Paul Ablack, CEO, OnApproach, joined Mike Lawson of CUbroadcast to discuss the state of the credit union industry, and why digital transformation is imperative. Credit unions have tremedous opportunities now with analytics and collaboration, but cannot afford to wait until banks and fintech disruptors get even further ahead in their analytics and member experience initiatives. As Paul states in the video, "If you don't start engaging with your members digitally, and giving them information that's very useful to them and helping them in their daily lives, you're not going to be able to compete, because there's lots of other people trying to think about that right now.... Now's the time to be really accelerating this strategy."
The term “digital transformation” has been a very hot topic recently, creating new conversations and products across a wide range of industries. This chart below, via Google Trends, displays the worldwide popularity of the phrase “Digital Transformation” in web searches since 2004. As shown in the graph, after remaining stagnant for over a decade, the term’s popularity has exploded in just the last year or so, raising questions about what this means, and how it will impact your organization.
An accepted definition for digital transformation is, “the application of digital technologies to fundamentally impact all aspects of business and society”. The important part of this definition is to realize that digital transformation does not mean just building a website, a mobile app, or obtaining a data warehouse. It means using your digital technology to transform your business processes. It means shifting your company culture to take advantage of your digital data to make informed data-driven decision and stay ahead of the ever-changing expectations of your members or customers.
At the 2017 NWCUA MAXX event, Paul Ablack joined Mike Lawson of CUbroadcast to discuss Digital Transformation, predictive analytics, chatbots, the upcoming Analytics and Financial Innovation (AXFI) Conference, the increasing popularity of blockchain, and more.
As a part of the conversation, Paul and Mike discuss member expectations and the interactions between credit unions and members. Members who shop at stores such as Zappos experience seamless and easy-to-use online shopping. These types of experiences alter the expectation for members when interacting with other organizations, such as their credit unions. In addition, credit unions have to learn how to connect with members throughout the entire decision making process. As Paul points out, credit unions need to position themselves to not just be in the business of loans or mortgages, but as an organization that is helping to make dreams come true. Rather than just being the last step in a process of getting a loan, credit unions could help with the decision making process, and truly be there for members before they've even decided whether they need a loan.
Earlier this month at at the CUNA Technology Council Conference, Paul Ablack caught up with CUbroadcast's Mike Lawson to discuss credit union real-time analytics, digital transformation, and machine learing.
Part of the conversation focuses on machine learning and artificial intelligence. Companies like Netflix and Apple are already using advanced machine learning and real-time analytics to provide quick, responsive, and superior member experiences. Your members are using these technologies today, which create higher expectations for service that must be met by financial institutions.
In the recent BIGcast, Analytics as The Fuel for Innovation – Implementing Analytics at OCCU, Andrew Bertrand, Data Analyst at Our Community Credit Union (OCCU) in Shelton Washington, discusses his role as a data analyst, getting started with data analytics, and data pooling for predictive analytics.
Getting Started with Analytics at OCCU
In the podcast, Andrew explains that prior to installing OnApproach M360 Enterprise, OCCU, a credit union with $360 Million in Assets, had an ODBC connection to their core system. It was a hassle to obtain data, and it didn’t meet the growing needs of a data-driven organization. Andrew realized that he could not possibly perform any predictive analytics without obtaining the history of member transactions.
After briefly (and in Andrew’s words, “foolishly”) considering to build their own data warehouse, Andrew decided it would be an inefficient use of time and resources. As John Best describes, for a financial institution to build their own data warehouse from scratch, is like building a “house of cards” as it has to be rebuilt repeatedly as elements are added and improvements are made.
In the recent BIGcast, To Fish with a Net or a Spear? Implementing Analytics at CUTX, John Best discusses data warehousing, core conversions, and winning at analytics with Keith Malbrue, CIO at Credit Union of Texas.
Core is NOT King
Years ago, in his previous role at another credit union, Keith Malbrue was the first OnApproach M360 client. He had come to OnApproach (a data consulting company at the time) with a problem. Keith realized member history was very important, and he didn’t want to lose all the member history when converting cores. According to Keith, it often feels like “Core is King” in this industry, but that doesn’t have to be the case. Credit unions should not have to lose member information because of a core conversion. To solve this problem, he engaged with OnApproach to discover a solution to this issue. The solution has come to be known as OnApproach M360 Enterprise.
By equipping his credit union with M360 Enterprise, Keith explains he gained 3 major advantages:
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”.
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.