Make Effective Data Management a Part of Your Strategy


Learn Why Effective Data Management Is Critical

Inefficient, stagnant data can derail the strongest compliance strategies. Is redundant, inconsistent data draining value from your business? Explore how to integrate effective data management into your compliance strategy in this webinar recording. 
Changing regulations and operational pressures add to your challenge. Constantly evolving regulations may pertain to some, but not all, of your areas of practice. Keeping track of all the changes and proactively implementing compliance protocols is extremely difficult. Regulators have the advantage of "shared hindsight" when sampling your enterprise data. 

An effective data management strategy includes:

  • Knowing where your data is located
  • Recognizing which data sets are deficient
  • Cleaning up and maintaining your data
  • Optimizing data modifying processes to ensure accuracy, and avoid unnecessary duplication
  • Understanding the interaction of your data with your compliance controls and processes
Good quality data is essential for:

  • Customer experience
  • Operational efficiency
  • Regulatory compliance
But experience shows that data can be:

  • Missing
  • Incorrect
  • Incomplete
  • Out of date

The cost of poor quality data

Poor data quality costs businesses $14.2M every year1

Keeping multiple different customer portfolios is costly

  • Unnecessary redundant processes and costs
  • Inconsistent customer records

An unconsolidated view of a customer is risky 

  • Partial and disparate assessment of risk
  • Disjointed operational processes

Unreliable customer records drive inefficiency

  • Incomplete customer information typically results in multiple touchpoints
  • Inaccurate or unavailable identifiers hamper the ability to automate processes
  • Always asking your customer for information results in customer friction and unnecessary added expense
  • Most businesses rely on the customer to notify of changes on their data
  • As more customer interaction becomes virtual, customers often do not proactively notify of changes or respond to requests for updated information 
  • Customer iterations create friction and expense

Helping To Meet Common Goals of Financial Services Organizations

Increased focus and scrutiny on customer data quality

  • Ensuring that organizations have complete and accurate data for their customers – Hygiene, Validating their customer data, “backfill of missing/incomplete data”
  • Credit Bureau Header reporting completeness and accuracy

Heightened regulatory expectations

  • Executive accountability for AML, KYC and filtering effectiveness
  • Increasing focus on risk data architecture and risk data aggregation


  • Increase efficiency, accuracy and ability to comply with increasing regulations
  • Screening accuracy and operational effectiveness and accuracy

1. Gartner,  “The State of Data Quality: Current Practices and Evolving Trends”

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