TeK Talk is a new podcast series, sponsored by Knowledgent, featuring data and analytics professionals discussing the latest trends and topics in data and analytics for Fortune 1,000 companies. Continue reading Second Episode of TeK Talk Podcast, Sponsored by Knowledgent, Available Now
As companies develop analytical capabilities and culture, there will be a strain on traditional IT models to support this highly iterative and data intensive enterprise needs. Business users and their application owner counterparts are quickly turning to cloud-based approaches to experiment with various data sources and software configurations, leading to drastically reduced time-to-market of analytical applications – decisions & actions are happening quicker. Continue reading Leveraging Cloud Agility in Data and Analytics
Knowledgent is excited to announce the release of our new Analytics and Visualization (A&V) Ecosystem, detailing the technology landscape for analytics, data science, business intelligence, and data preparation. Continue reading Knowledgent Releases New Analytics & Visualization Ecosystem
As all industries are embracing big data, and now quickly leveraging cloud, mobile applications, and IOT, self-service analytics has become an imperative to keep pace with rapid change. Continue reading Knowledgent Announces Continued Growth with Visualization and Analytics Practice
As a follow up to our white paper, “Risk Scoring: Big Data and Advanced Analytics Further Evolve the Healthcare Model”, Knowledgent announces our latest infographic “Improve Healthcare Risk Scoring Analytics through Hadoop”. Continue reading Infographic: Improve Healthcare Risk Scoring Analytics Through Hadoop
Rachel Sholder, Data Science Intern, Analytics and Visualization
Data analysis is the process of collecting, inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and then communicating your results to have the biggest possible impact. According to Merriam-Webster, a process is a series of actions or steps taken in order to achieve a particular end. While the “particular end” of data analysis might be a presentation to a client, my “particular end” I have been working towards achieving is a successful end to my summer internship. Continue reading Data Science 101: Three Takeaways from My Internship
What’s in a name? Everything.
A name is an idea. It carries with it many dimensions of meaning that shape understanding. So why do we continue to speak in the clumsy and imprecise parlance of the day, embracing such emergent monikers as “big data“? (Wasn’t data always big? Isn’t it the economics of using that data that have favorably changed?) Simply put, the trends are racing in so many directions, changing the data landscape so quickly, that in order to keep up, we must employ the language being used, lest we fall short of the productive connections we seek to make. Continue reading What’s in a name? DAaaS is more than semantics.
It’s hardly news to anyone that healthcare costs have long been on the rise in the U.S. For firms footing the bill for employee healthcare, this pain has been especially acute of late. According to the Kayser Family Foundation, employers’ contribution to an average family’s yearly health insurance premium grew over 77% from 2003 to 2013. Pharmacy benefit costs make up more than 16% of that premium. Healthcare costs make up the second highest proportion of corporate spending after salary (7.9% of total compensation, by the latest figures).
The ever-looming crisis has CFOs and benefit managers scrambling to cut costs and reduce benefits – with some choosing to attack costs through wellness programs, incentives for healthy living, and preventive diagnostics. A raft of data analytics outfits and consulting firms, along with motley startups, have rushed in to fill the demand for cutting costs, squeezing efficiencies, improving vendor sourcing, restructuring provider networks, and re-negotiating rates.
The smart money these days is on Big Data to solve all sorts of problems through the impartial, cold and calculating lens of Big Analytics. Can it help companies cut healthcare costs? Time will tell, of course, but the first signs are promising.
When it comes to enterprise health spend analysis, incumbents like Verisk Health, Truven Health, and MedAI, along with Big Data behemoths lBM, Oracle, SAS, and McKinsey have all jumped into the fray, offering reporting capabilities and predictive modeling to hospital systems and insurers.
At the same time, newcomers like Zakipoint Health, Health Care DataWorks, and Zephyr Health, among others, are seeking to shake things up with top-notch analytics and visualization tools and custom-tailored recommendations for CFOs and benefit managers to optimize cost-cutting.
Big Data also is making inroads in improving employee wellness. Well-funded Flatiron Health is partnering with cancer centers to create a Big Data cloud that captures more than 100,000 cancer patients’ data, giving unprecedented insight into the disease across a large patient pool. uBiome is building and studying a database of bodily bacteria profiles from across its customer base. Withings and Fitbit, plus the bigger players like Samsung and Apple are building wearables to track quite every aspect of one’s health and wellness, starting with sleep habits, exercises, running distance, calories, and weight. Apps engaging users to record, monitor, and track nearly every aspect of their lives have sprung up like a cottage industry.
All this brings us right back to cost containment. What is our surest bet when targeting the costs of healthcare all across the enterprise? Is it to visualize the costs and see the big cost drivers and to squeeze out inefficiencies, short-term? Or is it to hand out pedometers and FitBits and track the living tissue out of employees? Aside from simply cutting benefits, a third and harsher option is to penalize for non-compliance with directives to enroll in certain programs to improve employee health, based on criteria hand-tailored to maintain a healthy workforce.
Leaders among the corporate players with a keen and cost-effective eye already have some combination of these three that they are using. Studies are scant on what has worked; this seemingly quite obvious approach is only starting to firm up in numbers and specifics.
With U.S. healthcare spending hulking in at $3.8 trillion in 2013 and ever on the rise, companies are scrambling for magic bullets to improve their bottom line. Early observers feared that ObamaCare would lead to slashed hours to decrease employer health insurance liability, but the scenario simply hasn’t panned out.
All the same, health insurance costs are rising. As data on cost drivers, problem employee demographics, procedure effectiveness, doctor reimbursement rates, and others become available, enterprises will calibrate the rewards and the punishments more carefully to make employees more accountable for their healthcare. With better tools and expertise to understand that data and to make it actionable, employee health may soon be commoditized and scored like creditworthiness. In the meantime, enterprises will continue to grope around for short-term solutions.
Is your organization using these strategies (or others) to manage healthcare spending? Let us know in the comments!