Thanksgiving is a time of year when many of us eat too much food, watch too much football (though my husband would say that’s not possible), and spend too much money on Black Friday sales. The busyness of the holiday makes it easy to forget what the purpose of the day is all about: gratitude.
Tweet: Giving thanks: Gratitude in the workplace
I’ve been thinking a lot lately about the past year at work: the customers I’ve visited, the projects I’ve worked on, and the co-workers I’ve collaborated with. And if you’ll allow me a moment, in the spirit of the holiday, I’d like to share some of the things I’m most thankful for. (more…)
Dimensional Insight recently sponsored the Gartner Data & Analytics Summit in Frankfurt, Germany. The two-day event provided dozens of educational sessions from Gartner analysts and attracted 750 business intelligence and analytics leaders from around the world.
Tweet: Highlights from the Gartner Data & Analytics Summit in Frankfurt
What did these thought leaders say about BI today and where analytics is headed? Here are a few big takeaways. (more…)
If you could peer into the analytical mind, what would you find? Are business intelligence (BI) analysts independent thinkers who like to work out solutions to their questions alone? Or are they collaborative in nature, wanting to share their insights and gain perspectives from their peers?
The answer, as you might expect, isn’t that easy. But it is fascinating. Aberdeen set out to find where analytics users fall on the collaborative-independent spectrum and also examine the advantages and disadvantages of each frame of mind. Their findings are incorporated into a research report, “Analytical Mind Map: The Collaborative-Independent Spectrum.” Here are a couple of interesting pieces of information from the report:
- Need for speed: Collaborators are 29% more likely than independent analysts to say that the speed of their decision making has improved.
- More voracious appetite for data: Independent individuals are 77% more likely to access BI tools on a weekly (or more frequent) basis.
Tweet: What does the analytical mind look like?
In our last blog post, we talked about the importance of collaboration between IT and business leaders when it comes to analytics, and 5 fast facts about how a unified approach to analytics helps improve the business.
Those companies that are defined as “Best-in-Class” in new research by Aberdeen are those that excel in 3 critical areas:
- Data relevance & availability: They are able to draw out the most critical information to make decisions and make it accessible to business users.
- Portability of information: They are able to share information across business functions to drive analytics success.
- Self-sufficiency of business users: Business users can engage with self-service analytics without deep technical expertise.
Tweet: 3 steps on the path to performance improvement with analytics
I was a big fan of “The Office” when it was on prime-time, mainly because it nailed the dysfunction that’s a part of so many businesses today. One of my favorite scenes was in the pilot when Dwight opened his desk drawer to find his stapler floating in a Jell-O mold, courtesy of Jim. I mean, come on, at some point in your work career, you’ve encountered someone you’ve always wanted to do that to, right?
In many organizations, IT and business leaders have a Jim & Dwight type of relationship. Sometimes it’s mild intolerance of each other; other times it’s downright hostility. While this type of relationship makes for great TV comedy, it isn’t healthy, and let’s face it: it makes for bad business.
Tweet: 5 fast facts about the value of analytical collaboration
If you Google “business intelligence failure rate,” you’ll find varying stats on how successful (or rather, unsuccessful) business intelligence (BI) implementations are. One set of statistics estimates that projects fail half the time, while another set estimates they fail a whopping 70 to 80 percent of the time. While the stats vary depending on who is conducting the survey, who they surveyed, and how they define “failure”, the fact remains: BI implementations often fail to meet expectations.
There are myriad reasons that BI projects fail, from inadequately defining the need at the outset to improperly setting and managing expectations during the execution. But generally speaking, we can classify project failures into 4 broad categories: (more…)
Howard Dresner, noted BI industry analyst, recently hosted a “tweetchat” among his followers in which they discussed “why some organizations haven’t adopted collaborative BI technologies and why some adopted it but failed to achieve the anticipated value.” Howard posed the following question about slow adoption of Collaborative BI to his Twitter followers:
“If (Collaborative BI) is so effective, why aren’t organizations jumping on the collaborative bandwagon and why have some not seen the anticipated ROI?”
The discussion identified possible reasons ranging from the immaturity of the technology to the cultural gap between established BI practitioners and the younger, social media savvy, “share-all” generation of employees entering the workforce.