Tag Archives: Enterprise Data Warehouse
A couple comments on the importance of integration platforms like Informatica in an EDW/Hadoop environment.
- Hadoop does mean you can do some quick and inexpensive exploratory analysis with little or no ETL. The issue is that it will not perform at the level you need to take it to production. As the webinar points out, applying some structure to the data with columnar files (not RDBMS) will dramatically speed up query performance.
- The other thing that makes an integration platform more important than ever is the explosion of data complexity. As Dr. Kimball put it:
“Integration is even more important these days because you are looking at all sorts of data sources coming in from all sorts of directions.”
To perform interesting analyses, you are going to have to be able to join data with different formats and different semantic meaning. And that is going to require integration tools.
- Thirdly, if you are going to put this data into production, you will want to incorporate data cleansing, metadata management, and possibly formal data governance to ensure that your data is trustworthy, auditable, and has business context. There is no point in serving up bad data quickly and inexpensively. The result will be poor business decisions and flawed analyses.
For Data Warehouse Architects
The challenge is to deliver actionable content from the exploding amount of data available. You will need to be constantly scanning for new sources of data and looking for ways to quickly and efficiently deliver that to the point of analysis.
For Enterprise Architects
The challenge with adding Big Data to Your EDW Architecture is to define and drive a coherent enterprise data architecture across your organization that standardizes people, processes, and tools to deliver clean and secure data in the most efficient way possible. It will also be important to automate as much as possible to offload routine tasks from the IT staff. The key to that automation will be the effective use of metadata across the entire environment to not only understand the data itself, but how it is used, by whom, and for what business purpose. Once you have done that, then it will become possible to build intelligence into the environment.
For more on Informatica’s vision for an Intelligent Data Platform and how this fits into your enterprise data architecture see Think “Data First” to Drive Business Value
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The devil, as they say, is in the detail. Your organization might have invested years of effort and millions of dollars in an enterprise data warehouse, but unless the data in it is accurate and free of contradiction, it can lead to misinformed business decisions and wasted IT resources.
We’re seeing an increasing number of organizations confront the issue of data quality in their data warehousing environments in efforts to sharpen business insights in a challenging economic climate. Many are turning to master data management (MDM) to address the devilish data details that can undermine the value of a data warehousing investment.
Consider this: Just 24 percent of data warehouses deliver “high value” to their organizations, according to a survey by The Data Warehousing Institute (TDWI). Twelve percent are low value and 64 percent are moderate value “but could deliver more,” TDWI’s report states. For many organizations, questionable data quality is the reason why data warehouses fall short of their potential. (more…)
The CEO of a global technology products manufacturer had a simple question: “I need a list of our top 400 global customers by revenue—immediately.”
The boss got his answer—six weeks later.
That’s how long it took the company’s IT team to root through disparate applications, engage data owners in various business units and geographies, and manually reconcile granular data to answer a basic question that bears directly on the bottom line.
It’s a true story, and one with a happy ending. This multibillion-dollar company implemented Informatica MDM for multidomain master data management (MDM). It exploited the solution’s capabilities and followed best practices in aggressively tackling business-critical data challenges.