Unified data management (UDM) popularly known as enterprise data management or enterprise information management is used to achieve strategic and data driven business objectives. Organizations are looking for people who can collect all this unstructured data convert it into structured data or information and provide organizations with business insight.

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Building an Intelligent Organization - BI, UDM and Data
Analytics Strategies
By: Chirag Shivalker
What should a good unified data management and analytics
strategy look like? Here we explain it in three critical steps.

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Today in the age of digitization and information exchange, we are knowingly or
unknowingly contributing to an infinite pool of data. In most of the organizations, this ever
increasing data is managed in isolated silos. This data is processed using various tools for
data quality, data integration, information governance, master data and metadata
management, B2B data exchange, content management etc, to name a few.
Data management, processing and analysis, to derive meaningful information
that organizations can use to take informed decisions for the best business
outcomes, are a growing trend. This is exactly, why organizations are
increasingly embracing unified data management practices that help coordinate
teams and integrate tools.
Unified data management (UDM) also popularly known as enterprise data management
or enterprise information management is used to achieve strategic and data driven
business objectives such as business intelligence. The field of big data, data analytics and
hence BI- i.e. business intelligence, has become much more main stream in the recent years,
and is predicted to grow at a bullish rate.
As a result, organizations are looking for people who can collect all this unstructured data
convert it into structured data or information and provide organizations with business
insight. These professionals are called data scientists or business intelligence developers.
It is predicted that by 2018, there will be nearly 4 million positions in US alone
for such data scientists who can deal with any work related to big data, data
analytics, BI and UDM.
What should a good unified data management and analytics strategy look like?
Here we explain it in three critical steps:
1) Addressing your Analytics and Operational needs:
Today the volumes of unstructured data are growing at a great speed and the IT
environment has become highly complex, as a result the costs and risks for projects have
also increased. This necessitates information integration across legacy and distributed
systems. New and effective methods that support efficient collection consolidation and
provide access to mission critical data in real time for analytical activities lie at the core.
Data is a valued asset for an organization, and decision making for better business
outcomes, solely depends on how this valued asset – i.e. data – no matter from where it
originates or in what format it is available, is streamlined and leveraged to drive improved
performance and gain a competitive edge.

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2) Efficient Data Quality Management:
Availability of consistent and good quality information is crucial to ensure that all the
processes get executed seamlessly. Integration of new systems with the existing
infrastructure in a seamless manner while maintaining the data integrity to deliver efficient
and accurate services is crucial. This requirement can be fulfilled by enabling a data
integration solution that has data quality management capabilities built into it. This
ensures that the kind of sync and integrity requirement is maintained; as a result
information sharing amongst internal and external resources becomes more organized and
secure.
Before any data is unified with the existing enterprise data, it needs to be analyzed and
validated for its quality and accuracy. This can be done by embedding a data quality
firewall in the integration process. As a result the enterprise data always remains clean and
useful.
3) Business Critical Data should be amalgamated for Single View:
The last but the most critical step that helps deliver superior analytics is standardizing
costs, risk mitigation and revenue and mastering this mission critical data into a single
view. For instance, if an online retailer has sales data from different sources across multiple
systems and applications, then this data needs to be mastered and unified in order to
derive accurate analytics and information that supports the company to make decisions
that promote business growth.
Effective data management and structuring lies at the top of the pyramid of Business
Intelligence (BI). The rise of business intelligence and the willingness of companies and
organizations to bring data into decisions can definitely transform businesses across the
world and help them gain an edge over their competitors and drive business growth.
Article Source: http://goo.gl/MRLwe1#Building-an-Intelligent-Organization