With the volume of date increasing at a rapid pace, smart organizations are relying on reporting and analytics to efficiently achieve critical business objectives. In fact, a report from McKinsey & Co. stated the effective use of Big Data could drive $325 billion incremental annual GDP in retail and manufacturing by 2020 while cutting $285 billion in costs across the government and health care sectors.
Business intelligence, data warehousing, Customer Relationship Management (CRM) systems and enterprise resource planning are all applications that require access to data from a variety of sources. Subsequently, satisfying the information demands of these business applications becomes a primary objective. This means moving data from the original sources to the target business data systems; this can be accomplished through the traditional “Extract, Transform, Load” process. This process takes the data from its sources to a staging area in which data sets are manipulated and transformed into a target representation. An alternate approach includes data virtualization, where the data remains stored at the source and a conceptual view is materialized on demand.
The practical demands of data integration suggest focusing on a subset of data governance practices that directly support data integration, such as:
- Data requirements analysis: Enterprise projects such as data warehousing and customer relationship management cross line-of-business boundaries. As a result, there is a need for a well-defined process for soliciting, documenting and synthesizing the collected information expectations. Then, those expectations can be translated into data requirements to be imposed on all data sources.
- Data standards review: Defining data standards can address the challenge of inconsistency, especially aligning data element definitions and semantics.
- Metadata management: This includes processes for documenting and communicating the approved standard structures and definitions for reference data domains and data exchange.
The above data governance practices further help to ensure that centralized data is both consistent and complete. By introducing these process to simplify information sharing and reuse, trust in reporting and analytics will increase, benefitting all stakeholders across the organization. To learn more about incorporating data quality objectives within the integration process, you can read our white paper, “Data Integration Alternatives – Managing Value and Quality.”