How many of you suffer from at least mild “cyberchondria"? Do you run to the computer to Google your latest ailments? Are you often convinced that the headache you have is the first sign of some terminal illness you’ve been reading about?
Well, Symcat takes a new approach to Internet-assisted self-diagnosis. It provides not only the symptoms but the probability of getting the disease, using CDC data to rank results by the likelihood of the different conditions. It then allows users to further filter results by typing in information such as their gender, the duration of their symptoms and medical history. No, that headache you’ve had all week is likely not spinal stenosis or even viral pharyngitis. But if you’ve had a fall or a blow to the head you might want to consider a concussion.
As Symcat puts it, they “use data to help you feel better.” Never underestimate the palliative effects of peace of mind.
I had the chance to ask Craig Monsen, MD, co-founder and CEO of Symcat, a few questions about how they got their start with the business and their innovation with open data.
What was the genesis of Symcat? Can you describe the "ah-ha" moment of determining the need for Symcat?
There are multiple maturity models and associated assessments for data governance on the market. Some are from software vendors, or from consulting companies, which use these as the basis for selling services. Others are from professional groups like the one from the Data Governance Council.
They are all good – but frankly not adequate for the data economy many companies are entering into. I think it is useful to reshuffle some too well established ideas...
Maturity models in general are attractive because:
Using a maturity model is nearly a “no-brainer” exercise. You run an assessment and determine your current maturity level. Then you can make a list of the actions which will drive you to the next level. You do not need to ask your business for advice, nor involve too many people for interviews.
Most data governance maturity models are modeled on the very well known CMMI. That means that they are similar at least in terms of structure/levels. So the debate between the advantages of one vs another is limited to its level of detail.
As an analyst on Forrester's Customer Insight's team, I spend a lot of time counseling clients on best-practice customer data usage strategies. And if there's one thing I've learned, it's that there is no such thing as a 360-degree view of the customer.
Here's the cold, hard truth: you can't possibly expect to know your customer, no matter how much data you have, if all of that data 1) is about her transactions with YOU and you 2) is hoarded away from your partners. And this isn't just about customer data either -- it's about product data, operational data, and even cultural-environmental data. As our customers become more sophisticated and collaborative with each other ("perpetually connected"), so organizations must do the same. That means sharing data, creating collaborative insight, and becoming willing participants in open data marketplaces.
Now, why should you care? Isn't it kind of risky to share your hard-won data? And isn't the data you have enough to delight your customers today? Sure, it might be. But I'd put money on the fact that it won't be for long, because digital disruptors are out there shaking up the foundations of insight and analytics, customer experience, and process improvement in big ways. Let me give you a couple of examples: