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.
But as firms move into the data economy – with what this means for their sourcing, analyzing and leveraging data, I think that today’s maturity models for data governance are becoming less relevant – and even an impediment:
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:
Every company generates data that would be of significant value to its customers, partners and potential partners; information that could be combined with insights from this ecosystem, public data and other sources to generate significant new discoveries, products and business values. But making our data available, easily consumable and getting payback for sharing it are significant hurdles.
Over many years we have built up an ever-more complex web of security, legal and data management practices that make it nearly impossible to share valuable info between companies in an open marketplace style – which is exactly what is needed to open up this value.
But it doesn’t have to be this way. There is a new approach that leading enterprises and governments are taking today that is significantly simpler, more manageable and empowers companies to share their key data more freely, opening up massive new market opportunities for all. Here's how a few Forrester clients are taking advantage of this new model:
I met with a group of clients recently on the evolution of data management and big data. One retailer asked, “Are you seeing the business going to external sources to do Big Data?”
My first reaction was, “NO!” Yet, as I thought about it more and went back to my own roots as an analyst, the answer is most likely, “YES!”
Ignoring nomenclature, the reality is that the business is not only going to external sources for big data, but they have been doing it for years. Think about it; organizations that have considered data a strategic tool have invested heavily in big data going back to when mainframes came into vogue. More recently, banking, retail, consumer packaged goods, and logistics have marquis case studies on what sophisticated data use can do.
Before Hadoop, before massive parallel processing, where did the business turn? Many have had relationships with market research organizations, consultancies, and agencies to get them the sophisticated analysis that they need.
Think about the fact, too, that at the beginning of social media, it was PR agencies that developed the first big data analysis and visualization of Twitter, LinkedIn, and Facebook influence. In a past life, I worked at ComScore Networks, an aggregator and market research firm analyzing and trending online behavior. When I joined, they had the largest and fastest growing private cloud to collect web traffic globally. Now, that was big data.
Today, the data paints a split picture. When surveying IT across various surveys, social media and online analysis is a small percentage of business intelligence and analytics that is supported. However, when we look to the marketing and strategy clients at Forrester, there is a completely opposite picture.
On February 22, the Reserve Bank of India (RBI), an institution that supervises and regulates India’s financial sector, announced guidelines allowing corporations to enter the banking sector. Private companies, public-sector groups, and nonbanking financial firms will all be eligible to apply for a banking license. We expect RBI to start issuing new bank licenses by early 2014.
RBI guidelines state that companies receiving a banking license must open at least 25% of their branches in rural areas. Despite this guideline, I believe that new entrants will primarily target the same urban and semi-urban customers that existing banks target. The reason is simple: These are the most profitable customers. This helps explain why 85% of rural bank branches in India belong to public banks; it’s simply not an attractive market for private banks.
What it means for current Indian banking CIOs: Leverage big data to grow your business or prepare to be left behind.
As competition increases, businesses will expect new IT capabilities to understand and respond to customer needs better, faster, and cheaper. Banking CIOs who embrace this change will adopt big data technologies and become true business partners. The ones who don’t will be bypassed by new entrants (when they come to play) using big data approaches and internal data from whatever market they’re currently in to analyze the banking market. These new entrants will likely influence customer preferences, question existing assumptions, and look for ways to disrupt the market. I recommend that current Indian banking CIOs:
Whether you are just starting on your BI journey or are continuing to improve on past successes, a shortage of skilled and experienced BI resources is going to be one of your top challenges. You are definitely not alone in this quest. Here are some scary statistics:
“By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills, as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” (Source: May 2012 McKinsey Global Institute report on Big Data)
“… trigger a talent shortage, with up to 190,000 skilled professionals needed to cope with demand in the US alone over the next five years.” (Source: 2012 Deloitte report on technology trends)
“Fewer than 25% of the survey respondents worldwide said they have the skills and resources to analyze unstructured data, such as text, voice, and sensor data.” (Source: 2012 research report by IBM and the Saïd Business School at the University of Oxford)
Data management history has shown, it is not what you buy; it is how you are able to use it that makes a difference. According to survey results from the Q4 2012 Forrsights BI/Big Data Survey, this is a story that is again ringing true as big data changes the data management landscape.
Overall . . .
Big technology adoption across various capabilities ranges from 8% to just over 25%.
Plans to implement big data technology across various capabilities is as high as 31%.
Pilot projects are the preferred method to get started.
However . . .
High-performing organizations (15%-plus annual growth) are expanding big data investments by one to two times in many big data areas compared with other organizations.
The key takeaway . . .
For most organizations, big data projects aren't leaving the pilot stage and aren't failing to attain strong return on investment (ROI).
Why? What organization couldn’t benefit from making better decisions? Just ask the Obama campaign, which used sophisticated uplift modeling to target and influence swing voters. Or telecom firms that use predictive analytics to help prevent customer churn. Or police departments that use it to reduce crime. The list goes on and on and on. Virtually every organization could benefit from predictive analytics. Don’t confuse traditional business intelligence (BI) with predictive analytics. BI is about reports, dashboards, and advanced visualizations (which are still essential to every organization). Predictive is different. Predictive analytics uses machine learning algorithms on large and small data sets alike to predict outcomes. But predictive is not about absolutes; it doesn’t gaurentee an outcome. Rather, it’s about probabilities. For example, there is a 76% chance that this person will click on this display ad. Or there is a 63% chance that this customer will buy at a certain price. Or there is an 89% chance that this part will fail. Good stuff, but it’s hard to understand and harder to do. It’s worth it, though: Organizations that employ predictive analytics can dramatically reduce risk, disrupt competitors, and save tons of dough. Many are doing it now. More want to.
Few understand the what, why, and how of predictive analytics. Here’s a short, ordered reading list designed to get you up to speed super fast:
A year and a half ago I broke up with Blackberry and started dating iPhone. It was a clean but cruel breakup: AT&T cancelled my T-Mobile contract on my behalf, the equivalent of getting dumped by your girlfriend’s new boyfriend.
This year I’ve been cheating on my laptop with my iPad. But it’s an on-again, off-again relationship. While I tell my iPad it’s the only one, I keep going back to my laptop. When I travel, my iPad is with me meeting clients. Meanwhile my laptop is in the hotel room surfing the online menu for a turkey club.
The iPad beats my laptop on size, weight, connectivity, and battery life. It also improves the human element when I’m having a face-to-face conversation but need to take notes. These are all critically important to me when I'm out of the office visiting clients or at an event.
But my laptop wins when I need to perform other important activities. For example, the larger screen really helps to write and edit research reports (John Rakowski, you’ll have your edits soon!). Or when I need to approve expenses behind the VPN or access files on my hard drive that I haven’t stored in Google Drive (yes, Forrester sanctioned).
Now that I've had a few months of compare both devices, I come back to outcomes . . .