Data drives the modern world. Governments, schools, businesses, and communications worldwide require data to innovate, operate, and continuously improve. Data can be more valuable than money and with the advent of cryptocurrencies, data is money. But all data are not the same. Some data are captured through human observation, some through surveys, and even more through computer programs and other mechanical means. Data can be nominal, ordinal, ratio, interval, discrete, continuous, qualitative, quantitative, etc. Data has varying degrees of validity, bias, integrity, and reliability. Too little data can be a problem, but in the modern world, there's no such thing as too much data.
Good business decisions require valid, unbiased, reliable, high integrity performance data and there are many negative consequences for making decisions without it. Those decisions include how many workers to hire, who to hire, how much to pay workers, what and how many tasks to assign, who to train, how to continuously improve performance, and more. Valid, unbiased, reliable, high integrity performance data is not just valuable for worker-related decisions but also non-worker ones including process design and continuous process improvement, facility acquisition and expansion, and equipment repair and procurement.
In the past, it was difficult or impossible to capture valid, unbiased, reliable, high integrity performance data because it required people to assess performance. This is particularly true with performance assessment of people who work with their hands as the number of observations required to produce such data would be financially prohibitive. Furthermore, it is extremely difficult or impossible to remove bias from human assessments. Bias that enters human assessments is not just related to race, religion, age, gender, and disability, it also includes recency, halo, horns, central tendency, leniency, comparison, and false attribution. The effect of decisions made using poor performance data include high recruiting, onboarding, and training costs caused by increased employee turnover, unfair compensation for the best workers, greater amounts of waste, higher absenteeism, and poor service or product quality.
With modern technologies, systems like Pythia are able to capture the time workers spend on each process and process step along with their continuous improvement suggestions. By taking humans out of the assessment process, all bias is removed, data are always valid, high integrity is assured, and with a significant amount of usage, the data are reliable. These data can be used to reveal continuous improvement opportunities across all performance factors including worker and non-worker, less effective or efficient assessment measures can be reduced or replaced to save the organization money while accelerating continuous improvement. Data showing performance differences across multiple workers performing the same process steps can reveal specific training or coaching needs of individual workers at process step detail, which can greatly reduce training costs while incrementally improving performance. Comparing performance interval differences across multiple locations can reveal best practices, equipment needs, management effectiveness, and other non-worker performance improvement opportunities.
Once captured, valid, unbiased, high integrity, reliable performance data can provide the information needed to calculate the return on investment for any management initiative designed to improve performance. For example, the performance intervals captured before and after a performance improvement initiative can be converted to currency and compared to the initiative's cost. To be clear, training and learning data include numbers of people trained and test scores that are usually administered during or immediately after training, which is well before the "Forgetting Curve" begins taking effect. Training and learning data are largely nominal and ordinal and cannot be converted to time or financial savings.
A final note about automatically capturing and reporting performance interval data. Once captured, these data can be shared with external consultants such as training vendors, industrial engineering firms, and management consultants to enable higher quality services, faster, and at lower cost. For example, when providing your valid, unbiased, high integrity, reliable performance interval data to a performance improvement vendor, that vendor can deliver a more accurate needs analysis with little to no observation time, proactively propose performance improvement solutions as they're revealed, deliver accepted interventions, then report the return on your investment in financial terms.
Moving forward always begins with the first step. In this case, the first step is to get a free Pythia account and see how it works. If all goes well, you'll see how easy it is for workers and organizations to continuously improve performance by verbally delivering work instructions, then capturing and reporting the performance interval for each step. From there, contact me at Adyton or one of its distributors to arrange a pilot program. A pilot program will enable you to experience the entire Pythia system including entering your processes into the system, running processes in real time, and running reports to reveal continuous improvement opportunities across your organization.
Bill Crose is the founder and CEO of Adyton and inventor of the PythiaTM verbal workflow management system. He has 30 years of experience in the performance improvement and learning technology disciplines. Bill has an MS in Instructional Design from Western Michigan University, holds 2 patents, and 1 patent pending for performance improvement technologies.