A consumer-oriented company can use big data to listen to, learn from, and leverage consumer feedback to produce targeted B-to-C campaigns. All of the feedback collected from social media and surveys allows a company to build and update consumer profiles and then execute personalized marketing and advertising campaigns.
Today’s market is all about data. Consumers want to capture information relevant to their user experience; marketers want to capture that information to customize offerings for the consumer; enterprises want to turn data into business intelligence so as to secure a core competitive advantage; and data center vendors want to push virtualization so as to support the massive amounts of data to be captured, stored, mined and managed.
Businesses of all sizes are flocking to the cloud for business intelligence and analytics as it provides vast computing and storage resources without significant investment. Plus, the ability to gather and act on granular information is a key competitive advantage and one that is difficult and costly to achieve without business intelligence analytics in the cloud.
Existing business cultures will have a profound impact on how those initiatives are carried out. In other words, a company’s “personality” and working environment may have as much to do with data governance success as any other factor in the plan.
Bad data within a corporate environment can also lead to interruptions in core business processes. Downtime can be particularly significant if the poor quality information in question triggers or is consumed by automated workflows. These disruptions not only drain worker productivity, they can negatively affect revenue generation.
Data governance not only helps to ensure the integrity of the master data that stakeholders use to formulate important business plans and make critical day-to-day business decisions, it aids in effective compliance with regulatory and information disclosure demands.
Broad-reaching MDM implementations are highly inflexible because they fail to give project leaders the opportunity to assess the viability of the strategy, and make changes along the way. But, incremental implementations make it easy to assess goals.
In order for an MDM initiative to deliver optimum returns, fully-automated controls and validation must be put into place, to ensure that master data is accurate and up-to-date at all times.
