While Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and
Infrastructure-as-a-Service (IaaS) offer applications and resources at an
attractive, pay-as-you-go price, they also create silos of data that are
difficult to access by your analytics and BI solutions.
Likewise, Big Data offers new storage techniques, processing capabilities and
analytic opportunities. But traditional analytics and BI solutions are
typically built assuming relational, SQL-based sources. These often struggle
when trying to deliver value from these new No-SQL sources.
Cloud and Big Data Silos Add Value to Analytics and BI
Analytics and BI opportunities today are abundant and can significantly add
value to a business. According to the Professors Andrew McAfee and Erik
Brynjolfsson of MIT:
"Companies that inject big data and analytics into their operations show
productivity rates an... (more)
As competition accelerates, the business case for gaining a 360o view of your
customers, products, and employees is compelling.
360o View of Customers - Sell additional offerings to existing customers.
Improve satisfaction. Reduce churn. 360o View of Products - Optimize supply
chain decisions. Eliminate duplicate products. Cut costs. 360o View of
Employees - Improve employee productivity. Enable HR self-service.
MDM Applications Are Just a Start
Master data management applications maintain and control critical master
data. However, MDM applications alone ... (more)
An array of business intelligence (BI), predictive analytics, data and
content mining, portals and more tap a growing volume of information sourced
from enterprise data warehouses (EDW). However, significant volumes of
business-critical enterprise data resides outside the enterprise data
warehouse. To deliver the most comprehensive information to business
decision-makers, IT teams are implementing data virtualization to preserve
and extend their existing enterprise data warehouse investments.
This article discusses five integration patterns that combine both enterprise
data wa... (more)
Data virtualization is one of the fastest-growing segments in enterprise IT
today. Originally deployed for light BI federation needs, today's data
virtualization use cases span a range of applications, including customer
experience management, risk management and compliance, M&A transition and
Data virtualization adoption has evolved from project-level deployments to
enterprise-scale data virtualization layers that deliver data from multiple
sources (Relational, Semi-structured XML, Dimensional MDX, the new NoSQL data
types, and more) to multiple applications on demand.
If I could only recommend one data virtualization best practice, what would
Hmmm! With over a couple of hundred data virtualization implementations
under Composite's belt, the answer is simple - Implement a Data
Virtualization Competency Center!
Why a Data Virtualization Competency Center?
The reason is simple as well. A Data Virtualization Competency Center can
be IT's expressway to delivering Business and IT value.
Take a look at a recent Data Virtualization Leadership blog, by my colleague
Peter Tran What Business and IT Problems Can Data Virtualization Solve?
Peter p... (more)