IDG Contributor Network: A speedy recovery: the key to good outcomes as health care’s dependence on data deepens

It may have been slow to catch on compared to other industries, but the health care sector has developed a voracious appetite for data. Digital transformation topped the agenda at this year’s Healthcare Information and Management Systems Society (HIMSS) conference in Florida, and big data analytics in health care is on track to be worth more than $34 billion globally within the next five years—possibly sooner.

Electronic health records are growing in importance to enable more interdisciplinary collaboration, speed up communication on patient cases, and drive up the quality of care. Enhanced measurement and reporting have become critical for financial management and regulatory compliance, and to protect organizations from negligence claims and fraud. More strategically, big data is spurring new innovation, from smart patient apps to complex diagnostics driven by machine learning. Because of their ability to crunch big data and build knowledge at speed, computers could soon take over from clinicians in identifying patient conditions—in contrast to doctors relying on their clinical experience to determine what’s wrong.

But as health care providers come to rely increasingly on their IT systems, their vulnerability to data outages grows exponentially. If a planned surgery can’t go ahead due to an inability to look up case information, lab results, or digital images, the patient’s life might be put at risk.

Symptoms of bigger issues

Even loss of access to administrative systems can be devastating. The chaos inflicted across the UK National Health Service in May following an international cyberattack—which took down 48 of the 248 NHS trusts in England—gave a glimpse into health care’s susceptibility to paralysis if key systems become inaccessible, even for a short time. In the NHS’s case, out-of-date security settings were to blame for leaving systems at risk. But no one is immune to system downtime, as was highlighted recently by the outage at British Airways, which grounded much of its fleet for days, at great cost not to mention severe disruption for passengers.

Although disastrous events like these instill fear in CIOs, they can—and should—also serve as a catalyst for positive action. The sensible approach is to design data systems for failure—for times when, like patients, they are not firing on all cylinders. Even with the best intentions, biggest budgets and most robust data center facilities in the world, something will go wrong at some point according to the law of averages. So, it’s far better to plan for that than to assume an indefinitely healthy prognosis.

If the worst happens, and critical systems go down, recovery is rarely a matter of switching over to backup infrastructure and data—particularly if we’re talking about live records and information, which are currently in use and being continuously updated. Just think of the real-time monitoring of the vital signs of patients in intensive care units.

If a contingency data-set exists (as it should) in another location, the chances are that the original and the backup copy will be out of sync for much of the time, because of ongoing activity involving those records. In the event of an outage, the degree to which data is out of step will have a direct bearing on the organization’s speed of recovery.

To ensure continuous care and patient safety, health care organizations need the fastest possible recovery time. But how many organizations have identified and catered for this near-zero tolerance for downtime in their contingency provisions?

Emergency protocol

The issue must be addressed as data becomes an integral part of medical progress. Already, data is not just a key to better operational and clinical decisions, but also an intrinsic part of treatments—for example in processing the data that allows real-time control and movement in paralyzed patients. Eventually, these computer-assisted treatments will also come to rely on external servers, because local devices are unlikely to have the computing power to process all the data. They too will need live data backups to ensure the continuity and safety of treatment.

On a broader scale, data looks set to become pivotal to new business models (for example, determining private health care charges based on patient outcomes, otherwise known as “value-based medicine”).

While technology companies will be pulling out all the stops to keep up with these grander plans, maintaining live data continuity is already possible. So that’s one potential barrier to progress that can be checked off the list.

This article is published as part of the IDG Contributor Network. Want to Join?

Source: InfoWorld Big Data

IDG Contributor Network: Responsible retail: treating customer data with care

Retailers have become so adept at capturing and analyzing consumer data that there is now a real risk that they might alienate customers by revealing just much they know about our lifestyle, habits, and preferences. So if retailers want their big data investments to pay off, they must tread carefully. 

Big data exploitation in retail is no longer restricted to tracking and responding to broad trends; it’s become very personal. Which is great if the result is that customers find exactly what they were looking for; less so if it feels intrusive or invasive.

Analytics technology is now so sophisticated that, by drawing on an individual’s loyalty-card records, payment histories and browsing habits, retail marketing programs can detect an alcohol problem, whether someone has lost their job (because spending drops and premium brands are replaced by “value” purchases), if they’re away on holiday, and much more besides. (A few years ago, Target worked out that a teenage girl was pregnant before she knew herself.) 

This is not to imply that retailers are necessarily doing anything wrong or sinister (customers may well have given consent for this kind of data usage). But it can be unnerving to think that every time we browse online or in a store, that activity is being monitored to build a picture of our entire lives. Just think how often we are pestered with unsolicited promotions related to a product we may have glanced at only once.

Even in Europe, where measures to protect consumer privacy are fairly robust, customers are now being tracked via their mobiles as they enter or pass by stores. Their activity can be registered—even if they don’t have a loyalty card or store app. In the US, meanwhile, regulations are becoming looser rather than more stringent now that safeguards protecting internet search histories are being dismantled. So the scope for overstepping the mark is growing.

Snooping vs. problem-solving

If retailers want to impress and retain customers, rather than undermine their trust, they need to turn their attention to more beneficial ways of applying algorithms and data discovery.

In fashion, retailers are exploring ways of minimizing sales returns—a problem so costly across e-commerce that the likes of Amazon have gone so far as banning customers who do this too often. In the US alone, merchandise returns were valued at $260.5 billion in 2015, roughly 8 percent of total sales, according to the National Retail Federation. Returns are a pain for customers, too: who wants the disappointment and hassle of having to send something back because it’s not quite right? A common cause of apparel returns is over-ordering, because consumers haven’t been confident of getting the right size; this is something the industry is now trying to address with new combinations of technology and new data insight.

Another option is to use customer intelligence to provide a more responsive logistics service. Amazon has patented a shipping model that anticipates what goods certain customers are going to order, so it can have the products waiting in a nearby warehouse for faster delivery. Combine this type of strategy with automated drone deliveries and the customer experience might soar while the cost of logistics (even the need for delivery partners) diminishes.

Greater empathy, better service

To the customer, real service innovation reduces the sense of being spied upon because of the perceived personal benefit. The end justifies the means. Just as, if I go to my regular bar, it suits me that they’ll have my favorite drink ready for me before I’ve even taken a seat because of how well they know me. Though if that happened in a bar I’d never been to before, that would be unsettling. Context—and consent—matter.

If the result of deeper customer insight is something genuinely useful to the consumer, surrendering anonymity and sharing data becomes a lot more palatable. People do appreciate easier access to the items they want, it does make their life easier if they don’t have to parcel up returns, and a timely recommendation can be useful in the right circumstances. So really, retailers just need to be a bit more thoughtful about how they apply their knowledge.

What isn’t in dispute is the strategic value of data. Figures from Gallup Behavioral Economics suggest that organizations that are able to exploit customer behavioral insights outperform their peers by 85 percent in sales growth, and more than 25 percent in gross margin. So keep building those data vaults and adding ever more sophisticated real-time analytics; the rest is down to using the insights to best effect.

This article is published as part of the IDG Contributor Network. Want to Join?

Source: InfoWorld Big Data