Data integration is the practice of preparing a dataset that resides in a specific location and is structured in a particular way to be consumable in different places and other ways. To perform data integration, users need to have a firm grasp of the state of the data and the skills to engineer it for the desired alternate purposes, such as business analytics, operations functions, machine learning algorithms, and artificial intelligence implementations.
Let’s jump in and learn:
What Is Data Integration?
At its core, data integration means combining information from various sources into something useful. Data integration provides a framework for efficiently managing data and making it available where it is needed (i.e., by systems and people) in an accessible format, using discovery, cleansing, monitoring, and transformation.
Data integration includes several distinct sub-areas, such as:
- Data migration
- Data warehousing
- Enterprise application integration (EAI)
- Master data management (MDM)
Data integration is an important function for organizations, because it has the ability to:
- Deliver more valuable data insights and business intelligence
- Enhance customer experiences
- Improve collaboration
- Increase productivity by reducing errors and rework
- Save time by reducing the need for manual data gathering and analysis
Data Integration in Action
Data integration solutions are being enhanced to provide support for integrating high-volume, high-velocity data (i.e., big data) to process time-sensitive information. Examples of this include using sensor data to avoid production disruptions, storing transactions to prevent fraud while it is happening, or supply chain routing to avoid weather delays or optimize inventory levels. In these cases, data integration processes are generally performed in conjunction with cloud data warehouse and analytics solutions.
Data Integration vs. Application Integration
When considering data integration vs. application integration, the net results are more or less the same. Data is moved from one location to another, often with multiple data sources being consolidated in a single location. The difference is how the data integration is conducted.
Data integration is commonly used to migrate data from older systems to newer environments or move data from operational systems into a data warehouse. Data is moved via batch jobs that are processed periodically (e.g., weekly, daily, hourly, ad hoc).
Data integration is used to collect data for historical analysis. The process consists of integrating millions or billions of objects (e.g., sales transactions, orders, insurance claims, clinical tracking activities, machine or sensor data).
Application integration, sometimes called enterprise application integration (EAI), is the consolidation and enhancement of workflows and data across software applications with point-to-point integration. Data from disparate sources is located, retrieved, cleaned, and integrated using an API (application program interface) to communicate.
Often organizations use application integration to bridge existing and new cloud applications to either simply move data or allow the applications to work together and share data. Application integration enables the seamless connection of various on-premise and cloud applications (e.g., CRM, e-commerce, finance, ERP) to automatically transform and orchestrate the data required for workflows.
Data Integration Tools and Techniques
Storage Tools for Data Integration
- Database—includes both relational databases and NoSQL data stores
- Data warehouse—gathers data derived from multiple data sources in a central repository that can show how data types relate to one another
- Data lake—collects raw and unstructured data in a single storage system
The data warehouse is an important part of data integration, because it is often where collected data is aggregated and stored. The core benefit of a data warehouse is that it allows analysis to be performed in that environment.
How Data Integration Works
Data integration provides the map or model that defines the structure and meaning of data as well as the path it takes as it is moved from one system to another. It may also include cleansing, sorting, enriching, and other processes to prepare data for use. If this is done before the data is stored, the process is called ETL (extract, transform, load). When it is done after data has been stored, it is called ELT (extract, load, transform).
- Extract the necessary data from the source by using connectors or an API.
- Transform disparate data into a standardized format, enrich it, and validate it to ensure consistency.
- Load the data into the central location.
Data Integration Techniques
- Common data storage or physical data integration
Creates a new system, which keeps a copy of the data from the source systems to store and manage it independently of the original system using a data warehouse
- Data propagation
Uses applications to copy data from one location to another using enterprise application integration (EAI) and enterprise data replication (EDR)
- Data virtualization
An interface provides a near real-time, unified view of data from disparate sources using enterprise information integration (EII) for data abstraction
- Manual Integration or Common User Interface
Consolidates data by physically bringing it together from separate systems using ETL without a unified view of that data
Data Integration Processing
- Batch data processing
Runs data transformations periodically on a defined dataset, such as processing a dataset that is used for weekly, monthly, and quarterly reporting
- Micro-batch processing
Runs smaller datasets more frequently, such as periodic alerts that need to be frequent but not in real-time
- Stream processing
Runs processing on data flows from source to destination, such as digital assistants, recommendation engines, or event processing
Data Integration Tool Deployment Options
Provided as integration platforms-as-a-services (iPaaS) in most cases
Installed in a private cloud or local network
Used as an alternative to a proprietary solution and to have complete control over data in-house
Offered off the shelf and often purpose-built for specific use cases
Why Data Integration is Important
The reasons why data integration is important are many and varied, but all share a common thread—the reduction in time and errors realized by eliminating the need to manually transform, combine, and apply rules to data to make it easier to analyze. Following are examples that show why data integration is important.
Enhances Data Integrity
Data integration can be used to cleanse and validate the information that passes through its systems. The result of implementing a robust data integration plan is that it ensures data is free of errors, inconsistencies, and duplication.
Keeps Data Current
A data integration solution also makes it easy to keep information up to date. With data integration, one input can be propagated across all integrated systems, which keeps data current.
Reduces Data Complexity
Data integration streamlines data connections to reduce complexity and make it easy to deliver to any system. For instance, it can be used to create a data hub that can publish to and be subscribed to for simplified data access.
Uses Unified Systems to Increase the Value of Data
Data integration increases the value of data by bringing disparate sources together in unified systems. Data from internal and external sources and of different types (e.g., structured, unstructured, spatial, tabular, web, raster, big data) can easily be combined.
Data integration can improve collaboration across an organization and with third-party constituents by automating the flow of information. All users can easily access and share information between applications.
Ensures Quality Data
Data integration enhances data quality by ensuring its accuracy, consistency, and completeness. A data integration model can help reduce inaccurate, inconsistent, or incomplete objects or datasets by checking the data against validation rules.
Improves Data Availability
With data integration, the essential task of connecting all data sources is automated and accelerated. In addition, it provides ready access to any data sources in a unified way to improve data availability.
Increases Operational Efficiency
When data integration is automated, more time can be spent analyzing it. Also, data integration saves users time by eliminating the need to build the connections from scratch when they need to develop applications or create reports.
Provides a Competitive Advantage
Effectively used, data integration can fuel insights that allow organizations to provide better services that help them to stay ahead of the competition. In addition, this information can be used to develop new offerings that are tailor-made to customers’ wants and needs.
Reduces Errors Related to Manual Operations
Data integration reduces manual interactions with and aggregation of data, which is known to be error-prone. Because data integration automates the consolidation of data and keeps it synchronized, the chances of errors are significantly reduced, and accurate and complete records are increased.
Data Integration and Big Data
Big data is a term that refers to large volumes of data, both structured and unstructured, especially new forms of data that are produced at an overwhelmingly fast rate by machines (e.g., devices, sensors, equipment). Four “Vs” are often used to describe big data.
- 1. Volume, or the amount of data
- 2. Velocity, or the speed at which data is created
- 3. Variety, or the variation of data
- 4. Veracity, or the accuracy of data
Big data integration combines data originating from a variety of different sources, software, and formats, and then provides users with a translated and unified view of the accumulated data.
The amount, complexity, and rate of growth associated with big data make it difficult to process, but traditional ETL tools have evolved to organize this data. However, a common platform is needed to support data quality and profiling.
Master data management, or MDM, systems are commonly used to promote the collection, aggregation, consolidation, and delivery of big data.
Additionally, new tools are being used to support big data integration.
For organizations that use cloud services, data can be organized using integration platform-as-a-service (iPaaS). This service also makes it easy to include data from cloud-based sources, such as software-as-a-service (SaaS).
Data integration and machine learning help extract value (i.e., with analytics) from big data by providing automated solutions for processing it. The value provided with data integration and big data includes:
- Behavioral trends insights
- Cost reductions
- Faster, more informed decision making
- Greater agility and speed to market
- Improved customer service and customer experience
- Increased productivity and efficiency
- Predictive analytics
How Data Integration Helps Organizations Succeed
Data integration helps organizations succeed by organizing data to allow them to understand what it means and put it to optimal use. Several examples of how data integration helps organizations succeed are as follows.
Creation of New Products and Services
Data integration can provide a comprehensive view of the current market conditions and internal information. Combined, this helps organizations understand how offerings are performing against both internal benchmarks and competitors.
This information can be used to inform and direct the development of upgrades as well as new products or services that optimally align with market demands. This not only improves customer satisfaction and engagement, but can also provide a competitive advantage.
Optimized Customer Experiences and Improved Customer Engagement and Retention
Customer insights that used to take years of research and analysis can now be put into the hands of users in days, hours, or even seconds, using data integration to gather information from platforms that track customer purchases and behavior. This provides many opportunities to optimize customer experiences.
Marketers can build rich profiles of their customers to tailor messages. In addition, this rich customer data can be used to provide personalized, unique customer experiences that drive increased customer engagement.
Smarter Business Decisions
Data integration supports transparent processes and increased intelligence across an organization by making data accessible. This gives users the flexibility to use all data in any system, which allows them to understand the information better.
Data integration makes it possible to easily navigate through organized repositories that contain a variety of integrated datasets. The insights from this integrated data are near limitless.
For example, a user could apply location intelligence to a dataset to make it spatially comprehensive. This would offer a new level of insight around that dataset, resulting in more informed decision-making.
Data Integration for Accurately Aggregated Information
With data integration, any type of data from a wide variety of sources can be collected, stored, and made accessible to users as highly-accurate source information. Developing a robust data integration strategy ensures the ongoing availability of high-quality data to power informed decision-making and drive more positive outcomes. While it represents a fair amount of effort for IT teams, the result is accurate data that is more accessible and easily consumable by people and machines.
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Last Updated: 6th January, 2022