The world is dominated by data. In the last five years, the amount and types of data now available to us has exploded. Big data plays an important part in mining unstructured data to find the gems. DATAVERSITY discusses this in the article, “Four Phases to an Agile Data Governance Implementation.”
As the world’s data needs and capabilities evolve, we add even more data points to our collections. Frequently, the data represents concepts we never envisioned were even quantifiable at the advent of the Internet. For example, the Internet of Things (IoT) not only links our smart devices, but also gathers data on user behavior which then can be used to construct predictive models. In order to fully utilize the deluge of available data, it is imperative to make sure that data is correctly modeled and structured at the outset of its creation. Data governance, therefore, is a critical skill for both individuals and organizations in the 21st century.
Data governance is the process of owning a piece of data and running it through the organization without losing its value. This is a challenge because at any and every step of the way in the process, the data can be enhanced or modified. However, good data managers are aware that this process can create data silos if not managed correctly. Data governance is not for the faint of the heart–it should be designed as a long-term solution but must include “quick wins” in today’s agile business environments. Thus, the article discusses how an effective data governance plan includes four phases: Initiate, Plan, Build, Grow. Data is in constant flux because of the competitive pressures and the evolution of data itself. Modeling both your data infrastructure and your data management process for ongoing growth sets the stage for your organizational success.
Melody K. Smith
Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.
