Using Enterprise Information Governance to Balance Enterprise Information Management
TDWI Speaker: David Loshin, President of Knowledge Integrity
Date: Tuesday, December 3, 2013
Time: 9:00am PT, 12:00pm ET
Organizations are increasingly recognizing the intrinsic value in growing information resources to drive decisions on product and service innovation, customer relationships, operations, and value-chain activities. Yet management of information from a systemic perspective has historically reflected a dichotomy between the business side of the organization, where information is created, shared, delivered, and discovered, and the IT department, where information/data is being stored, protected, secured, and preserved.
Well-managed data can inform a broad spectrum of decision makers with insights to make better decisions. Yet the absence of good data management practices can increase operational costs, contribute to organizational inefficiency, allow opportunities to be missed, and increase risk. And because of the apparent management dichotomy, well-managed data must be a byproduct of enterprise understanding of all aspects of the end-to-end information life cycle: how data is acquired, consumed, repurposed, maintained, archived, and eventually retired. This must encompass the assignation of accountability and responsibility for collecting usability requirements and transitioning those requirements into production.
Enterprise information management requires a comprehensive information governance strategy that is far-reaching, involving a combination of people, process, and technology. In this Webinar, we focus on how organizations can develop a strategy for successful information governance and how those forward-looking enterprises can manage information in ways that reduce risk and costs, drive worker productivity, and enhance overall efficiencies.
You will learn:
- Challenges and opportunities of information governance
- Balancing the business and IT roles for creating, retaining, and disposing of information for optimum efficiency
- Leveraging the combination of existing data management solutions with new data-oriented technologies to best meet consumer data expectations