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I will be participating in a series of breakfast briefings on data quality with Identity Systems in the following locations:
Washington, DC on May 15, 2008
Chicago, IL on May 20, 2008
New York, NY on May 21, 2008
We will be focusing on how changing business requirements are creating new data quality challenges, and how we can adapt data quality solutions to help focus on providing the right data — in the right format, at the right time — to broad, global systems that drive strategic business goals.
Let me know if you are planning to attend and want to schedule some additional time to chat!
Hey, sorry it has been a while since my last blog entry. I have been focused on finishing up my book on master data management (MDM), which thankfully is now finished. Some interesting thoughts gelled over the past 6 months in which I have been furiously assembling material for the book, which is due now to be published in the Fall by Elsevier:
- MDM is more of a means than an end, and it is more likely to be justified in the context of other enterprise activities such as CRM or ERP.
- I have started to bristle at the phrase "golden copy." I now think that MDM is more about providing universal transparent access to a sngle representation of uniquely identifiable entity data, but that does not mean that entity data has to sit in its own silo.
- Comprehensive master metadata should include more than just data dictionary information
Stay tuned for more information on the book...
For anyone interested in learning about how to engineer data quality into the system development life cycle, sign up for my pre-conference session at Informatica World in Las Vegas on June 2, 2008. Contact me directly for more information!
I couldn't resist: Is disgraced super advocate governor Eliot Spitzer somehow related to super sailor Popeye?
Two interesting aspects of the Spitzer situation. First, his tactics at using information to track down targets for prosection as NY State Attorney General are prime exmaples of exploiting business intelligence to identify patterns of misbehavior. Second, one would think that, knowing the tactics to be used to seek out suspicious activity, would have hesitated to expose himself to discovery via the same tactics.
For some reason, I have acquired a habit of buying books at the airport. It could be that due to some lingering guilt about limitations on my personal productivity as I spend time getting from one place to another, I feel compelled to buy books that have some business relevance to read at the gate while waiting for all the business class and premier travelers to board the airplane.
I am finding, though, that I am building up an interesting set of books that provide value to the way I look at the use of information, so I thought I'd share a list of books that I have recently read, am currently reading, or plan to read some time in the near future. Each one deals with aspects of how we can learn from what we know, learn from what we don't know, then exploit what we can learn:
"The Wisdom of Crowds," by James Surowiecki
"Freakanomics, " by Steven Leavitt and Stephen Dubner
"The Tipping Point," by Malcolm Gladwell
"Blink," by Malcolm Gladwell
"The Black Swan," by Nassim Nicholas Taleb
"Fooled by Randomness," by Nassim Nicholas Taleb
"The Long Tail," by Chris Anderson
"Fortune's Formula," by William Poundstone
"Linked," by Albert-Laszlo Barabasi
"The World is Flat," by Thomas Friedman
"Collapse," by Jared Diamond
Earlier this week I attended the MDM Insight event that TDWI ran in Savannah, GA. The hosted event employed a different model than other TDWI events, in which qualified participants were invited to attend, and vendor sponsors were provided with direct access to demonstrate their products' capabilities.
One of my roles at the event was to moderate a short workshop session to help attendees articulate what they believed were their most critical needs for master data management. One interesting common reaction was confusion about what composed an MDM solution, and what were the vendors actually selling. Another frequent reaction was expressing difficulty in lining up the requisite set of ducks within a reasonable amount of time to garner enough "horizontal support. Third, a general consensus was that instituting MDM was best done as an adjunct to existing application development (e.g. to support BI), focusing on small projects.
Actually, that last one confused me a bit, since if it only centering on a small application area (and not the whole enterprise), could it really be "master data" management?
Oh, one more thing - it may be worthwhile to consider the qualitative (and feasibility) differences between creating a "single golden source of truth" and an environment supporting the transparent access to a unified view of uniquely identifiable master objects (my current definition of what MDM is, by the way).
I am sitting at McCarran airport waiting to board my flight back from TDWI, and am thinking about one trend I noticed at the vendor exhibits: there is a growing set of vendors selling high-performance columnar-based database systems. Interestingly, the common denominator is the positioning of the software as a means for virtualizing a data warehouse appliance.
Orienting the data in a columnar manner is nicely suited to analytic applications, so the clear opportunities for these kinds of products are partnered solution providers for specific types of analytics, or with data aggregators and providers to allow for data linkage and then analysis.
Some of the vendors (or vendor reps) I bumped into over the past few days include ParAccel, Vertica, Sybase IQ, Infobright. Kognitio, alternatively, is not columnar but through data distribution across parallel systems can also talk the virtual appliance talk.
One conclusion that can be drawn is an emerging market for providing high performance analytics platforms with a low barrier to entry points towards cracking open that small.medium business market. One interesting thing to watch is the ways these guys will partner with other BI vendors (e.g., OLAP, visualization, end-user analytics) to see who can put together a robust end-to-end BI solution suitably priced for the $50-$100 million company.
One of the hazards of advocating techniques intended to improve business through better customer insight is the occasional question of faith: does a good business intelligence strategy and program necessarily equate to greater profits? Sometimes I wonder: if customer analysis and predictive analytic techniques work so well, then one who is knowledgeable in the area should be able to apply the ideas directly to his/her own business, right? Isn't this just another example of eating one's own dog food?
Here is what I mean: using our business intelligence and data analysis and data mining and predictive analytics, we claim that we can increase response, reduce costs, extend customer lifetimes, improve lifetime values, etc. So as an experiment, I should be able to start a retail business and accumulate a bunch of customers who will always be satisfied, will never threaten to cancel their service, and will always be just about to buy the products I have already determined they need. They will each be at the center of a huge sphere of influence, and I will exploit the viral marketing opportunities by turning every satisfied customer into a walking advertisement for my products and services. I will have optimized my product and service offerings so that as one product becomes obsolete, the customer is dying to upgrade to the next level, and I will time their releases so that no follow-on product cannibalizes its predecessors' sales.
The idea intrigues me: pick a product or service to sell and then apply the performance improvement techniques driven by busiess intelligence. Some thoughts:
I would want to pick a business that is recession-proof (plumber? pest exterminator? funeral director?).
I would have to sell a product that needs updating or replacement within a relatively short cycle. Selling replacement windows is probably out. Selling office supplies is more like it.
You get the picture: a broad market where some knowledge of the customer community can drive repeatable sales, and where customer data is easy to get, maintain, enhance, analyze, and exploit.
There are a lot of success stories out there for applications of BI to business productivity improvement. Yet that is not true across the board, and that probably means that owning the software doesn't necessarily imply achieving the benefits without a little hard work. Ultimately, the successful organizations exploit BI by adapting their business processes to exploit the the knowledge discovered, and put practices in place to measure the value of each decision. Maybe that is what drives the belief in BI?
According to an article on a recent IDC report, small and medium businesses are rapidly making up a large piece of the business intelligence tools market. Adoption of BI tools by SMBs increased 40% in 2007.
Yet the increase in adoption of tools doesn't necessarily mean that there is an increase in value; it would be interesting to see some report documenting how SMBs adapt the way they do business as a result of integrating BI into their operations.
Why do so many people directly link master data management with customer data? Maybe because we have been dealing with customer data so long, that when a new buzz word appears, we immediately try to link what we are doing to the "latest craze" to ensure our mindshare among the stakeholders.
However, the more I think about MDM and product data, the more intrigued I am. I have said this in a number of metings: product names are curious because they often describe what they are. For example, a PHILLIPS SCREWDRIVER 6-3/4" is a phillips screwdriver that is 6 and 3/4 inches long. What is more, product descriptions carry a lot of information that can be relatively easily parsed out using standard text analysis and text mining techniques. So I would very much be interested in hearing more about some product information MDM projects - email me or post your success stories!
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