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.
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.
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 my previous blog, Big Data vs. Little Data: Part 1 - Structured and Unstructured Data, I discussed the two main types of data that should be top of mind for any organization thinking of becoming truly “Data-Driven.” In the world of data and data analytics, credit unions must leverage ALL the data accessible to them but the journey of mastering data analytics can be very tricky.
Determining the right tools will be critical. When it comes to data and data analytics, the order in which you introduce new tools is extremely important. In order to make each step up the analytics curve effective as the last, credit unions must consider the following steps:
As credit unions begin their journey into the future, they must rely on an industry standard analytics platform to guide them to their destinations.
Google Maps has revolutionized how we navigate our lives. It saves us from headaches caused by unnecessary traffic and other challenges in traveling. My journey from work to home has many different routes depending on traffic patterns. During days with slower traffic (i.e. - winter snowstorms), the Google Maps recommended route will change every 5 – 10 minutes. Using an analytics engine that informs me of the best route allows me to spend extra time on more important things in life. Credit unions have a similar opportunity when navigating their institutions into the uncertain future of financial services. Establishing an industry standard analytics platform will enable credit unions to cooperate on analytics and guide them to their desired destinations.
As 2016 draws to a close, it is time to look back on all that we’ve learned, and apply it to better our organizations for 2017. The credit union industry is rapidly changing as financial institutions are gaining better understandings of the necessity to optimize analytics and become truly data-driven organizations. As 2016 comes to a close, I have taken the liberty of compiling some of the industry’s favorite Big Data & Analytics related articles (and some you may have missed) from OnApproach’s blog, The Decision Maker. This is part two of two and features the top 5 articles you may have missed in 2016. Click here to see Part One highlighting the most popular blogs of the year. Enjoy!
As 2016 draws to a close, it is time to look back on all that we’ve learned, and apply it to better our organizations for 2017. The credit union industry is rapidly changing as financial institutions are gaining better understandings of the necessity to optimize analytics and become truly data-driven organizations. As 2016 comes comes to an end, I have taken the liberty of compiling some of the industry’s favorite Big Data & Analytics related articles (and some you may have missed) from OnApproach’s blog, The Decision Maker. This is part one of two and features the most read pieces of 2016. Click here to see Part Two highlighting the some good reads you may have missed over the year. Enjoy!
The Top 5 Favorites:
As the next generation begins making financial decisions, credit unions will be able to comfort them with data-driven product recommendations.
Recently, my wife and I were shopping for a mattress. We began the process by “trying out” mattresses by how they felt. My wife thought she preferred firm mattresses, while I thought I preferred soft ones. As we tried mattress after mattress, my wife would ask me, “what do you think about this one”, in which I would usually reply, “It feels pretty good to me”. We became frustrated by a complicated search for a large budget item until we found a mattress store that comforted us with data. The mattress store (Becker Furniture World) is locally owned with only 8 locations (does this sound familiar to your credit union?). They approached mattress shopping from a data-driven way. By using an analytic data model (developed by Sleep to Live Institute), they are using analytics to aid customers in their mattress investments through data sensors and user input. The data comforted us enough that we decided to purchase one of the mattresses it recommended.
Last week, OnApproach’s CEO, Paul Ablack discussed Big Data and Analytics with CUNA’s Senior Editor, Craig Sauer. In the podcast, we learn about the state of the credit union industry, what data means for financial institutions today, and how credit unions can thrive in an industry facing intense fintech disruption.
“95% of credit unions today are not able to truly integrate their data”, according to Ablack. Core vendor solutions do not allow credit unions to easily integrate data from disparate sources, or share and benefit from data of other credit unions. This means 95% of credit unions are at the bottom of the curve for analytics capabilities. As discussed in the podcast, less than 10% of credit union members are profitable. Unfortunately, credit unions at the bottom of this curve aren’t even capable of determining which members are not profitable, as factors such as product mix have proven to be an outdated and misleading determinant. Credit unions need to take action to integrate data and improve analytics to seize market opportunities.
As credit unions continue to invest in analytics solutions, they should focus on the purpose of analytics; Making data-driven decisions to better serve members.
Big data and analytics are a couple of the most used buzzwords throughout the credit union movement. You can’t avoid these terms no matter where you try to hide. Many vendors promise analytics that will be a panacea to the movement. They continue to make bold claims that are sure to perk an executive’s ears (and drive sales for the vendor). Although there are many powerful products available to credit unions, they must understand the purpose of analytics before they begin their journey.