Courtesy of IT Business Edge
Whether it’s user-driven, vendor-driven or some combination of the two, there’s a lot of fuss about getting business intelligence into the hands of more folks, not just the super users who pride themselves on their abilities to whip up data-intensive reports at the drop of a pivot table.
In order for this to happen, BI projects must move beyond selecting the right hardware and software to encompass change management, knowledge management and other cultural issues. That was a key takeaway from my interview with Desmond Mullarkey, Paulo Dominguez and John Brkopac, all of Technolab Corp.
Technolab, a consulting, software development and training company that provides business performance solutions, helps its clients establish competency centers. There’s a particular demand for BI competency centers due to companies’ largely unmet expectations for BI. Dissatisfaction with BI is “not a well-kept secret,” Brkopac told me. “As significant as the promise of BI is, many organizations haven’t gotten ROI from it,” he added.
Not only that, but BI technology is changing rapidly and so are business conditions in general, which means companies often find themselves scrambling to keep up with users’ demands for new reports or types of analysis.
Competency centers should be created and operated with four dimensions in mind, Mullarkey said. Technology, selecting the proper hardware and software, is one dimension. Human capital/culture is another, and it’s the one that relates most directly to getting users on board with BI. If users lack confidence in BI applications, they simply won’t use them.
“A lot of times you find BI applications are used by only a small number of people,” Mullarkey sruaid. “That could be due to a lack of confidence in the application itself or maybe a lack of a standard in the organization about what technology to use.”
Infrastructure, the third dimension, relates to both of the other two. While its relationship to technology is pretty obvious, the connection to human capital/culture is less clear. Mullarkey explained an underperforming infrastructure will result in slow response times, which is a major cause of user disappointment in BI.
The fourth – and trickiest – dimension is process. It includes both technical processes, such as creating and enforcing standards for data extraction, data staging and data quality, and human processes; such as training users.
A “big trap” associated with the process dimension is “is treating BI as a project for a particular area of users within the organization, so it doesn’t transform into a process,” Mullarkey said.
It’s important to consider all four dimensions when crafting a BI strategy, Mullarkey told me, “When you talk about less than optimal BI implementations, almost always it goes back to a strategy based on one of the dimensions without the other three involved.” And guess what? Companies tend to focus more on technology, while neglecting the other three dimensions.
My theory: Technologists continue to lead BI efforts at most companies, and they are more comfortable tackling technology challenges than cultural or process issues.
Ethics and Artificial Intelligence: Driving Greater Equality
FEATURE | By James Maguire,
December 16, 2020
AI vs. Machine Learning vs. Deep Learning
FEATURE | By Cynthia Harvey,
December 11, 2020
Huawei’s AI Update: Things Are Moving Faster Than We Think
FEATURE | By Rob Enderle,
December 04, 2020
Keeping Machine Learning Algorithms Honest in the ‘Ethics-First’ Era
ARTIFICIAL INTELLIGENCE | By Guest Author,
November 18, 2020
Key Trends in Chatbots and RPA
FEATURE | By Guest Author,
November 10, 2020
FEATURE | By Samuel Greengard,
November 05, 2020
ARTIFICIAL INTELLIGENCE | By Guest Author,
November 02, 2020
How Intel’s Work With Autonomous Cars Could Redefine General Purpose AI
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
October 29, 2020
Dell Technologies World: Weaving Together Human And Machine Interaction For AI And Robotics
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
October 23, 2020
The Super Moderator, or How IBM Project Debater Could Save Social Media
FEATURE | By Rob Enderle,
October 16, 2020
FEATURE | By Cynthia Harvey,
October 07, 2020
ARTIFICIAL INTELLIGENCE | By Guest Author,
October 05, 2020
CIOs Discuss the Promise of AI and Data Science
FEATURE | By Guest Author,
September 25, 2020
Microsoft Is Building An AI Product That Could Predict The Future
FEATURE | By Rob Enderle,
September 25, 2020
Top 10 Machine Learning Companies 2021
FEATURE | By Cynthia Harvey,
September 22, 2020
NVIDIA and ARM: Massively Changing The AI Landscape
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
September 18, 2020
Continuous Intelligence: Expert Discussion [Video and Podcast]
ARTIFICIAL INTELLIGENCE | By James Maguire,
September 14, 2020
Artificial Intelligence: Governance and Ethics [Video]
ARTIFICIAL INTELLIGENCE | By James Maguire,
September 13, 2020
IBM Watson At The US Open: Showcasing The Power Of A Mature Enterprise-Class AI
FEATURE | By Rob Enderle,
September 11, 2020
Artificial Intelligence: Perception vs. Reality
FEATURE | By James Maguire,
September 09, 2020
Datamation is the leading industry resource for B2B data professionals and technology buyers. Datamation's focus is on providing insight into the latest trends and innovation in AI, data security, big data, and more, along with in-depth product recommendations and comparisons. More than 1.7M users gain insight and guidance from Datamation every year.
Advertise with TechnologyAdvice on Datamation and our other data and technology-focused platforms.
Advertise with Us
Property of TechnologyAdvice.
© 2025 TechnologyAdvice. All Rights Reserved
Advertiser Disclosure: Some of the products that appear on this
site are from companies from which TechnologyAdvice receives
compensation. This compensation may impact how and where products
appear on this site including, for example, the order in which
they appear. TechnologyAdvice does not include all companies
or all types of products available in the marketplace.