I recently listened to Brian O’Neill’s excellent interview with Tom Davenport, headlined “Why on a scale of 1-10, the field of analytics has only gone from a one to about a two in ten years time.”
The conversation covered a lot of ground as Mr O’Neill and Mr Davenport explored the reasons why. Highlights included general lack of technical literacy and lack of an organizational data driven culture. But to their credit, they took responsibility on behalf of analytics professionals, emphasizing how we in the field could change in order to make more analytics efforts successful. Rather than focusing on providing technology-centered solutions, they recommended that data and AI professionals seek first to understand and empathize with their clients or internal customers, enabling data and AI pros to develop more effective analytics capabilities in light of that understanding.
I agree that analytics professionals can improve their game. However, as a former consultant who’s switched over to the client side, I think there’s room for improvement all around. To me, clients who work proactively to prepare for an analytics project position themselves for better outcomes. Continue reading →
Reading articles about data anonymization makes it clear that it is not an entirely effective security measure (here and here), but still part of a robust security capability, and required if your organization is affected by GDPR. (I use “anonymization” as a general term encompassing techniques that de-identify personal data within a given data set.)
But there’s a positive side of anonymized data that hasn’t received much press. Providing anonymous data to senior managers who don’t need access to personal data can encourage them to take a broader perspective, and thereby bring new energy to fact-based senior planning and analysis. Continue reading →
“At least 84 percent of consumers across all industries say their experiences using digital tools and services fall short of expectations.”* That quote headed a recent article by David Roe on the role of data integration in digital workplace apps. However, the opening quote reflects the pervasive dearth of integrated data among the companies most of us frequent.
We’ve all experienced the effects. Last week I was in a fender bender. Due to a mixup I didn’t have my insurance card with me, so I called the insurance company to get the info. They had no record of me associated with my car. It turned out that my car is insured under my wife’s name, hers under mine. Although I’ve been their customer for 25 years, and was driving my own car, they couldn’t give me insurance info. Sure, they were following good security practices. But I’m not letting them off the hook. Continue reading →
Recently my friend Mark Hudson posted about the inappropriateness of the term “sprint” for an agile project phase, preferring the cycling term “interval.” That post really struck a chord with me.
As a rugby union fan and former wing/fullback I’ve always thought the whole rugby analogy was wrong. Agile development is continuous and fluid, yet the agile originators chose the word “scrum” for its daily standup meetings. In rugby union a scrum is a set play resulting from a minor penalty, like offside in American football or a foot fault in tennis. If you like the rugby analogy the right term would have been “ruck,” which is kind of like a scrum but part of the continuous run of play (in the other kind of rugby, called rugby league, the scrum has devolved into an almost meaningless stylized ritual – which I guess happens on some agile projects). Continue reading →
“Our goals can only be reached through a vehicle of a plan, in which we must fervently believe, and upon which we must vigorously act. There is no other route to success.” – Pablo Picasso
It is an old story: about 30% of IT application projects succeed, 45% are “challenged,” and the other quarter fail altogether. That’s the consistent result over the years of the Standish Group Study of Project Outcomes. Jorge Dominguez, here, displays a chart of the remarkably similar results since 1994. Not a pretty picture, right? Some question the validity of the Standish studies, but Scott Ambler parallels the Standish story in a recent Dr Dobbs column called “Lies, Great Lies, and Software Development Project Plans,” which itemizes the strategies commonly used by IT project managers to “stay out of trouble” when schedule/budget results don’t match initial estimates. For example, “18% change the original schedule to reflect the actual results”. Continue reading →
In a recent very thoughtful post on data quality, Paul Erb plays out an analogy comparing data users with Don Quixote and data quality professionals with Sancho Panza, then reverses the analogy to cleverly coin the “Sancho Panza” test of data quality professionals. He encourages data quality professionals promoting the critical role of data quality to apply a what would Sancho say test to ensure that they are aligned with the needs and interests of data consumers. Continue reading →
Excerpt from “Show Me the Money: A DM/BI Business Value Primer”, Bob Lambert and Tri Truong, Information Management Special Reports, March 24, 2009
Before starting the Business Intelligence business case, the BI advocate should do the homework required to ensure its success, including these essential steps:
1. Know the organization’s goals and objectives.
2. Identify a BI champion.
3. Identify and work with BI stakeholders.
4. Identify an application with tangible business value.
5. Define and quantify a quick win prototype project. Continue reading →
History is littered with IT application projects that end late, go way over budget, or abandoned altogether. I was fortunate enough to see one work out really well (almost – please read on). It was no mistake. It came down to a simple method advocated by a gentleman named named John Carpenter.
The project was an HR management software conversion from one commercial off-the-shelf software (COTS) package to another. The company concerned was conservative about spending money. A previous business case had proposed a similar project. The problem with that business case was that the benefits were really tough to conceptualize, so the cost/benefit analysis relied on soft benefits like “improved access to information” and “more consistent reporting data”. The folklore was that the CFO had physically thrown that business case out of his office. Continue reading →