Solutions Review highlights the most common data integration use cases you need to know about so you can select the best software.
Evaluating data integration software can be a complex process. Organizations commonly use data integration software for enterprise-wide data delivery, data quality, governance, and analytics. Data integration tools allow organizations to better understand and retain their customers, support collaboration between departments, reduce project timelines with automated development, and maintain security and compliance.
Cloud connectivity, self-service (ad hoc, citizen), and the encroachment of data management functionality are major disruptors in this market. As data volumes grow, we expect to see a continued push by providers in this space to adopt core capabilities of horizontal technology sectors. Organizations are keen on adopting these changes as well, and continue to allocate resources toward the providers that can not only connect data lakes and Hadoop to their analytic frameworks, but cleanse, prepare, and govern data.
With these things in mind, our editors have compiled this list of the most common data integration use cases you need to know.
Business Intelligence and Data Analytics
Business intelligence is the use of data to help make business decisions. BI as it’s commonly referred to, is a broad umbrella term for the use of data in a predictive environment. Business intelligence encompasses analytics, acting as the non-technical sister term used to define this process. BI often refers to the process that is undertaken by business analysts in order to learn from the data they collect in a post-analysis phase. Conversely, business intelligence can also be used to describe the tools, strategies, and plans that are involved with data-driven decision making.
Data analytics is a data science. If business intelligence is the decision making phase, then data analytics is the process of asking questions. Organizations deploy analytics software when they want to try and forecast what will happen in the future. Data integration is often the first part of the analytic process, allowing organizations to extract data from source systems and transform and deliver it to its target where a BI system can access it.
Master Data Management
Master data is made up of essential company-wide data points. This data typically provides insight related to the core of the business, including customers, suppliers, accounts, employees, goals, and operations. Decisions about what constitutes as master data are made by management teams and business stakeholders. Once these data standards have been met, users can analyze the data as they need to identify key metrics that reveal areas of concern so appropriate actions can be taken to improve operations.
Master Data Management (MDM) enables business and IT leaders to ensure accuracy, stewardship and governance over an organization’s shared master data. At its core, MDM is a framework—and not a software—that focuses more on identifying data than measuring or processing the same. It is a level above transactional data that contains the actual information needed for business functions.
Cross-Application Data Consistency and Sharing
Data integration tools are necessary to ensure database consistency across applications. These processes most commonly involve synchronizing data for on-prem applications, or cloud data sources in SaaS. This is particularly important when multiple applications store the same data in their databases. Appropriate cross-system data distribution is paramount, especially among systems that are related.
Organizations are often required to send and receive data between external parties and partners. The data integration software you select should be able to meet specific data sharing requirements that may include on-prem, cloud and hybrid environments. You may also require big data integration. These tools can be used to support data acquisition, sharing and collaboration across business application and common data types.
Data Migration is a process where data is transferred between storage types, formats, data architectures and enterprise systems. Whereas Data Integration involves collecting data from sources outside of an organization for analysis, migration refers to the movement of data already stored internally to different systems. Companies will typically migrate data when implementing a new system or merging to a new environment. Migration techniques are often performed by a set of programs or automated scripts that automatically transfer data.
Data integration software can play a supporting role role to enterprise data migration and consolidation, though these tools are not recommended by the experts.