If you are a credit union still waiting on the data analytics sidelines, you’re already too late. Data analytics is not a fad – it is a major opportunity for credit unions to gain deeper insights and improve decision making to create a strong and competitive future. However, it is not always clear where credit unions should begin. To help answer these questions, John Best recently spoke with Clay Yearsley, SVP of Data Analytics at Texas Trust Credit Union about getting started on the analytics journey, the skills needed, and the value of data in the podcast, “Catching a Unicorn – Discussing Data Analytics with Clay Yearsley”.
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.
Apple has made a tremendously successful company off of one thing… Is it the iPhone, iPad, iPod, or Mac series? No. What makes Apple so powerful and successful is not its products, but rather the ecosystem it’s created through its standard operating system, the iOS. An operating system or “platform” that enables its users to connect with the rest of its users as a community and its developers. With this common platform, all users are on a level playing field with a similar access to all “apps” and services that have been created on the platform – rather than each user building everything themselves.
In my previous blog, “What is a Data Lake? Part 1”, I discussed how to define a data lake, and how it differs from a data warehouse. To briefly recap, a data lake is a massive data repository for raw data in its native format. To better understand the idea, let’s dive a bit deeper and get to know the advantages and disadvantages surrounding data lakes.
To start, there are a number of advantages data lakes serve for financial institutions:
Financial institutions all over are working to build effective data strategies and improve decision-making. With so many new technologies and innovations out there, it can get very difficult to keep up with the industry and even keep straight the buzzwords we hear throughout the day. In this piece, let’s dive in to better understand what makes a data lake.
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.