As another exciting year in the credit union industry comes to a close, it is a good time to reflect on everything we have learned throughout the year, and determine how to best prepare for success in 2018. It is an amazing time to be a part of the credit union movement as the entire financial services arena is changing before our eyes, and there is incredible potential for the movement with increasing collaborating and data analytics. As we look back on 2017, here are several of the industry’s favorite articles relating to credit union big data/analytics (and some other articles you may have missed) from OnApproach blog, The Decision Maker. This is part 1 or 2. To see Part 2, click here. Enjoy!
The Top 5 Favorites:
There is a reason for using the word “journey” to describe the investment in data analytics. A financial institution cannot simply purchase a shiny new computer or software system and immediately have every question answered and every problem solved. As Clay expresses in the podcast while discussing the analytics journey, “It’s a practice that you build, and a data warehouse is a vital piece in that practice.”
Put differently, by John Best, CEO, Best Innovation Group, “Analytics is a discipline, and not a product.”
Ultimately, there are a number of options to consider when beginning the journey. As stated by Clay Yearsley, “There are a lot of different pathways to take, but the biggest thing to beginning this, is to begin.”
Read the entire blog here: http://blog.onapproach.com/a-day-in-the-life-of-a-data-analytics-svp-making-use-of-your-credit-unions-data
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:
1. The data used for analytics must be “purpose-driven”.That is, data points used in analytics must be chosen carefully to ensure they align with high value processes within the organization.
2. The outputs of data analytics must drive action.To do so, analytics must operate across organizational silos, represent realistic scenarios, and most importantly, is actually used to drive decisions.
With these guiding principles, the authors suggest eight steps for achieving effective use of data analytics.
Read the entire blog here: http://blog.onapproach.com/8-steps-to-make-data-analytics-work-for-you
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.
What is a Data Lake?
Simply, a data lake is a data repository for raw data in its native format. As the name implies, these repositories are capable of holding massive amounts of data. Ideally, data lakes are available at an enterprise level and can be easily queried to find relevant data for managers to analyze.
How does it compare to a Data Warehouse?
Data Lakes and Data Warehouses have a number of similarities. Both are designed to:
- House disparate data sourcesin a single repository
- Allows improved data analytics
- Provide an enterprise source for querying data
However, there are distinct differences between a Data Lake and a Data Warehouse.
Read the entire blog here: http://blog.onapproach.com/what-is-a-data-lake-part-1-testing-the-waters
There has been a major emphasis on making banking friendly for millennials. Of course, this is a necessity as millennials make up a larger percentage of the workforce and have different expectations for their financial institutions than previous generations. However, there are bigger changes to prepare for. If you are struggling to please millennials, a generation of adults who were impressed by the ability to send text messages and pictures as high schoolers, how will you be able to meet the needs of Generation Z – the generation who has been operating smart phones and tablets (and in some cases coding) before they could walk or talk?
Who is Generation Z?
Before we discuss how to react to Generation Z, we need to better understand who Gen Z is. Generation Z, or as Business Insider has called them, “millennials on steroids”, represents the people born between 1996 and 2010, according to socialmarketing.org. That means they range from ages 6 to 20 years old. Here are a few fun facts to get you warmed up to who we are talking about:
1. They view technology as a given, not a reward or piece of equipment. It is a necessity[Thrivist]. In fact, Seventy-nine percent of Generation Z consumers display symptoms of emotional distress when kept away from their personal electronic devices [CMO].
2. The average Gen Zer has the attention span of about eight seconds. They have grown up at a time when they're being served media and messaging from all angles, and have adapted to quickly sorting through and assessing enormous amounts of information [CMO].
3. These consumers are digital natives, but even more so, mobile first. They are twice more likely to want to shop on a mobile than Millennials [Forbes]. To add to that, they spend 8-9 hours per day connected to at least one form of media [Thrivist].
Data continues to prove itself as a necessity for decision-making in financial institutions. For years, major banks and innovative companies such as Google and Amazon have taken advantage of “Big Data” to gain better insights into their customer base and make business decisions to position themselves for the future. The credit union industry is finally beginning to take advantage of their data and utilize new technologies. However, credit unions are much smaller than major banks and simply don’t have the same quantity of data that banks are able to collect from their customers. Fortunately, data pooling serves as a great solution to this problem. Here are 5 reasons your credit union should participate in data pooling:
1. Access to Diverse Data
“Why do I care about the data collected from a credit union on the other side of the country?” This is a frequently asked question when discussing data pools. Of course, it is a valid question. The economy may be different in December in Alaska compared to Florida. However, it is important to recognize that this diversity can actually be a major advantage that should not be overlooked.
As Joe Breeden of Deep Future Analytics explains in a podcast with Best Innovation Group, titled The CECL Effect – How the New Credit Loss Rule will alter Financial Analytics, data diversity is healthy for pooling and advanced analytics. In the podcast he states, “If we get folks spread around the country, in a shared blind repository, then it gives us a better overall view of the scaling of the risk versus economics and other things.” He continues to explain that “We leverage that pool to learn aspects that are in common, like economic sensitivities, but then also to calibrate to the individual… so you get the benefit of the whole, but specific to the individual institution.
2. Affordable Access to Data Scientists
Data scientists are highly skilled, highly demanded, and expensive resources. They play a major role in analyzing and creating predictive insights (such as ALLL forecasting for CECL) from raw data, which means there is a reason data scientists often earn $175k+ per year.
Credit unions simply don’t have the same assets and hiring power as Google, Microsoft or the large banks which makes hiring a single data scientist a non-option. This is where the power of the data pool comes into play. If a data scientist works on a pool of data, consisting of the data from, say, 50 credit unions, those 50 credit unions get to split the cost of the data scientist, making advanced analytics much more affordable.