The Decision Maker

Data Management Framework - 7 Essential Components

Posted by Merrill Albert on Oct 22, 2019 12:05:00 PM

Managing your data takes work. Just because you have data does not mean it is the correct data. You must make sure that you have useful data to inform the rest of your operations. People who are good at managing data are usually those who like having structure and disciplined processes that others will support. These are the people you want on your team to ensure you have data you can count on. If you can’t count on your data, it will not produce useful insights for reporting and business decisions.

Building data you can rely on means following a good data management framework. Data infrastructures built on sound management principles yield reliable data. This useful data, if used correctly, leads to engaged members who trust their credit union and want to do more business with you. Plus, you need to have reliable data when you are submitting reports to regulatory bodies and mitigating risks.

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Topics: Data Analytics, Data Quality, Enterprise Data Management

Data Insights - Building a Foundation

Posted by Merrill Albert on Oct 15, 2019 11:55:00 AM

Data insights start, naturally, with data. Get the data wrong, and your insights will be wrong.  Who wants to take business actions on insights based on bad data?

The real problem is when you’re acting on data insights that you don’t know are based on bad data. You need to prevent that from happening because you don’t want to make incorrect assumptions. If you’ve done nothing to build useful data, why should you expect it to be good? 

The data industry has been around a long time and has proven techniques for building that solid foundation upon which to base your insights. Using these techniques and following a structured methodology provide the discipline needed to make that foundation. But building a good foundation that produces valuable insights takes time. Don’t try to cut corners and don’t assume your data is useful.

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Topics: Data Standards, Data Quality, Enterprise Data Management

The Cost of Building a Data Warehouse for an Analytics Platform

Posted by CU 2.0 on Jan 25, 2019 10:02:00 AM

Credit unions can benefit greatly from collecting and storing information to leverage Big Data. The cost of building a data warehouse can be steep, though. If you’re considering building a data warehouse for your credit union, it’s important to know what you’re getting yourself into.

The benefits of building a data warehouse speak for themselves in the financial world. Getting into the data analytics game isn’t cheap, however. It’s not as simple as just buying a data warehouse and watching a video tutorial; no, getting started requires a large initial investment as well as ongoing support and upkeep costs.

Here are a couple of the common issues associated with building a data warehouse for the credit union industry.

Initial Investment Costs

There are two major expense considerations for any enterprising credit union looking to construct its own data warehouse. The most pressing of the two is the financial cost, and the second is the time invested. Because we’re talking specifically about credit unions, let’s discuss the monetary side of this investment first.

For an individual credit union, the cost of building a data warehouse or data lake for an analytics platform starts at around $500,000 at the low end. Most data warehouses and data lakes run well over the million-dollar mark. While it’s certainly a worthwhile investment, it can also be prohibitively expensive for smaller, more community-focused credit unions.

 The second major cost factor is time, though we could also say that it costs patience as well. Regardless of the size of the warehouse and the experience of the people putting it together, building a data warehouse takes an average of two or three years. If you want an analytics platform immediately, then creating one in-house from the ground up might not be your best option.

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Topics: Data Integration, Analytic Data Model, Enterprise Data Management, Data Storage, Insight Platform

The Analytics Talent Gap: Why the Middle Matters

Posted by Peter Keers, PMP on Oct 25, 2016 11:15:00 AM

 

There are many challenges to creating a data-driven credit union. One often cited issue is attracting and retaining qualified technical personnel. Hiring a Data Scientist is impossible for all but the very largest credit unions due to cost. Filling the position Data Architect is tough because of the heavy competition for qualified candidates. However, for many credit unions, having full-time employees with these skillsets is probably overkill anyway. There are many qualified vendors and consultants who can provide these services on either a project or retainer basis.

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Topics: Data Analytics, Enterprise Data Management, Leadership

The Data Imperative: Credit Unions Need to Up Their Data Management Game

Posted by Peter Keers, PMP on Apr 7, 2016 12:00:00 PM

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“The banking sector, especially when focusing on aspects that involve core banking functions, has largely been behind in the adoption of modern computing and data technologies.”

In a January 2016 research study by the Filene Research Institute (The State of Data Technology in Credit Unions: The Sink‑or‑Swim Crossroad Ahead), Professor Jignesh M. Patel argues that credit unions are falling behind when it comes to using data to make strategic decisions.

The study was based on an online survey that was followed up by in-depth interviews with a sub-set of survey respondents. While the study as a whole was full of valuable insights, the results of the interviews were particularly meaningful for credit unions that want to do a better job of harnessing their data.

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Topics: Data Analytics, Enterprise Data Management

The Data Warehouse is not Enough

Posted by Nate Wentzlaff on Oct 27, 2015 2:05:49 PM

Relying solely on a data warehouse, without an enterprise data management strategy, is a recipe for disaster.

 

Credit unions are beginning to invest heavily in big data and analytics.  When deciding how to allocate funds in this space, leaders are awash with buzzwords and conflicting advice.  One of the most common terms used within big data and analytics is: data warehouseDeciding whether to build or buy a data warehouse is an important strategic decision for credit unions.  Unfortunately, many decision-makers get lost in discussions about storage capacity, data processing, data visualization, etc.  All of these concepts are important.  However, data warehousing is not the solution.  It is a powerful tool in an enterprise data management (EDM) strategy. 

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Topics: Analytic Data Model, Semantic Layer, Data Analytics, Enterprise Data Management