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:
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even capable of determining which members are not profitable, as factors such as product mix have proven to be an outdated and misleading determinant.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.
By now, credit unions are aware of the industry’s changing landscape. Credit Unions are facing Fintech influence, industry disruption, and realize it is no longer an option, but a necessity to capture and optimize every piece of obtainable data to remain competitive in financial services. Large banks have been investing heavily in big data and continue to do so. Unfortunately, these banks are directly competing with credit unions, which don’t have the same resources available to effectively invest in big data. Awareness is step one, but one state is taking action to put the power of big data in the hands of credit unions.
Let’s face it: we live in a world where a strong data and analytics competency is becoming a “must have” for successful companies. Despite the growing significance of analytics, the majority of banks and credit unions are not “data-driven” organizations.
We’ve uncovered a number of common reasons why investment in data and analytics has been pushed off or outright rejected. Despite these challenges, most of the common reasons against data and analytics are driven by inaccuracies or misinformation.
In this post, we will address the common pushbacks against data and analytics projects and how to overcome those challenges.
For starters, yes the title is a terrible play on “when life gives you lemons, make lemonade”.
Bad jokes aside, I hear too frequently how organizations need more and more data. I’m a data guy – I’m all about data. But there is a subtle difference between having more data and more information.
Below is one of my favorite quotes about data:
Topics: Big Data
It’s clear now: Data can be one of a company’s most valuable assets if properly stored, managed and analyzed. What’s unclear to many however, is what data is the most valuable and how to harness the value of each type of data. There are two main types of data: “Big Data” and “Little Data” or, respectively, unstructured data and structured data. Both types of data can deliver a significant amount of value to a credit union. However, figuring out how to harness each type of data can be a challenge when dealing with the array of different data sources. Finding a healthy balance is key to delivering value without succumbing to analysis paralysis.
Collateral Valuations are essential while serving members and maintaining a healthy credit union. However, credit unions are relying on inaccurate valuations of their members’ collateral values because of disintegrated data.
The financial services industry is being constantly challenged with new regulations and outside threats. Two important developments in the financial world recently that are worth noting are the immense growth in blockchain technology utilization, as well as the impact of the Financial Accounting Standards Board’s (FASB) recent comments on Current Expected Credit Loss (CECL) guidelines. These are both undoubtedly items to keep top-of-mind, as they are impacting institutions from community banks and credit unions to the large banks.
A common misunderstanding with data analytics is how and when the various “tools” are used. Many think that a great data visualization tool (e.g. Tableau) will solve all of an organization’s problems. Often overlooked, however, are the many steps it takes for an organization to get from data ground zero to becoming completely analytically proficient. When it comes to data management and 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:
- Data Access – The first step in an analytics strategy is simply getting access to the data necessary for analytics. Although this may seem like a fairly easy task, credit unions may find it difficult to get access to the data they are looking for. It may be due to the level of skill needed to the extract the data or due to a vendor’s unwillingness to provide the data. Whatever the challenge might be, data access is an extremely important task in becoming analytically proficient and will need to be tackled right away.