Gregory Maciag

Iot and Business Data Standards

If you're looking for a crisp and up-to-date primer on IoT (Internet of Things), the European Parliament has a useful 8-page briefing, complete with explanations and capsule case studies.

The paper includes a section on data standards and interoperability, though the emphasis is on technical standards. The section discusses the tension between competition and consumer protection. However, I don't see any attention to the business data level.

I understand differing technical standards will cause investment headaches for buyers. But this isn't just VHS-vs-Betamax all over again. Market dominance in a technical standard no longer guarantees dominance in content provision. Specifically, the promise of the IoT is a vision of machines communicating with each other about things that matter to humans. For this promise to be realized, we need agreement on definitions and structures for the things-of-human-interest.

So, off the top of my head, I'd say an agreed, stable, and comprehensive business data definition for PARKING SPACE is going to be more important to making this corner of the IoT world a success than how cars, parking lots, satellite systems, and mobile devices chatter to each other. The machine-things can use all the mapping and translation layers they want. As long as they speak human to humans.   Download EPRS_BRI(2015)557012_EN

Health IT Standards - Progress

The Journal of the American Medical Informatics Association (JAMIA) has devoted an entire issue to health IT (HIT) data standards. Skimming the intro, I get a quick sense of a familiar evolution story in the data management field – along with a robust, informed response to today's state of play.

The authors suggest that HIT data standards arguably “date to the canonical list for causes of death in the London Bills of Mortality of 1528”. Since then there has been proliferation of standards, leading to the inevitable silos and problems with interoperability. But today's healthcare professionals are acutely aware of the joined-up nature of medical practice. For them, being “patient-centric” isn't a buzzword. The need to tie together all the available information about a patient is critical to individual care. And the need to pool data about patient populations is crucial for understanding the mechanisms of disease and the effectiveness of interventions. Of course, joined-up data is also key to efficient and effective funding of the healthcare system.

The authors also say: “While far from comprehensive or fully coherent, the current state of HIT standards is at a turning point, where we appear to be making more effective progress and practical applications than most would have predicted from the bad old days of just a few decades ago.” Yes, there are plenty of acronyms to wade through, and many different perspectives must be reconciled. However, this mature attitude to making a concerted effort toward comprehensive, consistent data standards provides an example many other fields of activity can learn from – not least in business.   Oxford

Keep benefits in Sharp Focus

Here's some good advice on how to keep focused when driving out data standards – while continuing to serve business-as-usual. It comes, as so often these days, from healthcare. Dave Reed at Cook Medical says you need to remember why you're rolling out standards. My reading of his detailed advice is that you should stick to the big, compelling benefits, and remind everyone of their value.

His first reminder is the value of standards to patients. Patient safety absolutely relies on data standards: “Data standards give us the assurance that expired or recalled products will never make it to a patient’s bedside. It sounds like a simple enough goal, but the existing processes don’t allow for it. Using GTIN and GLN numbers, suppliers and providers can track and trace products all the way from a manufacturers warehouse to a patient’s bedside.”

There's a version of this need in every business domain. For example, a financial services company can get into real problems if it sells a version of a product that no longer meets regulatory requirements – or a version that should not yet be released. Without standards for version and effective dates, such mistakes can happen easily, and cause significant business impact.

Dave's second group of reminders are all around operational efficiency, both inside an organization and across the value chain. It can be tempting to see operational efficiency as a nerdy obsession – something that's good, but a bit dull and not worth getting too exercised over. For this reason, I think it can be useful to change your vocabulary when talking about efficiency. You have more impact if you use language like waste, delay, loss, expense, confusion, stress, rework, remediation, and write-offs.

We understand “patient safety” at a visceral level because the phrase summons up a plethora of negative images. We put ourselves in the patient's place and we imagine what could go wrong. This motivates us to act. It's different with “operational efficiency”, which sounds like an abstract concept affecting nobody. So I make no apology for recommending the use of a little fear-inflected language. Inefficiency can kill patients. And inefficiency can kill businesses – and entire industries. Standards save lives.

Returning people's attention to the big reasons why you're rolling out standards will keep the program on track, while simultaneously reinforcing a culture where not using standards is regarded as pragmatically, professionally, and morally unacceptable.    Cook Medical

Virtuous Circle

Cay Hasselmann has been writing a series of posts on various types of stupidity and I really enjoyed his attack on data standards. He describes a real circle of stupidity whereby organizations start with data architects, become disillusioned with them, allow the business to look after data, watch that fall apart, and then hire data architects.

There's a great deal in Hasselmann's story which rings true and will make you chuckle. But I'd want to add there is a way out of the circle of stupidity. The circle's structural weakness is poor governance and the resulting inability to deal with change. Adopting an industry standard is a quick and safe way of addressing this weakness. By using an industry standard, implementation and maintenance of data standards is no longer a local problem. The heat is taken out of the politics. You leave your own circle of stupidity and join a bigger virtuous circle, dedicated to making data standards work well for everybody.    EA Voices

Take a Number

Unique identifiers solve a lot of business problems. Take barcodes. Without them, organizing and monitoring the flow of stock would be immensely complicated.

But the implementation of unique identifiers has traditionally met with problems of its own, stemming from the homogeneous and networked nature of many domains. It's easy enough to impose a unique Social Security Number, because there's one body responsible for social security. It's been harder to launch unique identifiers in the financial world because only a relatively small number of transactional items (currency, treasury bills etc) are originated by monopoly organizations.

The internet is fixing this problem. Say a big welcome to UTIPrefix.org. This is a facility launched by the International Swaps and Derivatives Association (ISDA). Users can go to the site and get themselves a unique trade identifier (UTI) for their derivatives trades. The service is free.

It's simple, secure, and in the interests of everyone involved – from clients to trading partners to regulators. No laws need to be passed, no one needs to be coerced. It must be the closest thing to a no-brainer I've seen in a long while.   SFM

Ignore Standads and Self-Destruct

It's maybe my all-time favorite IT saying: Garbage In, Garbage Out. No matter how smart our technology, if we're less than smart about how we feed it data, we'll get bad – and even dangerous – results.

So it's almost spooky to see GIGO in the headline of a June 2015 Forbes article on data standards in healthcare. It's also encouraging to see the Global Trade Identification Number (GTIN) as the focus of a mainstream business article – everyone's waking up to the fact that data standards really are about the business. And the article goes straight to the heart of the slow progress in standards adoption in healthcare. The big stumbling block, as I've blogged about often is commoditization:

“Manufacturers fear commoditization. While they see the value in an improved data standard, they don’t like the idea that the adoption of a 14 character product code will make it easier for their customers to analyze cost, identify equivalent alternatives and measure value. In other words, there’s a belief that such transparency may not be good for business, despite the positive experiences of the few manufacturers that have already embraced it.”

Notice how the fear continues in the face of contradictory evidence. Commoditization is such a frightening prospect it's almost impossible to question whether it's a real threat, or just a bogeyman.

Writer Paul Martyn notes that healthcare providers are pressuring manufacturers to get with the standard (often just called GS1 for simplicity). They can't defend buying into proprietary systems. The additional costs and restrictions on trading are simply unacceptable. Martyn concludes: “An enriched data standard – GTIN – is essential to healthcare’s successful transformation. The idea that some manufacturers would continue to deliberately interfere is worse than short-sighted, it’s self-destructive.”

Hear, hear. To vendors in all industries: Data standards will save your business. Proprietary data standards belong in the past.   Garbage in Garbage Out

Data Standards - Taking Action

If data standards are such a good and natural thing – an enabler and an asset that benefit everyone who uses them – how come they don't just emerge by themselves? The answer is that evolution is blind. Stuff happens, and then other stuff happens. There's no benevolent guiding hand making sure events turn out the way we want them. The data landscape we inherit is a patchwork of isolated solutions. It's not a planned economy.

The case for data standards pretty much writes itself. Anyone who thinks about the value of data from a non-local perspective will quickly see it makes no sense to have different formats and semantics for the same items. But seeing the obvious isn't the same thing as making it happen. We need to promote the incredible value of joined-up data.

Bill Anderson is on this path in a recent blog post. I think those of us who know standards are the answer must never forget to highlight the questions they are the answer to. It's the questions which motivate action. Anderson's example is the need to know how many women around the world die in childbirth. It's incredible we don't know this. Data standards would enable us to collect more data, analyze patterns, and relate outcomes to practices. Physicians around the world would save more lives.

The Irish statesman Edmund Burke said: “The only thing necessary for the triumph of evil is for good men to do nothing.” Those of us who understand data standards, and how to apply them, can be a force for good.   Open Data

Standards Create Value

It's not always easy to point to the organizational tools, techniques or assets which directly and unambiguously create value. Maybe you have a fantastic brand that creates value. Or a secret sauce. Maybe you have a captive genius whose product ideas never miss. Or maybe you have a proud bank of relationships that guarantee profitable activity.

It's right to treasure and nurture these value creators wherever we find them. But of course we want to do more. We want to find new sources and generators of value. Allow me to spotlight the greatest unsung value creator of them all: Data standards.

First, standards destroy waste. By using standards, you remove activities that suck up costs and gum up the works.

Second, standards enable innovation. Once you're running in a standards-based environment, you can move faster, and introduce new products, services, and processes faster. Even better, you have an actionable, legible environment that inspires new ideas rather than frustrating people.

Third, standards make connections. When you take up an industry standard, you gain a new kind of passport. You're now in a position to collaborate with partners like never before.

Fourth, standards stimulate growth. That's right – growth from standards. Because, with standards, you can replicate successful solutions easily, cheaply, and confidently. This means quicker market entry and lower risk.

I struggle to think of anything comparable which can produce such benefits. Standards are simple, safe, and people-sized. Yet they can transform your business.

Standards Empower

Set aside the reference to open data and feel the resonance of this quote: “Far too often users of open data face small but time-consuming technical hurdles that slow down progress towards open data impact [...] With good standardisation practices, it becomes possible to innovate more rapidly, and to identify the solutions that work in one place, and build on them to apply that solution elsewhere.”

There's a lot of wisdom in this, so let's parse it a little. First, those small but time-consuming hurdles. Fans of “lean” techniques will recognize this pattern immediately, but anyone with an interest in process improvement will spot it too. People are routinely plagued by minor inconveniences that don't seem worth fixing. It's like if you need to use a certain action to make a door close properly – every time you use the door you're slowed up and distracted, but then you forget about it. If you were completely rational, you'd allocate some time to fix the door – once and for all. But this would be to break a habit, to take an overarching view of your processes, and even learn a new skill outside of your comfort zone (fixing doors).

Our lives are full of bugs. So are our business processes. And no more so than when it comes to managing the flow of data. Too many people accept mismatched and inconsistent data as a fact of life, rather than a process flaw they could fix. As an industry – heck, as a species – we waste way too much time working around these bugs. This is time we could spend on the goals that matter to us.

Second, take a look at where bug removal takes us. By standardizing around data, you save time, and the organization becomes smarter. You can innovate more rapidly – and, I would argue, with more certainty.

Third, standardization gives you a route to replication. This is not to say standardization offers a cookie-cutter approach to business practice – it doesn't, any more than speaking English condemns people to say the same things to each other. However, standardization is the best way to identify, promote, and distribute effective solutions.

By using a trusted industry standard, you also get to inject effective solutions discovered by your peers direct into your business. It's like giving evolution a boost.

One of the jobs we do in the standards movement is to help people see, respond to, and remove the bugs in their work environments. This is real empowerment – for individuals and organizations alike.   Open Data

Intelligence from Drones

Chris Ketcham reports: “State Farm, AIG and USAA have received preliminary approval from the Federal Aviation Administration to test drones for their claims and underwriting functions.” He muses on the wider implications of drone usage in insurance, focusing on what happens if or when drones see something they're not supposed to see. If a drone happens to “witness” a crime being committed, is the insurer obliged to report it? If a drone happens to see something in a neighborhood that could be interpreted as a symptom of a fraudulent claim, should the insurer act on that intelligence?

These are fascinating and important questions. You could widen the inquiry to include the image streams from proliferating fixed and mobile cameras, and the data streams being generated by the emerging Internet of Things. We're now in a position to collect great quantities of peripheral data.

But there's a major caveat. If your drone is programmed to survey, say, building damage, and it records details of an apparent crime taking place inside the building – who recognizes the apparent criminal activity? This is to assume a human agent reviews all the drone's video and takes decisions based on unstructured visual information.

While this may be the way the first generation or two of such systems work, we should expect additional automation. First, the building's smart devices (or the ones that aren't damaged) will communicate directly with the drone. Second, the drone will most like carry mapping tools that enable it to capture structural data as well as visual images. And whatever additional programming is built into the drone system, it will not contain the ability to recognize potential criminal activity unless there's a great advance in artificial intelligence.

Much of the data being generated today and in the future will be noise. It may be possible to use some of that noise to prospect for additional information. However, such nuggets will not appear spontaneously. People will have to look for them, and look hard. The issue, then, is not so much the possibility of an insurer finding out too much. It's whether or not the insurer cares to look hard. Or to sell its data by-products to organizations who know how to look hard.   Insurance Thought Leadership

Data Strategy and the Insurer Board

Majesco's John Johansen writes well on why data strategy is a board level issue. He breaks his argument into three areas: downside risk, opportunity risk, and upside risk. This is a great way of approaching the strategic importance of data.                        

Johansen's downside risks are those most commonly called cyber threats. His opportunity risks are issues related with failing to exploit IT and/or being displaced by a disruptive competitor. The upside risks are related to being a data-driven organization.    IIR