Gregory Maciag

Industry Data Standards: Long Useful Lifecycle

Data standards in the UK government are led by the Cabinet Office. The Cabinet Office sounds like it would be a pretty specialized kind of unit, but in reality they get to deal with all the stuff that doesn't fit in any of the other departments – including the efficient running of the government itself.

The business of making standards work across a large community is daunting. You've got to articulate a vision of the future, acknowledge the messy nature of the present situation, and create a way forward that everyone can sign up to. Interestingly, the Cabinet Office is having to protect an aged de facto standard in order to keep the government show on the road. They've paid Microsoft £5.5m to support Windows X for another year following its official withdrawal from support.

Windows XP is thirteen years old. Many users around the world have never seen any need to move off of the platform. It's only the removal of continuing support that's forcing such users to make new plans.

One of the cool things about industry standards is that this doesn't happen. The composition of sponsoring bodies may change, and industry standards may be transferred between custodians, but the plug seldom (if ever) gets pulled. There's no incentive to end the life of a successful industry data standard.  ACORD EDI Standards date back almost three decades and they are still being used today.  theguardian

Quote of the Day

“We have more and more data we know less and less about.”  (Stuart Madnick, Professor of Information Technologies, MIT Sloan School)

Madnick recently urged an audience of data executives to consider value and veracity alongside the usual Big Data criteria of volume, velocity, and variety. He's so right to make this plea. Unless data is meaningful and trustworthy it is at best useless, at worst hazardous.  Data Guardians

Time and Cost

Mick Smothers at Capco summarizes the typical forces that act against standards adoption in the financial sector. He notes the arguments for standards outweigh those against – in fact, “it is self-evident that data standards are crucial to financial organizations – from insurance firms to mortgage companies.  Firms resist adopting standards due to the costs and time involved in enterprise-wide implementation.”

The problem here is perceptions. The “costs and time” objection arises from short term thinking and a tendency to confuse investment with cost.

The objection about alignment with business cycles is the good old “yes, but not now” argument. This is a powerful tool of obstruction, because it's very hard for anyone to determine with accuracy exactly which business cycles are relevant, where we are in those cycles, and what we're doing to manage them. The phrase “business cycles” is being used a little like “will of the gods”.

Leaders have to take a lead. But we expect them to base their decisions on analysis, not voodoo.You've got to fight inertia, because inertia is generated by people.  It's a product of fearful thinking and avoidance of evidence

CDO's on the Map

e.Republic E.Republic are publishing a Google map of the states and cities which have appointed Chief Data Officers. It's a small but growing population. Colorado raised the first state CDO flag in 2010, New York City the first local government CDO in 2011. Expect to see more pins appearing on the map this year and next.   Public Sector Innovation

CDO's Step Up

Over in the world of banking, researchers say there could be up to $10bn savings from implementing the Legal Entity Identifier (LEI). Yet only 17% of surveyed firms say they have clear responsibilities for implementing.

The article I read on this suggests Corporate Data Officers (CDOs) are unclear about their role in dealing with regulatory change. I wonder if the problem is more fundamental. For one thing, few organizations have CDOs, and among those organizations which do have them, I'm not convinced the title means as much as it could.

We're struggling with the familiar issue of data's apparent origins in technology. “Data” is a nerdy word. “Data processing” was and is a technical activity. And so we keep trying to make the argument that IT folks – data professionals – must move over to the business, or align better with the business.

But the reality is that data always belonged to the business. Technologists never originated data, they just looked after it. The business never really lost ownership to the techies.

If a CDO's job is to mean anything at all, it must at least mean identification and enforcement of data standards across the organization. If a regulatory requirement points to the need for implementing a standard, then I don't see how there can be any confusion. There's only going to be doubt if the CDO somehow gets the impression that he or she isn't really at the big table, taking business decisions.  Banking Technology

Solutions Thinking

You often hear business people saying IT people think too much in terms of “solutions”. The beef here is the idea that technologists always want to build a new system, whenever there's an opportunity.

You also hear IT people making the same complaint about business people. In this case, the technologists fear their colleagues have been sold an out-of-the-box – or off-of-the-cloud – “solution” which will be incompatible with everything else in the applications porfolio.

So I paused when I noticed Ken Collier of ThoughtWorks advocating “Solutions Thinking” as one of his touchstones for agile analytics. But he's coming from a different angle. And what he says might just help to rehabilitate the problematic “solution” word:

“Solutions Thinking. The best analytics are not simple answers to singular questions. Rather they are the complex heart of powerful solutions to challenging business problems. Examples like using machine learning on traffic flow data in real-time to reduce gridlock and traffic jams or using dynamic, real-time bioinformatics embedded in a sports bra to detect breast cancer in women are at the pinnacle of such solutions thinking.”

Collier has picked up on the effects of a little noticed cultural clash that's under way in our organizations. We have two kinds of data analysts in the analytics space. There's the IT version of data analyst, who is concerned with formats, consistency, quality and so on – but not actual content. Then there's the scientific data analyst, who is interested in working with the data to find patterns – and answers to questions.

When Collier asks for solutions rather than “simple answers to singular questions”, he's asking for the IT view to be joined with the specialist view. This is about adding repeatability, auditability, and shareability to the specialist's work.

From time to time, business absorbs new kinds of specialties. Each profession joining an organization has to adapt to the needs of business. Just as a research scientist moving from a university lab to a clinical setting needs to adapt to a new environment, so analytics experts must recognize the economic, regulatory, and collaborative drivers of the businesses they serve.   Thought Works

Investing in Standards

In the days when people didn't yet “get it”, standards were looked at as a cost. It's now clear to everyone that standards are an investment. Furthermore, people are looking back to past decisions and realizing the costs of non-standard development are truly enormous – and recurring.

Our community has invested in data standards now deployed in businesses around the world. Each organization which implements standards reaps the rewards of that investment. And in implementing standards, they also invest in a robust, business-driven, architectural process that guarantees continued information excellence as both business and technology evolve.

The Future Customer

Writing about the new generation of insurance customer, Toan Huynh at Cloud Sherpas says data privacy concerns will be negligible and “[f]urthermore, these new customers will have little or no brand loyalty when it comes to insurance. Instead, they’ll seek out individual products that are easy to purchase (for example online and via mobile), guilt-free and innovative.”

I want to push back on this a little, without sounding cynical or old fashioned. First, I agree carrier brand loyalty has taken a beating. But I notice brand loyalty now attaches to aggregators and portals. One function of brand loyalty is to deliver ease of purchasing, so brand will always be important. The interesting question is – whose brand?

Second, I'm not so sure we want to make insurance too easy to purchase – a thought that's linked to “guilt-free”. I wouldn't say feelings of guilt put people off buying insurance. In fact, feelings of responsibility and concern for the future are obvious motivators for purchasing. If we make insurance too easy and thoughtless, then we're encouraging people not to buy it. There should be a simple button at the end of the purchasing process, and this should deliver the goods. 

And this is where innovation comes in. We need product innovations, for sure. We will also see novel bundling of insurance with other products and services. However, a major focus for innovation is surely in the sales channel. The always-on era allows us – invites us – to treat the sales process as a conversation carried out over multiple channels. It may be a stop-start conversation, controlled by the customer. The conversation may also intersect with marketing activities.

Dialog with the customer does not just help to drive sales. It's also at the heart of brand building. I suspect we are on the cusp of a new age of insurance branding. Yes, the customer will be king or queen. But insurance will never sell itself.   The New Insurance Customer

Home Performance Standards

The real estate business now has a data standard covering the energy efficiency of homes. Known as BPI-2100 and BPI-220, or more simply as Home Performance XML, the standard enables home buyers to compare potential properties on this important dimension.

This is a neat example of how standards arise to meet new market needs. Realtors and buyers alike need to be able to assess the efficiency of properties, and they don't want to spend time and money trying to compare apples with oranges.

Smart industries evolve their standards to meet emerging needs – and evolve them effectively. Real estate is one business where friction is glaringly obvious to everyone involved. Standards remove friction, speed transactions, protect profits, and enlarge markets.   IMT

Standards Mindset

We often talk about the need to update our mindsets, especially in order to move away from “legacy thinking” toward a more strategic, data-centric view of business. Often people talk about getting the standards mindset. But what is the standards mindset?

Different people will have different ideas about what makes for a standards mindset. I'll offer what I  think are the two main parts.

First, when considering approaches to a problem or opportunity, you're likely to ask: What can we bring in? Old-style thinking tends to start by asking: What can we do with our existing resources? This is why the wheel gets reinvented – because we're good at inventing wheels. It takes a shift in thinking to set that expertise aside, and bring in a pre-existing wheel from outside.

This is subtly different from “thinking outside the box”. That's great advice, but it's hard to implement. It's easier to ask what you can bring into your box from outside. Look outside the box, but keep thinking about your own goals.

Second, I think the standards mindset pays attention to reducing dependencies on proprietary solutions, software, processes, and formats. To stick with the “box” analogy, ideally you want nothing inside the box that you're going to be stuck with – unless it's core to your profitability.

Now, I'm not saying that the standards mindset is all for scrapping every proprietary feature of the existing business. It's about reducing dependencies, not going cold turkey. And it's also about recognizing where unique features – the secret sauce – add real benefit to the business, and where they really just add costs, reduce agility, and degrade competitiveness.