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    Understanding Infonomics — and What it Means to Your Organization

    One of the big buzzwords in the data field today is infonomics, along with such similar terms as the economics of data and data monetization. The essential idea behind all of these terms is that organizations can change how they manage and use data to optimize the impact on their Profit & Loss position and on operational efficiency.

    At its core, infonomics is all about finding a better way to process and extract value from the data that one creates. Many organizations have robust processes for generating and gathering data … as well as data scientists, business analysts, and others who are eager to consume the data. The problem is often that they lack a critical step in between.

    Digging for gold

    To illustrate the important role that process can play, consider the popular Discovery channel reality TV show, “Gold Rush,” in which different teams of miners compete to see who can find the most gold. Watching it over the last few years, I’ve been interested in the phenomenal success of one contestant in particular — I think of him as “The Kid.” The chief reason for his success is that he stepped back and took a fresh look at the entire process of gold mining. He then reengineered the process to create an entirely new approach, and has since produced consistently better results than the seasoned mining veterans he’s competing with.

    Basically The Kid starts the same way everyone does, by locating an area (or a “cut”) that has a high probability of containing gold, based on drilling samples. But then, instead of going straight for the gold, he takes the time to excavate down to the bedrock around the cut, removing enough dirt and rock so that he can position his processing equipment and dump trucks adjacent to the cut. This allows him to perform more mining, processing, and hauling cycles per hour. By processing material more efficiently, he’s finding far more gold. It seems so simple, why has no one else thought of it?

    Three steps in value creation

    In the same way, organizations need to take a new look at how they gather, process, and share data to eliminate waste and maximize efficiency. There are three major steps of the process.

    The first step involves how one creates, touches, and changes the actual data. This function is generally the responsibility of the CIO, CTO or CEO, with influence from the CDO and other executives. Most organizations collect data through a variety of processes, including interfaces with web apps, data collecting machines, telematic devices, and so on. By and large, organizations have this step pretty well figured out. The problem is that while they’re piling up massive amounts of raw data, they often lack the processes, technology, and culture to convert it into actual value. In a gold mining operation, this would be like excavating large amounts of raw material, but then leaving it untouched — no more than piles of dirt and rocks.

    That takes us to the second step of the data supply chain: the processes for managing, curating, and enriching the data. In many organizations, this step is either ineffective or missing altogether. As a result, even if the organization collects data in great quantities, it may still fail to adequately collect associated metadata. At the same time, it may lack effective processes for improving or enriching its data quality. A bigger problem is when data goes directly from being collected to being used. This can create an enormous amount of waste (that is, data which is essentially useless to the organization because it is inaccurate, incomplete, or discrepant) that becomes “baked into the system.” In fact, evidence suggests that a typical medium-sized organization can have as much as $20 million worth of basically useless data sitting inside its operational model.

    The third and final step is the consumption of data. As an organization gets better at optimizing its consumption of data, it can also become more effective in deriving insights and making decisions based on that data. These benefits can feed right back into its operational model, and give it a higher probability of generating new revenue.

    These benefits can take several forms. One could be finding new ways to monetize your data. For example, we’re working currently with a client that has created a reliable predictor of certain economic metrics based on data the company already collects, and it’s selling this indicator (“sanitized” of any customer-specific information, or course) to other organizations. Essentially, the organization has created a brand-new line of revenue, based on a new use of existing data. In other organizations, revenue enhancement can come from the ability to make better marketing and product development investments, improve financial decisions, do a better job of managing and preventing risk.

    Before you can start reaping the benefits of your data, however, it’s critical to make sure you’re consuming the right data. You may have heard some of the data problems that have been plaguing Target, including misuse of customer data in marketing, and in major data breaches. But a more recent controversy involves how the retailer used the wrong data in its ambitious attempt to enter the Canadian market. In essence, they aggressively set up a sizable chain of stores, and then watched as sales stumbled.

    The problem? Although they’d used all the most recent sales data for stocking their Canadian stores, incredibly, the data they used was all from Target’s U.S. operations. That meant that some shelves were almost always empty, because their products happened to appeal to the Canadian consumer, while products on other shelves hardly moved, no matter how they were priced or merchandized. The key lesson is that it’s not just the data quality that’s important, it’s also using the right data.

    Focusing on the fundamentals

    Clearly, infonomics can work — and there are many examples of companies that use data thoughtfully to generate new sources of revenue and improve business decisions.

    But you also need to make sure you’ve taken care of the fundamentals — having a solid solution for processing the data to ensure its quality, and at the same time providing the right data for your decision-makers to use.

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