In his book, The Knowledge Work Factory, Fortune 500 consultant and thought leader William Heitman helps you recognize opportunities for knowledge work standardization: the same keys to success that have unlocked breakthroughs from the Model T through Amazon. William is a managing director of The Lab Consulting, a company that has been standardizing business processes for Fortune 500 businesses for 25 years.
“Boards and bots” refer to two hot topics in knowledge work today: dashboards, also known as business intelligence (BI) or advanced analytics; and robots, or robotic process automation (RPA). While both of these disciplines hold great promise and opportunity, they are continually hobbled by a lack of standardization that limits their implementation potential and related benefits. In fact, 90 percent of today’s knowledge-work activities are based on “standards” that are actually rules of thumb, workarounds, and tribal knowledge.
In this article, I’ll provide you with some real-life examples of how standardization enables these new technologies. But first, I’ll set the stage for you with the most low-tech examples imaginable: a grocery store and the ocean floor.
Unlock standardization by chasing down the long tail
What is standardization? One of its ironies is that there is no standard way to achieve it! (You can read more about that paradox, types of standards, and why standardization is important in this article here.) Yet you can find simple examples in everyday life that can guide you along your journey, and make it far less daunting, too.
Let’s start with a standardization example as common as the general ledger in business accounting. The ones that my company is typically tasked with standardizing are, well, a mess. That’s because there’s no disciplined process for defining, creating, and maintaining ledger line items. To put it bluntly, there’s no standardization of procedures: Far too many people in a given organization have the ability to create line items however they like, park them wherever they see fit, and name them using whatever personal coding format they like. It can often reach 50,000 line items—85 percent of which are virtually empty. This makes the summary version of the ledger bloated, un-navigable, and incomprehensible. There is no technology on earth designed to unsnarl this mess and make it usable for something as powerful and valuable as advanced analytics.
Contrast this scenario to something as simple as your neighborhood supermarket. A store like this will stock about 50,000 items. Yet when you shop there, you’re not confused, lost, or overwhelmed. That’s because the shelving process is highly standardized. You can easily see the dozen or so overhead signs that logically tell you how the aisles are organized; within those aisles, products are clearly clustered by application, and easy to recognize by shelf tags. All the shelves are generally full. And no technology is required for human navigation!
You can apply the supermarket analogy to a general ledger. So, even if you wanted 50,000 ledger line items, you could standardize them into intuitively arranged groups, like grocery aisles. And you can do the same for any part of the operation: business processes, activities, work products, performance measures… anything that needs to be standardized to take advantage of “boards and bots.”
And one more thing. Never assume that just because things appear “average” at the summary level, they’re already nearly standardized, i.e., not worth the investigative effort. Once you dive down, from the top-level organization structure to the “ocean floor” of individual knowledge-worker activity, you may be shocked to find variances—for the exact same activity or knowledge-work product—of up to sevenfold. Put it this way: You can’t standardize this variance until you dive deep and identify it.
To find the organization’s true level of performance variance, go all the way down to individual-employee data, i.e., the “ocean floor” of operations. That’s where the valuable “long tails” of wide variance hide.
A real life data standardization story
My consultancy recently worked with a company that used a home-grown tracker for work activities—in the form of a 1,000-row Excel spreadsheet created by a single knowledge worker to monitor supposedly “unique” activities.
This company wanted to take advantage of the power of advanced analytics so they could get real-time, visual dashboards and use them to make speedier, better-informed decisions. But how do you visualize those thousand “unique” rows on the Excel sheet?
This was first a definitional standardization exercise. Reducing the false precision that generated the 1,000 rows of “unique” activities collapsed the list of definitions to about 50 “standards.” From that point on, it was a simple meta-tagging exercise for us. We took each type of activity being tracked, tagged it, and tied it to a new, simple, standardized master data table. From there, it was pretty straightforward for us to park a business intelligence (BI) tool atop the newly standardized data, from which it now cranks out visually stunning, executive dashboards in real time.
Standardization and the robotic challenge
Robotic process automation or RPA is the latest rage in knowledge-work operations. Think of it as a super-powerful Excel spreadsheet macro—one that can span multiple applications and systems, reading, copying, and pasting information that’s tedious yet essential.
Companies often turn to my consultancy to find use cases (or, opportunities) for RPA. Just as often, we find that fully 40 percent of these opportunities require no additional standardization; the keystrokes simply need to be revealed, documented, and configured.
That may sound encouraging, but the flip side is a downer. If 40 percent of use cases don’t require standardization, that means—you guessed it—that the other 60 percent do. Without standardization, those are lost RPA opportunities that never get recognized. (And neither does the simple, non-technology savings from the standardization—a double loss.)
Whether you’re looking to implement “boards” (advanced analytics) or “bots” (RPA), standardization is your essential first step. It can appear daunting, but if you think of simple examples like the supermarket and the “ocean floor,” you’ll be better equipped for the challenge. William Heitman is the founder and managing director of The Lab Consulting, which has been implementing knowledge-work standardization for Fortune 500 clients since 1993. His book The Knowledge Work Factory provides a practical approach for “industrializing” the knowledge workforce to achieve gains in efficiency, productivity, and effectiveness.