Rethinking Metrics: Using Technology to Manage the Organization in the Information Age
I looked at my watch and said, “I know we only have another hour together, but let me give you a short answer of why I think this has happened, and maybe we can pick it up when we meet again. The ability to drive data-driven decisions has improved through the use of technology. Dashboards, scorecards, in-memory reporting, and visibility technologies make it easier to manage metrics within a company, but companies have to be clear on the metrics strategies. This is the challenge for every Joe like you.”
With that, Joe laughed. “So, I am not unique? Do other organizations have the same problem?”
I nodded, saying, “Many times companies will leap to improve metrics through technology without doing the hard work of figuring out which metrics matter and how to align the key performance indicators into a metrics strategy.
“There is also an issue of functional myopia. The views of operations and finance do not easily align. One of the problems is that financial metrics are backward-looking and transactional, while operational metrics are forward-looking based on flows. But the metrics you get from technology are based only on historical data. It's like trying to drive a car on a winding road at 60 miles an hour while looking in the rear-view mirror to steer. Closing this gap requires descriptive, predictive, and prescriptive analytics. While descriptive analytics enable reporting and data analysis, predictive and prescriptive analytics enable the management of operational flows. In contrast, predictive analytics enable operational alerting while prescriptive technologies recommend actions to take. Robust analytics are essential to ensure metrics alignment and are an important step in driving success on a metrics journey.”
“This has certainly been one of our issues,” said Joe. “We have a guy on the sales team who's really smart and can put together spreadsheets so we can analyze all kinds of things, but they're all about what's already happened, and the sales forecasts… Well, you know, they're really only good about three or four months out, and the sales team always inflates the numbers. It's an ongoing problem.”
“When embarking on a project to improve metrics, the average Joe, like you, will need to work with the information technology (IT) department to build measurement systems. This includes self-service reporting, dashboards and scorecards, and alerting systems. Analytics technologies are closely woven into a metrics project to make progress possible. It should be easy, but it is not. I would like to tell you more, but right now I really must be going. As much as I have enjoyed the discussion, I am late.” I then suggested that Joe read the article that I had just completed, “Managing Metrics in the Information Age.” We agreed to discuss it at our next meeting.
With that, we shook hands and I left my article on Joe's desk. Some excerpts are offered in the following feature.
Managing Metrics in the Information Age
Today, business leaders live in the Information Age. Technologies make new ideas possible. Data flows quicker and computational power enables quicker assessment of complex problems. Decisions can be more data-driven and real-time information enables new capabilities. More and more, metrics can be measured. Targets can be assessed more quickly. However, this only adds value if the technological advancements can be successfully aligned with business outcomes. This is the challenge.
Why is there a problem? Simply put, companies are new at it. We are only 40 years into the Information Age. The adoption of technology in the Information Age followed the Industrial Revolution. The Industrial Revolution was all about mechanization. There was a shift from making things by hand to the mechanization and adoption of manufacturing processes. The focus was on the management of physical assets. It was all about the control of financial assets and liabilities.1
The Information Age started in 1975 with the widespread adoption of computers. The practices and policies were a stark contrast to those of the Industrial Revolution that stretched from 1850 to 1975. It makes possible global connectivity and new forms of analytics to drive business insights. Today, most organizations are retiring leaders from the Industrial Age while trying to maximize the potential of the Information Age. This changing of the guard is not easy.
Impact of the Information Age on Metrics and Corporate Performance
This shift was a fundamental change at the core of the organization with intense repercussions:
What drives value. In the Information Age, companies are wired differently. Products and services are enhanced by data. OnStar differentiated General Motors while Pandora redefined the music experience. Around us today, digital data transmission improves the value of molecules and atoms. As a result, manufacturing is more information-intensive with less labor and capital dependency. Ironically, workers are more productive today, yet their wage rates are less. Market drivers are constantly changing. Metrics are more complex.
Redefinition of management principles. In the traditional management models of the Industrial Age, investments in people were the primary predictors of a new venture's future performance. This is not so today. It is now possible for a group of relatively inexperienced people – as demonstrated by Facebook, Microsoft, and Twitter – with limited capital, to succeed on a large scale. Metrics help to ignite groups of people to action.
Global workers. As connectivity has removed the friction from borders, workers now compete in a global economy. For example, in the United States, from January 1972 to August 2010, the number of people employed in manufacturing positions fell from 17,500,000 to 11,500,000 while manufacturing value rose 270 percent.2 In the next decade this pattern will continue. Metrics have different definitions in different countries. With language and cultural issues, it is critical that the metrics are simple and clearly communicated.
Redefinition of the office. More and more employees telecommute. The water cooler is now a virtual experience. In 2012, 2.6 percent of the U.S. employee workforce (3.3 million people, not including the self-employed or unpaid volunteers) considers their home as their primary place of work.3 As a result, it is more important for companies to have metric dashboards. Leadership teams have to focus more energy on the communication of goals and results. Organizational alignment is more difficult.
Management of the global company. Over the past decade companies have become global. However, companies define “global” differently. While each organization we study defines global governance models slightly differently, they will all agree that the management of a global organization is growing increasingly more difficult. The definitions of regional/global governance, and the evolution of KPIs for a global organization, are critical elements to manage a successful global organization.
Processes that have not caught up with the change in technology. Our technologies are digital. Our processes are not. While the cost of computing has moved from $222 to $.06 per million transistors and storage costs have moved from $569 to $.03 per gigabyte of storage, and Internet bandwidth has improved from $1,245 to $23 per megabits per second, the organization's operational processes remain relatively unchanged.4 Most companies have digital technologies with analog-based processes. We have not redefined metrics based on what is possible. New possibilities abound. The impact of less data latency and increasing capabilities of mobility and sensor data offer a wealth of opportunities.
Proliferation of data and a need for insights. We are living in the Information Age. Data abounds. Global connectivity transcends borders. Third-generation analytic systems improve workflows. Real-time data is now possible. Yet, as shown in Figure 1.7, companies struggle to use data. Employees cannot get the data that they need. It is their number-one business issue. As a result, metrics need to be defined very clearly while understanding the limitations in data availability.
