Data Aggregator

What are data aggregators?  

A data aggregator is a company that collects data from various sources and consolidates it into one place to make it easier for businesses, researchers, or individuals to access and analyze. Data aggregators source data from public records*, web searches, surveys, and other providers.  

Some key things to know about data aggregators: 

• They compile both structured and unstructured data from disparate sources. This includes numerical data like statistics, metrics, and ratings, text data like documents, notes, and social media posts, images, video, audio files, and more. 
• Aggregation helps overcome issues like data being stored in incompatible formats across sources, lack of data standardization, and sources using different identifiers for matching records.  
• They use big data infrastructure including cloud computing, distributed storage systems, and high-performance processing engines to store and process large, complex datasets. 
• Data is normalized and standardized to a common schema and set of formats. This makes the data more usable for analysis. 
• Some perform enrichment by appending additional contextual data like demographics, firmographics, or social media profiles to the records. 
• APIs, bulk data files, cloud data services, and interactive dashboards are provided to enable easy access to aggregated data at scale. 

Industries like marketing, finance, healthcare, insurance, and retail use aggregators for powering data-driven decisions, analytics, and applications. 

How does data aggregation work?  

Data aggregation is conducted using the following steps:  

• Identifying relevant data sources like public records, web scrapers, surveys, sensors, commercial data providers, and internal systems. 
• Cleansing and standardizing through processes like fixing inconsistencies, removing duplicate records, normalizing data formats, parsing unstructured data, and enriching with unique identifiers.
• Enrichment by including additional attributes like locations, demographics, firmographics, or social media profiles to provide contextual data.
• Integration by compiling and linking related data points from all sources into unified, consistent datasets.
• Storage in structured databases like data warehouses, unstructured data lakes, or specialized time-series databases to store aggregated data. 

The process brings together data from many places into one clean, integrated set that can power analytics and applications.  

Uses of data aggregation 

Key business uses of aggregated data include: 

• Market research: Analyze consumer demographics, psychographics, buying behavior, sentiment, competitive intelligence, and market trends. 
• Business intelligence: Identify growth opportunities, benchmark performance, analyze operational efficiency, and support strategic decisions.  
• Risk modeling: Build predictive models to detect crime, analyze credit risk, model insurance risk, and make actuarial projections. 
• Sales prospecting: Enrich B2B lead and account data with attributes to enable targeted sales and marketing efforts.  
• Compliance reporting: Streamline financial, regulatory, and compliance reporting using aggregated data. 
• Personalization: Create highly targeted marketing campaigns and personalized user experiences based on aggregated customer data. 
• Product development: Analyze aggregated usage patterns, feedback, and metrics to improve products. 
• Data monetization: Package and sell access to aggregated datasets to other parties. 

Benefits of data aggregation 

Benefits that data aggregation provides include: 

• Wider data access: Consolidates data from many disparate sources into one place, overcoming barriers. 
• More comprehensive data: Offers increased breadth, depth, and diversity of information not available from a single source.  
• Higher quality data: Consistent formats, accuracy, and removal of errors improves overall reliability. 
• Greater context: Linking related data points provides a 360-degree view of entities like customers, products, etc.
• Operational efficiency: Reduces staff time spent collecting, cleaning, integrating, and preparing data.
• Faster analysis: Clean, unified data speeds up analytics, reporting, modeling, and other data-driven processes. 
• Reduced costs: Less manual effort needed for data management, so data usage and analytics costs are lower.
• Enhanced insights: More robust data sets drive better insights from analytics, reporting, and data science.  

Risks and challenges of data aggregation 

Some top risks and challenges with data aggregation include: 

• Privacy issues: Collecting personal data from multiple sources raises consumer privacy concerns if not handled properly. 
• Biased data: Aggregated datasets may propagate biases that exist in their underlying data sources. 
• Security risks: Centralizing data creates risks of data breaches and cyber-attacks by presenting a single point of failure. 
• Data provenance: Understanding the source and context of aggregated data elements can be difficult. 
• Licensing issues: Usage restrictions around some types of data like open data, public records, and commercial data feeds. 
• Costly infrastructure: Requires significant investment in storage, processing, and data management technologies. 
• Data rot: Keeping continuously updated datasets current, relevant, and accurate as new data arrives.  
• Compliance: Adhering to evolving data regulations, privacy laws, and consumer transparency requirements.  

How LexisNexis supports data aggregation 

LexisNexis provides essential resources for data aggregation with an industry leading database with unmatched quality and depth of data. With the Nexis^® Data+ flexible API, you can access our expansive source universe of data, featuring petabytes of semi-structured, normalized, and enriched text-based data to meet a wide range of data needs.  

Whether forecasting with predictive analytics or conducting trend analysis, improving competitive intelligence, or ensuring accuracy and compliance, we provide the complementary third-party data your organization needs to make strategic, data-driven decisions. Enhance your data analysis and business intelligence with data that can be trusted, accessed, and analyzed when and how it’s needed. 

* Access to U.S. Public Records content is subject to credentialing. Due to the nature of the origin of public record information, the public records and commercially available data sources used in reports may contain errors. 

Due to the nature and origin of public record information, the public records and commercially available data sources used in reports may contain errors. The LexisNexis Public Records services are not provided by “consumer reporting agencies,” as that term is defined in the Fair Credit Reporting Act (15 U.S.C. §, et seq.) (“FCRA”) and do not constitute “consumer reports,” as that term is defined in the FCRA. Accordingly, these LexisNexis services may not be used in whole or in part as a factor in determining eligibility for credit, insurance, employment, or another eligibility purpose in connection with which a consumer report may be used under the FCRA. 

Learn more about Nexis^® Data+