Data integration: The changing nature of tools and experiences

When I first thought about writing a blog post about data management, picking a topic was an obvious, if daunting, first step. The challenges facing both IT and business are increasing in the era of bigger, more complex and more immediate data. My experience working with organizations across a variety of industries has taught me one thing: the management of a company’s data is the foundation for any business.

Another thing I've learned is that data integration is one of the most common and most established methods of data management. In today’s organizations, with data arriving from a multitude of sources, making sense of varied and disconnected information is a concern for both business and IT. The business side of the organization needs a cross-functional view of data to make good decisions, while IT needs to make sure a trusted, coherent view is available, safely and securely, for business users.

As a result, a comprehensive and flexible data integration strategy is necessary for any organization. For example, the need to capture marginal revenue, increase market share, and improve customer experiences result requires a company to truly “know the customer.”  And knowing your customer means knowing your data.

Data integration technologies were formerly known as ETL tools for the “extract, transform and load” processes they managed. For years, organizations viewed data integration as the process of getting information into a data warehouse or similar consolidated data target. Organizations now view data integration as a practice that doesn’t exist in a vacuum – it is part of a larger data management construct.  Data integration affects (and is affected by) a variety of initiatives, including:

  • Enterprise data governance
  • Changing business processes and data consumer requirements for data
  • More and different types of data and their sources (social media, RFID, etc.)
  • Decision support, business intelligence and advanced analytical needs
  • Real-time and near-real-time data requirements that fall outside of typical batch data integration

These initiatives are changing data integration technologies into more mature platforms.  Data integration is often part of larger implementations, such as SAS Solutions, where data integration is critical precursor to analytics and reporting efforts. Data integration is also a part of operational master data management (MDM) initiatives, helping create a more unified view of customers, products or other information types to support enterprise applications.

To accomplish this larger role, data integration suites, part of SAS DataFlux Data Management offerings, include functionality for a broader set of functionality, including:

  • Data access – Access and manage enterprise information and publish integrated data back into the IT environment
  • Data quality – Enforce data cleansing and data standardization rules to produce accurate, consistent information
  • Data governance – Manage and enact business rules to ensure data is “fit for purpose”

In the next post, we will discuss some of the interesting attributes of SAS Data Integration, including its role in high-performance computing, high-performance analytics and decision management.

tags: data integration, data management, data quality, information management

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