The success of business models lies in their agility, the extent to which they adapt to changing market conditions and how they maintain efficiency and competitiveness in international supply chains. Today markets are changing at an unprecedented pace, driven by mobile, social, cloud and data technology. 

IDC states that “by 2020, a third of the data in the digital universe (more than 13,000 exabytes) will have Big Data value, but only if it is tagged and analysed”

In an ideal world, large enterprises want to be able to respond to market change, with the speed of smaller competitors. This agility lies in the organisational ability, to create innovation cycles at the technology, people and process levels- the development of new applications based on insights crystallised, from the effective collection, management and analysis of data.  These cycles have the potential to continually drive, innovation and solutions.

An Enterprise Data Architecture identifies the strategic data requirements and the related components of the information management solution at the enterprise level, and supports the ability to leverage data into business intelligence.
Such architecture informs organisation strategy and provides a formal approach to creating and managing the flow of data and data processing across IT systems and applications. This includes defining objectives for the improvement of data collection and use, process improvement, effective decision making on new and modified solutions, data warehousing, integration and reporting initiatives.

The dollars are in the detail when it comes to data management practice. If organisations are to reap this value, they will need to enable data synthesis on a shared, intra-organisation basis, and for this, modelling of data assets is imperative. Enterprise Architect has a built in data modelling profile and further information can be found here http://www.sparxsystems.com.au/enterprise-architect/information-data-modeling/information-data-modeling.html

Gartner recommends that “enterprises should adopt a portfolio of data integration tools that support a range of data delivery styles” including “federated and virtualized views of data.” It is recommended to take into account both existing data integration processes and future needs relative to a range of use-cases including data warehousing, operational application integration, system migrations and data conversions, and intra-enterprise data sharing.

The Big Data Survey http://www.gartner.com/newsroom/id/2593815 conducted by Gartner in 2013 reveals that “64 Percent of organizations have invested or plan to invest in Big Data in 2013”.