Blog by Philip Russom
Research Director for Data Management, TDWI
For about a month now, I’ve been interviewing users on the phone, in search of speakers for upcoming TDWI events. I need speakers who can share their organization’s best practices and strategies for data management. As you can imagine, I’ve heard a lot great tips in these interviews, many of them concerning master data management (MDM).
A tip I’ve heard from people in multiple organizations is that MDM solutions achieve a higher level of success when they adopt some of the techniques and best practices of data quality (DQ). Let me give you some examples of DQ practices applied to MDM.
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Posted by Philip Russom, Ph.D. on September 8, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
Allow me a moment to parachute into the middle of an issue that’s come up a lot this calendar year, namely multi-data-domain master data management (MDM). I assume you are familiar with MDM; if not, spend a few minutes on Wikipedia.
The issue is that most user organizations deploy single-domain MDM solutions. The most popular data domain is customer data, but other common domains for MDM are (in priority order) financials, products, partners, employees, and locations.
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Posted by Philip Russom, Ph.D. on August 24, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
The current hype and hubbub around big data analytics has shifted our focus on what’s usually called “advanced analytics.” That’s an umbrella term for analytic techniques and tool types based on data mining, statistical analysis, or complex SQL – sometimes natural language processing and artificial intelligence, as well.
The term has been around since the late 1990s, so you’d think I’d get used to it. But I have to admit that the term “advanced analytics” rubs me the wrong way for two reasons:
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Posted by Philip Russom, Ph.D. on August 5, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
Do you know what a cul-de-sac is? In French, it literally means “bottom of the bag.” But figuratively it means what most Americans would call a “dead-end street.” In residential real estate, a cul-de-sac is a desirable place to live. In analytics, a cul-de-sac is where the epiphanies of advanced analytics never get off a dead-end street to be fully leveraged elsewhere in the enterprise.
The current hype around big data analytics has most discussions of analytics focused on “discovery” analytics. That’s where a business analyst or similar user employs an advanced analytics tool (based on data mining, statistics, natural language processing, complex SQL, etc.) to discover facts never known before. For example, the analyst may discover the root cause for a new form of customer churn, a new partner behavior that’s potentially fraudulent, or the hidden costs that erode otherwise profitable customers.
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Posted by Philip Russom, Ph.D. on July 21, 20110 comments
Delivering value sooner and being adaptable to business change are two of the most important objectives today in business intelligence (BI) and data warehouse development. They are also two of the most difficult objectives to achieve. “Agility,” the theme of the upcoming TDWI World Conference and BI Executive Summit, to be held together the week of August 7 in San Diego, is about implementing methodologies and tools to that will shorten the distance to business value and make it easier to keep adding value throughout development and maintenance cycles.
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Posted by David Stodder on July 14, 20110 comments
Blog by Philip Russom
Research Director for Data Management, TDWI
While researching a new TDWI report on big data analytics, I’ve run across a few BI professionals who are concerned about the seeming lack of data preparation that’s common with some forms of advanced analytics. Allow me a moment to sort this out.
On the one hand, all of us in BI and data warehousing are indoctrinated to believe that the data of an enterprise data warehouse (EDW) (and hence the data that feeds into reports) must be absolutely pristine, integrated and aggregated properly, well-documented, and modeled for optimization. To achieve these data requirements, BI teams work hard on extract, transform, and load (ETL), data quality (DQ), meta and master data management (MDM), and data modeling. These data preparation best practices make perfect sense for the vast majority of the reports, dashboards, and OLAP-based analyses that are refreshed from data warehouse data. For those products of BI, we want to use only well-understood data that’s brought as close to perfection as possible. And many of these become public documents, where problems with data could be dire for a business.
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Posted by Philip Russom, Ph.D. on July 12, 20110 comments