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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

Why Credit Union Digital Transformation Can’t Work Without Credit Union Data Integration

Posted by CU 2.0 on Jan 17, 2019 11:01:00 AM

It’s no good to be a dinosaur in the financial sector. Not only are dinosaurs notoriously temperamental, but they can’t type. Oh, and they’re extinct. If branches don’t want to go the way of the dinosaur, then a little credit union digital transformation is their best hope.

(Hint: credit unions aren’t the only industry affected by digital transformation and the emerging primacy of data.)

While digital transformation is certainly the goal, it can’t just organically happen. Credit union digital transformation is a strategic process that incorporates several approaches, from digital engagement to data integration. In this blog, we’ll talk about the challenges of credit union data integration and collaborative analytics strategies.

Tying Together Data Sources

Typical credit unions have somewhere around six to eight data sources. Some have more. While having the data is certainly nice, it’s not much good to just sit on it.

Core and ancillary systems produce data at prodigious rates. These streams of data are all separate, too. Siloed data streams are great when you need to understand only the data produced by one source. However, individual sources of data have a nasty habit of not producing a clear, complete, actionable picture.

Making matters worse is that each system stores its data differently. If you want to perform data analysis on any of your credit union members, you have to check in on each system and pull different data sets from them.

This lack of robust credit union data integration hampers solid, actionable analytics. The first challenge for credit unions then is reconciling individual data streams into one single source of truth.

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Topics: Credit Unions, Data Integration, Digital

How is Digital Growth and Transformation Vital to my Credit Union?

Posted by CU 2.0 on Jan 10, 2019 11:00:00 AM

The traditional brick and mortar model has worked well for credit unions over the years. So long as they continue to deliver superior member experiences, that won’t change anytime soon. However, as more financial institutions offer mobile and online services, digital transformation will quickly grow in importance for your credit union.

Increasingly, the platforms on which credit unions engage and support their members are in the digital realm. While credit unions still offer phenomenal services in their brick and mortar branches, not all have the same level of digital sophistication typical of tech-savvy newer businesses. As younger generations begin their financial journey, poor technological performance will become an issue.

Who is the Competition?

Normally, credit unions have to deal with fairly predictable competition: big banks, community banks, other credit unions, and boxes hidden under mattresses. Credit unions have been able to distinguish themselves well in the field of financial institutions.

Currently however, entire generations feel comfortable bringing their phones or other mobile platforms with them to disrupt perfectly good dates, game nights, dinner parties, and so on. It’s not abnormal to see an entire group of young adults sharing a table at a bar while staring at their phones.

Businesses that have embraced technological growth by developing applications, online support, and mobile functionality have prospered: think Netflix, Amazon, and any pizza company that lets you order delivery with an app (that’s phone application, not appetizer, although personally, I want the option for both).

If Netflix is going to build a media production and streaming empire by beginning with mail-order DVD rentals, then credit union digital transformation can turn a small brick-and-mortar branch into a mobile-friendly powerhouse.

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Topics: FinTech, Disruption, Digital

How Data Integration Helps Credit Union Analytics Platforms

Posted by CU 2.0 on Dec 11, 2018 10:59:00 AM

The immediate future of banking and the financial industry is in analytics. The ability to draw conclusions from massive sets of data helps financial institutions improve their advertisement targeting, their ability to underwrite loans, and a whole host of other things. One of the sticking points in the machinery is in credit unions’ ability to perform adequate data integration.

Data integration sounds relatively simple on its own. Data integration is the practice of combining multiple streams or forms of data into a single readable format. The extent of data integration needed increases as the amount of data—or the number of data sources—increases.

Why do Credit Unions Need Data Integration?

This will be a long answer, and so I’ll break it up into three parts. The first part will address the many sources of data that credit unions deal with on a daily basis. The second part will introduce the necessity of analytics platforms in finance. The third section will explain the role of credit union data integration in the grand scheme of things.

1.     Credit Unions Generate Lots of Data

Credit unions exist in the financial sector, which is technologically fast-moving. Partially because of this, and partially because credit unions must record financial and member data, credit unions are inundated with a massive amount of data daily.

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Topics: Data Integration, Data Analytics, Insight Platform

How Credit Unions Can Win the Big Data Play

Posted by CU 2.0 on Jul 2, 2018 12:47:00 PM

Ask executives at the money center banks how they plan to win, against both fintechs and smaller institutions like credit unions, and they smirk as they say two words, big data.

Big data is today’s magic.  How does Amazon knows what book you want to read next, or what music you want to buy, or when you are about to run out of cat treats? Those are simple examples but the answer is big data. Amazon crunches a lot of data, in a blink of an eye, and it knows what you want, maybe before you know.

The race now is on inside financial institutions to crunch lots of data and to achieve similar predictive intimacy about their customers and members. 

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Topics: Big Data, Data Pool, Analytic Data Model, Collaboration, Data Lake, Data Ownership