American colleges and universities have a problem and—like baseball teams and health care companies—they are turning to predictive analytics and big data to help solve it. The problem is retention. Fewer than half of U.S. students graduate in four years and about 60% finish in six years, at a big cost to both families and institutions. The statistics are even worse for first-generation students and those from low-income backgrounds, perpetuating social inequities.
Tweet: Big man on campus? Now it’s big data
A solution lies in predictive analytics: analyzing large sets of data to discern patterns and spot students in danger of dropping out. A growing number of colleges and universities are incorporating predictive analytics and big data to monitor student progress and intervene quickly when there’s trouble. Here’s how. (more…)
Each year, the United States spends billions of dollars on cancer research. In fact, the National Cancer Institute’s budget is $5.665 billion for 2018, and that does not include money from private donations. Despite all the money and effort spent on cancer research, there is a bit of a “big data crisis” preventing healthcare institutions from optimally learning from each other.
Tweet: Want to cure cancer? First liberate the data
The New York Times recently published an article titled, “New Cancer Treatments Lie Hidden Under Mountains of Paperwork,” which talked about this problem and the difficulty of extracting meaningful data from medical records. And I have to admit, the article struck a nerve. (more…)
A few weeks ago, I attended the Real Business Intelligence Conference, which was hosted by Dresner Advisory Services on the campus of MIT. The show was packed with great presentations by speakers such as Dr. Theresa Johnson of Airbnb, Professor Thomas Malone of the MIT Sloan School of Management, and David Dadoun of the Aldo Group.
Tweet: The downside of data science
One of the more thought-provoking presentations was given by Cathy O’Neil, author of the book, “Weapons of Math Destruction”, in which she argues that mathematical models and algorithms give the illusion of being impartial, but in actuality, many of them end up perpetuating stereotypes and inequality. Following Cathy’s presentation, I sat down to read her book. Here are some of my thoughts.
At our recent Dimensional Insight Users Conference (DIUC17), industry analyst Howard Dresner, who coined the term “business intelligence” back in 1989, joined our CEO Fred Powers on stage to talk about some of the trends in business intelligence today. The timing was serendipitous, as Howard had recently released the results of Dresner Advisory Services’ 2017 Wisdom of Crowds Business Intelligence Market Study.
Tweet: 3 business intelligence trends and how they impact your organization
BI trends that Fred and Howard discussed at the conference: (more…)
When Dimensional Insight’s Model engine was introduced in 1989, it had many advantages compared to other relational databases at the time. Today, as columnar databases grow in popularity – and in performance – we have again outpaced the competition with the introduction of our new Spectre data engine.
Tweet: 3 reasons you should upgrade to Diver Platform 7.0 and Spectre
If you’re a Dimensional Insight customer, here are 3 reasons why you should upgrade to Diver Platform 7.0, which includes Spectre technology. (And if you’re not a Dimensional Insight customer, here’s why you’ll want to take a look at what we have to offer.) (more…)
What do you think about when you hear the words “HC big data”?
Do you think “opportunity”?
Think they’re overused buzzwords?
Or do you get that feeling of dread?
HC big data has generated a lot of talk over the last few years, but as far as companies doing something about it? Er, not so much. Many organizations know they need to harness their big data in order to derive insights that will help them cut costs and improve revenue, but they aren’t quite sure of how to put a plan into action.
Tweet: 5 ways healthcare organizations are deriving insights from big data
We all know that data is growing, and it’s growing fast. In fact, IDC statistics show the big data market is growing about six times as much as the overall IT market is.
To address the need for super speed and the ability to handle high data volumes, Dimensional Insight has developed a new business intelligence (BI) data engine. Our lab in Cambridge, Massachusetts has been hard at work in developing this project, which we previewed to customers at our annual users conference earlier this summer.
Interested in getting inside the head of the lead developer of Diver’s data engine? Here’s an interview with Dimensional Insight’s Jamie Clark, who has the scoop on this exciting new technology.
Tweet: Need more speed? Learn all about Dimensional Insight’s new big data engine
Anyone in the business world has heard the urban legend about “Big Data” and how it’s growing at an abnormally fast rate, leaving companies in shambles. But how much of a threat can it really pose? It’s not like it’s growing at an alarming rate (oh wait, it is), or costs businesses money (oh yeah, that too), or leads to less accurate decisions (whoops).
But really, let’s forget the facts for a moment. Implementing a business intelligence (BI) tool is clearly a horrible idea. Here are 5 reasons why. (And yes, this is totally meant to be taken tongue-in-cheek, with a nod to BuzzFeed.)
Tweet: 5 reasons BI tools are totally useless
When it comes to business intelligence, it’s clear that the current modus operandi won’t cut it for organizations moving forward. Too much is changing: the amount of data within organizations, the way users are accessing and analyzing it, and much more.
With that in mind, here are 4 business intelligence trends that are shaping where the industry is heading in the near future. They are also impacting how we at Dimensional Insight are developing products and applications.
Tweet: 4 trends shaping business intelligence in 2015 and beyond
As a child, I was a big fan of Nancy Drew. I owned tons of Nancy Drew books (the old school kind from the ‘60s and ‘70s with the yellow cover) and would read them any chance I had. In fact, on a recent visit to my parents’ house, my mom dug up some of my old Nancy Drew books, and I was thrilled to be able to take them home and pass them on to my own daughters.
What fascinated me so much about Nancy? (Other than that she was much older than me – 18 years old! – and seemingly so sophisticated?) Mainly, it was her ability to piece together hard-to-decipher clues and – voila! – solve the crime. During the years I read those books, I used to fancy myself a detective, trying to solve my own neighborhood mysteries (which unfortunately never quite had the drama or thrill of the Nancy Drew capers).
Now, as a marketer who relies on data to guide program decisions, I’m finding myself more and more relying on these detective skills that I honed as a young wannabe-sleuth. (more…)