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The cost of data abundance

Financial firms rely on global company data to drive decisions, reduce risk, and follow regulations. Many organizations need this data for important tasks but face a basic problem: they have too much information and not enough ways to use it well.

For most financial firms, the problem isn’t getting large datasets but using them effectively. When dealing with tens of millions of records from global sources, processing power becomes the main issue rather than data access.

When more data creates less value

Financial institutions typically face several challenges when trying to use large-scale company data:

• Slow processing: Handling 30+ million company records can take months instead of days
• Outdated information: Weekly or monthly updates miss important changes
• Tracking problems: Events like companies moving to new countries create duplicate records
• Resource limits: Processing huge amounts of data strains existing systems

This creates a strange situation – the more data they have, the less useful it becomes. Financial firms often can’t act on information they already own, making valuable data practically worthless.

The price of inaction: Why firms delay solutions

Despite knowing these problems, many organizations put off finding solutions because:

• Updating data systems seems too expensive
• Old systems don’t work well with modern data tools
• Finding skilled workers with the right expertise is hard
• Separate departments make working together on data projects difficult

According to research, financial firms often underestimate how much doing nothing costs them. Duplicate data increases storage costs, slows down searches, lowers data quality, raises operating costs, and leads to wrong reports that affect important business decisions.

Breaking the data gridlock: Expert strategies

Data experts recommend several approaches to solve these challenges:

For data processing:

• Use batch processing for large historical datasets
• Use near real-time processing for ongoing updates
• Consider a mixed approach with “fast batches” for balance

For removing duplicates:

• Apply matching techniques that track companies across different countries
• Use exact matching for records with consistent IDs
• Use fuzzy matching where exact comparisons won’t work

For keeping data fresh:

• Use files containing only changes since the last update
• Set clear expectations for how often data gets updated
• Balance update frequency with available resources

Avoiding data integration obstacles

During implementation, organizations often face several obstacles:

• Data quality varies across different sources
• Mapping between different data models is complex
• Security and privacy concerns arise with expanded data access
• Regulatory compliance requirements affect data usage

Companies that overcome these challenges typically succeed by:

• Partnering with data providers that offer flexible delivery options
• Creating clear agreements about data accuracy and timeliness
• Implementing sophisticated matching for accurate record linking
• Using historical snapshots to track entity changes over time

Core approaches to unlocking data value

The most effective approach to managing large-scale company data combines several key elements:

Change-only files for updates

• Process only changed records rather than entire datasets
• Reduce resource use while keeping data fresh
• Enable more frequent updates without system strain

Historical snapshots for entity tracking

• Capture data at specific points in time to identify trends
• Compare snapshots to detect when companies change countries
• Create reliable audit trails for compliance purposes

Mixed processing approaches

• Use batch processing for initial data loads and historical analysis
• Implement near real-time processing for critical data elements
• Balance frequency and resource use based on business needs

The biggest challenge organizations face is the initial processing of massive historical datasets. Leading companies overcome this by:

• Breaking data into manageable portions by country
• Prioritizing high-value regions or data elements
• Using scalable cloud infrastructure for processing
• Implementing parallel processing techniques

Organizations that successfully navigate this challenge complete the process in weeks rather than months, with major improvements in data usability.

The ROI of making data usable again

Financial institutions that successfully implement these strategies typically experience:

• Significant reduction in data processing time
• Significant improvements in data accuracy and completeness
• Enhanced ability to track companies across different countries
• Reduced storage costs through elimination of duplicate records
• More timely insights supporting better decision-making

Organizations that master large-scale company data operate in a fundamentally different way:

• They process comprehensive global data without bottlenecks
• They maintain up-to-date company information across countries
• They accurately track companies through complex events like changing countries
• They make decisions based on complete and current information

Summary

For financial institutions seeking to improve their data management capabilities, several key recommendations stand out:

• Start with clear business objectives rather than technical challenges
• Choose data providers that offer flexible delivery options and strong data quality
• Implement both batch and incremental processing approaches based on data importance
• Use historical snapshots to track entity changes over time
• Establish clear agreements covering data accuracy, freshness, and delivery reliability

By addressing these basics, financial institutions can transform massive data volumes from a burden into a strategic asset that drives competitive advantage.

For more information

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