There’s a lot of talk about infonomics, and how companies could (or should) figure out ways to leverage their data to achieve some type of financial or strategic gain.
To me, the real power of infonomics is being able to turn all your data assets — text, spreadsheets, legacy, new — into an aggregated set of analytics that gives you insights into the value, health, and understanding of those assets. Let’s consider three interesting areas of insight that can be especially powerful at a macro level:
1. Determine the health and well being of your data. Knowing whether or not your data is trustworthy is an enormous benefit to your organization. If you can demonstrate that your data can be trusted, you can realize several benefits, including:
your CEO and management team can be more confident in their decision making
your marketing team can make longer range and more accurate forecasts
your IT department (or better yet, your Office of Information) can know better where and how to allocate resources
I’ve described in a previous post a comprehensive approach to conducting data quality assessment, but it boils down to few essential steps. First you need to choose a data profiling tool, then establish a standard analytical approach. Next you need to create a findings repository, and set up a centralized place for feedback from your data community. The final step is to create a common measurement system with which you can record all the rules, logic, and algorithms in a normalized analytical module. Being able to quantify all your data quality and metadata boils down to aggregating your data quality findings to a single, statistically-valid and empirical score that represents each data asset’s well being and health.
2. Gauge the value of the data to the organization. One can look at the value of data from two lenses. One looks back at what the data originally cost you to produce or acquire, and the other looks forward to gauge how much financial impact the data will have in the future. The former is the more difficult of the two approaches, since data values are constantly being changed. But determining what data will allow you to earn in the future is definitely an equation that can be solved, even though there’s a certain element of guesswork involved.
Your goal should be to create a mechanism that every day, minute, or even every second quantifies the demand being placed on individual data assets inside the company. This will give you the ability to correlate the demand that is most meaningful to your business model or key financial and operational metrics. For example, you could correlate the demand on a particular data asset to net income, EBITDA (earnings before interest, taxes, depreciation, and amortization), or even monthly cash flow. The point is the demand indicator will have correlations to the outputs of the business which you can visualize via analytics.
3. Increase your understanding of the data. We’re experiencing a time when Baby Boomers are retiring at a rapid pace (roughly 10,000 per day) — an unprecedented transformation that would be hard enough to weather in any era. But we work in the Information Age, so the exodus of this unique generation will take with it a vast amount of “tribal knowledge” regarding the data that companies rely on for day-to-day operations.
For many companies, this means that a huge portion of their workers will be out the door in just a few years, and with them goes all the knowledge they have about the company’s data as well. Not only is the size of this cohort of retirees large, but often they’re the ones who have been in your workforce the longest — and who had a ringside seat as your organization was accumulating, improving and using its data.
In the average company today, it’s very common for an individual’s work product to completely infused with data. This is true no matter their role, or whether their function is to create, use, or change data. So my thinking is, while you still have a little more time (although the clock is starting to run out even as I write this) why not treat the data these individuals work with — and the tacit knowledge they carry — as artifacts to be conserved? That data is at the center of the universe for your business, and the power of infonomics is in being able to quantify this understanding.
When you put all three areas of insight together — the health of your data, its value to your organization, and the understanding and tribal knowledge around it — you have what you need to make operational forecasts. You can also calculate how changing the dimensions of any one of the three areas will affect your forecast. That gives your leaders the insight they need to make truly smart decisions about not only your data, but also the very future of your organization and its people.