Data integration is the collection of technical and business processes used to combine data from disparate sources into meaningful and valuable information. A data integration system plays a crucial role in managing and merging data from various sources into a unified format, supporting tasks like data cleansing, quality validation, and the automation of data processes. Most organizations have many separate applications and systems that contain large amounts of data, which could be combined in various ways for business intelligence and analysis is, improved operations, and other activities. Data integration is a complex initiative, one that requires attention to enterprise architecture and its components: data architecture, application architecture, technical / infrastructure architecture; metadata management, master and reference data management , data quality management, and data governance . Many organizations consider data integration to be only a part of a data warehouse effort, when actually it is a valuable function in data management and technical design in its own right.
What is Data Integration?
Definition and Examples
Data integration is the process of combining data from multiple sources into a single, unified view. This process involves data replication, ingestion, and transformation to create a standardized format that can be used for various applications such as business intelligence (BI) and data analysis. By integrating data, organizations can ensure they have a complete, accurate, and up-to-date dataset, which is critical for making informed decisions and optimizing business processes.
For example, a retail company might integrate data from its online store, physical stores, and customer service systems to gain a comprehensive understanding of customer behavior and overall sales trends. Similarly, a healthcare provider might integrate patient data from different departments to improve patient care and streamline operations. Data integration is a fundamental aspect of modern data management, enabling organizations to harness the full potential of their data assets.
Data Integration Concepts
Data integration involves combining data, either whole sets of data or selected portions based on business requirements, from more than one source into a specific target, to provide a unified view of the combined data.
Selecting the right data integration tool is crucial for effective data management, as it can streamline processes, automate systems, and support various integration approaches, such as ETL and application integration.
Many data integration projects are part of a data warehouse initiative since the definition of a data warehouse includes the collection of data from multiple sources. According to much research over many years, approximately 80% of the effort expended in a data warehouse project concerns the mapping for data and the extraction/transformation/loading (ETL) that is collectively known as data integration.
The data integration process covers several distinct functions:
Source-Target Mapping
Source-Target Mapping is the process that defines the metadata for the data elements for each file / table for every source system involved in the data integration effort, aligned to the corresponding attribute in the target application table. It contains the relevant metadata for the chosen data to be included in the ETL process, and therefore can be considered as the blueprint for the ETL design.
Extraction-Transformation-Loading (ETL)
Drawn from the data warehousing domain, extraction/transformation/loading are the processes that take the data from the sources, transform it from its current state into a unified version, and load the unified version to the target application’s database. To improve data quality, this set of processes includes data cleansing, which are the routines that parse the data against parameters set by subject matter experts for accuracy, completeness, validity, and other data quality dimensions (characteristics) . Cleansing includes the application of data unification rules (e.g., converting addresses to standard format, replacing null values with N/A, etc.)
The extraction step should be designed so that it does not affect the source system negatively for performance, response time or encounter any form of lock (database, table, row, etc.). The transform step applies a set of rules to transform the data from the source format to the desired target format, applying any required connections to other data in the process, and validating any additional business / technical rules. The load step places the cleansed and transformed data into the target database as smoothly and quickly as possible, while maintaining all referential integrity in the database.
Figure 1: Non-Data Warehouse Data Integration Example
Figure 2: Data Warehouse Data Integration Example
Data Integration Cycle
The typical data integration process, commonly called an ETL Cycle , has the following steps:
Cycle initiation
Build source-to-target mapping for business area
Extract (from sources)
Validate process and data
Transform (clean, apply business rules, check for data integrity, create aggregates or disaggregates, perform any conformance processes)
Stage (load into staging tables)
Publish (to target tables)
Prepare audit reports
Archive
Close out, prepare for next cycle
Each of these steps is a process and would include a set of activities that may involve a variety of people, technologies, and tasks. Neglecting any step listed would mean the cycle was not fulfilled and that the ETL process as a whole was not completed, reducing the effectiveness of data integration.
Foundations Of Data Integration 4 Data Integration Process
Steps Involved in Data Integration
The data integration process involves several key steps that ensure data from various sources is combined into a cohesive and usable format:
Data Ingestion: This initial step involves collecting data from multiple sources, such as databases, files, and applications. Effective data ingestion ensures that all relevant data is captured and made available for further processing.
Data Transformation: Once data is ingested, it needs to be converted into a standardized format. This step involves cleaning, normalizing, and enriching the data to ensure consistency and compatibility across different data sources.
Data Loading: Transformed data is then loaded into a target system, such as a data warehouse or a data lake. This step ensures that the integrated data is stored in a centralized location where it can be easily accessed and analyzed.
Data Quality: Ensuring the accuracy, completeness, and consistency of the integrated data is crucial. Data quality checks and validations are performed to identify and rectify any issues, ensuring that the data is reliable and trustworthy.
Data Security: Protecting the integrated data from unauthorized access, theft, or corruption is essential. Implementing robust security measures, such as encryption and access controls, helps safeguard sensitive information and maintain data integrity.
Approaches to Data Integration
There are several approaches to data integration, each with its own advantages and use cases:
ETL (Extract, Transform, Load): This traditional approach involves extracting data from various sources, transforming it into a standardized format, and loading it into a target system. ETL is widely used for batch processing and is ideal for integrating large volumes of data.
ELT (Extract, Load, Transform): A variation of ETL, ELT involves loading data into a target system before transforming it. This approach leverages the processing power of modern data warehouses and is suitable for handling large datasets and complex transformations.
Real-time Data Integration: This approach integrates data in real-time, providing immediate access to up-to-date information. Real-time integration is essential for applications that require instant data availability, such as fraud detection and real-time analytics.
Cloud-based Data Integration: With the increasing adoption of cloud services, cloud-based data integration has become popular. This approach integrates data in the cloud, supporting cloud-based applications and services, and offering scalability and flexibility.
Data Virtualization: Data virtualization creates a virtual layer that abstracts data from underlying sources, providing a unified view without physically moving the data. This approach simplifies data access and integration, improving operational efficiency and decision-making accuracy.
By understanding and implementing these approaches, organizations can choose the most suitable data integration solutions to meet their specific needs and achieve seamless data integration.
Data Integration Tools and Technologies
Data integration tools offer powerful solutions to streamline and automate the process of integrating multiple data sources into a single cohesive data ecosystem. These tools connect to a wide array of sources, including databases, APIs, and cloud platforms, facilitating efficient data extraction, transformation, and loading (ETL). Many modern tools also support real-time data access and processing, which enhances operational efficiency by ensuring immediate availability of integrated data.
For example, Integration Platform as a Service (iPaaS) solutions offer pre-built connectors and workflows to simplify data synchronization between cloud and on-premise systems. Key functionalities of these platforms often include data mapping, data aggregation, and data transformation, essential for unifying data from disparate systems. By maintaining data accuracy, consistency, and integrity across environments, these tools help organizations achieve a “single source of truth” for analytics, providing valuable insights from consolidated data.
Business Rules as Part of Data Integration
One of the tasks in the Transform step of the ETL Cycle is to “apply business rules.” Business rules are abstractions of the policies and practices of a business organization. They are essential metadata for any data integration effort, since they specify the actions that must occur (or not occur) to the data in a record/table/file/database under a set of conditions. Applying the business rules to the data set is an iterative process, which includes reviewing the existing business rule documentation, validating assumptions, applying the rules to the data set and confirming the act, and documenting the relevant metadata for the data associated with each business rule. It is important for all data integration efforts to include business rules as part of the project since they provide valuable information concerning the operations of organization and its data in business and technical contexts.
Overcoming Data Integration Challenges
Data integration projects face several challenges, including the complexity of consolidating data from multiple data formats and sources. The involvement of both structured and unstructured data from data lakes, databases, and APIs adds further complexity to the integration process. Real-time data integration, which supports continuously streaming data, requires a robust data integration platform that can maintain data accuracy and integrity during processing. Ensuring data security is also critical, especially when sensitive data is transferred or consolidated across systems. By implementing proactive risk management strategies and fostering stakeholder collaboration among data engineers, data scientists, and business units, organizations can address these challenges effectively. Additionally, solutions like data virtualization allow for a more efficient data integration process by creating a unified data source, improving both operational efficiency and decision-making accuracy.
Data Quality Challenges in Data Integration
Poor data quality (perceived or real) causes distrust from business users when using data in data warehouses and data integration projects. This lack of trust may cause customers to look for analytical solutions elsewhere. Additionally, poor data quality leads to poor decisions and improved costs due to re-work, missed opportunities, error corrections, etc. Almost half of all firms do not have a data quality plan, according to a 2010 Gartner Group study. This lack can severely affect the performance of all data integration efforts, and produce erroneous mistakes in the integrated data sets, leading to poor decisions or ineffective operations. Ensuring that data quality management is part of all data integration projects is essential for success and optimal results.
Data Integration Audit Processes
Data integration processes produce metadata that must be recorded and analyzed. It is important to study this metadata to understand the performance of the integration processes for quality control and improvement. Metadata from DBMS system tables and from schema design tools is easy to capture, but probably composes only 25 percent of the metadata needed to understand and control the data integration process. Another 25 percent of the metadata is generated by the cleansing step.
The biggest metadata challenge for the data integration team is developing and storing process-flow information. In ETL tool suites, this process-flow metadata is maintained automatically; however, if the application is written specifically, the project must implement a central repository of process flow metadata.
Every data integration audit process requires the creation of several tables and programs. Following is an example of an important table that records the metadata in the data integration process.
Column Name Description JobNumber The unique number of the job that this step is associated with. StepSeqNumber The step number within the object that executed the unit of work StepDescription A description of the step. For example, “Inserted records into tblA.” Object The name of the object. For example, the name of a stored procedure or package that accomplishes the step. NumberRecords The number of records affected by the step. MinThreshRecords The minimum “acceptable” number of records affected by the step. MaxThreshRecords The maximum “acceptable” number of records affected by the step. MinThreshTime The minimum “acceptable” execution time for the step. MaxThreshTime The maximum “acceptable” execution time for the step. CreateDate The date and time the record was created. StepNumber A unique value assigned to the record, generally an identity column.
Table 1: AdminStepMaster
Many ETL jobs are multi-step, complicated transformations. It is important to record metadata that tracks the successful completion of an operation, when it happened and how many rows it processed. This information should be stored for every step in an ETL job.
Data Integration Testing
Testing the data integration process, and the data that has been moved, is a crucial component of the effort. There are three phases of any test process :
Unit testing – occurs during and after development, performed by the developer and the systems analyst in the development environment
Quality Assurance testing (QA) – occurs by a separate group in a separate environment mirroring production
User Acceptance Testing (UAT) – occurs by the user group in a separate controlled environment created from the QA environment; letting users have a hands-on look at the data to ensure processes are running as expected
At the end of UAT, the test manager should obtain sign-off from the user representative. Once sign-off has been received, the data integration application is ready to move to production.
When testing new ETL processes, it is important to have users test for known data issues and source system anomalies. Not only will this validate the project’s efforts; exposure to the clean data will excite users and make them eager to use the new data source. Clean data can create some positive effects. Users will communicate the success of the data integration project, and this will lead to additional requests for integrated data that is based on a confirmed approach, governed by best practices, and filled with high quality data that adheres to organizational business rules.
General Data Integration Challenges
Data integration processes can involve considerable complexity, and significant operational problems can occur with improperly designed data integration applications. Following best practices in enterprise application / integration / data architecture can alleviate or eliminate many of the issues that are encountered by poorly designed data integration systems.
The range of data values or the level of data quality in an operational system may not meet the expectations of designers at the time validation and transformation rules are specified. Documenting business rules, including data quality expectations, can reduce the issues that arise due to data anomalies. Data profiling of a source during data analysis can identify the data conditions that must be managed by transformation rules and their specifications. Doing so will lead to changes to validation rules, explicitly and implicitly, that are implemented in the ETL process.
Data warehouses, a major form of data integration, typically are assembled from a variety of data sources with different formats and purposes. Therefore, ETL is the key process to bring all the data together in a standard, homogeneous environment. Without error-proof data integration (ETL), the data warehouse will not succeed.
Data integration architects should establish the scalability of any data integration process across the lifetime of its usage. This includes understanding the volumes of data that will be processed according to service level agreements. The time available to extract from each source system may change, which may mean the same amount of data may have to be processed in less time, or there may be additional sources to process in the same window. Data integration scalability is essential for a successful system.
Conclusion
Data integration is a critical process that allows an organization to combine data from disparate sources into a unified version for a variety of uses. Not just for data warehousing, proper data integration starts at architecture, includes understanding requirements and the data, and ends with trusted valid results. With data integration informed by best practices, any organization can achieve collated data that becomes information to improve operations and make decisions.
