Data Integration 

What is data integration?  

Data integration refers to the processes and technologies used to combine data from multiple sources into unified, coherent views and data sets. It involves consolidating different types of data from disparate systems and applications across an organization to provide users and applications with consistent, reliable access to timely, accurate information.  

Some key things to know about data integration: 

• Enables a holistic view of key entities like customers, products, accounts, etc. by linking data across departmental systems, databases, and applications. 
• Consolidates structured and unstructured data from diverse sources including databases, APIs, cloud apps, files, etc. into a cohesive whole. 
• Helps organizations gain greater value from their data assets by making data more accessible, understandable, and usable to more users. 
• Typically involves processes like standardizing formats, cleansing dirty data, transforming schemas, matching records, and enriching data from sources. 

Why is data integration important? 

Data integration provides a range of benefits that make it a critical capability for most organizations: 

• Supports data-driven decision making through unified views of business entities like customers, products, and transactions. 
• Enables reporting, analytics, business intelligence, and advanced applications by unifying data from across systems. 
• Eliminates data silos and inconsistencies across departments, systems, databases, and applications. 
• Saves time and costs by reducing redundant data capture efforts and manual data reconciliation.  
• Improves data quality, accuracy, and reliability by consolidating data in one trusted source. 
• Allows building centralized data hubs, data warehouses, and data lakes to support analytics. 
• Simplifies access to integrated data for reuse across the organization by many applications. 

How does data integration work?  

A typical data integration workflow involves: 

• Identifying data sources like databases, APIs, files, cloud apps across departments and systems. 
• Extracting data from sources via built-in exports, APIs, queries, connectors, or real-time streams. 
• Cleansing data to fix inconsistencies, errors, duplicates, missing values, and outliers. 
• Enriching data by appending attributes like location codes, product categories, or other reference data.  
• Matching and linking records across datasets through unique identifiers or fuzzy matching. 
• Loading integrated data into target databases, data warehouses, data lakes, etc.  
• Providing access to integrated data via APIs, reporting tools, dashboards, and applications. 
• Scheduling and orchestrating recurring ETL (extract, transform, load) processes and data flows. 

Specialized integration tools and platforms help manage and automate these workflows.  

Data integration and technologies 

Key methods and technologies for data integration include: 

• ETL tools for batch data integration and warehousing. 
• Data virtualization providing real-time integrated views of data from multiple systems. 
• Enterprise service buses (ESBs) enabling integration via APIs, messaging, and adapters.  
• Data replication to synchronize and share data across multiple databases and systems. 
• Master data management (MDM) for consistent reference data across systems. 
• Data warehouses and data lakes to store integrated data at scale for analytics.  
• APIs and web services for programmatic data access and integration.
• Streaming, events, and change data capture (CDC) for real-time integration. 

Challenges of data integration 

Some common data integration challenges include: 

• Incompatible data formats, schemas, semantics, and APIs across source systems. 
• Technical complexity in managing large-scale integrations across many systems and data types. 
• Security, privacy, and regulatory requirements around sharing and combining certain data.  
• Legacy systems with limited export, integration, and API capabilities.  
• Lack of common identifiers, schemas, and attributes across systems for matching records.  
• Poor data quality requiring extensive transformation, cleansing, and standardization. 
• Real-time integration and latency issues for operational systems. 
• Maintaining accuracy as source data changes rapidly in systems.  
• Manual efforts needed for mapping, transforming, and monitoring integrations.  

How LexisNexis supports data integration 

LexisNexis provides robust systems that can support data integration. With Nexis^® Data+, you gain access to a vast array of reliable and up-to-date information from diverse sources. This extensive database facilitates comprehensive research, empowering users to extract valuable insights for data mining processes. 

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