Gregory Maciag

Mobile Insurance Gets Going

A good sign that a technology has matured is when the promised benefits start to defeat the barriers to entry. In the far-off early days of the PC, the breakthrough point was around the availability of spreadsheets. With mobile insurance, there isn't such an obvious catalyst. This channel has come of age through simple evolution. And the barriers – on the consumer side – turn out to be illusory.

Research by Everquote suggests mobile insurance users are not so very different from other types of customer. In fact, they're somewhat more “normal” than people have assumed. For one thing, the belief that mobile users don't complete quote requests is wrong. And it gets better: “mobile consumers are actually 17% more likely to complete a full quote request form than desktop users.” Without a keyboard.

The research also finds the median age of mobile insurance customers is 29. This is higher than usually assumed, but still significantly lower than desktop users.

Everquote's findings undermine all sorts of easy opinions about today's consumers. One lesson I take from the research is that we should stop thinking in terms of separate groups like “mobile users” and “desktop users”. These days people use all kinds of devices to do all kinds of things. It's best to think of customers as being in different situations, rather than different demographics.

Organizations also need to be able to offer consistent, joined-up service across all their channels. Data standards play a big role in making multi-channel operations work.   PC360

Governing External Data

Phil Simon at SAS wrote an interesting blog post on whether or not you can extend data governance to data sourced from outside the organization. He points out that many people are still struggling to make data governance effective within the organization – that is, managing their structured, “small” data.

He concludes that although social data, linked data, and open data may be very useful to the enterprise, the enterprise can't apply data governance to them. I think this is what he means by: “it's never been more important to govern what you can.”

I accept that no single consumer of external data can control its original format, semantics, or quality – unless the relevant sources are signed up to a set of common data standards. But I'm worried that by saying external data isn't covered by data governance processes, we risk letting the business work with bad, incompatible, and uncertain data. Data professionals surely have a responsibility to apply best practice processes to any data used by the business.   SAS

The Need for Standards Bodies

The insurance industry showed great foresight in forming ACORD as the custodian of its data standards. ACORD is such a familiar part of the landscape we take it for granted. But take a look at some other industries, and it's clear their development and productivity has been held back by the lack of similar organizations.

Take real estate. Matt Cohen writes how the Real Estate Data Standards (RETS) have been in development since 1999, but are only getting traction since the formation of the Real Estate Standards Organization (RESO) a few years ago. I'll bet Matt has noticed RESO is the front part of the word RESOURCES. A volunteer group can do great work – but getting standards adopted takes more resources, including, as Matt notes, professional project management.

Real estate is an interesting comparison to insurance because its challenges with multiple data entry are large, dispersed, and entrenched. There are over 800 Multiple Listing Services (MLSs) in north America. And MLSs have been around for over 100 years. These services are how realtors collaborate and compensate each other. People put up with giving the same information to different services because it has always been necessary.

I have no doubt ACORD's success owes a lot to the support shown by the industry from the get-go. We've proved you don't need a big organization to create, maintain, or drive standards. But you do need a group that will educate, promote, and assist. The insurance industry's mature data standards are a great foundation for innovation. Standards have lifted everyone's game.    Brokers and Agents

Patience and Persistence Drive Success in Standards

Progressing with standards demands a range of skills – few of which are technical. Although data standards are used by machines, they describe the business. And adopting and exploiting data standards requires intervention in the way processes are carried out inside the organization, and across boundaries. So there's a deal of understanding, relationship building, and negotiating to be done.

Here's what Arien Malec of RelayHealth says will achieve health data interoperability: “it will be patience, coupled with persistence, cooperation, and a commitment to innovation”.

You don't need sophisticated tools. You don't need super smarts. You just need to know where you're headed, keep going, and take people with you. Sounds a lot like leadership, right?

There's often a problem here. Most standards movements are grassroots phenomena. They come from a community of users who have got sick of the waste and delay caused by data fragmentation – all those tailored connections, translations, workarounds. So the impetus for data standards tends to lie with people who are passionate, democratic, and practical. There may be leaders among them, but there often isn't a recognized leadership role in the standards context. This can mean the group ends up generating a lot of heat, but little forward motion.

The insurance industry can thank the visionaries who started the process that quickly led to the founding of ACORD and ultimately to the suite of standards and frameworks we maintain for the global industry today. ACORD was created to supply the leadership role – without taking ownership from the community. As a neutral non-profit run by the industry, for the industry, we can lead with standards in the knowledge that everything we do is accountable to the industry.

Other sectors are not so lucky. Standards proponents in such sectors should look seriously at what an industry body like ACORD can achieve.   Health IT

Integrating Clinical and Claims Data

The American Medical Group Association (AMGA) has written to Congress about the data requirements for supporting chronic care. Here's a key passage: “Claims data is needed for providers to understand the care that happens outside of the medical group office. Claims data is also needed to better predict risk and identify chronically ill and high cost patients.

In other words, medical groups need both clinical and claims data to manage a patients’ care and their costs. However, access to claims data is uneven. Some commercial payors will share its claims data with providers while many will not. CMS shares its Medicare claims data with ACOs but there are limits to the effectiveness of this data share effort.”

This is a very clear statement of why two domains need to collaborate. AMGA goes on to point out  the current fragmentation “takes up resources on the provider end and diverts attention from building the infrastructure necessary to effectively manage population health.”

The benefits of data standards are many and demonstrable. AMGA is here reminding us of the costs of not adopting standards. These costs are real, recurrent – and unacceptable. Organizations exist to serve their stakeholders, not to provide a home for make-work.    Health IT

End 2 End Thinking?

End-to-end is a useful term, it crops up a lot, I use it myself. Its use reflects a growing understanding that problems and opportunities need to be addressed holistically. We're trying to move away from silos, fragmented processes, and needless variation. End-to-end thinking is healthy and beneficial.

Yet, the term suggests the business world is made out of component parts that start and end, and live on their own. So, for example, there's the claims process, or the procurement process. This is correct – but it misses out the fact that all these components are also connected. Our organizations, our businesses, our activities – they are webs. Not spaghetti.

And if we're to think holistically in the true sense, we need to see the whole. It's good to think end-to-end. We also need to think systemically – across the whole piece. This isn't easy, but there's an entire, mature discipline of systems or business analysis we can use.

End-to-end is great for shifting our thinking away from isolated details. For the digital businesses we're building today, we need also to think in the round.

Opening Up Innovation

Hats off to John Hancock for their solid moves toward driving innovation in insurance. Innovation is the big driver in the industry today and there's a real urgency to change the way people think and to accelerate change. John Hancock is just one of the companies reaching out to new partners and stimulating new approaches.   INN

Standardizing Processes - Important Next Step

I agree with most of the recommendations about enacting data standards taken from a recent big data event held in Europe. In particular, I'd underscore this: “Best practices and guidance are just as important as the standards themselves.”

But there's one finding I think needs the addition of a little light and shade. I'll quote it in full: “Don’t standardize processes: Not everything should be standardized. In particular, the processes followed by a particular company should not be standardized as it’s the processes that capture innovation. That has to be down to individual entrepreneurship!”

I believe when we standardize something, we're recognizing an inherent similarity in a set of superficially diverse things. Here's an easy example. Organizations evolve different ways of representing “customer”. But you can standardize this, because everyone recognizes there's a common concept hidden behind the different realizations of the concept. The clue is that we already have the name “customer”, even though we've got a pile of differing data structures.

In the same way, I don't believe anyone who attempts to standardize a process is looking to impose some straitjacket on the business. They're just trying to find the commonality underlying a load of superficial and unnecessary differences. Again, the clue is in the name. If you can talk about “the claims process”, then you can bet such a common concept exists.

Some of what you do provides your organization's differentiation. But not all of it. The truly valuable parts of your processes may account for 10% or less of their steps. In fact, the more effective your business, the smaller this percentage is. This is proven every day by businesses who buy in “white label” business operations and add a small – but supremely valuable – top layer of customer service or branding.

We began to look at process at ACORD and member reaction has been very positive.  So I'd change the recommendation to this: “Standardize everything you can. Invest in what is truly unique to your organization.”    Big Data Europe

Public Isn't Open

I learned a small but interesting fact today while scanning a piece about public records by Kim White at Lexis Nexis. It's one of those things you tuck away (and probably forget) so I'll share it here instead.

It's this: “Public” doesn't mean “open”. Some public records are open for anyone to see, but others are closed to everyone except certain types of organizations, and they have to have special permissions. Also, what's deemed a public record varies from state to state – but I could probably have guessed this.   This fact will be useful to anyone interested in open data.   LexisNexis

Cyber Continuity

The Insurance Information Institute (III) has some very useful information out about cyber threats and the insurance coverages available. Looking down the summary of coverages, I note how data loss is only one part of the picture. There are equally important risks to organizations such as inability to conduct business because of a network attack – and extortion based on threats of mounting a network attack.    III

Organizations need to secure their data, for sure. But they also need to secure their processes. Cyber is not just about losing data. It's about losing the business.

Data Breaches and Paperwork

The Information Commissioner's Office (ICO) in the UK publishes statistics on the data breaches it learns about. I guess it doesn't learn about every data breach. In fact, I doubt whether every organization knows exactly how many data breaches it suffers and what the damage is.

The most fun part of the current quarterly report is the analysis of data breach incidents by type. The leading type is: “loss/theft of paperwork”. The next is “Other principle 7 failure” – whatever that means – and the third is “data posted/faxed to incorrect recipient”.

So, the impression I get is that the leading types of data breaches are not actually data breaches in the sense most normal people would understood. Numbers for actual hacking are a fraction of the combined totals for the top three causes.

My next point is this: If paper is the leading cause of (let's call it) information leakage, and hacking is comparatively rare, then you can reduce leakage by digitalizing more of your business and cutting out paper.    ICO

Bill Hartnett, Head of Innovation at ACORD

Bill Hartnett (from Microsoft) joined ACORD recently to become our first Head of Innovation.  He is not only focused on making ACORD more innovative but helping members innovate faster.  Listen to this podcast and you will definitely decide to attend ACORD 2015 in Boca Raton on November 1-4 to see the future.  

Powered by InsuranceJournal.tv

 Click here to:  Attend ACORD 2015 in Boca Raton Novemver 2-4

Signs and Symptoms

Every so often I see someone using “data standards” to mean data quality standards – while also talking about problems connected with what I mean by data standards. At ACORD – and within other standards organizations across industry, commerce, government and the open data movement – data standards refer to agreed formats and meanings for data elements, whether at a technical or business level.

A recent article highlighting eight early warning signs for data governance problems has these as numbers 2 and 3 on the list: “No defined and managed metamodel” and “No centralized master inventory of business data elements (BDEs)”. The author explains why these omissions would cause problems – basically, I'd say you haven't got a hope of carrying out any kind of data governance without these assets in place.

But there's no recognition here that organizations of all kinds can rapidly develop or directly import these assets from existing industry standards. The ACORD Framework provides all the models you need and our message standards cover data element meanings and formats.

I have a feeling most data governance problems give themselves away through process failures rather than structural symptoms. What I mean by this is an organization with data governance problems is one that can't share information – within the organization or with partners or customers. You can often truncate the diagnosis of an under-performing data governance regime with one question: Are you using the relevant industry standards?    betanews

Banking on Data

As we've built out business data standards for the global insurance industry, people have often made comparisons with the banking industry. They have pointed out how banks got their data standards together a long time ago – that's how they could clear checks, and share ATM networks, and launch credit cards. The implication was that insurance was slow off the mark.

There's some truth in this. But it's a partial view and it's focused on retail banking, and high-volume, low-transaction business. In one important respect, the banking industry is way behind in the use of business data standards. This is the area of regulatory reporting for investment banking.

This is made clear in a fascinating post by John Barclay, a specialist in market and credit risk. (Risk, right – something we know about in insurance.) Banks have different data models and uneven data governance regimes. It's expensive and time consuming for regulators to deal with this diversity. More important: “In the future, banks will have to manage their balance sheets and risk in a more controlled and stringent way. They will need to be able to make decisions around asset optimization, liquidity, and funding on an intraday basis. In most cases their current data models cannot support these requirements. The question we need to ask is whether regulators will acknowledge that this is a huge challenge for banks and will they give them sufficient time to implement any new data standards that emerge?”

This is a tantalizing question. For me, I want to know how such standards are going to emerge without some kind of concerted effort to produce them. Standards don't self-construct. Someone's got to do the work. Barclay says banks have an opportunity “to start investing in cleaning up their data models and simplifying them”. They need to do more than that. They need to come together and agree common data standards for regulatory reporting, based on the principles set out by the regulators together with their own management information requirements. The banks' opportunity is bigger than the chance to clean up their separate data models. It's the chance to make much, much more of their data.    Finextra

Innovations with ACORD

My column at PropertyCasualty360.com

Maciag on Innovation

Sideswiped - People Confuse Standards

Every so often I get sideswiped by someone using “data standards” to mean data quality standards. Usually the context makes this usage clear right away. But when I saw the headline “Finding: Local Government Agencies Ahead of Fed in Data Standards” – on a site called Data Management Resource – I thought I was going to get an eye-opener into how local government is using data standards to improve information flow, cut out duplication, and join up business processes.

However, the article is not about data standards. It's about things like “cleansing, matching, merging and data enrichment”. And it gets a little weird: “What poor data management does is have a negative impact on time and resources, and perhaps the starting point is to make the capturing of data more efficient through the better use of technology. For smaller businesses, there are choices like Evernote, and cloud storage services like Google Drive and Dropbox [...]”.

What just happened? I can't see the logic in this argument – “perhaps” doesn't cut it. And I can't see how expanding your use of applications has any positive impact on data management.  But using data standards does have a great impact on data management. It puts an end to all that cleansing, matching, and merging.  Housekeeping is an important aspect of data management. But it's not the point of data management.   Data Management

Getting Utility from Data

The world's full of data – but how much of it is being used? All data is potentially useful. But it's not enough just to collect it.

An article by A. J. Shirley on the implications of the DATA Act's data standards makes this clear. First, government agencies already have plenty of data, because they're legally required to collect and report data. But the data is in silos – sets dictated by the reporting requirements. They can't use the data for other purposes because it's not organized the right way. A philosopher might say there's a problem of incommensurable paradigms. We'll just call it a mess.

Second, none of the agencies can share data without data standards. So no one is able to see the big picture, or trace a process from end to end, or understand how waste could be cut, or detect business factors which don't fall in the remit of any single agency.

Third, without consistent data, no one can make truly informed decisions. If you have a ton of data, it may look like you're running a rational decision making process. But if you can't use all that data as a collection then you're guesstimating – at best.

The data standards established by the DATA Act do more than enforce a little housekeeping. They're  about more than saving money, although they'll save plenty tax dollars. They're even about more than the principles embedded in the DATA Act's name, accountability and transparency. They're about good management, effective leadership, and a more intelligent government.   Management Concepts

Innovation Inside and Out

I like the way Celent's Jamie Macgregor summed up today's priority for business strategy when he gave a keynote at ITC 2015: “If you can't make money out of money, you have to look at your core business.”  The current poor returns on financial investments add an edge to innovation. We have to look at improving and expanding what we do instead of looking for pre-built investment vehicles outside of the company. This has got to be a good thing.

At the same time, there's huge interest in investing in the new generation of fintech startups and more of these are young companies working in the insurance field. Some of the core business developments we'll see going forward may come from this source of innovation too.

Jamie acknowledged these complementary paths and noted that the innovation labs being set up by the likes of Aviva and Progressive are internal to their organizations, although some insurers are investing in fintech startups as well. As he said, this is a big shift from the default-to-outsourcing behavior we've got used to. I think it's good news for anyone with a bright idea and the determination to make it happen.

Data is Not a Department

There's a great start to a piece on Chief Data Officers. It's a dialog with Capgemini's Zhiwei Jiang, head of the firms' insights and data practice for financial services:

Capgemini has recognized four opportunities for big data in the financial services: risk management, operational efficiency, customer experience and identifying new business models. It makes you wonder, could one person be good at all those things?  Zhiwei Jiang: Ha! The short answer is no.

Now there's a longer answer as well, but I'll let you read that if you're interested. I just want to use this as a springboard for an observation I've been making in various ways for a while. My point is that data is everybody's responsibility. By all means, have a Chief Data Officer. But make that person a leader and a focus. Data isn't a department.     Big Data

Google and Oscar

The Wall Street Journal noted Google's investment in health insurance startup Oscar. On first hearing the news, you might think – well, Google has so much money to spray around, who can say whether any single move they make is significant or not? But Google isn't sloppy with its investment decisions, and this one seems to have a real dose of that elusive quality known as synergy.

Oscar's pitch to customers is lower insurance costs through tech-assisted monitoring. Oscar customers get free fitness-tracking devices and free access to unlimited phone consultation with a physician. Now, the WSJ article says another part of Google is working with Novartis to develop a contact lens which measures glucose levels in tears... You can see how these things start to come together.

Connect the dots. Sometimes you only need to draw a couple lines and a whole new picture leaps out at you. Are insurers ready for the big, big effects of these seemingly small additions to the healthcare landscape?   WSJ

The Cyber Gap

ITC 2015's panel on cyber insurance set out some good outlines for the topic, but I'm not sure we're ready to color in the picture yet. The center of this subject is a region of uncertainty.

Some of the speakers seemed to regard cyber risk as purely about data breaches. Loss of data is important for sure, but so are the other kinds of disruption (and destruction) that can be carried out using information and communications technology.

I want to pick on one comment made by the panel: We don't have good data on cyber – we lack a measure of the losses.  Without loss data, we can't pool risk. But it's unlikely any one insurer will amass enough credible cyber loss data any time soon. So the industry needs to find a way to share cyber loss data without impacting competitiveness or breaking any rules.

If cyber loss data was in the public domain, or measurable by external actors, we'd be okay. As one panel member said, we have data for earthquakes but not for cyber. Information professionals within the insurance industry must see the cyber data problem as a trigger for collaboration. We need to share if our rating models are to have any meaning – and our customers to have the products they need.

Two other points from the discussion: Regulators could help by extending cyber protection requirements to small and medium sized enterprises; and insurers could help by having cyber insurance on their own risks! Both of these moves would definitely help awareness.

Standards Evolve Industries

You've hear me say this for decades.  Data standards enable business. They also enable business change. And because they enable business change, they must change themselves.

This is an organic cycle which has been under-appreciated in the IT world. Developers, integrators, and vendors are focused on the next upgrade, the next release. They try to adapt to changes in the business environment via chunked-up sets of “new requirements”. It's a freeze-change-freeze model. But the pattern is rarely recognized as a true business cycle.

I think of this normal pattern as in-domain change. But there's another kind of change: When the boundaries or location of the domain alter. Often it's only in retrospect that we recognize a real domain change for what it is.

There's a good example of a responsive domain change in the recent release of OpenOil's API for open data on oil rights. The oil industry has had data standards for years, enabling exchange of geological information on reservoirs, and drilling and production data. Now OpenOil is adding an embryonic standard on rights, so that people will be able to see which companies have been granted exploration or extraction rights in which blocks in which countries. It's not an upgrade – it's a rethink of boundaries.

Notice how access to information and the sharing of information are bringing pressure to bear on this industry. Data standards are actively underpinning a renewed demand for transparency and accountability. This effect will impact all industries over time. Data standards are a force for change.   GOXI

Relevant Data

Swiss Re's innovation manager Oliver Werneyer was a big hit at ITC 2015 – one of a series of real stars who made this a vintage year for the event and made me excited for the future of innovation in our industry.

I'll pick out two of the points which resonated with me. First, Oliver remarked how most managers are in post for three to five years, while the horizons on the really innovative ideas are more like ten to fifteen. I think we have real continuity problems when it comes to achieving significant change. We at least need to hold on to core teams in our innovation practices, even though we have to recognize creative people may want to move to different opportunities.

Second, Oliver mentioned how too many people today are happy to visualize the data they have available – but you need to decide what you want to achieve and then get the relevant data. This theme is a constant in the information business, and it needs to be reinforced regularly.

Elsewhere, conference speakers talked of the ability to exploit “exhaust data” generated by other organizations. No, that's not a reference to Volkswagen – it's the idea that data collected by another organization as part of their business process, but not directly needed by them, could be useful to you.

The temptation is to assume all the data you need can be sourced from someone else's exhaust. But these opportunities to spin data gold from some other company's are rare and they are not advertised – some smart person has to spot them.

Let's remember what scientists do. They decide what they want to know. They look to see if the data exists. If it doesn't, they figure out how to get the data. They don't just grab whatever data is kind of in the right area and then believe they've done their job.

Big Does Not Mean Difficult

It shouldn't need saying, but volume is not the same thing as complexity. In the business of data, the little word “big” causes a lot of trouble these days. “Big data” sometimes means large volumes of differently formatted data, much of it unstructured. But the term is also used to imply that just having a lot of data is a problem. However, if you have large quantities of data which all conform to a standard, you don't really have a problem. You might need to wait a few more seconds for the answer to a complex query, but those wait times are reducing all the time. And you might need more space to keep all your data, but storage is also becoming ever cheaper and more convenient.

We need to bear this in mind when thinking about the data management issues of large organizations – and the government in particular. I'm a fan of Hudson Hollister at the , who continues to do a great job focusing folks' attention on what needs to be done.  And when he points out the government now represents 21% of our economy, we should all take note.

However, let's recall it's not size that matters here, but difference – and, specifically, needless difference. If the IRS can't coordinate its internal systems and consolidate its customer data, then that's clearly a problem that needs to be fixed. Across government as a whole, however, the number of entities for which standards need to be agreed is relatively small. What's more, the core shared concepts are well understood in the corporate domain and can be readily applied with little adaptation. “Party”, anyone?

I know the scale of the challenge of digitizing government is large. But I firmly believe that with the right standards and incentives in place, a transition to much smoother and cost-effective government business will be faster than many might think.   I don't want Hollister to soft-pedal though. Let's keep pushing.    Government data

Will Insurance Lead IoT?

Gary Nuttall of Chaucer thinks the insurance industry won't lead the development of the Internet of Things because IoT leads to premium erosion. Speaking during a panel session at ITC 2015, he said the spread of IoT will most likely be driven by device manufacturers and logistics companies. He added that insurers don't have the ability or capacity to analyze IoT data and take advantage of it through dynamic pricing.

IoT data will definitely have an impact on how risks are calculated. I think it will lead to changes in insurance products and services. There may be erosion of premiums in traditional lines, for sure. The wager is this erosion will be more than matched by new lines of business.

Also, I can see a leadership role for insurance in the IoT – at the business information level rather than the technical level. Insurers may not lead the world in putting devices out in the field – although personal health monitoring could be the exception that proves the rule. But I believe insurers will quickly become involved in establishing business data standards for the IoT, because insurance will have the greatest overlap with the largest number of business domains where IoT is being deployed. We're going to get involved because it's going to impact our business right across the board.

Salesforce on the Customer Experience

Good to see Salesforce presenting their insurance strategy at ITC 2015. These folks invented “as-a-service” and they see farther down the road than many of us.

The company's Jeffery To spoke eloquently on providing a seamless, omni-channel customer experience. He also brought a fresh view to the kind of layered technical architecture you need to provide this kind of experience. The line-of-business systems sitting at the core of the business make up the “system of record”, while the enabling layer which mediates between them and the customer facing applications is the “system of engagement”.

Technologists have been promoting layered architectures for a long time. They are the way to optimize your existing assets, be open to third party systems, and ensure you can change your operations as business needs dictate. What's different is hearing this from a company which is synonymous with providing services rather than systems integration. You could call it Integration as a Service: Systems collaboration created for you in the cloud.

Don't

Rick Sherman's article on worst practices in data governance first appeared in 2011. It's still relevant, still unflinchingly accurate.

It's easy enough to set out what you should do. But following the best advice is not necessarily easy. Sherman makes excellent points about common flaws in execution, such as setting up all the right teams but doing so with the wrong timing, or ignoring the “data shadow systems” where the true, trusted business data often lives – in Excel hell.

It's not negative to take note of advice that focuses on what to avoid. Plenty of mistakes have been made – let's all learn from them.  TechTarget

Disruptive Technology or Disruptive Business

Jeff Ward of Ebix, speaking on a panel at ITC 2015, said people often mistakenly refer to disruptive technology when they should be talking about disruptive business plans. It's a good point, because it's easy to mix up the potential of a new technology with the uses to which the technology is put.

However, there are many cases where the technology and the business plan are so tightly associated with each other that we probably need to accept the combination as the truly disruptive force. This is maybe best appreciated in the military context. A new weapon system enables new ways of planning and executing campaigns. The campaign – the “business plan” – is definitely the part which matters in the real world. But the campaign wouldn't even be conceived without the technology.

I guess also you could say “disruptive technology” means technology which disrupts business as usual. So every truly disruptive technology implies the emergence of new business plans. New business plans can come about without new technology, but it does seem the main driver for innovation today is technology.

Jeff made a different point in the same session, which maybe speaks to a similar interest in downplaying technology as the only answer to every problem. He wondered whether we're conceding too much control of our businesses – and perhaps our lives? – by automating more and more. For example, if you run a small business on eBay, you could be wiped out by a rule change made by eBay.

These are important thoughts and they act as a balance to the adulation that's inevitable in the tech world. We have to keep reminding ourselves: Technology is here to serve human interests.

Health Records

Cerner's Neal Patterson makes an impassioned plea for “policymakers in Washington to do what they have to do” to enforce interoperability for EHRs (Electronic Health Records). The image of his wife, who has cancer, carrying a shopping bag filled with printed records because none of her carers has the full picture of her health, is hard to forget.

Data has a huge part to play in healthcare, no question. One route to ensuring interoperability is for citizens to make more noise about it. You don't have to be a current patient to care about getting a complete, secure, current file on your health. EHRs are at the core of preventive medicine too – that's the business of keeping us out of the doctor's room.  lifehealthpro.com