Emerging Trends in Metadata Management
The push to become a data-driven enterprise is a growing trend in the industry as more and more organizations see the value of data as a strategic asset. In order to effectively leverage this asset, data must be of high quality and clearly understood. As a result, metadata is more important than ever before, with more than 80% of survey respondents stating that Metadata is as important, if not more important, than in the past. Read this research paper to find out why.
Why a Data Model is Important to the Business
A high-level data model conveys the core concepts and/or principles of an organization in a simple way, using concise descriptions. The advantage of developing the high-level model is that it facilitates arriving at common terminology and definitions of the concepts and principles that drive the organization. This whitepaper provides an overview of why a data model is important to the business.
Integrating Packaged Applications: The Business Value of Metadata for Data Governance
CRM and ERP systems contain some of the most valuable data assets in the organization around customers, sales, campaigns, accounts payable, and more. Achieving a comprehensive view of systems such as these, however, is a daunting task due to their size and the high degree of complexity required to execute their business processes. These two conflicting aspects are what often create frustration and challenges many organizations—while these systems contain some of the most valuable information, they are also the most difficult to decipher. Data Governance and Metadata Management can play a critical role in integrating these systems with other business-critical systems in the enterprise to achieve business value.
Getting Your CRM Data Right: The Law of 80-20
Data is the bedrock of any effective CRM initiative. Lack of data focus will undermine and potentially sabotage your best efforts to build and sustain effective CRM processes and supporting platforms. This whitepaper outlines a simple set of steps that will help to identify and tackle the most important CRM data quality problems.