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March 2, 2021
Data integration is a major pain point as companies seek to become increasingly data-driven. Even simple cases are subject to data integration challenges. These challenges only multiply as complexity grows, amounts of data increase, and the desire for real-time functionality is introduced. The reality for most companies involves data silos, brittle APIs, and struggles to build and maintain the data pipelines required to provide good data to the right end users to drive better business decisions.
Data integration often involves a combination of legacy systems and new technologies. Plus, as business needs evolve and require new use cases, the number of different sources and destinations can rapidly grow and change. Building and maintaining a clean architecture with robust automations and connectors to handle these challenges in a scalable manner requires a thoughtful data integration process.
A successful data integration strategy leads to effective and accurate data analysis when it comes to data warehouses.
The Four Components of Data Integration
Let’s highlight a few important components of data integration:
- Data Migration
- Enterprise application integration
- Master data management
- Data aggregation
Data migration is the part of data strategy that refers to transferring data from one location, application, or system to another. A simple example of data migration is moving on-premise applications and data storage to cloud-based.
The following are the three main data migration types:
- Storage Migration refers to transferring the data from pre-existing volumes into new and modern counterparts to achieve better scaling, faster performance, and more efficient data management tasks such as cloning, snapshots, and backup.
- Cloud Migration refers to transferring data, applications, and other business elements from one cloud infrastructure to another.
- Application Migration refers to transferring an application from one environment to another.
Enterprise Application Integration
Enterprise application integration (EAI), is an approach commonly used to manage the interoperability between the separate systems that businesses utilize to manage big data. This enterprise data process requires solving problems related to the organization's modular architecture. It includes the following key factors:
- Interoperability: Management of different languages, data formats, and operating system components so that a connection is established without any hiccups.
- Integration: Setting a standard process to allow data flow management between different source systems and applications while ensuring consistency.
- Stability, Scalability, & Robustness: Ability to adjust to the implemented solution seamlessly.
Master Data Management
MDM, or Master Data Management, is a discipline focused on the cooperation between IT and the business to achieve accuracy, uniformity, accountability, stewardship, and semantic consistency of shared data sets. Master data includes core attributes such as suppliers, customers, and sites, among others.
There are a few challenges regarding the implementation of an MDM strategy. These usually refer to the complexities, overlaps, governance, and setting up the required policies and ownership standards.
Data aggregation combines disparate data sources and is bifurcated into two major aspects - data warehousing and data federation.
Data Warehousing: Data Merged into a Physical Database
Data warehousing is a technology that aggregates structured data collected from one or multiple sources for effective data analytics and achieving cutting-edge business intelligence. This is a very effective means to better understand a business’s overall performance given the wide range of data available for extensive analysis. Rapid access to data, improved business Intelligence, increased query, system performance, and access to historical intelligence are the benefits of a data warehouse.
Data Federation: Data Merged into a Virtual Database
This is yet another approach used to get an integrated view of your data. It creates a virtual database that doesn't store the data itself. Instead, it contains information on how and where the actual data is stored.
Data federation involves processes like cleansing, transformation, and data enrichment.
The benefits, as a result, are reduced latency, simplified BI, and replacing the ETL (Extract, Transform and Load) processes for organizations.
Three tips for a Successful Data Integration Strategy
Find the best data integration providers
There are many vendors out there with various data integration platforms that are both efficient and resourceful. Therefore, choosing the most suitable data integration solution for your business should be your number one priority.
Finding the right vendor who can overcome all the data integration challenges while implementing the right data management strategy with timely delivery and speed is the most important piece of the puzzle.
Establish a data governance process
To unlock your data's full value, you should establish and implement a set data governance process in your organization. This process needs to prioritize and include managing risks, data quality, business processes, and data management as a whole.
Having a set data governance policy in place will help you improve your operational processes. Also, it will help you ensure that your data is present in the right format, with the right quality and utmost availability for your stakeholders.
Implement data security
Businesses in touch with the latest data integration trends also need to find a way to safely and securely connect on-premise data using different cloud applications and systems.
Taking action on this subject should be a priority, considering the large volume of data that keeps growing.
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Content Manager at RudderStack