How Data Abstraction Works Part 1

Organizations today understand that better access to information assets can improve their bottom-line.

But they struggle with the variety of enterprise, cloud and big data sources, and all their associated access mechanisms, syntax, security, etc.  Further, few data sources are structured properly for business user or application consumption, let alone reuse.  And often the data is incomplete or duplicated.

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Data Virtualization’s Logical Role in the Logical Data Warehouse

I have been closely following Gartner’s Logical Data Warehouse (LDW) strategy for the past year and a half.

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Ten Mistakes to Avoid When Virtualizing Data – Part 2

In my prior blog post, I revisited mistakes one through five from my November 2008 Virtualization Journal cover article entitled Ten Mistakes to Avoid When Virtualizing Data.  In this post I will address mistakes six through ten and summarize what has changed since 2008.
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What Is Your Strategy for Responsive Enterprise-wide Data Sharing?

Sharing Data Is Difficult, Yet Important

Large enterprises learned long ago that effective sharing of data across lines of business was a critical success factor. But achieving this objective in large organizations has been especially complex due to:

  • Thousands of information consumers with varied roles and responsibilities;
  • A range of analysis and reporting applications addressing different business problems;
  • Multiple approaches to access, combine, and deliver data to these applications; and
  • Extreme data source volumes, variety, velocity and complexity.

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Hey Architects, Why Bother to Abstract Your Data?

The benefits of providing better access to information assets are many.  So too are the integration challenges. 

Consider the typical enterprise information architecture.  It includes:

  • Large volumes of complex, diverse data spread
  • A wide landscape of application silos and fit-for-purpose data stores
  • Each data source has its own schema and syntax
  • Few sources are structured properly for consumption by other applications
  • Many sources are incomplete, duplicated – or both!

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