Monthly Archives: December 2007
Cautious Tech Spending for 2008?
No longer do companies keep increasing tech spending “like it’s 1999.” Companies have become more selective in what they invest in. The key word is “investment.” Most companies don’t just go out and buy the latest and shiniest technology, no matter how much hype there is, unless there is a solid business ROI (return on investment.) No discretionary project gets funding without the ROI.
I am an avid reader of the Wall Street Journal (WSJ) Business Technology blog written Ben Worthen. In his post “Oracle and Accenture: Not a Rising Tide”, December 20, 2007, he postulates that although Oracle and Accenture just had terrific quarters and are forecasting good times in 2008, tech spending will be flat and other tech vendors will not experience the same solid growth as these two companies. Unlike in 1999, there will not be a rising tide for all tech companies. I couldn’t agree more.
I don’t know (nor does anyone else, really) whether tech spending will be flat or will grow. (It could grow because we will avoid a recession or because people’s estimates are too conservative, as they have been the last few years.)
(more…)
Better management through measuring data quality
I recently asked a customer of ours why they invested so much in monitoring and publishing key performance indicators for their data quality. “Believe it or not, the biggest reason we measure data quality is not to correct bad data” came the reply. “The reason we monitor data quality is to detect problems with our business processes.”
Indeed, as I mentioned in my last blog post, business users look to investments in people and processes in addition to technology in order to address poor data quality. For example, if a bank branch manager received a report showing that customer data originating from his branch office had much higher incidents of duplicate entries and was putting the entire bank at risk of massive regulatory fines, he is not going to throw technology at the problem. His response might be mandatory training for tellers or better hiring practices to screen for adequate computer skills.
Experts in quality control methodology refer to this as addressing “root cause.” Common starting points of measurement involve completeness, accuracy, consistency, conformity, duplication, and integrity. Eventually, as the business culture matures its data quality practices, timeliness and data lineage (origination) are used to evaluate quality of data. Of course, software technology that automates the process of parsing, standardizing, matching and consolidating data is of immense value and is an absolute requirement in any data integration project. However, the issue of data quality goes beyond these IT projects. Ongoing measurement and monitoring of data quality provides value directly to the business because it helps them to better manage their people and processes.
The self-service data quality minefield
Marshall Field, the American department-store owner and retail merchandizing pioneer, is usually credited with coming up with the now much abused adage “Right or wrong, the customer is always right”. These days the saying is usually shortened to the more functional “the customer is always right”. But with self-service data entry, where more and more customers are responsible for supplying their own contact and order details via web applications, it is worth revisiting the entire quote.
The fact is even when the customer is responsible for “bad data” the vendor still has to shoulder the blame and do something about it.
Take the example of a friend of mine who recently signed up with a well known social networking website. Let’s ignore the reasons why he signed up – he’s not entirely sure – or what social networking sites are for – none of us is entirely sure yet, but I guess we’ll start to get the point as more and more of them are sold to industry giants for n billion dollars.
Is there life on Mars?
This week NASA announced that it may have discovered evidence of water flowing on the surface of Mars in the recent past. This raises the possibility of life existing on Mars in the past and even to the present day.
A lot of talent, time and money has gone into addressing the fundamental question – is there life on Mars. In addition to the two rovers on the surface, there are currently three spacecraft in operation around Mars; Mars Odyssey, Mars Reconnaissance Orbiter and Mars Express – sadly the Mars Global Surveyor which is responsible for this weeks exciting news has probably suffered a severe failure and is in effect lost. Each spacecraft has been sent out to gather some basic statistics about the Red Planet such as; how the surface changes over time, the percentage of carbon-dioxide at the poles or how the temperature varies throughout the atmosphere. All of this in the hope that we move closer to a definitive answer to that fundamental question. But forget the answer: are we asking the right questions?
Your 2008 Data Integration Plans, Part 4:Getting your Service-Oriented Architecture (SOA) in Order
I am seeing a disconnect between many clients’ 2008 plans for service-oriented architecture (SOA) and the expectations they are setting with the business groups that are funding them.
The good news is that people have been pervasive in getting sponsorship and funding for SOA projects based on anticipated business benefits. The bad news is that despite good technology, many IT projects fail when business group don’t get what they think they were promised. It’s like the old saying “you can talk the talk, but can you walk the walk?”
The promise of SOA is to establish reusable services that can be deployed throughout the enterprise to deliver accelerated application implementation at lower risk and effort. Reusable business services reduce redundant or overlapping development efforts and increase IT productivity. They also enable applications that were previously siloed to exchange data and interoperate.
(more…)
Start small with monitoring, but always think big to achieve data quality goals
I attended my first parent-teacher meeting the other day for my five-year old daughter. Another one of those “life stage” events done and dusted – I remember dreading the annual meeting when I was a kid. The notion of my parents and my teacher comparing notes on my behaviour was too much to bear – somebody was eventually going to put two and two together and find out I was up to no good.
It all got me thinking about a recent blog post by my esteemed colleague Garry Moroney. His post Mobilizing the Data Quality Army outlined the level of effort, thought and planning that the US Department of Education is putting into data quality.
As Garry points out dealing with data quality in a large, disconnected organization such as the US schools system is not a trivial exercise. But if you were to only read that one post you might be overwhelmed by the potential size of the data quality task in front of you.
(more…)
Business and IT Collaboration is Essential for Data Quality
A recent InformationWeek article* described the growth in IT employment across the US as a result of a shift in skills. Rather than focusing on pure IT proficiency, organizations are looking for talent with “a more hybrid mix of technology skills, along with an understanding of the business and its customers.”
IT departments are highly motivated to increase the level of collaboration with their counterparts in the business. Nowhere is this more critical than in the area of data quality and the trend is causing a shift in the way companies are looking to solve their data quality issues. First generation data quality tools had a natural focus on technology, instead of business. Here are some of the differences between technology focused data quality solutions and business-focused data quality solutions.
Tools vs. Process
Technology focused data quality solutions provide tools that automate data processing. Evidence of this type of focus can be seen in the way that vendors will tout the sophistication and type of their algorithms over and above their ability to support ongoing data quality management processes. While technology is extremely important, its relevance cannot eclipse the overall data quality management process. Even if your data quality tool can automate the correction of 95 percent of the data, if the remaining five percent cannot be managed properly, you will continue to suffer from poor data quality.
(more…)
Your 2008 Data Integration Plans, Part 3:Getting your reference data in order
Do you have projects lined up for next year that will deal with reference data? It’s a good idea, especially if you want to better understand your customers, determine the profitability of your products or work more effectively with your suppliers.
If your projects are formal they may be identified as master data management (MDM), customer data integration (CDI) or product information management (PIM). All of these are examples of reference data.
Getting your reference data in order – reflecting what is and was – helps to establish data consistently. This, in turn, enables historical analysis such as trending, and helps you plan for the future. Some of you will not have projects with the MDM, CDI or PIM labels, but if you look beneath the covers you almost inevitably will see efforts to improve your reference situation.
The reasons the MDM, CDI and PIM acronyms have emerged in our industry is because most businesses have not done a great job with reference data yet and because it is difficult. But, as with many things in life, if it was easy it probably wouldn’t be worth doing.
Don’t let a vendor convince you that all you need is their shiny new MDM, CDI or PIM product. Software alone won’t solve the problem; otherwise it would have been solved a long time ago.
(more…)
Loading ...