The 3 things every credit union needs to know about data science
To unlock the full potential of data, the industry must demystify the discipline and establish strong data cultures.
Data science is often confused with other disciplines, like data engineering. Knowing the difference is important because it helps credit union leaders more confidently take the first steps on a journey to data transformation.
So, what is data science? To be fair, there’s a reason it’s so often misunderstood. Data science is a broad, interdisciplinary field, drawing from many other scientific disciplines to inform all kinds of strategic pursuits. However, the field becomes easier to wrap our minds around when we focus on the singular thing all data science projects have in common. That commonality is the strategic objective to extract actionable insights from large collections of data. It’s not so convoluted when we look at it through that lens.
Data engineering, on the other hand, is a discipline focused on the tactical elements of data science, such as building and maintaining databases.
Once credit unions have base knowledge of data science as a discipline, they can begin to explore the strategic use cases that make the most sense for their overall business objectives. Of course, it’s not all that easy. There are several common stumbling blocks credit unions find themselves confronting as they pursue data science projects. Here are a few we’ve encountered in our work.
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