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Organizations across all business sectors are awash with data, from supply chain monitoring to customer statistics and business process metrics to performance parameters. Digitization programs are adding to the vast quantities of information that businesses need to process and share.
The problem is that typically achieving compatibility and interoperability across the entire dataset requires surmounting significant challenges. Companies that can leverage the value embedded within this data will have the best chance of prospering in a competitive and volatile marketplace. This situation is where a data integration process will help.
Data integration is the principle of combining data of varying types and formats into a single coherent entity that enables its use. In essence, it is integrating data from multiple sources. Data contains information that has value to a business; maximizing this value requires that the information be readily accessible to business processes. Data integration facilitates this accessibility.
Data integration combines technical and business processes, the first implementing the mechanisms for obtaining, transforming, and storing data. The latter defines the accessibility requirements and purpose of that data.
The implementation of data integration can employ various techniques, including:
- Data replication techniques manage the transfer of data between storage repositories to maintain synchronization. The use of this method applies to situations where no transformation of the data is necessary.
- Data consolidation techniques support data integration by reducing the volume of stored data. Using methods such as the extract, transform and load (ETL) process, collect data from various sources and translate it into a standardized accessible format for storage in a centralized database. Data may also be subject to cleansing and filtering as part of the translation process.
- Data propagation techniques move copies of data between information repositories for distributed storage to support accessibility and responsiveness needs. However, this technique creates challenges in maintaining synchronization between data copies and managing bandwidth consumption during the transfer process.
- Data virtualization techniques provide an interface for accessing unintegrated data from multiple sources using real-time interpretation based on a user-configurable data model. While requiring significant processing resources, it offers maximum flexibility and adaptability characteristics.
- Data federation techniques leverage data virtualization to create a standard data model using a virtual database, using data abstraction to provide the unified view. The advantage is that it preserves the integrity of the source data where compliance or security restrictions are necessary.
- Data warehousing is the centralized storage of integrated data following its cleansing and transformation into a unified dataset. By contrast, a data lake is the centralized storage of consolidated but unintegrated data, retaining its native formatting.
- Data change capture techniques track changes to source databases to maintain the synchronization status of any centralized repository.
There are distinct types of data integration to choose from:
- Application integration utilizes software applications to access data from various sources and perform processing to make the data compatible and accessible through a centralized mechanism.
- Middleware integration utilizes logic sitting between the application layer and the hardware infrastructure to transform and integrate data. Compared to manual integration, this introduces automation but is inflexible and non-portable, often subject to compatibility issues with data sources.
- Uniform Data Access Integration (UDAI) utilizes translational logic that copies data from its various sources and transforms and stores the data in a centralized repository for access. This method leaves the source data unchanged.
- Common storage integration copies data from various sources and stores the data in a centralized repository accessible through a unified view.
- Manual integration relies on users collecting and processing data from multiple sources to extract information. This inefficient, resource-intensive, and error-prone technique is available as a last resort.
Let’s consider a data integration example. Imagine a sales forecast team requiring information to support short-term tactical forecasting over a seven-day horizon. The team needs the forecast for planning transportation schedules and routing. To produce accurate estimates, they need access to a diverse range of data such as retail outlets’ demands and stock levels from warehousing and distribution centers. They will also include external factors such as weather forecasts, planned road closures, and driver availability.
If all these data sources require manual interrogation, extraction, and interpretation, gathering the information may take most of the planning period. Manual processes are also laborious and error-prone, not great for a dynamic business in a competitive market. In addition, this manual approach would leave little time for managing exceptions and responding to disruptive events.
Data integration reduces workload and errors, improves data quality, and operates autonomously in near real-time. In addition, having all the information integrated into a single entity would allow forecasting to be automated and advanced techniques such as machine learning-based predictive logic employed to improve forecast accuracy.
The forecast team can achieve more with better results using fewer resources.
- Data integration offers a range of benefits beyond simply unifying the dataset.
- Data integration is a critical element of good Business Intelligence, the process of analyzing business data to inform decision-making processes and support reporting.
- Data integration allows stakeholders across the business, indeed along the entire supply chain, to eliminate silos and let everyone access coherent shared data and make consistent decisions.
- Data integration reduces error and improves data quality, leading to more effective use of available information and tangible business performance improvements.
- Data integration reduces the time and work required to analyze business data by leveraging automation and eliminating the required effort to find, gather and process data.
- Data integration supports continuous improvements to business information by highlighting sources with data quality or availability issues, allowing corrective actions to focus on those sources.
- Data integration supports Master Data Management (MDM) processes by ensuring the quality and correctness of the data that underpins the single version of the truth generated by the MDM solution.
- Data integration enables the creation of an integrated data warehouse to combine disparate data sources into a centralized relational database where information is more accessible.
Embracing the use of big data can bring business benefits. Still, companies are often reluctant to manage high data volumes too large and complicated for conventional data processing models. Big data analytical processes require high-performance and scalable tools to manage the volume of data. Data integration can aid the adoption of this technique, leveraging automation to minimize management overheads.
One of the challenges multinational businesses and international collaborations face is the semantic differences seen in business data. Subtle differences such as date or accounting formats or significant differences seen in units of measurement can make sharing data challenging. Data integration can resolve these issues by standardizing the dataset and providing geographically based views of the data.
Companies operating multiple legacy systems that develop over time often face the problem of maintaining the same data in various locations that use different formats. In consequence, errors can quickly creep in when data divergences when updating one system in isolation from the others. Data integration can resolve these issues by creating a single rationalized baseline data element against which all the legacy systems can synchronize.
Successful implementation of data integration faces several challenges.
- When sourcing data from external parties, the quality and trustworthiness of the data may be unknown
- Integration with legacy systems may cause technical challenges for the extraction and synchronization of data.
- Implementing data integration requires careful planning based on a thorough understanding of the data sources and the goals of the integration.
- Maintaining a coherently integrated dataset using dynamically changing data sources requires careful management processes that support synchronization.
Data integration tools are commercially available to enable businesses to maximize their information management potential. The key features of any tool are:
- Simple to use for all stakeholders
- Simplification of complex data integrations
- Automation of business processes
- Facilitation of data sharing across organizational boundaries
- Centralized data management
- Maximization of the value of integrated data
- Scalable and portable enough to adapt to business changes
The growth in data integration has culminated in the development of Information Platform as a Service (iPaaS). These typically include a suite of cloud-based services to implement integration dataflows necessary for data integration.
When selecting a tool, businesses need to consider whether they need on-premises or cloud-based data integration solutions and whether they should invest in a proprietary solution or go down the open-source route. But, of course, these influencing factors will vary by business, its information systems, and its existing infrastructure.
Data integration is simply the process of consolidating data from diverse sources into a single reliable and coherent dataset. The aim is to provide users with a unified information source built from data of any type, format, and structure. Its importance is growing with the increasing need to process and share business information. This expanding use of big data analytics across business sectors further underlines this need.
Grouparoo is a modern reverse ETL data pipeline tool that enables you to leverage the data you already have in your data warehouse to make better-informed business decisions. It’s easy to set up and use, and it integrates with a wide selection of CRMs, data warehouses, databases, ad platforms, and SaaS marketing tools.
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See all of Stephen Mash's posts.
Stephen is a UK-based freelance technology writer with a background in system development and assurance, primarily focused on high-integrity applications.
Learn more about Stephen @ https://www.linkedin.com/in/steve-mash-exosure