Making Data Models Actionable with Data Integration

To address the growing need for business users to harness the benefits of data while
being shielded from technological complexity, data solution providers are developing
graphically-driven products that allow business users to manage and manipulate data
through point and click interfaces. Data integration is a specific challenge, as the typical
organization has information scattered across a myriad of technologies — from relational
databases, to Big Data platforms, to Cloud applications, and more.


Not only is data in disparate technical formats, but teams within an organization often
have different business definitions for core data elements, which adds to the complexity.
Even a term as seemingly straightforward as ‘customer’ can have differing meanings
across teams and be stored across multiple technologies. Two techniques that have
proven helpful in managing the complexity associated with data are data modeling
and data integration.


Using these techniques together can provide a powerful way for business stakeholders and
technical staff to collaborate in creating business value from complex data sources, in an
efficient and cost-effective way. The impetus of this white paper was to showcase what can
be achieved, specifically by building a bridge between data models and data integration
software. With such bridge, organizations can go from data models to production flows
quickly and repeatedly in rapid, automated and inexpensive iterations. This paradigm shift
also nurtures better collaboration and communication between business and technical
teams. In this way, the power to innovate beyond existing organizational structures,
processes and technologies becomes very real, and the following case study details
precisely how this was achieved in a global financial services organization.


There can be little argument that data is at the core of a growing number of new and
innovative business models. As a result of these opportunities, an increasing number
of business stakeholders are becoming more involved in data. The positive implication
of this trend is that the key stakeholders most influential in using data are also closely
involved in its preparation, reporting, and management. The downside however, is that
many of the new data-centric technologies are increasingly complex, and learning the
details is beyond the scope of the typical business user’s knowledge and responsibilities.
We will now look at this in more detail, and uncover how data models can work directly
with data integration to provide enhanced synergies between business and technical
teams, driving business value and innovation for the organization.

 Data Center

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