In my last post, I explained why organizations need to consider data as an asset rather than a cost center. When we deem something to be valuable, we then need to determine how and when we’ll use it as well as secure it. We do this by establishing standards, policies and processes to define how this asset will be utilized and protected.
Let’s look at the example of an office building. Furniture and equipment are inventoried and tracked. Employees are trained on safety and security, with some developing expertise in the use of specialized equipment. Office managers know which conference rooms and desks are available for use and their locations.
Keeping this office building clean, secure, comfortable and well organized adds to the productivity of its occupants.
Without such office governance, this office building could become unsafe, unsecure, unproductive and underutilized. Do you see the parallel between this office asset example and your data? Transforming data into an asset also relies on effective data governance.
Successful data-driven companies embrace and implement continuous improvement activities to enhance results, providing a structured approach for business improvement projects. Steps include problem identification, data collection, root-cause analysis, planning process changes, implementing the changes and monitoring the results. This cycle is known as the Plan-Do-Check-Act cycle of continuous improvement, or PDCA.
Organizations committed to a continuous improvement culture, based on the PDCA cycle, depend heavily on data at every step. Business problems can be defined in terms of waste, delays and re-work. These problems need to be quantified with actual measurements to help analysis teams detect and prioritize the next set of improvement activities.
After improvement activities have been completed, it’s important to monitor the results through feedback. It provides evidence of success, and it also helps improvement teams learn about the processes on which to focus.
Data collected about improvement processes will show symptoms of inefficiencies and waste. The analysis team then carries out root-cause analysis to determine the “levers” that can be adjusted to reduce them. Assumptions and hypotheses will be tested and validated to find the real forces at play so the appropriate management and operational levers can be adjusted accordingly.
Companies that implement a PDCA cycle of continuous improvement realize there will be challenges in scaling and sustaining the program across multiple business areas over time.
Data collection can be tedious, especially if the associated data management activities require significant manual activity. It is common that the data available from operating databases has many problems related to quality, security, confidence, accessibility and overall understanding. These are all roadblocks the will delay the improvement activities.
If data isn’t readily available, accessible, trusted or understandable, the analysis and improvement teams can’t do their jobs effectively. This will lead to a slowdown in momentum or cause companies to abandon the improvement approach altogether. The necessary data to drive the improvement cycle must be in an “asset class” form to sustain the improvement cycle.
Scaling the PDCA cycle involves multiple teams working in different business areas to broaden the reach of the improvement activities. Processes for finance, human resources, operations, sales, supply chain, customer service and IT may all be under analysis and evaluation.
The path to operational excellence is based on the ability to scale and sustain continuous improvement.
Consider a utility company that operates a physical network delivering energy to customers. The executive team wants to reduce the time it takes for newly constructed assets to go online and reap the financial benefits of commissioning them for service more quickly.
The business improvement team starts gathering performance data from previous construction projects to determine potential areas of improvement.
They soon realize a new work management system was implemented, and the conversion of historical construction data was deemed as “non-critical” to keep the project on schedule and in budget.
The implementation team didn’t view the historical construction data as valuable from an operational perspective, so they archived it rather than covert it to the new system. This decision was made within the context of a “local” project without considering the larger analytics needs of the company.
Unfortunately, data governance was not understood or in place at this utility. If it were, the historical construction data would have been cleansed and converted as part of the new work management system’s deployment. This company failed to recognize this data as an asset with downstream analytics applications.
In this example, the decision not to convert historical data was based on managing cost at the project level. A data investment was not considered. But well-governed data is a true asset. Quality, accessibility, timeliness and understandability are fundamental to the productivity and sustainability of continuous improvement processes.
If your company is implementing any form of program to improve results, such as specialized management systems, balanced scorecards, lean management concepts, Six-Sigma or total quality management, data governance sits is at the core of long-term, sustainable success.
Improvement programs require motivation, energy and commitment at all levels of the organization. To maintain momentum, governed data assets are the key enabler, making it easier and faster to detect and diagnose problems, improve processes and validate results. There’s a direct link between the quality of improvement programs and the data assets that power them.