Understanding Data Integration.

Data integration and interoperability (DII) encompasses the processes related to the movement and ultimate consolidation of enterprise data within data marts, hubs, warehouses, and lakes. DII solutions enable the basic data management capabilities that any organization relies upon, including:

  • Data migrations
  • Data consolidation
  • Vendor package integrations
  • Data sharing between systems and organizations
  • Data distribution
  • Data archiving
Data Architecture 3D Rendering

Definition of Data Integration

Within most data management frameworks, the data integration and interoperability (DII) knowledge area is concerned with the movement and consolidation of data within and between applications and organizations.

Data integration is the process of consolidating data into consistent forms, from multiple sources into a single, unified view. This process typically involves the extraction, transformation, and loading (ETL) of data from various sources, such as databases, files, or other systems, into a central repository or data warehouse. The goal of data integration is to enable the consolidation and analysis of data from different sources, making it more accessible and useful for business intelligence and decision-making. Data integration can be achieved through a variety of methods, including data warehousing, data federation, data virtualization, and data replication.

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What is big data integration?

Data integration and interoperability is central to big data management. Big data integration refers to the process of combining and integrating large and complex data sets, often referred to as “big data,” from multiple sources into a single, unified view. This process can be challenging due to the volume, velocity, variety, and complexity of big data. Data can encompass both structure and unstructured data. Big data integration typically involves the use of specialized tools and technologies, such as Hadoop and Spark, to manage and process large data sets in a distributed and parallelized manner.

Big data integration also requires additional steps such as data cleaning, data transformation, data governance, and data quality management to ensure that the integrated data is accurate, consistent, and usable. Additionally, big data integration often requires the use of distributed data storage and processing systems, such as data lakes, to handle the scale and complexity of big data. The goal of big data integration is to enable organizations to gain insights from their big data and make better-informed decisions.

Benefits of Data Integration

The main benefit of data integration and data integration solutions is to consolidate multiple data sources into a unified target data source which can be used for further downstream data projects. Subsequent benefits include:

Unifies Systems and Enables Collaboration

By consolidating data sources, data integration brings an enterprise’s data into singular view. This means that multiple departments are not only contributing their data towards improving the organization, they are able to find insights from combinations of other datasets that were unavailable before.

Supports Business Intelligence and Data Visualizations

Data integration is the foundation of further BI and data visualizations, two tools critical in discovering trends and patterns within enterprise data that lead to actionable insights.

Supports Machine Learning Applications

Data integration is also necessary to support big data and machine learning applications. Data integrations are critical for providing quality, clean and comprehensive data sets to train models on, as well as providing new information to improve the performance of the models over time.

Propagates Systemic Efficiency Throughout the Organization

Data integrations eventually improve data systems as a whole, and subsequently reduces costs, time expenses, data errors, and overall increases systemic efficiencies.

Data Integration Techniques

Data integration is critical to any data management strategy. The basic goals of these techniques are:

  • To keep applications loosely coupled using techniques like APIs or SOA,
  • To limit the number of interfaces developed,
  • To manage by hub and spoke,
  • To create standard (or canonical) interfaces.

There are several techniques used in data integration, including:

  • Extract, Transform, and Load (ETL): This is a process used to extract data from multiple sources, transform the data into a common format, and load it into a central repository, such as a data warehouse or data lake.
  • APIs: Application Programming Interfaces (APIs) allow systems to speak a common language, the API language, and work together without having to reveal their inner workings to each other.
  • Data Warehousing: This involves the use of a central repository, such as a data warehouse, to store and manage data from various sources. The data is extracted, transformed, and loaded into the data warehouse, and then made available for reporting and analysis.
  • Data Federation: This is a technique that allows organizations to access and query data from multiple sources as if it were in a single location. The data remains in its original location and is accessed through a virtual layer that integrates the data.
  • Data Virtualization: This is a technique that allows organizations to access and query data from multiple sources as if it were in a single location. It uses a virtual layer to integrate the data without physically moving the data.
  • Data Replication: This is a technique that involves creating copies of data from one location and storing them in another location. This can be used to improve performance, ensure data availability, and reduce data latency.
  • Data Cleansing: Data Cleansing is the process of identifying, correcting, or removing inaccuracies, inconsistencies, and incomplete data. This can be done by using various techniques such as standardization, data matching, and data de-duplication.
  • Data Governance: Data Governance is the process of ensuring that the data is accurate, consistent, secure, and compliant with relevant regulations. This can be done by implementing policies and procedures to manage data and ensure data quality.

These techniques can be used individually or in combination to achieve the desired level of data integration, depending on the specific needs and requirements of an organization.

Examples of Data Integration

Examples of data integration include:

  • Combining customer data from different sources, such as CRM, email, and social media, to create a unified customer profile
  • Merging data from multiple sensors or devices to create a more complete picture of an individual or environment
  • Joining data from different financial systems, such as accounting, inventory, and sales, to provide a comprehensive view of a company’s financial performance
  • Combining data from different healthcare systems, such as EHRs, lab results, and insurance claims, to provide a complete view of a patient’s medical history
  • Integrating data from different marketing platforms, such as email, social media, and web analytics, to understand how customers interact with a brand
  • Combining data from different transportation systems, such as GPS, traffic cameras, and public transit schedules, to optimize transportation routes and improve traffic flow.
  • Integrating data from different systems to build a data lake or data warehouse for analytics or machine learning.

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