Monthly Archives: November 2008
Five Things to Look for When Hiring an Enterprise Architect (Part 2)
What does it take to make a good enterprise architect?
In my previous post, I mentioned the five top qualities that are essential for enterprise architects. These individuals are the critical bridge between business and IT, and can help ensure the success of important initiatives such as data warehousing, business intelligence, and service oriented architecture. (more…)
Five Things to Look For When Hiring an Enterprise Architect (Part 1)
Judy Ko recently discussed the difficulties business users have communicating with technical staff, and visa-versa. “How many of us have spent days or even weeks in tedious requirements gathering sessions, asking what the business wants, and getting very fuzzy answers back?” she asked, taking the technical side. Conversely, business people frequently complain that technical folks speak a different, strange language. This makes key enterprise projects such as data warehousing, SOA, or data integration that much more difficult, if not impossible, to implement. (more…)
Cloud Presentation Stuns Conference
Last month I posted an article about cloud computing and cloud integration (see Keep your feet on the ground and your head in the clouds for the full article) and encouraged readers to come to the Architecture and Integration Summit to see Informatica, salesforce.com and Amazon.com tell the story and see a demo. Those that came were not disappointed – the keynote presentation by Sanjay Krishnamurthy, Jeff Barr and Peter Coffee was electrifying! (more…)
Can You Trust Your Metadata If You Have Poor Quality Data?
Over the past several quarters, I’ve had the privilege of speaking with a number of companies involved in data governance. The interesting thing I found: firms who identified both drivers as critical, but only invest in one and not the other.
Case in point: a leading financial services firm implemented a data governance program to improve the comprehension and accuracy of the company’s existing board reports. I learned that one of their goals was to define their business terms and definitions (i.e. business metadata) to help non-technical users improve their understanding of the data used to run the business. What I found fascinating was that this was being done prior to addressing their data quality issues. In fact, when asked, “Do you have data quality challenges?” most business users said “yes”. Unfortunately, no one at this company knew to what extent. Instead, their focus was on defining their business metadata. This leads me to ask, “Can you trust your metadata without addressing your data quality issues as part of a data governance practice?”
If metadata is information about your data which your business users are relying on to drive decisions, but the source data is not clean, how will that affect your business? The answers seem self-explanatory. Of course you can’t trust your metadata if you have poor quality data. For example, business metadata is defined from an approved list of valid values. Unfortunately, if the data used to define those values are incorrect, the downstream impact is you end up with inaccurate metadata.
Organizations implementing data governance programs need to consider the lifecycle of how data is captured, processed, and delivered to downstream systems— whether that is your data warehouse, master data management application, data hub or CRM system. Creating, defining, and publishing business metadata without addressing your data quality issues may not help companies looking to benefit from data governance.
Can Data Governance Help Wall Street Firms Survive?
Now that the $700 billion dollar Troubled Asset Rescue Program (TARP) has been approved by the government, firms on Wall Street are preparing themselves for even more oversight and scrutiny by lawmakers and taxpayers. Survivors from the market meltdown will be required to establish tighter controls, policies, standards, and processes for managing and delivering trusted information for decision making, auditing, and regulatory reporting than ever before. (more…)
Turbocharging Data Warehousing Performance with ELT
Flipping around traditional ETL (Extract-Transform-Load) on its head is not a new practice. ELT (Extract-Load-Transform), where processing is handled in the database, instead of the ETL server, has been proven to enhance performance in many types of data warehousing deployments.
For example, Oi, a leading telecom provider in Brazil, implemented an enterprise data warehouse (EDW) consolidating information on 36 million customers, speeding response time to customer requests. The right-time EDW also enabled Oi to rapidly launch a successful new service offering, which made it easier for customers to recharge their pre-paid accounts for telecom service.
By implementing ELT with Informatica’s pushdown optimization capabilities for this Teradata data warehouse, Oi accelerated its data warehousing loading process two-fold. This has led to even more timely updates of Oi’s customer information, while lowering costs.
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Data Quality in Voter Rolls: A Big Problem with a Familiar Ring
In Chris Cingrani’s recent post the question: “Data quality, does anyone care?” was posed. The answer is yes, of course people care about data quality – in fact, there are a lot of good reasons why a lot of people should care very deeply about data quality. Let’s look at the most recent example of where data quality makes a big difference, and that is in the federal election process. (more…)
Informatica Inside – Continued Momentum with OEM partners
As a follow up to my Alliances momentum post, I wanted to call attention to a recent OEM partners press release highlighting recent wins with OEM ISV partners leveraging Informatica for their own applications, be they “in the cloud” or on-premise installation based. Much of my time in Worldwide Alliances is spent working with prospective ISVs explaining the benefits of taking a ‘buy’ approach versus ‘building’ a hand-coded solution for data integration.
I continue to see market evidence that also explains our momentum. If you read the interesting October 20th InformationWeek cover article entitled, “SaaS Under Stress; Integration’s the next big test for software as a service”, you’ll understand why SaaS software providers are trying to fill this large gap in their offerings.
This makes perfect sense when you take a step back and think about the moving parts of a SaaS application, namely data that needs to (more…)
Business Intelligence, Light and Fast (Part 2)
In my last post Business Intelligence, Light and Fast (Part 1), I talked about how Web 2.0 technologies hold a lot promise for the spread of BI. But how are organizations putting this approach into everyday practice? (more…)
Data Quality – Does Anyone Care?
Over the last few months, I have had a number of discussions with clients at various stages of planning a data quality initiative. Some clients are just starting to take the data quality plunge, while others are evaluating how to leverage the successes of past projects into building out a formal data governance initiative. When I start talking to clients about their goals around data quality, I often start with the same basic question, regardless of where they fall from a maturity process around data quality. The question is simply – does the business care?
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