-context data so that context can be added by T-K’s modules: not just the breakdowns and charts but the order, concurrency, and flow of activity. The goal is not rigid procedures and complete control, but rather to reveal meaning and understanding of the group dynamic, the behaviors that generate successes and failures.
In concrete terms, the effort model enables a manager to prioritize actions. Volumes alone cannot answer any of the following questions, but an understanding of effort provides the basis for answering all of them:
- For what types of work will automation truly pay off?
- Which part of my process is wasting the most time and therefore should be retooled?
- Where will worker training most improve outcomes?
- What is the optimal staffing level and schedule to satisfy the demand for workers’ effort?
Normally this information would arrive rather imprecisely through subjective judgments and “rules of thumb” understood by managers – or through the faux precision of BI tools that present information but can’t directly answer the questions above. At CKM, we prefer to bracket expectations, cut out the noise and let the data speak to the truth of these questions (with help from T-K). This way our clients’ organizations can evolve to achieve their potential through collective learning and continuous innovation, unburdened by human bias and false expectations. Our design does not end at analysis, but rather at a catalytic starting point to reorganize the effort within a business in order to achieve the desired outcome of enhanced and invigorated production.