A “data-driven” operation is defined as one that harnesses its operational data to augment the experience and judgement of operatives, managers, analysts and engineers as they plan, organize, conduct and control their processes. It is noteworthy that data-drivenness is not high-tech or new-tech as much as it is modern-day knowledge, skills and software.
Almost universally, every operation’s current-day challenge is to find the actionable training to modernize the role holders in their operational processes. Maybe the greatest challenge is to find training without conflict of interest; meaning that the content is not self-serving to the its source. Instead, with the content, the operation can proceed, self-directed, without the offered services or products of the presenting instructor or enterprise.
The training sessions of this page meet that standard. This can be confirmed by inspecting the description and slides to each session. Furthermore, an organization can confirm in the slides that the sessions are new knowledge to them.
The training sessions mandatory to starting out and reaching data-drivenness are as follows:
The sessions have a logical order. First, it is obligatory to set the horizon upon the components of what is possible, necessary and why. Second, because absolutely all components of data-drivenness rest on the ability to get at our data, it is mandatory to gain the skills to work with the data in operating systems; such as a CMMS. Finally, the operation role holders must know how to rethink their processes through the lens of these component new knowledges and skills
First Step To Becoming a Data-Driven Operation: The first step to becoming a fully data-driven operation is that process role holders must reach a clear, implementable understanding of data-drivenness. The purpose of the training session is to be the first step.
To make what is possible readily doable by the attendees, the session presents the principles and practices of data-drivenness in context of “critical-mass.” Critical mass is defined as the threshold set of knowledge, skills and software that must be in place to be fully, effectively and efficiently data-driven. Accordingly, critical mass and has the following characteristics:
Build Super Tables from Operational Data: From normal functioning, massive troves of data are captured in thousands of tables in the background of a firm’s operating systems such as a CMMS. To be capable of becoming data-driven; role holders across an operation must know how to extract tables from their systems and join and mold them into single “super” tables. The purpose of the session is to teach its attendees how to build super tables.
Four characteristics make the session maximally relevant and consequential to its attendees and their firms:
Data-Driven Maintenance Operations: The purpose of the capstone training session is to act upon the implementable understandings and skills learned from the above two training sessions.
The training session, as a case, works top-down through the steps to rethink the management processes of a maintenance operation through the lens of what data and analytics make possible. At the top is to establish how the firm, through the plant, competes and wins in its industry and how the competition is scored financially. Next, is to establish a proxy to the top measure of competitiveness by which the maintenance operation would be measured in the same financial terms as the grand score. In turn, is to establish the set of operational scenarios that would constitute maintenance performance at the pinnacle of the proxy. These constitute the North Star to the maintenance operation. In turn, is to establish and chart the structure of maintenance management processes that are required for the plant to be able to operate at the pinnacle.
As presented, the session will be interactive. Attendees will find themselves offering ideas to the case flowcharts as their perspectives of data-drivenness mixes with their personal experiences and the ongoing challenges of their roles.