As businesses grow into large-scale enterprises, they meet a variety of challenges. Departments are quickly created and grow to adopt their own organizational tools; information systems are adopted to solve problems and become outdated as technology advances; and mergers and acquisitions result in the integration of different systems and data sources. It is no wonder that 95 percent of businesses cite the need to manage unstructured data as a problem for their business. That’s why every business must eventually adopt a data integration strategy at some point in its growth.
Having data scattered across different departments, systems and formats leads to data silos and duplication. The resulting data inaccuracies and inconsistencies lead to ineffective decision making. Enterprises that have a high volume of data can experience performance issues, slowing down critical processes, reducing efficiency and making it hard to meet contractual SLAs. A lack of integration can also lead to a fragmented view of the enterprise, making it difficult to gain a complete picture of the business, its customers and its operations. Enterprises with integrated data systems are also prepared to take advantage of modern analytics and reporting tools to gain stronger insights into their business performance and drive better decisions.
Ultimately, efficient data integration enables enterprises to consolidate and harmonize their data from various sources into a unified view. This helps to eliminate data duplication, improve data consistency and reduce errors, leading to higher data quality. By integrating data from different sources, companies can gain a more comprehensive understanding of their operations, customers and markets, which enables them to make more informed and accurate decisions.
The data integration strategies below increase efficiency by automating processes and reducing manual data entry. They can also improve customer experiences by providing a more personalized approach based on a complete view of the customer.
Each of these strategies has its advantages and disadvantages. It’s important to properly assess your enterprise before implementing a large-scale data integration strategy. You will find that some of these strategies are not mutually exclusive, rather they can complement each other nicely as part of a larger data integration effort.
Data standardization is a useful strategy that helps reduce inconsistencies and improve data quality. Often, this strategy includes defining data standards and implementing data governance policies to ensure compliance.
This is a useful strategy for when an enterprise has multiple data sources with information stored in different formats. Standardization makes it easier for organizations to integrate and analyze data by consolidating data from different sources, such as separate departments or systems and ensuring that the data is consistent and comparable. Data standardization is often a crucial step in other data integration strategies.
However, it’s important for enterprises to undertake a data standardization plan with great care. While a common set of standards makes it easier to compare and integrate data, it may make it harder to utilize the full value of certain data as it’s easy to inadvertently remove important information or attributes from a particular data source.
There are several crucial steps involved in implementing a data standardization strategy:
An enterprise must first identify all relevant data sources, including structured and unstructured data. It is also important to determine how this data is currently stored and being used.
The next step is to map the data from each identified source to a common data model. This model defines a set of common data elements as well as their relationships to each other.
Mapped data can then go through a process of data normalization. This includes transforming data into the appropriate data types, such as dates or numbers, or transforming data into a common case or format.
The final step is to validate the data and ensure that it meets the standards and requirements defined in the data model.This helps ensure the data is accurate, consistent and up-to-date.
Enterprises use data warehousing to centralize data from multiple sources into a single repository. This strategy gives organizations a definitive, unified view of its data, making it easier to analyze and use for decision making.
This data is optimized for querying and reporting, making it an effective tool for consolidating siloed data.
This strategy is especially useful for enterprises that have a large volume of data that needs to be stored and analyzed. Because all the data is stored in a single repository, organizations can use powerful analytics and reporting tools to analyze the data and gain actionable insights.
Data warehousing can be a slow process, as all the data needs to be extracted, transformed, then loaded into the central repository. However, hosting data in a centralized location can make data analysis faster and more efficient since it won’t need to be pulled from multiple locations.
Here's how data warehousing works:
The first step in data warehousing is to extract data from sources throughout an organization, such as databases, spreadsheets and transactional systems. This data will likely come in many different formats, making analysis difficult.
The extracted data is then transformed into a standardized format, removing any inconsistencies and duplicates and transforming the ideal format for storage and analysis.
The transformed data is then loaded into the data warehouse, a central repository designed for storing large amounts of data.
Note: The three steps above for another common data integration strategy called extract, transform, load (ETL). We go into this strategy in more detail below.
The data in the warehouse is then aggregated to provide a single view of the data, allowing organizations to analyze data across multiple sources.
Finally, the data in the warehouse can then be analyzed using a variety of business intelligence and analytics tools, allowing organizations to make informed decisions based on historical data.
As discussed above, ETL is a process that is used to integrate data from multiple sources into a central repository, such as a data warehouse or a data lake. However, ETL is not limited to these applications.
ETL is a process that can be used to integrate data from various sources into a variety of data management systems, such as data marts, operational databases and business intelligence systems. By standardizing, cleaning and transforming data before it is loaded into the central repository, organizations can ensure data is of high quality and consistent.
Enterprises can use ETL tools to extract data from sources across their organization, such as transactional databases, log files or APIs, transform the data into a consistent format and load it into a target system for analysis and reporting. This is a versatile data integration technique that can be used in a variety of situations.
There are a wide variety of ETL tools on the market, such as IBM DataStage, Oracle Data Integrator, Hadoop, AWS Glue and others. It is important to understand an organization’s needs before settling on the correct tool.
ETL processes can also be automated to run on a predetermined schedule, ensuring that the consolidated data is regularly updated with the latest information.
However, data integration via ETL can also have some drawbacks, such as the need for specialized skills to set up and manage the ETL process and the potential for data latency if the ETL process takes too long to run. Additionally, data integration via ETL may not provide the same level of real-time access to data as a data virtualization solution or APIs, as the data is only updated in the central repository after the ETL process has run.
Here's how a basic ETL process looks:
The first step in the ETL process is to extract data from the source systems. This may involve connecting to databases, spreadsheets and transactional systems to retrieve data.
Typically, data is read from the source systems and stored in a temporary storage location that acts as a buffer between the source and target systems. The extract process is typically designed to be run at a specific time or on a schedule, such as daily, weekly, or monthly.
This process can also involve filtering and cleaning data to remove duplicates, incorrect records and irrelevant information. This helps to improve the quality of the data and ensure that only relevant information is processed and loaded into the target system.
The extract process is a critical step in the ETL pipeline, as it sets the foundation for the rest of the process. A well-designed extract process can improve the efficiency and accuracy of the ETL pipeline and ensure that the target system receives high-quality data.
Once the data has been extracted, it is then transformed to meet the needs of the central repository. This may involve standardizing data, cleaning data and transforming data into a standardized format.
In this process, an enterprise manipulates and converts data into a format that can be loaded into the target system. This can involve approaches such as data cleansing, data enrichment, data validation, data mapping and data aggregation.
For example, data cleansing might include removing duplicate records, correcting faulty values or providing missing data.
Enterprises can accomplish the transform process through a variety of approaches, including in-memory transformations, stored procedures and custom scripting. The approach depends on the specific requirements and capabilities of the organization’s infrastructure.
The load process refers to the final step in the ETL pipeline, where an enterprise loads data into the target system. The transformed data is then loaded into the central repository, such as a data warehouse. This process may involve creating tables, defining relationships and indexing the data to optimize performance.
Enterprises ideally design the load process to be efficient and fast, utlizing bulk loading techniques, such as batch loading or incremental loading, to minimize the impact on the target system.
In order to ensure the load process is reliable and loads data into the target system accurately, organizations can include error handling and data validation steps into their load process.
Data migration involves transferring data from one system to another. This can be a complex process but can be necessary depending on an enterprise’s needs. It is important to ensure the data is accurately mapped and validated during the migration process.
This strategy is useful when an enterprise needs to move data from one system or database to another. Data migration is a complex process that requires careful planning and execution.
It is often used when organizations are upgrading systems, consolidating data, or merging with another organization. Data migration is particularly useful for when an organization already needs to move large amounts of data and ensure that it is accurately transferred to the new system.
Moving such a large amount of data can be a time-consuming and risky process that requires significant effort and resources to implement. If performed without the proper care, enterprises could lose crucial data with a significant impact on business operations. It is important to work with stakeholders to minimize any disruption during the migration process.
Data migration processes include:
The first step in data migration is to assess the data that needs to be migrated, including the volume of data, the complexity of the data and any dependencies on the existing system.
Once the data has been assessed, specialists prepare it for migration by cleaning and transforming the data into a format that is compatible with the new system. This may include removing any inconsistencies or duplicates and transforming the data into the required format for the new system.
The organization must then transfer the data into the new system, either through a direct transfer or through the use of an intermediate storage mechanism, such as a data staging area.
After the data transfer, it must then be validated to show that it has been transferred accurately and completely and that it meets the requirements of the new system.
Finally, it is time to perform a cutover from the old system to the new and put the new system into production. A data cutover is the process of switching from the old system to the new system in a data migration project. It involves the final transfer of data from the old system to the new system and the transition of users, processes and systems to the new system.
Data federation is a strategy that allows businesses to access data from multiple sources as if it were stored in a single repository. This approach provides a unified view of data, abstracting the underlying data sources and enabling real-time data integration and consolidation. This is particularly useful for organizations that need to minimize the impact on existing systems and reduce the need for data migration. Instead, the data remains in its original location.
Data federation provides a flexible and scalable solution for integrating data from multiple sources, without the need to physically move or duplicate the data. By virtualizing the data, organizations can access and analyze data from multiple sources in a single, unified view, improving the accuracy and efficiency of decision-making.
However, data federation can also have some drawbacks, such as increased complexity and the need for specialized skills to set up and manage the federation system. Additionally, data federation may not provide the same level of performance as a centralized repository, as the data remains in its original location.
The following processes are involved in a typical data federation strategy:
The first step in data federation is to discover the data sources that need to be integrated. This may include databases, spreadsheets and transactional systems.
The data from the sources is then virtualized, which means that a virtual representation of the data is created. This virtual representation can be accessed and manipulated as if it were a single, unified data source.
The virtualized data is then integrated into a single, unified view, providing a single source of truth for the organization. This may include combining data from multiple sources, aggregating data and transforming data into a standardized format.
The integrated data can then be accessed through a single, unified interface, allowing organizations to access and analyze data from multiple sources as if it were a single, centralized repository.
Enterprises can use APIs to securely exchange data between systems and consolidate data in real-time. This strategy provides a method for different systems to communicate with each other, allowing them to seamlessly exchange and integrate data.
APIs can be a useful tool for when organizations need to exchange data among different systems in real-time. It helps bypass the significant time and resources it takes to migrate data or establish a data warehouse.
Additionally, APIs allow organizations to control access to their data, ensuring that sensitive information is only shared with authorized systems and applications.
It is important to recognize that data integration via API can come with its own issues, such as the need for specialized skills to create and integrate APIs and the potential for security risks if the APIs are not properly secured. Also, an API-driven approach to data integration may not provide the same level of performance as a centralized repository, since the data is retrieved from the original source every time it is needed.
An enterprise must first create the necessary API to begin integrating data. Typically, this involves defining the data that will be exchanged, the structure of the data and the methods that can be used to access the data.
Developers then deploy the API and make it available for use. This could involve hosting the API on a server, or making it available through a cloud-based service.
The next step is to integrate the API into the systems and applications that need to exchange data. This may involve writing code to connect to the API and retrieve or update data as needed.
Once the API has been integrated into the systems and applications, developers and analysts can begin to exchange data between them. This may involve retrieving data from one system and updating it in another, or vice versa.
MDM is the process of defining, managing and maintaining a single, consistent view of critical data elements, such as customer and product data. This can make it easier to integrate data from different systems by improving the accuracy and consistency of data. Although MDM does not integrate data on its own, the efficiency it offers makes it worth exploring as an organization plans out its data integration strategy.
MDM is best suited for an organization that has a wide range of data entities and needs a system to help them keep track of this data and ensure its quality.
MDM can, however, be complex to integrate with other systems and processes. In addition, this strategy, on its own, can be limited in terms of the level of data integration it can achieve as it is dependent on the underlying data sources that already exist and the level of support provided by these systems. That is why it is often used in conjunction with strategies like data warehousing.
Common steps in implementing a MDM strategy include:
The first step in MDM is to collect data from various sources within the organization. This may involve connecting to databases, spreadsheets and transactional systems to retrieve data.
Once the data has been collected, it must be cleansed to remove duplicates, correct errors and standardize data. This may involve using data quality tools and processes to improve the accuracy and completeness of the data.
The next step in this strategy is to consolidate the cleansed data into a single repository, such as a data warehouse or a master data hub. This may involve creating a data model, defining relationships and indexing the data to optimize performance.
The final step in MDM is to implement data governance processes to ensure the accuracy, consistency and completeness of the master data over time. This may involve defining roles and responsibilities, establishing policies and procedures and monitoring the data for changes.
Cloud-based solutions can help enterprises integrate siloed data by providing a centralized platform for data storage and management. This helps reduce the cost and complexity of data integration and enables real-time access to the data from anywhere with an internet connection, providing organizations with greater flexibility in how they access and use their consolidated data.
Cloud-based data management solutions can be used to store, process and consolidate data from multiple systems. This strategy eliminates the need for on-premise hardware, making data consolidation more scalable and cost-effective.
Cloud-based data solutions are best used when an organization has a need to store and process large amounts of data that are generated and updated frequently. The cloud’s ability to scale data management capabilities as needed makes it ideal for organizations that have rapidly growing data needs.
An issue with cloud-based data management is that the tools involved can limit the level of control and customization available to its users, as these solutions are typically provided by third-party providers.
Steps involved in cloud-based data management include:
Naturally, an enterprise must start by collecting data from various sources within the organization, such as databases, spreadsheets and transactional systems. This can be done through APIs or by using data migration tools to move data into the cloud, both tasks related to strategies we’ve discussed above.
The business will then consolidate the collected data into a central repository within the cloud-based data management solution. This could involve creating a data model, defining relationships and indexing the data to optimize performance.
Next, the enterprise standardizes the data to ensure consistency and accuracy. This may involve using data quality tools to clean and standardize data and to resolve data discrepancies.
The consolidated and standardized data is then made available to the various teams within the organization through secure access controls and user-friendly interfaces. This may involve using data visualization tools to help teams gain insights from the data.
The final step is to implement data governance processes to ensure the accuracy, consistency and completeness of the data over time. This may involve defining roles and responsibilities, establishing policies and procedures and monitoring the data for changes.
Data virtualization is a technique for accessing and integrating data from multiple, disparate sources as if it were stored in a single location. Data virtualization servers act as a single point of access for data, abstracting the underlying data sources and enabling real-time integration and consolidation of data. This can help to reduce the need for data migration and minimize the impact on existing systems, while providing a single, unified view of data across the enterprise.
This strategy involves creating a virtual layer that sits on top of multiple disparate data sources, such as databases, data warehouses and cloud services. This virtual layer provides a unified view of the data, allowing users to access and analyze the information as if it were all stored in a single place. Data virtualization is particularly useful for organizations that need to access and combine data from multiple sources in real-time, while mitigating the need for data migration.
Data virtualization may not be a strong option for businesses that need a high degree of control over their data, as the virtual layer is dependent on the underlying data sources and the level of support those systems offer.
A typical data virtualization strategy includes the following:
Data virtualization works with data from various sources, such as databases, spreadsheets and transactional systems. The business must identify these sources and understand the data provided by each.
The enterprise then creates a virtual layer that acts as an intermediary between the data sources and the applications that need to access the data. This layer abstracts the underlying data sources, providing a unified view of the data.
Applications and users access the data through the virtual layer, which translates their requests into the appropriate format for the underlying data sources. This eliminates the need to replicate or physically move the data.
Data virtualization technology can also include features such as caching and indexing to help improve performance and responsiveness.
The data integration strategies exist to help organizations integrate their disparate data. While some of the strategies can be taken on their own, they are not all mutually exclusive and are often used together in order to fulfill specific enterprise needs.
To standardize data, organizations can adopt common data definitions, formats and structures. Data warehousing involves storing data in a centralized repository to provide a single source of truth. Data migration involves physically moving data from one system to another, while data federation involves accessing and combining data from multiple sources without physically moving the data.
APIs allow systems to communicate and exchange data, while ETL (Extract-Transform-Load) extracts data from source systems, transforms it into a consistent format and loads it into a target system. Master Data Management (MDM) ensures that an organization uses a single version of the truth for critical data elements. Cloud-based data management solutions provide a unified view of the data stored in multiple cloud services. Data virtualization creates a virtual layer on top of multiple disparate data sources to provide a unified view of the data.
Every organization must keep in mind its specific needs and circumstances before settling on a data integration strategy. Regardless of the approach, most companies must develop a data integration strategy at some point in their growth in order to better take advantage of all the data they have accumulated and develop a unified view of their operations.
