Credit union interest in Big Data is at an all-time high. The promise of predictive analytics and other Big Data opportunities will be a key part of helping the industry compete more effectively with traditional banks and fintech upstarts.
In two previous OnApproach blogs, the concept of a data lake was defined and differentiated from a traditional data warehouse. Yet, a key point was a data lake and a data warehouse are not mutually exclusive. In fact, a structured data warehouse could be a subset of an overall data lake architecture.
Simply stated, a data lake is an effective way to store and access very large quantities of data.
What does this mean for credit union decision makers?
This is Part 2 of 2 in a blog series on how credit unions can catch up in data and analytics. In Part 1, we discussed which questions credit unions need to be asking to get off the bench, the issue with data silos, and what it will take to move forward with data analytics. In Part 2, we will further discuss the concept of big data, staffing for data analytics, and creating value from the data.
"A recent McKinsey & Company report emphasizes the fact that many industries are achieving only a fraction of their “digital potential”. However, the report observes, “In the United States, the information and communications technology sector, media, financial services, and professional services are surging ahead…”. This means other players in the marketplace served by credit unions have a big head start.
Credit unions that have been sitting on the sidelines can wait no longer. To get off the bench, these organizations need to ask:
- What are the basic questions about the organization’s strategic direction that cannot be answered today?
- How can existing data be better “generated, collected, and organized”?
- What data outside the organization would be useful?
- What skillsets are missing internally and to what degree can they (or should they) be outsourced?
- Once “insights” are uncovered from analytics, what are the practical steps to leveraging them to create value?"
The message has been ringing load and clear throughout the credit union industry for years: make better use of data and analytics or lose “member share” to more progressive CU peers or (horrors!) banks and fintech startups.
Despite the warning cries, the proportion of credit unions embracing this trend is (horrifyingly!) low.
Forward-thinking credit unions are tuning their internal data for improved decision making. Previously, data was locked up in multiple, “siloed” transactional systems. Now, innovative credit unions organize their critical information within integrated data warehouses.
However, once the data is made available, how does a credit union make best use of it? An apt analogy is the XBOX and other gaming platforms. The game console by itself is a marvelous piece of technology. Yet, without the games, it is not very useful. By the same token, video games without a console are useless. Put the two together and wonderful things happen for video game aficionados.
Credit unions are awash in data, but until recently there were few options for leveraging this data for better decision making. That has changed with the emergence of two major innovations.
A recent Forbes magazine article by Randy Bean and Thomas H. Davenport notes how General Electric (GE) is making a bold transformation into a “digital industrial” company. In the past ten years, GE has taken important steps to capture massive amounts of data (massive = “Big Data”) from devices throughout the enterprise. At first, it seems GE applied conventional analytics to find ways to increase revenue, cut cost, and many other beneficial outcomes. While analytics continues to be a critical part of GE’s evolution into being a digital industrial company, GE is taking a further step forward into the emerging areas of artificial intelligence and machine learning.
The report recognizes that financial services analytics has reached a point where marketing was in the 1970’s for the banking sector. Prior to that time, sales and marketing initiatives for credit unions and banks were rare. In 2017, a credit union would find it difficult to survive without some level of marketing effort.
In the world of credit union data and analytics, there are hot topics galore:
These subjects fall under the trendy term, Data Science. Yet, when it comes down to the practicalities of everyday business at a credit union, plain old reporting still provides the most information used for decision making. An O’Reilly/TIBCO Jaspersoft e-book released in March 2017 quotes a recent InformationWeek survey that found 88% of organizations are using reports while only 34% use data science.
Credit unions interested in advancing their data analytics efforts will find a wealth of information in a recent article in the McKinsey Quarterly. Simply entitled, “Making Data Analytics Work for You – Instead of the Other Way Around” (Mayhew, Salah, and Williams), the article provides an easy to follow list of steps for any organization to get the most out of their investment in data analytics.
The authors emphasize that improving corporate performance is the only meaningful reason for organizations to pursue data analytics. As a result, they state two important principles: