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Want Better Analysis? Consider The Data

Credit unions now have many initiatives to better understand each member, including improved marketing and member loyalty. Unfortunately, many CUs are running into an unexpected challenge: inaccurate data.

According to a recent Experian Data Quality study, 92% of financial institutions suffer from common data errors. The most common data errors are incomplete or missing data, outdated information and inaccurate data.

Because of the prevalence of these common errors, the vast majority of institutions suspect their member contact data might be inaccurate in some way. On average, financial institutions believe 22% of their data is inaccurate.

Garbage In, Garbage Out
The level of inaccuracy is creating many problems for credit unions.

  • 80% believe their bottom line is affected and on average 13% of revenue is believed to be wasted.
  • 79% of financial institutions are encountering problems with their loyalty efforts, mainly due to poor data quality.
  • 87% of institutions have problems generating meaningful business intelligence.

The main cause of data inaccuracy is human error. With information collected through many different channels and by many different individuals (both members and individual institutional employees), it is extremely challenging to maintain accurate data.
However, the level of inaccurate data directly relates to a lack of sophisticated data management strategies. Many financial institutions struggle to centralize data management, producing disjointed practices and inconsistent standards. Currently, only 28% of institutions manage their data quality centrally through a single director. That means that the majority of financial institutions lack a coherent, centralized approach to data quality.

Without a centralized approach, credit unions are unable to prevent human error across channels and standardize member information for analysis. One-off, ad-hoc projects of the past no longer suffice. Given the importance of data-driven efforts, credit unions need to centralize approaches to data management.

A central approach to data quality ensures consistency across departments, access to many data sources for member information, and improved best practices related to data.

There are three steps credit unions should take to improve data quality.

1. Move to a central approach — Credit unions should start by creating a task force around data management. The group should consist of stakeholders and individuals to execute plans from across the organization. IT should be involved to prioritize and source new data management technology. Benchmarks should be taken on a regular basis to check progress as new solutions and processes are implemented.

2. Consolidate member data — The average large institution has eight different databases and that statistic does not include other spreadsheets or sources of data that may exist outside of a database, which can be numerous.

To consolidate data, credit unions should start by identifying the various sources of data and determine where they can be consolidated into a central location. Then, clean and standardize as much information as possible to better identify duplicates and consolidate records.

Software should be used to identify duplicates. While many institutions may look to manual processes to identify duplicate member records, this can lead to human error, which should be eliminated whenever possible.

3. Verify data upon entry — Inaccurate data can hinder operations, but also any analysis. Software tools can be put in place to verify member data and standardize information according to data policies. This allows credit unions to more easily find member accounts, but also keep information consolidated into the central record for easy access.

Creating a central data source can take some time. However, this will be invaluable for quickly accessing member information and transforming your institution into a data-driven business. Data quality is the foundation for a number of key initiatives and credit unions need to ensure the accuracy of their member data to succeed and gain better insight.

Thomas Schutz is general manager of data quality at Experian, Boston.

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