Big Data: The New Oil Fields

“Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals … to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”

- Clive Humby, British mathematician and data scientist

Since Clive Humby made his statement that the new most valuable commodity in the modern economy is data, and more specifically analytics resulting from that data, numerous high-profile individuals have gone on to echo his sentiments. Peter Sondergaard, SVP of Gartner, said that “[i]nformation is the oil of the 21st century, and analytics is the combustion engine.” Then, IBM CEO Virginia Rometty encouraged those at the Council of Foreign Relations to think about data as “… the next natural resource.” But is it?

To answer this question we first must examine, evaluate and define our comparison point: Oil. Oil’s value comes from its multitude of uses and our dependency on those uses. But when we take a step back and examine the more general values of oil, we discover that it is a fungible, tangible, finite, transportable and liquid (in more ways than one) commodity. Data is not fungible – you can’t replace your credit union’s data with another’s and get the same results. Data is not finite, and you would be hard-pressed to define data as something you could touch and feel. So, in the most literal, metaphorical ways – no, data is not the new oil. It is better.

Data is a renewable resource that is the byproduct of interacting with your member, doing business and existing as a financial institution. An army of people and machines is not required to hunt and collect data. In fact, most organizations are drowning in it. Data’s form is easy to alter and the original product is not consumed by its transformation. The steady decline in data storage costs means that millions of dollars’ worth of data can fit in your back pocket instead of an oil tanker of a data warehouse. While data alone will not power a machine, it has the potential to systematically optimize and improve every facet of business operation from strategy to execution to culture. The new oil is better than anything crude – it is the pure commodity whose use will decide the fate of financial institutions over the coming decades.

For a credit union, this always ties back to the member. At tech firms there is a mantra of “know your customer just short of creepy.” We see this during those moments when we swear our phones or home devices are listening to our conversations. No, it is far more likely that given the vast data at Google, Amazon or Apple, they can simply see that you are likely to be in the market for a product you happen to mention once. Our data reveals far more about our desires than we think it does. For an enterprising credit union, this means it is sitting on a vast oil field of data, but it needs to be distilled, purified and altered to offer the value it has hidden inside.

Data’s nature of being easy to alter means that it is easy to transform incorrectly. Data by nature is fragile, easy to corrupt and hard to make homogenous. In addition to the ability to manage the sheer amount of data, not all of which has the same value, it takes specialized skillsets to shape datasets into the right tool for the job. Data that can improve efficiency has a different focus than data aimed toward enhancing culture, the same way data that can predict losses is processed differently than data engineered to improve product adoption.

The key to transforming data into the correct shape highlights yet another difference between data and oil. To get value out of data, you must first start with a question and adapt the data to suit the needs of the problem being solved as opposed to trying to engineer solutions that will fit the data. Once an unknown is identified, the value of data can shine through its delivery of an unbiased answer. Data-driven decisions are the most potent ones, as they are the most repeatable, lead to the most consistent results and all stem from the statement, “the data suggests.”

Knowing which problem needs to be solved and working backwards means that institutions must first start with strategy. Displaying curiosity and a sense that there are hidden answers in your data comes from the talent of a credit union’s people. The more detailed the question being asked, the better the results. The refining process of changing data from 1s and 0s to actionable insight, after a specific question has been posed, is made up of six distinct steps:

1. Assessment: This is the who/what/when/where stage of data refining. This stage not only includes completing an inventory of the volume, details, accessibility, quality and integrity of the data, but expands to include identifying the talent and technology available or needed to execute the data refining process.

2. Preparation: Once the data is identified, the first hands-on step is preparing the data for analysis. This includes any cleaning, normalization, organization and movement of the data from its original location to a system or platform made for exploration and analysis.

3. Augmentation: Chances are low that the data needed to answer the original question will be contained in the initial data set. Augmenting, or enriching existing data with additional information, becomes important for filling in any missing data points or to give context to data and any trends.

4. Analysis: There are several types of analysis, ranging from descriptive to prescriptive, that can be completed once data is cleaned and organized. The type and method of analysis will depend on the type of question being asked and the quality of the data available.

5. Action: The final realization of all the work is in the action. The goal of analysis is to transform something digital into a map of tangible steps. Commitment to taking action as recommended by the analysis is crucial to success. It is important to note that often data analysis can challenge long-held beliefs and uncover non-intuitive answers. Having trust in the value of the data and quality of the analysis is vital.

6. Retrospective: Capturing data on data is as meta as it is important. Refining the process of cleaning, analyzing and acting on data will improve performance, enhance data culture and ultimately make a more positive impact on the return on the data assets. Asking questions like, “How well did the data inform our actions?” and “What data are we missing?” are important. A credit union will run through these steps time and time again as it drives a better member experience with data as a guide.

The world credit unions do business in is rich in data – data that can help institutions better understand their members, refine how their products and services are offered, positively impact key metrics and even improve culture. Is data the new oil? Will it drive the future of economic development? Will it empower the mobility, potential and technological capacity of the next several generations? Yes. Credit unions are sitting on top of the new oil fields of data and analytics – all that remains is to drill and prosper.

Ray K. Ragan, PMP is the co-founder of Clear Core, a data cleaning and transformation provider focusing on increasing the value and accessibility of data for financial institutions, in Tucson, Ariz.

Timothy “Buck” Strasser is the founder of Clear Core in Tucson, Ariz.