Have you ever wondered what it takes to enable your organization to become data-driven? For a limited time, I am giving away my “Credit Union Executive’s Guide to Data and Analytics” eBook FOR FREE! To get the eBook, complete the form below and press “Sign Up”:
It’s no secret that credit unions are becoming more data-driven organizations. As the amount of digital channels increases and the usage and utilization of those digital channels increases, so too does the amount of data available and accessible to those organizations. Throughout this eBook, I will discuss how credit unions can become data-driven organizations, what that really means, and some simple ways to get started on analytics journey.
What Will I Read About?
First, I’ll help you understand what it means to be a data-driven credit union. I don’t want the term “data-driven” to become just another buzzword. I’ll address what it really means for an organization to use data properly and how organizations can take advantage of data and analytics.
Second, I will address some key definitions and common misconceptions surrounding business intelligence, data, and analytics. For example big data, my least favorite buzzword, is drastically misused on a regular basis. I will discuss what big data really means, and what people tend to mean when they say the term “big data”.
Next, I’ll discuss the importance of a data strategy. I’m a firm believer that strategy without execution will fail and that execution without strategy will also fail. Business intelligence and analytics requires a top-down approach. This means that an organization should start with a strategy and then figure out how to execute that strategy. I will address this concept further and the details associated with a data strategy when we get to that section.
Next, I will teach you how start building your credit union’s data strategy. Answering the question, what can you start doing today to begin the discovery process and gather all the relevant information necessary to begin formulating your credit union’s data strategy?
I will then go over how to gain greater clarity around the technical aspects of building a data and analytics platform. By downloading this eBook, I do not assume you have a technical background. In fact, when I discuss the technology that’s going to be used, I do so from the perspective of someone who does not have much, if any, technical expertise. At the same time, we do need to have a discussion about the technology that your credit union could or should be using in order to maximize your data and analytics knowledge. This will help you become aware and understand what is involved for analytics. It will also better prepare you for conversations with both technical and business people.
Next, I will go over a common question that I often get about analytics: “How do you address data quality?” Data quality issues are something you can never get rid of. There will always be bad data somewhere. The important thing is not to try and have perfect data, but that you have a plan to address data quality issues when they inevitably arise. Data quality issues will always present themselves as long as there is the possibility of human error (which is always)! Being able to address those data quality issues as they arise is essential, so they do not snowball into bigger problems and compound going forward.
Next, I will address how you can build and evolve a data-driven culture. Not properly developing a data-driven culture is one of the most common reasons that data and analytics initiatives fail within credit unions. As a result, I will address what constitutes a data-driven culture, how you can create a data-driven culture, and some techniques that can help you out along the way.
Lastly, I’ll go over the importance of “quick wins” along with something known as the “Flywheel Effect”. For those of you who have read Jim Collins’ book, Good to Great, this “Flywheel Effect” concept should be familiar to you. It has an interesting application when it comes to analytics. Learning how to gain early momentum is critical in implementing a successful and sustainable data strategy and a data and analytics platform.
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