Data strategy, data architecture, and data governance: these are the first three steps in building a solid data foundation for your business. In this post, we’ll talk about the final piece of the puzzle: data management and integration.
DAMA International, the premier international body for data management professionals, defines data management as “the development and execution of architectures, policies, practices and procedures that properly manage the full data life-cycle needs of an enterprise.”
Both data management and data governance have similar goals: ensuring that everyone within the organization can enjoy continued access to the high-quality, accurate data that they need to effectively do their jobs. Yet while data governance focuses on high-level conceptual issues, data management is concerned with the low-level technical implementation—essentially, the difference between theory and practice.
Also, like data governance, data management includes multiple related subfields:
- Master data management (MDM): Organizations need a single source of truth for their enterprise data, helping resolve conflicts between different sources of data and get everyone on the same page. The practice of master data management (MDM) seeks to create this single source of truth by cleansing data and enhancing it with reference data and metadata in order to remove inconsistencies.
- Data standardization: Even if you have a single source of truth, the same data can be represented in countless ways—just think about the same measurement in imperial or metric units, or the variety of date and time formats around the world. Data standardization aims to improve data quality by making information clear and consistent, making it easier to analyze multiple data sources at the same time.
- Data integration: Once your data has been systematized and standardized, it needs to be integrated and centralized for use in your business intelligence and analytics workflows. Data integration is heavily linked with concerns such as the design and development of ETL (extract, transform, load), which is the most common method of implementing data integration.
- Data performance and tuning: After your ETL workflows are up and running, you need to continually monitor and optimize them, fixing any slowdowns and removing bottlenecks. Data performance ensures that information can flow freely throughout the enterprise from source to target during data integration.
Backed up by a solid data governance strategy, data management is the key to achieving world-class, data-driven decision-making. For more information on the 4 crucial steps of building a solid data foundation, download our white paper “Build a Business Foundation on Trusted Data” now.
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Ultimately the goal of commentary in OBIEE is to have a system for persisting feedback, creating a call to action, and recognizing the prolific users.