Standardization is the process of developing – and implementing – measures, metrics, (or, “standards”) used to specify essential characteristics of just about anything: rules, technologies, behaviors, measurements and countless other items. However, one of the most important benefits of standardization in business is that throughout history it has always provided a powerful and valuable competitive weapon.
Four categories of standards are helpful for understanding where the competitive advantages of standardization in business can be developed:
On one hand, standardized work can be defined as simply any work that meets an established standard, like those in the categories described above. However, within businesses that implement Lean Six Sigma standards, the terms, “standardized work”, or “standard work” take on a slightly different meaning. In these situations, the terms imply that the standards reflect the “one best way” to perform the work.
Standard work has been researched and measured to identify the operating details that deliver the optimal performance standard. These operating details are typically documented with standard work templates, tables and work charts that describe the following:
– Work sequence: the best process flow for performing work activities
– Takt time: the production rate required to meet demand and avoid backlog and overage
– Standard work-in-process inventory: the ideal level needed to maintain a smooth, Takt time flow
The typical history of standards begins with ancient weights and measures. Then it jumps thousands of years to relatively modern times – the industrial revolutions that began in the late 1700s. Finally it ends with a dry discussion of the regulatory bodies that began to spring up in the early 1900s to set standards for power, communications and a host of then-emerging technologies. That’s a simple, straight line narrative of the conventional history of standards. Unfortunately, it overlooks essential facts related to the competitive importance of standardization:
Four sources, each with its own standardization process, have delivered business standards over the long course of history. However, these same four standardization processes are as active and relevant today as ever.
And businesses that understand the critical importance of standardization to emerging technologies such as robotics process automation (RPA), Business Intelligence (BI), and customer journey mapping can benefit immediately.
At first, the modern business advantages of standardization can be difficult to detect. Distinguishing between four different sources of opportunity to focus a standardization process effort can be even harder.
The lines that divide them are not rigorously defined. Standards can evolve and migrate across different sources. The following life-cycle standardization example describes how standards evolve from successful innovations.
In business, data standardization is often a secondary priority during core IT system implementation. This results in large and increasing amounts of business operations data that, when left un-cleaned or un-wrangled, is not usable for historical trend analysis, management reporting, business intelligence, machine learning, or predictive analytics implementation.
A simple definition of data standardization
Data standardization is defined as the process of establishing and implementing a common framework, hierarchy, organization or classification method for cataloging and storing business data on an ongoing basis.
Two decades of core IT system implementations with little focus or investment in operational data structure and management reporting have resulted in businesses with massive amounts of operations data at their fingertips, but can’t do anything with it.
Historically, primary goals of workflow automation and core IT system implementations have been process focused, while operational management reporting elements were always viewed as secondary “nice-to-haves” by large technology integration and software firms.
New technologies and services firms have emerged that solely focus on solving these problems by bridging data gaps between systems and standardization of “big data” at scale.
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