Gregory Maciag

Adopting Standards: Best Way to Survive a Downturn

This headline is from an article about the oil industry. But take a look at the piece – because unless you're very alert, it's not evident until about halfway through what sector the writer is talking about when she refers to “the industry”.

Here's a quote from Ross Philo, who heads up “the industry's” main standards body: “There has never been a better time to work on adopting them [standards] in order to deliver dramatic cost savings for individual companies and for the industry as a whole.” I guess the dramatically low oil price is the unspoken driver here.

The article suggests players in the oil industry are only just beginning to appreciate the obstacles and inefficiencies created by the lack of data standards for collaboration among companies and breaking down silos within them.

Does every industry need a downturn before it “gets” standards? I don't think so. Business leaders and new entrants in many sectors are focused on innovation rather than simple continuation. They're less concerned with trying to protect their eroding power bases. The thinking that inspires their actions is less about riding out the storm than transcending to a new environment – a world they are working to co-create.

And what if the downturn doesn't switch back into an upturn? Maybe this is still unthinkable to some oil industry executives. But the unthinkable has become the norm elsewhere. Everybody seems to be looking for disruptive innovation not business as usual.

I wonder how much longer an offer like “cheapest way to survive” will be attractive to decision makers. And of course the arguments for data standards are so much more positive than this. Organizations use data standards so they can grow and evolve, not just so they can scrape through another period of challenging market conditions.   RigZone

End Data Wrangling Now

One of the jobs computers superseded was that of... the computer. A “computer” was somebody who worked out things like tide tables or mortality rates using slide rules and hand-cranked tabulators and good old mental arithmetic. (What we call a computer was originally given much sexier names – like “electronic brain”.)

As data standards become more and more accepted as the gateway to understanding – and exploiting – data, there's another role at risk of redundancy. This is the labor of wrangling disparate data so it can be analyzed as a single collection. The work is usually buried under a more positive sounding role designation. Many well qualified and highly motivated data scientists are frustrated that they spend so much of their time dealing with disparate data instead of developing insights and actionable intelligence.

We can achieve a lot for business and society by implementing effective data standards. We know data standards save money, speed up processes, and enlarge networks. But we should remember data standards also liberate people from pointless drudgery. No one trains for a profession in hopes they'll spend most of their work life sweeping the floor or rearranging the shelves. Let's remember data standards are the major enabler for real digital business.

Big Data Standards

The International Telecommunications Union (ITU) has developed technical standards for big data. These standards are aimed at making sure big data service providers don't lock their users into particular tools for collection, analysis or visualization of big data.

This is a positive step although I note the point made by Gartner's Alan Duncan, who says interoperability has already emerged through the vendor community. It's not in vendors' interest to lock anybody in because no one vendor can hope to address every potential type or source of big data.

My instinct is naturally toward the use of standards. I think we also need to be aware of the forces for consolidation in the software business. Today there may be a great diversity of vendors with a shared willingness to promote – or at least not block – interoperability. Tomorrow could be different.  ITU

 

Inspired by the Health Story

Sometimes innovation comes about by transferring ideas between domains. Picasso is supposed to have said “Good artists copy, great artists steal”, but what he actually said was “When there's anything to steal, I steal” – which I think is much more useful. It goes along with not reinventing the wheel. Maybe the guy next door has a better wheel you can steal.

If you're advocating data standards or managing their implementation, you could steal the word “Story” from the health standards community. The Health Story project has been running for almost a decade now and has produced several important data standards which improve interoperability across the healthcare system. HIMSS, who look after the project, are working toward “Comprehensive electronic records that tell a patient's complete health story.” This goal has analogs in every area of endeavor.

Are you in a retail business? Then you're dealing with the story widely known as the customer journey. If you're in industry, your story is likely to be about the supply chain. In government, you'd be concerned with the rights and needs of the citizen – the story of everybody's life.

Thinking in terms of stories helps people appreciate the systemic nature of today's challenges. Stories help to make complexity manageable and to keep goals in sight. Above all, stories humanize activities that can all too easily become mechanical, hidebound, and inflexible processes.   Health Story

 

Data Sharing is New Default

The indemnification provisions of the Dodd-Frank Act have been removed. Rejoice!  Okay, so this is an obscure piece of tweaking to a law governing the conduct of financial organizations and I guess it's not going to inspire many all-night celebrations. But it's a sign of a shift in thinking about data that shows how leaders are getting the importance of data sharing.

The provisions which have been annulled “effectively blocked the sharing of over-the-counter (OTC) derivatives data among regulators and complicated efforts to improve market transparency and mitigate systemic risk”. Where previously people thought walls were automatically a good thing, they're now seeing how artificial barriers increase risk for the whole community while obstructing oversight.

If even the government has got this message, there's no place for enterprises to hide. Sharing is the new default. In turn, that makes using industry data standards a no-brainer. DTCC
 

IoT in Retail

One area of the IoT that's getting traction is the world of consumer retail. The buzz phrase is Micro-electromechanical systems (MEMS) – which, as far as I can tell, are sensors. Retailers are looking at the sensor data captured in baseball and thinking how it can be applied to the shopping experience. Baseball has always been a great producer and consumer of data, so it's a natural leader in this area. They track items like a player's eye movements or a team's coordinated response to a fly ball. The idea is to use similar ideas in stores so as to better understand consumer behavior.

But in a familiar pattern, the early promise is already being obstructed by the old enemy, the lack of data standards. According to Josh Knauer, “1) Most sensor and MEMS data is proprietary; 2) The data lives in database silos and doesn’t come in one format; 3) Siloed data in proprietary formats can’t just be exported into a spreadsheet and compared easily”. And this means retailers are not making the progress they want.

Knauer's company, Rhiza, is in the business of helping device managers package and monetize their data. They're in a good position to advocate standards. It would be good to hear from the major retailers too, who must want to avoid proprietary lock-in.

RHIZA

Data Leaders of the Future

Who are the data management leaders of the future? As I look toward my retirement from the role of President and CEO of ACORD, I'm optimistic about the future for data as a resource. ACORD has always been a strong voice for the value of data, the importance of sharing data, and the competences needed for good data management. I think the next generation of leaders will not need the same level of education about the benefits of data. But they may need more help in understanding the obstacles in the way of success.

It's a conundrum. Before, people in management were often hazy about data and not always able to appreciate the asset nature of data. But they were very aware of the difficulty of coordinating activities in a complex environment, and the challenges of managing change. Now things have switched around. People are comfortable with data because they have been interacting with it, producing it, and consuming it, all their lives. And maybe it's because they have a close affinity with the world of data – a realm of smooth interaction, instant results, and intuitive progress – that they are less aware of the issues around human processes. By which I mean, getting things done with, by, and for, people.

So I'm generalizing widely here. I'm drawing a caricature. The picture I'm drawing is a product more of industry comment and anecdotal evidence – maybe even prejudice – than science. But that's okay, because what I'm concerned with is the way the rhetoric is running. Received wisdom has it that the leaders coming up are technology savvy but organizationally limited. If there's any truth to this, then it's our duty to do something about it.

I don't mean we should spray cynicism all over everybody – tell them how difficult it is to get anything done “in the real world”. I'm all for simplifying processes, reducing bureaucracy, spreading empowerment. Organizations need to change in order to deal with change. All I'm saying is we need to look for leaders who don't just appreciate the value of data, but who are also people people. And this is a very positive message, because there are many more of these balanced individuals around then there used to be.

Related articles

The New Vision Gap

Data Standards are a Necessity - Not a Choice

Metabolomics is yet another critical area of activity which is crying out for data standards. What is metabolomics? It's the “systematic study of the unique chemical fingerprints that specific cellular processes leave behind” – so it's very important for understanding cell biology and diseases including cancer.

The situation regarding data in metabolomics mirrors so many domains. Vital need for sharing data? Check. Huge growth in data volumes? Check. Problems with data diversity? Check. And get this analog to regulated commercial activities: “Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency.” In other words, the only data analysis and design work done was in response to mandatory third party requests, not the primary mission of the data creators and users themselves.

It's becoming a challenge to identify any worthwhile sphere of human endeavor which does not require industry data standards in order to function correctly. I'm finding it hard to think of any significant (legal) business process that doesn't include some requirement for machine-readable data at some point. And wherever more than one person or organization needs to connect with that data, industry standards are logically implied. Standards can no longer be spun as a choice. They are a necessity.    Wikipedia

Paperity

Data Act Benefits Everyone

GovLoop, a social network for people in federal, state, and local government, ran a neat piece on the benefits of the DATA Act. I'll summarize their points in an even shorter format: The benefits are better accountability, better management, and better compliance.

These all sound great to me. While people are right to focus on innovation, we must remember there's always a need to improve the mechanics of how organizations work, and how they serve their stakeholders. It used to be called management science – I guess it still is, but nobody seems to use that label much today. You've got to pay attention to accountability, management, and compliance – because if you don't, you don't get to play any more. Doing well means doing better. Always did, always will.   GOVLOOP

Down with Dirty Data

Gotta love the 80/20 rule. It's a universal phenomenon that applies in every aspect of data management and exploitation. Here's an instance from a data scientist working in housing: “Anecdotally, a data scientist will spend 80% of his or her time cleaning a data set.”  This is tragic. The valuable time and skills of motivated professionals are being squandered on dumb, repetitive tasks that could be removed at a stroke – with industry data standards.

Whenever we see people in this kind of data mess, we don't need to look far for either the cause, or the solution. Every community that suffers needless data complexity is generating its own problem, which it can cure with data standards. Let's stop wasting people lives with thoughtless data creation habits. This is a business hygiene issue. Down with dirty data!   HACT

Never Too Late for Standards

When is the right time to adopt industry data standards? Reading some robust remarks by Ross Koppel on electronic medical record (EMR) systems, I'm tempted to answer: Yesterday.  Koppel says the health industry urgently needs data standards to serve healthcare professionals better. Existing systems were built with billing rather than clinical needs in mind. “Now the existing structures are so embedded that it’s very costly to reach the kind of standards that are needed to allow the interoperability that is the hallmark of a reasonable system,” he says.

Note the implication here – the current ecosystem is unreasonable. It doesn't make sense, and we shouldn't stand for it.  No one can travel in time. But Koppel has a shortlist of immediate actions that could be taken to move the industry ahead in priority areas. He says his ideas could be implemented in six weeks. And he says: “We’ve been facing the same bloody problem now for 15 years. It’s enough. We just have to deal with it.”

He's right. It doesn't matter how long something suboptimal has been the case. If it's wrong, get rid of it.  It doesn't matter how hard it's been in the past to create standards. Standards are still absolutely required. Past difficulties should act as signposts to prioritization exercises, such as the one Koppel has performed.

And it doesn't matter how much anyone has invested (monetarily or emotionally) in the existing set-up. Spending more isn't going to trigger a reversal in values – it's just going to compound the problem and further delay the inevitable.

The article citing Koppel is titled “Overcoming the Madness of Flawed EMRs”. Not using industry data standards is a form of madness. It's unreason. I see more and more evidence that people will no longer tolerate the unnecessary expense and limitations created by non-standard data environments. They're right to call out the madness – and to get mad.   WTNnews

 

Identifying the Industry Standards Leader

A common challenge in every sector is figuring out who should take the lead in industry data standards. The global insurance industry made the wise decision to create ACORD to take this role. People across the industry recognized standards were in the interests of the whole community and wanted a commonly owned, volunteer based organization to take standards forward.

Insurance was lucky because the interconnected nature of the business is understood by all the players. Companies compete, but they also need common structures and protocols.

Where should we look for data standards leadership in a domain like connected cars? The answer doesn't leap out. There are many diverse interested parties but no encompassing culture, as there is in insurance. Vehicle manufacturers don't have much existing business with traffic management authorities, for example. And although insurers are leading the way in generating and exploiting telematics data, insurance isn't the obvious area to look for leadership across the whole connected car piece. Sure, we have the expertise and a big interest – but other types of organization might have just as great a claim.

So let's look for an alternative way of discovering candidates for leadership. I like what I hear in recent comments by Ashley Sowerby, managing director at Chevin, a fleet management company. Sowerby is voicing questions about connected car data ownership, access – and standards – when few others seem to be. He sees these questions as complex and pressing. And he's thought them through: “There are all kinds of arguments to consider. For example, if there had been public access to the environmental performance of fleet cars, then the recent diesel emissions scandals would probably not have remained hidden for so long. However, there will also be parties who want to keep close control over their data for a wide variety of reasons, ranging from basic concerns over information security through to less open-handed motives.”

It's Sowerby's position as a fleet manager that gives him this perspective. Chevin has operational data on more than 850,000 cars under its management. Fleet managers know more about cars in the wild than anyone else. They're in the car data business.

Absent a pre-existing industry culture around the value of data, we need to look for the operational data specialists within any domain. These are the natural data standards leaders. This is not to say they should get to dictate data standards for the community. It's to recognize that the folks with the clearest, broadest, and deepest appreciation of the value of the domain's data are well placed to lead the community in creating standards which will benefit everyone.   Fleet World

Federal Blow to Proprietary Data Standards

Early in 2015 the Treasury and OMB opted to continue using the proprietary DUNS Number for identifying organizations that contract with federal government agencies – at least for the time being. It turns out this period was pretty short. The DUNS Number is no longer mandatory.

Why does this matter? It's not that proprietary data standards are bad in themselves... Oh, all right – yes they are. If an organization gets to charge you money for something that's forced on you then that's bad business and it's not democratic. DUNS officially stands for Data Universal Numbering System. But it's owned by Dun & Bradstreet. The DUNS Number is Dun's number.

To be crystal clear: Until now, it was illegal for government contractors not to use the DUNS Number. And to use the DUNS Number, they had to pay Dun & Bradstreet.

So now NASA, the General Services Administration, and the Department of Defense are deleting all references to the DUNS Number from the Federal Acquisition Regulation. It's a big step forward. And it establishes a very important principle: No data standard whose use is mandated by law should be owned by a commercial entity.    Data Transparency Coalition

Data Science Bowl

I'm a fan of the Data Science Bowl, a competition co-sponsored by Kaggle and Booz Allen in partnership with The National Heart, Lung, and Blood Institute (NHLBI), part of the National Institutes of Health. They're inviting people to develop an algorithm to empower doctors to more easily diagnose dangerous heart conditions. There's $200,000 to be won. The contest is also positioned as a rallying point and resource center for data science as a whole.

It's great to see awareness of data science being promoted. In fact, it's great to see “data science” getting traction as a function, and a career option. Finally we're seeing the separation of data from “IT”. Understanding data has value is accelerating – and the practical benefits of working with data are multiplying daily.  http://www.datasciencebowl.com/

 

Barcodes of Finance

Let's hear it for three little acronyms that are set to bring straight-through processing (finally) to the finance industry. They are: LEI (legal entity identifier), UPI (unique product identifier), and UTI (unique transaction identifier).  Fans of abstraction will notice these are identifiers for the three core entities that turn up in every business: party, product, and transaction.

A progress update on the uptake of these identifiers by Allan Grody of Financial InterGroup is frank and instructive. There are more acronyms to negotiate, but I take two key points from this useful report. First, I note the impetus for standardization is coming largely from regulators even though financial institutions stand to make great gains in understanding their risk exposures.

Second, I appreciate the small but significant point about difficulties implementing the LEI. The LEI is 20 characters – too long for many legacy systems. It could easily be halved and still have all the coverage and longevity it needs. This looks like it was a miss-step – but kudos to Grody for exploring the issue. Human beings aren't perfect and we learn from our mistakes.

I want to add that “barcodes of finance” is a great phrase. Grody and his colleagues are to be congratulated for creating an image with great power and resonance. This is a great way of encapsulating the nature, universality, and value of standardized identifiers for a general audience.   TABB

Sharing Bikes - Sharing Data

I'm not going to tell you bike sharing is a super-important part of the economy or something business strategists need to stay awake at night worrying about. That said, it's important for those for whom it's important – if you see what I mean. And if the spread of technology has done anything, it's empowered people to bring efficiency to the activities they care about.

But I do want to offer what's happening in bike sharing as an example of the changed expectations I'm seeing in business, government, and social media. Bike sharing in the US is adopting data standards: “The North American Bikeshare Association, working with representatives from the largest bike manufacturers, owners and operators in North America, is happy to announce the adoption of the open data standard for bikeshare. The open data standards will make real-time data feeds publicly available online in a uniform format so that map and transportation based apps such as Google Maps, RideScout and Transit App can easily incorporate this data into their platforms.”

Parse this statement and what do you see? First, you've got an industry association working on behalf of everyone with a stake in bike sharing. Second, you've got openness to applications and organizations that might want to use the data. Taken together, this represents a default assumption that people need to share data.  This is a big shift in our culture. People now acknowledge they're part of a bigger reality. Isolation is now unusual and unnatural. The benefits of sharing data across a community are so obvious they barely need stating.

Some enterprises are still to catch on with this way of thinking. Tradition and legacy structures hold them back – it's not their people, who are regular folks like you and me: people who appreciate data as a value in itself.    Bikes

 

Standards Outside and Inside

A good piece in Government Technology offers ten tips for how cities can exploit big data. “Use Standards” is listed at #5. No prizes for guessing I'd put it at #1.  Also, this article perpetuates the idea standards primarily have an “external” use. I see this idea around a lot. It's not stated explicitly, but it accounts for some of the disordered priorities I often see in capsule advice about data management.

So: “Use Standards” is at #5. Ahead of that at #3, we get “Integrate and Unify Internal Data”. The gist of this tip is that you do data analysis on your existing systems. This is weird because one of the points made about using standards in tip #5 is: “Applying standards to the data you store will simplify data management and provide new opportunities to analyze and benchmark your data across disparate systems.”

I think what's going on here is an understandable attempt to break the data management problem down into digestible chunks. It seems sensible to call for getting your own house in order before looking to “external” issues like data standards – and, in this context, “gaining the ability to benchmark your data with other agencies”.

The error here is to believe there's still a hard and fast boundary between internal and external. But despite agencies and departments having separate names, staffs, buildings, budgets, and so on, the boundaries between them are porous to information. From the point of view of data, there are no natural parking places – only the artificial dam sites we know as silos.

And from the pure pragmatic perspective, the very best way to bring order and consistency to your internal data is to apply the relevant data standard. Whoever you are, whatever you do, there's a pre-existing data standard that covers at least 90 per cent of your data. Believe it. The Government Technology article points to Code for America's useful list of standards in the public sector. If you're in commerce or industry, your trade association will be able to direct you.   GT

Data Standards Infrastructure

A small thing, but it gets my yellow marker pen. This is National Credit Union Administration Chairman Debbie Matz speaking to the House Financial Services Committee about her agency and the Financial Stability Oversight Council (FSOC):

"The [FSOC] is performing a valuable service for the American people by identifying risks, promoting market discipline and responding to emerging threats to the U.S. financial system on critical issues such as the oversight of key financial market utilities, data standards infrastructure and cyber-security threats.”

Data standards infrastructure. That's exactly right. Data standards aren't an optional extra. They're not some kind of outer shell. And they're not an abstract good. Data standards are infrastructure. We need data standards to run our businesses, our nations, our lives. We rely on them. They are an integral part of the modern world. When we run right, we run on standards.   CU Journal

Standards - Who Pays?

Cay Hasselmann wrote an interesting blog post about data standards in investment banking. In particular, he thinks about why there tend to be more standards in the public sector than in the private sector. He says it's about “money and motivation. In governments standards are vital as they often define the reason for doing something.  In the commercial sector standards are used as a means to a goal and only seen as a requirement if a public body forces them to be used.  Also in public service there is a much higher tendency to pay for standards then in the private sector, unless the standard is included in the product cost for a software (this is why you will find a lot of vendors on the standards committees).”

I agree commercial organizations often evaluate standards according to some single end, rather than appreciating the systemic value of standards. Part of our job as leaders in the standards movement is to get people to appreciate the role of standards as business infrastructure. The ability to share data is a vital prerequisite for doing business, not a one-off project with a discrete, one-time payoff.

I also agree the role of regulators in mandating standards is significant, especially in the investment industry. I'd add that regulators play an important role in encouraging standards in sectors where making a practice mandatory may not be necessary.

I'm not sure about Cay's point on the public sector appetite for paying for standards. I guess where the public sector is the main paymaster, agencies have a clear interest in funding standards because they stand to recoup the costs directly. It's less clear in mainstream commerce who should pay for standards development, promotion or implementation.

Or is it? I think there's a kind of myopia affecting some sectors. Any business domain today is defined by its inter-dependencies. There's no such thing as an isolated business – not, at any rate, an isolated business with a future. Whatever line of work you're in, you're part of an ecosystem. If you don't recognize that reality through cooperation around data standards, you fade out of the picture. If enough current players do the same, then a new generation – with greater self-awareness and environmental savvy – will move in on your niche. And eat your lunch.

The community pays for its standards, because the community benefits.   EA Voices

Follow the Money

How about this: “Currently, a personal package being sent to your home is more easily tracked than a retailer’s money.”

That sounds odd, because retailers' cash is transported in armored vans, and surely the carrier services know where the vans are and what's inside them? Well, apparently not. One retailer says “if he has a missing deposit, he has to get proof from the store that it was picked up, proof from the carrier that it was delivered, and proof from the bank that it was received and deposited. It can take several days to get responses from all parties involved.”

Maybe you guess the way this story unfolds. There are several major cash carriers in the US and they are starting to introduce cash tracking services, so retailers will be able to follow the movements of their money just like a package from Amazon.

And here's what one retailer has to say about these developments: “Our concern is that you’re going to have Garda’s version, Dunbar’s version, Loomis’ version—everyone’s going to have different versions. I’d like a standard. We use Garda a lot and we do use their current system. It’s not ideal. Since it’s standalone, they can’t share information with the financial institutions properly. We want something that everyone can use; something that doesn’t matter if Garda’s going to U.S. Bank or Fifth Third or Chase—I can still see the same information.”

That's a long quote – but it contains the major arguments for data standards in any sphere of activity. Vendor lock-in? Check. Inability to collaborate? Check. I think you can also hear a plea for sanity in this quote – between the lines I hear suppressed disbelief that a non-standardized, needlessly disparate data tracking situation should even be contemplated. It's the twenty-first century. We don't need to repeat the mistakes of the past.

AFP

Data Security for Surety

The surety industry has launched a three month pilot program to evaluate the use of structured data, conforming to data standards, for improving surety underwriting. The program is based on XBRL, the standard for financial reporting. A surety or surety bond is an arrangement where one party stands good for the potential liabilities of another. Sureties have to be reviewed on a regular basis. Greg Davenport of Liberty Mutual Surety says: “Sureties evaluate hundreds of contractors every 3-6 months. The WIP (Work in Process) is typically sent in PDF format and must be hand-keyed into our system before analysis can begin. This process is time-consuming, labor-intensive and costly.”

The inefficiency of re-keying data is a well-worn theme, so I won't spend time reiterating Davenport's message. Instead, I'd like to draw attention to the role of image-only PDF in delaying straight-through processing. PDF is a wonderful format, but not (in its plain vanilla version) for data sharing.

Many organizations which formerly opened snail mail and keyed information into applications now use email instead. Here, PDF attachments rule. PDF lets you keep to your old forms and your old business processes while cutting out trips to the printer and saving time in the mail room. At first glance, it looks like a great move.

There's a thinking error here. Just because a few people spend more time glued to their chairs doesn't mean you get a major boost in productivity. The re-keying is the real problem here, not the paper. I include copy-and-paste here – it's not much more efficient than re-keying although less likely to result in mangled data. (However it's easy to lop off characters when you're using a mouse to highlight text.)

The aim of the paperless office (remember that?) is not primarily to save filing space or trees. It's to speed up the business flow. Replacing actual paper with virtual paper isn't the solution. You have to liberate the data from the substrate.

The PDF format does recognize data fields and you can do fillable forms with PDF – we have ACORD Forms like that. But even if you use PDF for structured data, you still need data standards regulating their use.

Business Wire

Information Centric

I'm always looking for new ways to articulate the importance of data standards. I like to see how people in industries other than insurance describe the value of standards for their communities. The messages are similar across sectors, but sometimes the language used in one industry can yield interesting ideas for addressing your own colleagues. Here's a fantastic paragraph from PPDM, who have an ACORD-like role in the oil industry:

“In an information centric economy, good analytics depend on data sources that are integrated, accessible, trusted and unambiguous. They cannot be hampered or misled by regional data constraints or local dialects. Industry standards are a solid foundation upon which regulators, operators, and other key stakeholders can establish technology solutions that are fit for purpose and robust.”

All of this good, but I particularly like how it starts with the information centric economy. Every data professional – and every business leader who wants to shape the future – must take every opportunity to remind people that information is the lifeblood of our economy. Folks said this 40 or 50 years ago, when it had the status of a prediction based on a few leading cases. But now it's a fact of life for us all. It's come true. We need to put the crucial value of information upfront and ensure data is at the heart of everything we do.

PPDM

Leading Standards

An article about progressing with health data interoperability between the VA and DOD in the absence of full industry data standards leaves me unconvinced. I agree sharing data is critical and standards take time to evolve. But I'm not so sure there are technological short-cuts.

Lynda Joseph cites some technical approaches which might help parties to interoperate, but I believe they'd still need to create a lot of workarounds in order to get any real utility. In the past, piecemeal solutions based around current technical capabilities have served as obstacles to growth in data sharing and exploitation. Workarounds are expensive to implement and difficult to decommission.

I think a better approach is to reject the idea that standards have to take a long time to emerge. Sure, standards will be late to the party if there's no leadership and they're developed against a background of heterogeneous, ad-hoc solutions. But we can do better than this. If you're involved in health care data, you've got to agitate for standards – and maybe you need to ask yourself whether it's time to step forward and lead the necessary acceleration. You'll be doing all humankind a favor.     NextGov

3D Data Standards Needed Now!

Discussions about the need for 3D data standards in transportation and building services highlight an important point about how the need for standards emerges.

There are many 3D mapping applications in many fields. Today, it's becoming clearer how data from one area might be joined with data from another in order to create more value. Also, people are appreciating that universal standards for 3D data will help to accelerate business development, reduce market entry costs, and add customer value. One of the catalysts for this new perspective is the development of autonomous vehicles.

The psychology insight here is how as soon as you see why data standards would benefit a field, you immediately wonder why they don't already exist. Isn't it obvious that a self-driving car is going to need “see” or “sense” the world around it in a standard manner?

And yet, the need for standards never comes ahead of the vision for what can be achieved better with standards. That's how our minds work. If you'd stood up ten years ago and called for 3D data standards, people would have been puzzled and then asked you about business benefits. Today, those benefits are blindingly obvious.

Incidentally, standards for 3D data are also vital for the future of insurance, with the increased deployment of drones and ideas around self-monitoring assets – including buildings.   ENR

Standards for Genomics

You know how people refer to DNA as the genetic code? And they explain how every piece of DNA can be represented as a string of 4 characters, namely G,T, C and A? In my naivete, I thought genomics researchers had the greatest – and most natural – data standard in the world. Turns out life isn't that simple.

The Workgroup for Electronic Data Interchange (WEDI) in genomic medicine issued a report including nine big problems with genetic data, and one of these is data standards. Others are to do with the sheer size of records, the rapid ageing of data, security, and ownership. In fact, another of the issues cited is problems with data exchange, which is also a standards matter.

Here is the workgroup's key point about data standards: “Genetic tests are typically presented in a narrative text report that must be transmitted via scan, fax or PDF attachment prior to being uploaded into an EHR [electronic health record] or repository.” The report goes on to say: “Until genomic data is provided in standardized, structured and discrete formats that are both human- and machine-readable, value will be limited.”

You could take that last sentence, drop the word “genomic”, and you'd have a neat description of the problem for any information-intensive domain that doesn't use industry standards.

I don't claim to know much about genomics, but I know that if experts are writing free text which is supposed to be used by other professionals, there's a data standards gap. This is a symptom telling us that professionals need to get together – right across the healthcare chain – and agree the core concepts they want to share with each other. Genomic information is too important to waste.   Health IT

Short IoT Window? Not Without Standards

Are businesses taking IoT seriously? You bet. How about this: “A 90-year-old industrial device manufacturer that mFrontiers works with estimates that it has four years to make its analog devices more digitally savvy. They need to be internet-connected so that they can generate data on performance and monitor it remotely. After that, [mFrontiers'] Ottoman says, the company thinks internet-connected devices will be the standard, and vendors that don’t offer IoT functionality won’t win new business.”

This company is looking at a four year window. It's a make-or-break time period. And it's really short.  Forbes

Data Modeling is Data Governance

This is the title of an article and webinar by Robert S. Seiner. It's designed to ruffle feathers, but Seiner backs up the assertion.  For me, the clincher is the negative argument Seiner mentions – this is: “Ask any organization that does not model their data if their data is being governed. The sure answer will be 'no'.”

Data modeling is about defining data, and data governance is about managing data. If you haven't defined it, you can't manage it.  Note that data modeling doesn't automatically mean defining data from scratch. Organizations which use industry models as the basis for their own data models are exercising the same kind of discipline – while saving time and money.

So, if you want to be picky, I guess you might want to say: Data modeling is essential for data governance. But it doesn't make for such a great slogan.   TDAN

Data Management in China

The Chinese have noticed the DATA Act. China plans to create a unified open data platform for government by 2018. They'll have joined-up information across the state and one-stop citizen services.    Any doubt that data management is a vital aspect of national infrastructure ought to be dispelled by this news.   China

Backtracking to Standards

There's a familiar pattern to a recent article about interoperability in military robotics and autonomous systems: “Despite substantial increases in capability and applications, U.S. and multinational robotics and autonomous systems have limited information interoperability, convoluting an already complex data-sharing environment. The U.S. Defense Department finds itself in a predicament created by rapid and independent fielding of systems over the past 10 to 15 years along with the use of proprietary software and payload and bandwidth restrictions.”

We see this in business too. Organizations can't wait for data standards – they need to execute today.  Effectiveness today generates ineffectiveness for tomorrow.

There's no way out of this dilemma. If data standards don't exist, you have to go with what you have. It's good to be honest about the remedial work you'll need to do farther down the pike. It's also sensible to set a standards development activity in progress alongside the development of point solutions.

The development of industry data standards means many sectors, like insurance, don't need to take this kind of risk. I hope the close involvement of insurance players with the development of IoT will mean the commercial sector does not repeat the experience of the military with robotics and autonomous systems. Our industry needs to take a strong lead.

The article which inspired this post is fairly dense and technical. But there's a catchy nugget of business insight there too. The writers observe: “Data is to information as sand is to silicon.”    AFCEA

Information Blocking

The terms of debate in the realm of data management are undergoing radical change. With data at the heart of so many activities – not just in business, but in government as well – we're seeing a shift away from arguing about the value of data. This argument has been won. The friction now is about impeding the flow of data. Flow is now a given: obstruction is a problem.

This is clear in a recent piece about “information blocking” in healthcare. Are EHR (electronic health record) vendors blocking the exchange of data across the value chain? Or is this a simplistic attempt to assign blame for problems in an inherently complex business area?

Whatever the rights and wrongs in the case, we need to notice that data flow is being seen here as a kind of right in itself – or as a natural feature of collaborating organizations. It's a great step forward to see things in this way. Every multi-party endeavor must have unimpeded, secure, and meaningful data flow. Otherwise the value of the whole will be even less than the sum of the parts.    Health IT

Loose Standards

Is a “loose data standard” a contradiction in terms?

The idea of “loose or even ad hoc” standards for metadata pops up in an interesting post by Charlie Greenberg. One of his fictional examples has the term “Retired Employees” defined in different places as “age >65 years” and “age 65-79 years”. This would lead to different counts in different parts of the organization – and perhaps even truncation of benefits to some people of 80 and over.   Is that a loose definition? I think it's a case of a faulty standard. Because it's not standard.

But I think the idea of loose standards does have some merit. This is where data items are optional. The optionality of data items, and the logic which can group certain optional items together, is an important aspect of the business. For example, if you're insuring home contents, it's optional for people to have a bicycle. But if they have a bicycle, you want to know whether it's the kind of bicycle people like to steal.

Creating standards around optional items needs the kind of looseness engineers call tolerance – or, less formally, wiggle room. This is why we talk about standards being robust and resilient, rather than rigid. They need to be created with flexibility in mind.   INFOSEC

Open Data Gets Joined Up

The launch of the International Open Data Charter in New York is a welcome step forward for the data management community as well as everyone concerned with the open data movement. This is the first time we've seen a proposed data standards approach for everything and everywhere – that is, an attempt to enable data access across existing boundaries.

Also, as Bill Anderson says in his report on the Charter: “the inclusion of the principle of 'Comparability and Interoperability' [is] the first time that a charter like this has moved beyond transparency to properly consider the whole point of open data: usage. Most datasets are pretty meaningless on their own. It is only when they are combined and contextualized do they generally make sense. Joined-up data creates the information we need.”

There's long been a problem in the field of open data around the idea that just publishing something is good enough. It's finally getting through to more people that open data needs to be usable – and that means it must conform to some declared set of data definitions.    Open Data

Making Data Interesting

Socrata's Q&A with Tyrone Grandison, Deputy Chief Data Officer for the U.S. Department of Commerce, includes some wonderful advice on “making open data interesting”: “Whenever possible, tie it to a standard. Always ensure that it meets one or more use cases. Provide an example of a story that can be told with the open data you are creating.”

I believe this advice can be extended to the corporate setting as well. We need to keep humanizing the value of data and relating data to processes and outcomes people care about.

Note how and continues into stories. It's as if you need data standards as Grandison's advice begins with standardsthe foundation for the picture you're going to paint about the usefulness of the data. Even if you're only going to produce one case, one story, you still want to be sure – whenever possible – that the data you're talking about has multiple uses in different scenarios, and data standards are about making sure of exactly that.

From the perspective of commerce, I don't think the open data movement has too much to worry about when it comes to generating interest. There's a natural enthusiasm for open data which is expressed by the great levels of volunteering we see in open data, and the growth of related start-up activity. We could do with more of this spirit inside our corporations. And I believe it's coming – the zest for data is leaking into organizations and the excitement about what can be achieved with data is continuing to grow.   Socrata

Blockchain

The Securities and Exchange Commission has approved a plan from online retailer Overstock.com to issue company stock via the Internet, signaling a significant shift in the way financial securities will be distributed and traded in the years to come.    SEC

 

Securing/Exploiting Data with Standards

Data isn't a systems concern any more. Data is everybody's business. Going forward, I believe organizations' investments in data will be mostly in two areas: securing data, and exploiting data. Data standards are at the heart of both areas.

To insure all your data is secure, you need to know you're addressing all the relevant data types before you start applying a security regime. You can have the safest, most bullet-proof security systems on the planet – but if some of your crucial business information has been left outside of the umbrella, you're in trouble.

You think you're covered but you're not. Many organizations are uncertain about how much important corporate data is exposed to threats by virtue of its not being known about by data management. Using data standards – and continuously assessing their use – enables you to bring all your data under one management canopy. You can then be certain that your security regime is covering all your data.

In order to exploit data successfully, you need to be sure you're comparing like with like. Data standards are there to insure your information is meaningful, so you can take decisions based on analysis with confidence.

The original motivation for standards – the exchange of information between systems and business partners – hasn't gone away. It's still a big driver for adoption and an obvious entry point into measuring the business benefits of standards.

This motivation is absent in the cases of data security and data exploitation. Organizations may only appreciate the benefit of standards for security when they suffer a breach. They may only recognize how standards can help them make informed decisions after they make a damaging decision based on faulty data.

It's on leaders to look ahead to these outcomes and make sure they don't happen. Twenty-twenty hindsight isn't much use if you're using it from the vantage point of the scrap heap. To keep your data secure, and to leverage its incredible value, you need standards.

Learning from Food Safety Standards

The creation of business data standards is spreading out from commercial and government organizations to the big wide world. The motivations for new standards, and the methods being used to formulate them, are instructive. Looking at how these new standards movements are faring may well help business organizations to improve their own standards processes – saving time and money, and opening up new potential benefits.

A paradigm case for the new, in-the-wild standards world is that of food inspection. Food inspection could sound like something dull and technical – a box-checking exercise designed to add costs to restaurateurs. But badly sourced or prepared food has major health impacts. An e-coli outbreak in 1993 claimed more than 700 victims in four states. And this wasn't just a few upset stomachs: over 170 victims were left with permanent kidney and brain damage, and four children died. It was traced to under-cooked burger patties.

Data standards for this domain are a two-level job. The standards must define the key data elements from the ground up as well as ensure compliance with the definitions. In business, it's been usual for the first level of this task to be reasonably straightforward. Say you want to define a “product”. You can usually come to an agreement about what a product is, its attributes and relationships, even if there's a deal of discussion and compromise along the way. The data associated with food safety is much more diverse and there's less of a common mental model waiting to be dug out of the participants. And, which is very interesting, a large part of food safety monitoring comes from the experiences and impressions of customers.

Now, the challenges facing the creators of food safety standards look very much like those facing businesses in an age of big data and social media. Business isn't fully in control of the data model any more. Consumers, partners, and regulators contribute as much – if not more – to the model than the business entities who think they “own” the domain.

What can standards advocates and adopters in business learn from areas like food safety? First, remind people of the stakes. Bad food kills people – get the data right and you save lives. Characterize the risks of bad data in your own domain, whether those are lost sales, reduced market share, slow response, rework costs, reputational damage, blocked access to markets etc.

Second, expect to discuss data items that are completely new to the domain. Many of these will come from the Internet of Things, initially from wearable tech. What do we want the sensors in our environment to measure? What scales should these measurements use? What are the threshold points we're interested in?

And third – solicit and embrace data from other parties. A diner's assessment of how clean a room is matters to other diners, and therefore ultimately to the restaurant's owners. Your customers' feedback is not just useful for informing how you develop the business. It's also a primary source for other customers' behavior. Everything you do is now public.    Government Technology

Data Standards and Cloud Service Providers

Much of the innovation in farming today is around what they call precision agriculture. Precision ag means using data across farming processes to manage the business. It's like bringing industrial control to agriculture. You could also say it's about bringing real-time, data-based management to farming.

There are many precision ag products and services out there. But: “Farmers and others agree on the need for some sort of industry body that will define and govern the standards our industry should follow when it comes to data platforms.” There are two groups working on standards, one which seems to come at the problem from the systems point of view and one that runs under an open data banner. Of course these two viewpoints are not incompatible – they could just represent different starting points, one inspired by current offerings from vendors and the other by users.

The pattern of an industry becoming more centered on data and then seeking to build out standards is familiar from other domains. I guess the difference industries are dealing with today, in comparison with even five years ago, is the growth of cloud services. It's easier to sign up with a cloud service than to trial an out-of-the-box solution. The economics look a whole lot better and there's less apparent risk of lock-in. However, until a community agrees its core business data standards, users of these services face potential difficulties with shifting their data from one service provider to another.

I'd like cloud service providers to take a more proactive approach to industry standards. I know this hardly looks like it would be in their interests. They want to offer agnostic services and they're not in the business of helping customers move to other suppliers. But as the cloud services business matures, customers will be looking for value-add. It doesn't cost much for a cloud service provider to say “we recommend and support the industry data standards from XYZ”. Doing so would show some added customer orientation on their part. Most importantly, promotion of industry standards grows the entire business for everyone involved. There's market share – and there's helping to grow the market.    Ag Industry

Industry Innovation from ACORD

Bill Hartnett, ACORD Head of Innovation was interviewed in a podcast from Insurance Journal:

IJ COMMENT:  "It is easy to talk about disruption and the need for innovation. It is quite another thing to actually implement new and innovative processes, products and servides. ACORD recognizes this and under Bill Hartnett's leadership (as Head of Innovation) is challenging carriers, agents and brokers, associations, and solution providers to become innovative brands and drive innovation in our industry.

Bill recognizes that the insurance industry has always talked about innovation but historically implementation has been lacking. According to Bill, what is different today is that instead of the industry setting its own agenda it is being set from outside the industry. That is, there is external pressure from consumers being placed on our industry to innovate and adapt. Are you an innovative brand? Bill Hartnett and ACORD would love to hear from you."

Here's the link: Insurance Journal

Data Agnostic

This is a good quote from Chuck Helveston at Archer, over in the investment management business: “Until the SMA [separately managed account] industry universally adopts standards for a common global language, any system supporting SMAs will need to be data agnostic.”

This is sound advice for buyers of systems. But it's also kind of sad for vendors. To be data agnostic, you have to “believe in” every proprietary data standard in your industry and be able to translate between them. This adds development and maintenance overhead to systems. So it's bad for buyers of systems as well, who end up paying higher prices. By delaying agreement on industry standards, everybody suffers.  ARCHER

Data Skills Everywhere

I keep reading that organizations need to hire more people with skills in big data analytics and informatics. It's probably true. But who are these people, and where are they going to come from?

For the insurance industry, I see a close overlap between actuarial, underwriting and analytics skills. They're not the same functions, but they speak to similar interests and aptitudes.

But I believe the spread of technology must inevitably enable more people to realize they have an affinity for data analysis – people who otherwise would not have recognized their calling. Our stereotyped ideas about the kinds of people who are interested in this stuff and are good at it are probably outdated. Especially with the great advances in visual modeling. You no longer need to have some mysterious ability to “see” data patterns or relationships in your head. 

Also, it's vital to remember data is central to the business. We should not be corralling “data scientists” off in a sealed room and thinking of what they do as magic. Data analysis needs to be located in, and performed by, the business. Let's look for data analysis potential in everyone we recruit. And let's offer training and encouragement to everyone who shows an interest in working with data.   Health IT

Some Proprietary Standards are Harmless like DUNS

Here's a small mystery. At the end of August 2015, federal government officials finalized 57 data standards enabling the DATA Act. GitHub – the open source web platform which hosted much of the discussion about the formation of the standards – said: “Notably, Treasury and OMB chose to adopt the proprietary DUNS Number as the favored, government-wide identifier for recipients of federal funds – even though every GitHub comment said they shouldn't.”

What could the thinking be here? Maybe it's that the DUNS (Data Universal Numbering System) number is so well entrenched in existing systems that people see no reason to change to something else. If you're looking to standardize, wouldn't you want to preserve any pre-existing standard used across the domain and regard it as a time saving?

I guess the issue here is whether you see the proprietary nature of the DUNS number as a problem or not. For straight serial numbers, I'm not sure I care whether a private or public organization allocates the numbers. It's not as if they can lock anyone out.

I can also see how the adoption of any proprietary element could look like a precedent for favoring proprietary standards in general. But I don't think standards leaders think like this. Using the DUNS number doesn't mean government will lean toward other proprietary standards that potentially distort competition. It's not in anyone's interest.   FIERCE

Zombie Data

Forrester's Michele Goetz is a great phrase maker. She's coined the term zombie data to refer to the fact that many (most?) organizations are still grappling with basic data management issues – and this is blocking their efforts to get more value out of data through analytics. I'd add that zombie data is also impeding organizations' ability to collaborate with each other and become more agile.

One of the questions Goetz identifies as a sign of zombie data is “How do I get the business to invest in data?” This is a great question, because it's a symptom rather than a problem. It needs to be taken as an invitation to discuss the organization's stated goals and actual behaviors with regard to data exploitation.

Unfortunately, the loose use of the term “big data” has led some people to think there's a discontinuity between the mainline concerns of data management and the bright world of a big data-powered tomorrow. In Goetz's words, “what is bright, shiny, and new is always more interesting than the ordinary”.

There's two ways we could spin data management. We could spin “ordinary” data up, and say it's not ordinary at all – try to give it some glamour. Or we could spin big data down – put it in its place.

I'm reluctant to do either. Sure, data needs to be managed, or else the organization won't obtain its value. But we need a shift in thinking, so that data is no longer seen as a technical issue. It's technical issues which suffer from “old/new” prejudices. Business issues don't suffer the same fashion anxieties. No one tells you you're being old-fashioned if you think customer service is important. And they don't think you're from the future if you try new ways of improving customer service.    Let's domesticate data. Let's make it “ordinary” in a good way.   Forrester

Valuing Data

Gartner connected a couple of dots in a recent report on companies' attitudes to data. Companies tend not to manage data as a corporate asset and they tend to ignore the monetary value of data.

I say they're connecting the dots here because the idea that data is an asset has been around a long time. But the idea that data could be worth money is kind of new. And yet the two ideas are really saying the same thing.

So we have people in organizations – sometimes from the tech side, sometimes from the business – promoting the exploitation of data for business benefit. The business case tends to be generic. The better your data, the better your decision making, the more accurate your targeting, the more complete your actions etc. But we don't usually point at a class of business data and say: This item is worth $ to the company.

The monetary value of data is becoming clearer thanks to companies' interest in selling data on. Thanks to greater connectivity – and data standards – markets in data are growing. Simple, atomic data streams like stock prices achieved market pricing a long time ago. In more recent times, “eyeballs” achieved value on the web.

The next step in our thinking should be to apply market pricing methods to all our corporate data, whether or not we're thinking about trying to sell it. Look at it this way: If we didn't have the addresses of our customers, what would we pay to get them? Areas for investigation can be prioritized – you don't have to boil the ocean on this one.

This practice would bring a new dimension to data audit. I have no doubt iterations would be needed to improve initial data valuations. But placing monetary values on data would mark a real transition to an asset view of data, with beneficial secondary effects in attitudes, processes, and skills.   Monetize Data

Lessons from 911

Wherever you find a group of committed professionals who want to ensure efficiency and effectiveness in their industry, you'll find a standards body. It might be called a committee or a working group. It's likely to be part of an industry association. I just came across one – the standards body that looks after 911.

But wait... 911 is a standard already. In fact, it could be the most successful standard introduced in modern times. Everyone knows what 911 is for – and everyone knows in an emergency you dial 911. It's a fact you grasp at an early age.

The work of the Agency Systems Committee at the National Emergency Number Association (NENA) is all about insuring “the reliable interoperability of all systems that public safety agencies use to handle emergency calls and associated data”. The 911 number is a key to a plethora of collaborating agencies which must work together seamlessly in real time to deal with emergency situations.

The need for reliable interoperability is highly visible to emergency agencies. I bet they don't agonize over mission statements.

Today, many industries are still getting used to the idea they're parts of connected webs. Phrases like  reliable interoperability still sound a little alien to them. They're used to making one-off dedicated connections with business partners. This is probably because the culture is about deal-making or individual agreement-setting rather than infrastructure development. It's to the great credit of the insurance industry that its leaders recognized early how the industry is not just about deals, but the infrastructure needed to do business.  NENA

Data Responsibilities

Coverage of Google's Nest product for home telematics is always respectful because, well, it's Google. I see the logic – Google is smart and if they're investing, we ought to take note. Liberty Mutual and American Family are on board with Nest. And as Insurance Networking News says: “As more information comes online from vehicles, homes and individuals themselves (through mobile and wearable devices), insurers are destined to become data companies.”

Okay, so insurance companies have always been data companies, but I guess it's a revelation for many. Whatever – INN's Joe McKendrick goes on to point up what being a data company means today: “With that comes great responsibility, not the least of which is building and managing an infrastructure – cloud-borne or with data centers – that can maintain the security, quality and accessibility of the data.” Insurance has always been about data – but now data is a much more fluid and shareable resource than it was back in the days of all-paper systems.

Home telematics is the next area for digital insurance, following vehicles. Personal telematics is not far behind. The central value of data in insurance needs to be recognized with the kind of responsible actions implied by McKendrick's analysis. Don't wait to make data count.   Telematics

Minimal or Maximal Standards

Sometimes people debate whether data standards should be minimal or maximal. A minimal data standard covers just the core area of concepts needed to help a group exchange data. A maximal data standard attempts to cover an entire domain.

Pragmatists vote for minimal standards. Idealists vote for maximal standards. To my mind, both types of thinking are wrong.

First, the minimalist approach. If you're sure you can identify a small common core of concepts, then you're in danger of setting current practices in stone and ignoring innovation. The growth in any industry comes from concepts connected with the common core. You have to follow some of the connections out of the core if you're to enact a standard that offers enduring value.

The maximalist approach is easy to criticize – it's an attempt to boil the ocean. But a more important objection is that maximalists repeat the error of the minimalists in assuming there's a defined limit to a domain, and that this boundary is understood by everyone. Again, innovation comes from challenges to a domain's definition, so you need to keep the edges blurry.

I guess it's no surprise that the right approach is somewhere between minimal and maximal. This is not just because the middle way is a neat compromise. It's because by abandoning the extremes we can return our attention to where it belongs: What's important to the business? The middle way helps us junk our prejudices about abstract principles of correctness and keeps us focused on serving the business.

 

What is data ownserhip

Hat tip to Christophe Marcant at Stibo Systems for his clarification of what “data owner” means. This is a term that's often used ambiguously. Marcant makes a clear distinction between the owner of content and the owner of structure and quality rules. This should have a prominent position in any organization's Data Management definition.  Data Informed

Enabling Data Governance

If you're looking for guidance about data governance in the big data era, this post from Informatica's Rob Karel is a great place to start. Rob has a very useful graphic showing the management cycle, plus a strong set of recommendations for enabling data stewards throughout the organization.    Informatica

 

Smart Cities Need Data Standards

Mark Fox, a smart cities expert with the University of Toronto, says: “We need to imagine a world when all the information about cities is published in a common way. We need to automate city analysis.” 

Fox and his team have developed an international standardized methodology for monitoring the urban environment in 17 areas, including sustainability, energy, education, and basic city services. Cities and companies need a common set of metrics, or they will have a hard time understanding their performance and developing their systems.

It seems to me that data standards must be seen as core requirements for the smart city. Standards didn't feature in all those “city of the future” illustrations we used to enjoy. But neither did depleted natural resources, mass urbanization, or population growth – forces which demand intelligent, dynamic management.

For the cities we're building today, data standards are as vital as power grids and waste disposal. Data standards are the foundation on which services are constructed – and communities created.   Future of Cities

Automated Transactions Need Standards

Every so often a sentence pops up and gives us a fresh, new way of communicating a benefit of data standards. Here's one: “The Real Estate Standards Organization (RESO) hopes to use data standards to make a fully automated real estate transaction a reality.”

Can you think of a complex, multi-party industry that doesn't dream of implementing fully automated transactions – right across the value chain? This has been the ultimate goal of every IT strategist for generations. The vision has always been just out of reach.

Failure to automate transactions across complex business processes can have many underlying reasons. But not using common data standards is likely to figure in all of them. For example, it may look like the inability to fully automate is down to departments not communicating well – nothing to do with systems, right? Except data standards help different departments talk to each other meaningfully – their use in software is almost secondary.

Let's take RESO's insight and apply it everywhere. If you really, truly, seriously want to realize your vision of fully automated transactions, you need to use data standards.   INMAN