faster than the speed at which complex enterprise systems can adapt. When systems have data in incompatible formats, or when that data is of poor quality due to manual processes and conversions, making the data compatible adds another layer of friction. Friction comes from many sources, including out-of-date information, lost content, incorrect or poor quality data, disconnected processes, incorrect measures, inconsistent communications, excessive meetings, overcommunication, and undercommunication. These sources of friction become impediments that inexorably slow information flows.
The challenge is that this happens in myriad incremental ways that are difficult to pinpoint, hard to quantify, and challenging to remediate. In most cases, this friction is accepted as business as usual or as the nature of the beast—“that’s just the way things work.” Or leaders decide that it is too expensive to fix and that adding some people to deal with the issue is the most economical (short-term) solution. The problem is that these incremental friction points, which individually do not make sense to address, are never considered as a whole—or if they are, they become too difficult and disruptive (not to mention costly) to tackle.
AI projects can provide incremental value by addressing these friction points. As Tom Davenport points out in his book The AI Advantage, AI and cognitive technologies augment human work and provide incremental value in many areas that accumulate to transform the enterprise.
Since organizations operate on information flows, the leadership of the enterprise needs to invest in the things that smooth, speed, and improve the efficiency of those flows. The problem is knowing what to invest in and how to harvest the benefits of investments. When executed correctly and based on a solid ontology, investments in data and configuration of the infrastructure through which data flows can lead to enormous value. One implementation that I will describe led to a $50 million annual savings from a $1 million investment. Another digital transformation, the core of which was built on investments in data, data architecture, and technology infrastructure, led to an $8 billion increase in market value from a $25 million investment in the foundational principles that I will discuss throughout this book.
Things are accelerating as new entrants arrive that are “born digital”—like Airbnb in the lodging business, Tesla in the automotive business, Google in media, or Uber in transportation. Such businesses have the luxury of reinventing the end-to-end value chain and building integrated processes that speed the flow of information and data using a green field/ clean sheet approach. It’s a huge competitive advantage—like the speed that warm-blooded animals tapped during the evolution process to allow them to compete effectively with sluggish reptiles.
THE PROBLEMS THAT KEEP BUSINESSES FROM MOVING FASTER
Businesses know all of this. So why can’t they get out of their own way? Let’s review the three most common problems that stop corporate organisms from competing effectively in the economic ecosystem.
Problem One: Friction and Siloed Communication
Companies, especially those born in the pre-digital age, are created not holistically but in a piecemeal fashion. Departments and functions operate independently. When a signal comes in, it doesn’t instantly go everywhere it needs to go; it must be routed from one place to the next. Internal communications, databases, and other systems act as messengers to relay a signal throughout the organization. Imagine if the brain told the legs to run, then the legs had to go and ask the adrenals for some energy to execute the movement, and the adrenals had to then go and verify that the decision was authorized by the brain. Before you could move, you’d be run over.
In most companies, there is a lot of friction along the way. Challenges like differing or competing priorities, incomplete understanding of the big picture, dropping the ball, and double-checking all create drag as information makes its way through the organization. That’s not fatal when the information being handled is human brain–sized, which is why manual systems and processes have worked for companies so far. But it collapses under the weight of the enormous data sets required for AI and high-tech interventions and for the rapid response required in a digital economy. The key to making AI work is reducing that friction and speeding the flow of information.
Problem Two: Incompatible Data and Language
From time to time, every organization needs to do housekeeping on its digital working files, archived data, and other forms of information. But how do you do that consistently and cleanly when every process or function uses different terminology or a different way of organizing things? Too often, the various groups within an organization are speaking their own languages—or at least their own dialects. Information is stored differently, and teams use different terminology that speaks to their needs and processes but that doesn’t consider the broader organization. This leads to manual workarounds and to information getting lost in translation. As I’ll show in more detail in chapter 8, technology companies designed collaboration tools to make it easy for people to create and use data, not to make the data effective in the context of a corporation. The result is an information environment full of poorly designed, fragmented, and disconnected systems. It’s not a surprise that many of these systems don’t share a consistent set of data language.
Problem Three: Junk Data
Entropy is another fact of the physical world mirrored by the digital one. Every system tends from order to disorder, and reestablishing order requires energy. We all experience this in our day-to-day lives: our desk gets messy and we need to put energy into organizing it. The house gets dirty and requires energy to clean. Information gets messy, too, and organizing it also takes energy. For example, we have to delete or label our email messages, put files into folders, or cleanse a document repository of any out-of-date material. Productive activity seeks to reduce the amount of disorder and therefore reverse the entropy of a local system through the application of energy.
Most organizations today are drowning in junk data as the incredible volume of digital information produced massively outstrips the energy available to manually practice good data hygiene habits. This book will show you how to get your digital house in order so the robots can keep it clean for you.
TAGGING UNCLOGS THE FLOW OF INFORMATION
Tagging is a central part of what makes ontologies able to speed the performance of enterprises.
A manager’s objective is to give employees direction; provide resources and the information necessary to solve problems; and allow creativity, hard work, and expertise to generate solutions that have value to customers and the marketplace. All of this activity is fueled by knowledge, and the way that knowledge flows through the organization’s networks is key to its efficiency and effectiveness. Important information needs to be flagged, tagged, and held up as meaningful. This information includes, for example, the needs of customer segments based on market research, solutions to engineering problems, the current quarter’s strategic objectives, and the features of a new product and how it is different from the competition. These are all signals that need to be separated from the noise of day-to-day communications.
The separation comes from tagging that identifies the information as important. Then someone can take that important piece of data and use it to solve their problem. (As I will describe later, that tagging, or separation of signal from noise, can happen at multiple levels—from manual information and data curation performed by humans through AI and machine learning approaches.)
Not having the right tags causes meaning to be lost—the noise drowns out the signal. Inefficiencies in information flows, lack of consistent terminology, and systems that don’t talk to one another bog down operations and create waste. We’ve all seen what this looks like and felt the pain: for example, folder structures on shared drives
