The Decision Maker

Big Data vs. Little Data: Part 1 - Structured and Unstructured Data

Posted by Austin Wentzlaff on Aug 4, 2016 11:06:00 AM

Big Data vs Little Data Part 1: Structured and Unstructured Data

It’s clear now: Data can be one of a company’s most valuable assets if properly stored, managed and analyzed.  What’s unclear to many however, is what data is the most valuable and how to harness the value of each type of data.  There are two main types of data: “Big Data” and “Little Data” or, respectively, unstructured data and structured data. Both types of data can deliver a significant amount of value to a credit union. However, figuring out how to harness each type of data can be a challenge when dealing with the array of different data sources. Finding a healthy balance is key to delivering value without succumbing to analysis paralysis.

Little Data: Structured Data

Little Data, typically, is found within a credit unions operational systems.  Systems that are highly structured and require proper inputs to make them function properly.  These types of systems include, but are not limited to, Loan Origination Systems, Core Systems, Credit Card Processing Systems, etc.  These systems collect member and account information in a conformed fashion and, even more importantly, the transactions that are generated. These transactions represent the behavior of a member. It is fairly easy, with the right tools, to find specific member and the account-related (transactions) information that member has done with the credit union in a structured data source and thereby track behavior.

Big Data: Unstructured Data

Big Data is found in unstructured data sources that generate far more data points (behaviors) than the structured data sources.  A few examples of unstructured data sources are social media (Facebook, LinkedIn, Twitter, etc.), CRM, ratings/comments, etc.  These sources are very difficult to analyze manually on a case by case basis.  The sheer amount of data generated by these sources often causes this data to be underutilized or completely not used at all.  This data, however, is just as valuable, if not more valuable, than the structured data from the Little Data sources.

The Tools Needed

Mining value out of Little Data and Big Data is equally important but just as challenging and complex in both.

Little Data (Structured Data) can be mined with common business intelligence tools and languages such as Structured Query Language (SQL).  In order to maximize the value of the data, however, the structured data sources must be fully integrated and normalized.  The integration is achieved by establishing data infrastructure, commonly implemented via a data warehouse or data model.  Integration using a data warehouse can allow a credit union to get a complete 360 view of their members by linking transaction data across all subject areas. Data integration also creates a “a single source of truth” so a credit union can “know” everything about their members through their behaviors (transactions).

Big Data (Unstructured Data) can be mined with more advanced “Big Data” tools such as Hadoop, Cloudera, and MongoDB.  These tools are not for the data beginner but are necessary for the evolution from Little Data to Big Data.  For example, Big Data tools can allow credit unions to make sense out of data that is not easily done manually.  These tools allow a credit union to comb through tens of thousands of comments, ratings, and likes in seconds and finding relationships that would otherwise be overlooked. Insight derived in this way in areas such as member sentiment can often more valuable than data collected in a structured form such as a loan application.

Conclusion

In the world of data and data analytics, credit unions must leverage ALL the data accessible to them.  Credit unions should start with the structured data within their own operational systems by developing the data infrastructure to manage, store, and analyze the data.  Once the credit union has all of their structured data in a single repository, planning should begin to leverage unstructured data from available data sources.

Determining the right tools will be critical.  It is very important that Little Data tools (SQL Database / Data Warehouse) are connected to, or compatible with, the Big Data tools (Hadoop, Cloudera, and MongoDB).  If the data warehouse does not support the move from structured data to unstructured data there will be a serious loss of value.  While both Big Data and Little Data are extremely powerful, the marriage of the two is where the real value lies. 

Big Data vs. Little Data: Structured and Unstructured Data is Part 1 of a blog series on Big Data vs Little Data, the tools used and the value the can be found in the various sources.  Subscribe to our blog to learn more and stay up-to-date on the latest in data analytics for credit unions.

Watch the Ideal Credit Union VIP Case Study Video

Topics: Big Data, Little Data, Structured vs. Unstructured Data

Subscribe to Email Updates

You now have more information at hand about your credit union than ever before. But are you using it to "out-think" your rivals? If not, you may be missing out on a potent competitive tool.

This blog will:

  • Educate subscribers about data integration and Big Data and Analytics.
  • Provide tips and best practices.
  • Provide entertainment.
  • Share ideas and expertise.

Recent Posts

Posts by Topic

see all