The Decision Maker

What is a Data Lake? - Part 2: Sink or Swim

Posted by Mark Portz on Jul 26, 2017 11:07:00 AM

In my previous blog, “What is a Data Lake? Part 1”, I discussed how to define a data lake, and how it differs from a data warehouse. To briefly recap, a data lake is a massive data repository for raw data in its native format. To better understand the idea, let’s dive a bit deeper and get to know the advantages and disadvantages surrounding data lakes.

To start, there are a number of advantages data lakes serve for financial institutions:

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Topics: Data warehouse, Data Pool, Data Lake

What is a Data Lake? - Part 1: Testing the Waters

Posted by Mark Portz on Jul 18, 2017 11:03:00 AM

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.

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Topics: Data warehouse, Data Pool, Data Lake

Building for the Future

Posted by Michael Cochrum on Apr 5, 2017 11:15:00 AM

When my brother and I were kids, we liked to build things. We built forts, ramps and anything else we could fashion out of scrap wood.  Typically, our projects served a specific function, to ward off rival “street gangs” of preteens from another block, to propel our dirt bikes into the air, or whatever else we decided could or would result in our becoming temporarily disabled.  We thought we were good builders, but the greatest evidence that we were not, is that our work does not exist in any form today.

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Topics: Big Data, Data warehouse, FinTech

Looking to the Future of Data Warehousing

Posted by Mark Portz on Jan 30, 2017 11:01:00 AM

In the fourth Data Analytics Series BIGcast, From Questions to Answers: Becoming a Data-Driven Organization, John Best speaks with Brewster Knowlton of The Knowlton Group about data-driven decisions, data warehousing and successful data integrations.

The Six Characteristics of a Data-Driven Organization

According to Brewster, there are six characteristics to determine whether your organization is really data-driven:

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Topics: Data warehouse, Data-Driven, BIGcast

What’s Holding You Back from Being Data-Driven?

Posted by Brewster Knowlton (The Knowlton Group) on Sep 20, 2016 11:08:00 AM

Let’s face it: we live in a world where a strong data and analytics competency is becoming a “must have” for successful companies. Despite the growing significance of analytics, the majority of banks and credit unions are not “data-driven” organizations.

We’ve uncovered a number of common reasons why investment in data and analytics has been pushed off or outright rejected. Despite these challenges, most of the common reasons against data and analytics are driven by inaccuracies or misinformation.

In this post, we will address the common pushbacks against data and analytics projects and how to overcome those challenges.

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Topics: Data warehouse, Big Data & Analytics

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: Analytics, Data warehouse, Semantic Layer, data management

Having It All: The Keys to Comprehensive Data Capture

Posted by Peter Keers, PMP on Jul 1, 2015 9:57:33 AM

Credit unions aiming to build Big Data & Analytics capabilities have a lot of decisions to make. One of the most fundamental decisions is how much source data to capture. The two dimensions of “how much” are depth and breadth.

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Topics: Big Data, Data warehouse, Data Integration, Analytic Data Model, Data Standards

Contact Analytics: Going Beyond the Call Center

Posted by Nate Wentzlaff on Jun 10, 2015 12:00:00 PM

As digital strategies continue to proliferate throughout the credit union industry, the contact center has become essential.

Calling a company about an issue can be a miserable experience.  Being transferred to four departments and having to explain an issue, not to mention basic account information, four different times will frustrate the most patient consumers.  These experiences have given the call center a bad image in the minds of consumers.  Stereotypes of call center agents who do not speak fluent English failing to understand a problem have been burned into the American culture.  However, the necessity for remotely assisting consumers has never been greater.  Redesigning the call center into a contact center will enable credit unions to give their members excellent service.   It will also empower credit unions to continue learning about their members through every interaction.

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Topics: Analytics, Data warehouse, Members

Credit Unions and Big Data/Analytics [VIDEO]

Posted by Austin Wentzlaff on Mar 16, 2015 12:13:00 PM



 

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Topics: Analytics, Business Intelligence, Big Data, Credit Unions, Data warehouse, Data Integration, Data Pool, Big data/analytics, Analytic Data Model

The Next Big Idea for the Credit Union Industry

Posted by Paul Ablack on Feb 23, 2015 11:11:57 AM

The credit union industry is on the cusp of significant challenges with the potential to disrupt the financial services landscape as we know it. Big Data and Analytics is driving a new breed of competitor into what has been a very traditional marketplace. The industry will need to envision and build out the “Next Big Idea” for credit unions to stay competitive and successfully navigate the next 10 years.

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Topics: Business Intelligence, Big Data, Credit Unions, Data warehouse, Data Integration, Data Pool, Big data/analytics, predictive analytics, CUSO, Cooperation

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