When it comes to healthcare analytics, there’s a lot of fiction that’s presented as fact. This can muddle the buying process, which is already more complicated with more decision-makers involved. In fact, statistics show that the average buying team is 5.4 people, and that the more stakeholders involved, the lower the likelihood of any purchase.
Tweet: Healthcare analytics myths busted: Why the biggest BI companies aren’t always the best
When lots of people are involved in a purchase decision and reputations are on the line, unfortunately, the best solution often isn’t picked, but rather, what’s thought to be the safest solution. This brings us to our last myth in our blog series in which we’re busting the biggest pieces of fiction in healthcare analytics. (more…)
In this blog series, we’ve been busting some common healthcare analytics myths, and it’s time to bust yet another. So far, we’ve tackled these 3 myths:
Today’s myth: that the business intelligence (BI) tool that provides the slickest visualization is the best one.
Tweet: Healthcare analytics myths busted. The slickest BI tool isn’t always the best. (more…)
In this blog series on healthcare analytics myths, we’ve been busting some common myths that threaten to derail your business intelligence (BI) buying journey. Our first post focused on why you don’t need a data warehouse, and the second focused on why the analytics in your EHR might not cut it.
Tweet: Healthcare analytics myths busted. You don’t need to know all your healthcare analytics goals as you start BI.
This third myth has to do with planning your business intelligence implementation and the belief that you need to know the exact end result you want before you even get underway with your project. Truth is, that’s just a myth, and BI often works better when you adopt some flexibility into your planning process. (more…)
When you’re buying healthcare analytics, one of the first steps is to go out and research potential vendors and products, and learn as much as you can about each one. Unfortunately, though, during this process, you often come across a lot of information that’s presented as fact, but isn’t necessarily so. It’s hard to become the superhero of your analytics project when you have to wade through so much misinformation.
Tweet: Healthcare analytics myths busted. Why the analytics in your EHR might not be the best option.
In this blog series, we are taking some of the biggest myths about healthcare analytics and debunking them. In post #1, we talked about how you really don’t need a data warehouse to do analytics correctly. In this post, we’ll tackle myth #2. Don your cape and superhero mask and let’s take a look… (more…)
Face it: picking out a healthcare analytics solution is not easy. In fact, it often feels like a job meant for a superhero. You need to jump through hoops to determine your specs and what you need in a solution. But that’s hard to do when there are so many myths out there about what you need or don’t need in a healthcare analytics solution. Add to that the number of people involved in purchase decisions (an average buying group size of 5.4, according to statistics by SAVO), and it’s easy to see how buying analytics turns into a daunting task.
Tweet: Healthcare analytics myths busted. Why you don’t need a data warehouse to do analytics right.
In this 5-part blog series, we’re going to take a look at some of the biggest myths being perpetuated about healthcare analytics and debunk them. You’ll learn the truth about what you really need to do healthcare analytics well, and in the process, become the superhero of your buying team.
Without further ado, here’s myth #1: (more…)