Gregory Maciag

Data Goes Wild

The continued spread of technology into every area of our lives continues. This is a well-known headache for corporate IT folks, who now face enormous problems supporting different access devices and channels. They also worry that business data “is now strewn everywhere. No longer locked behind firewalls in managed data repositories, data lives many places outside of the enterprise”.

The consumerization of IT has been going on ever since the rise of the (IBM PC) clones in the late 1980s. For thirty years or more, corporate IT has struggled to keep technology in its box. The liberation of data, first enabled by floppy disks and now accelerated by ubiquitous connectivity, has made every organization a potentially leaky boat.

So, data management must look outside of the organization. If you're responsible for business information, your remit now includes the entire world. You have no choice but to negotiate data administration rights with owners of devices which you allow to connect to your networks.

I believe organizations can also work together fruitfully to safeguard business data. This is an important role for industry groups, and another important reason for adhering to the data standards produced by such groups. Standards make it much easier to track the usage of data in the wild. The standards-setting process also provides an ideal forum for pursuing joint work on “wild data”.   Data Integration Blog

Unbundling and Rebundling

Younger consumers don’t want to buy general insurance products – they want cover associated with specific products and services. So says Mila Singh, who convincingly argues why a generation of renters don’t buy renter’s insurance, but do buy cover for their cell phones. 

I agree this is a generational effect in that younger people have no tradition of buying general coverage. However, the decline of general coverage is being felt in every demographic. It’s part of the drive toward personalization, on the one hand, and the construction of new kinds of product and service bundle on the other. You could say renter’s insurance is fragmenting into different types of cover that are being bundled with their risks. Equally you could say that utilities, such as your phone company, are creating bundled services that could also incorporate insurance coverage.  Consumers Refuse to Buy

No Bad Data

The New York Times ran a piece airing concerns about the collection of big data and privacy. George Orwell gets a name check. More to the point is a comment from Microsoft's Craig Mundie: “There's no bad data, only bad uses of data.”

I know this is only a soundbite, but it's kind of chewy. Clearly, there is bad data. There's data that's wrong, and data that is wrongly tagged. There's also data that's badly used from structural causes. That is, rather than some evil genius cackling and using honest data for bad ends, the clean data has been incorrectly related to other data within a storage schema.

If you model your data incorrectly, you create structural misuse of data. And this is where the real dangers lie. The way to guard against structural misuse is to exploit established, trusted data standards wherever possible.

Equally, safeguarding privacy concerns will have to mean the incorporation of privacy markers in the data models for relevant applications. For example, mobile healthcare apps should be using personal data standards that include a privacy marker for age (or date of birth).

Big Data is Opening Doors

Data for Evidence

Thank you, Kristian J. Hammond. Writing in the Harvard Business Review blog, Hammond talks about the value of “evidence-based decision-making (aka Big Data)”. At last – someone with the sense to put the horse back in front of the cart.

You need evidence to run the business successfully, and data can be the basis of evidence. Hammond develops this theme by arguing we need to turn data into narrative; that is, build a story from our data.  I'd put it like this: If big data is the answer, what's the question? Data can help you take decisions, but it won't – on its own – tell you what decisions you need to be taking.

There's more and more data every day. But it doesn't automatically make us any smarter. If we're prepared to ask questions about the business, to construct theories and test them, then big data is a boon.The Value of Big Data Isn't the Data

Travel Light

Perspectives from different industries often shed new light on my own industry, the global insurance business. For example, I immediately get a couple of useful insights from a piece by David Rogers, who is a consultant in the travel world. 

First, he says that despite being leaders in technology, the travel folks have not led in understanding customer behavior. One big reason is they don’t usually maintain a history of the customers they deal with: “As of recently, the airline industry has not even retained historical purchase records, literally ‘throwing out’ the unique ID of each customer transaction after the trip, to recycle the record locator number for another customer.”

This failure to recognize a customer between transactions has long been a challenge in insurance, but one I think we’ve tackled well. What interests me here is that one kind of data excellence can create a situation detrimental to another kind.

Second, Rogers points out that “data is political, not just technical”: “Sharing and linking of data, in order to assemble a complete picture of customer behavior, is very frequently obstructed by reluctance to share between divisions of a single company, or between partner companies. Whosoever ‘owns’ the data wields a great deal of power. The travel industry is struggling to shift to NDC, a new data standard for airline reservations. Why? Because while airlines claim it will allow for much better servicing of customers, online travel agencies (OTAs) fear being cut out of the value chain – disintermediated by their partners.”

Rogers believes data “ownership” is the wrong mindset. I agree. Data is the core resource of any advanced industry. Lock it or block it, and you kill the value chain. But the travel industry is facing the same kind of partnership issues insurance has faced. Travel agencies need to adapt to changing times. Carriers need to help them adapt. Data standards are the ideal way to evolve the relationships in the industry, as we have seen and continue to see with ACORD standards in insurance.  Why Big Data is so Hard

The Era of the Chief Data Officer

Data is everywhere in business today and you could say that every business is its data, especially in insurance.  Download Chief Data Officer 

Standards: Infinite ROI

New legislation which reuses procedures from a previous Act will deliver the USA's international treaty obligations on child support recovery. I mention this because the legislation is said to reuse “no-cost data standardization provisions” from three other social programs.

As well as making sure attempts to recover support payments don't stop at national borders, the move will save up to $500,000 over 10 years by improving efficiency.  Cost zero, return one half million. That's an infinite Return on Investment. Crazy not to.  Dave Camp

Digital Protectionism

The Chambers of Commerce of the US and Canada have joined forces to urge common data standards in future free trade deals, starting with the Trans-Pacific Partnership talks. They want to stop governments putting up borders around personal and financial data. Their argument is that data is a massive part of trade, so not having interoperability via common standards is a form of protectionism.

Governments' requirements for data residency in certain applications clearly reflect wariness about overseas hosts. Sensitive data must not be shipped to places which don't have the means or motivation to guard that data well or respect its privacy. Unfortunately, the blanket-rule approach to keeping all data “at home” means that competent data handling businesses are unfairly excluded.

Free trade is an evolving issue. It is good to see data taking its rightful place on the agenda. It is also encouraging to see that people understand standards as the means of ensuring openness, transparency, and security.  But wait. Look across the Atlantic ocean, and the Transatlantic Trade and Investment Agreement (TTIP) talks, and it's a different story. European countries want the US to become as protective of data as they are. It's as if we're living in two different worlds, depending on whether you look east or west.  Globe & Mail

ISO 20022 Financial Services Standard is Now 10 Years Old

ISO 20022 was intended to provide standards coverage for the entire financial services industry. Here is a progress check.  Download ISO 20022 Progress Report

Understanding the Data You Have

“Just as knowing the process of baking bread will never result in a tasty loaf without a sack of flour, Big Data will never result in anything actionable without appropriate data.”

Patrick Gray makes a good case for organizations getting better at “little data” before they go overboard on Big Data. He has a particularly resonant question for those who want to run before they can walk: “If you lack confidence in whether the datum you’re considering is 'the truth' how can you have any confidence in your analysis based on that datum?”  Tech Republic

Clearing Digital Payments

The newest payment technology is to be standardized by the oldest financial institution in the US. The Clearing House, an organization of 22 banks, is developing a “dynamic credentialing solution” for digital wallets. The aim is to make digital payments more secure for consumers. Presumably, transaction processing costs will fall too. This is one to watch. The mainstreaming of the digital wallet has been announced many times before. Will The Clearing House's program make the difference this time?  Mobile Payments Today

The Mosaic Effect

Back in the day, data was a topic for geeks – and these were not even alpha geeks (sorry guys). Now data is everybody's concern, because data about everybody is getting everywhere.

The British commentator Simon Jenkins recently devoted a column to the dangers of government data getting into the wrong hands. As well as likening the situation to the early days of atomic power, he said: “Data protection is a blazing contradiction in terms.” Now, data protection is not a contradiction in terms, let alone a blazing one. Jenkins probably means he doesn't trust claims made by technologists that data protection is possible.

Though I think his tone is a little hysterical, it's true that not enough has been done to guarantee the anonymity of personal data released in the public domain. And yet this issue has been flagged up by government itself. The so-called mosaic effect is when you combine two or more data sources to reveal a pattern that doesn't exist in any single source.

It was the Health and Human Services Department that discovered this, when it combined data about Medicare payments with Census data.  This inadvertently led to some health information about people in low population areas being potentially identifiable.

The “mosaic” terminology comes from database marketing, the field that discovered long ago how combining different data sets could lead to increasingly accurate predictions about people's income or propensity to buy certain products and services.

You could argue that data standards only help people to create mosaic views from multiple data sources. You might conclude that the more disparity we have in public data sets, the safer the public will be.

I don't think that's right. We actually need data standards in order to map potential mosaics before the hackers get to them. Then we can restrict the relevant data sets accordingly. Standards will let us see actual business relationships – such as the ones revealed by the Medicare/Census mashup – ahead of data release. In this way, standards can help us safeguard privacy.  Digital Revolutiong   Open Data Policy

Open Data Tools

Librarian and archivist Shannon Bohle has a comprehensive post about using open data, including lots of examples and good quotes. She has also compiled a list of 349 subject specific tools (mostly scientific) and 123 general tools, all with links. Very useful.  SciLogs

 

Caring About the Future

It's a pity the word “legacy” has come to have negative connotations. What we inherit in business isn't all bad. More important, people should be actively trying to create great things – brands, processes, techniques, markets, and so on – that will continue to power the business when they themselves move on.

We rightly try to link rewards with achievements. But this encourages a short term perspective. People are incentivized to meet goals tied to months, quarters or years. Planning to such a level of detail on a longer timescale isn't worth it, because too much is likely to change. We can't set hard, long term goals, so we sometimes neglect the long term altogether.   How can we reward people for caring about the future? For making the effort to build a better future for the business and all its stakeholders?

The way forward lies in the investment mindset. Business leaders must model and encourage behaviors geared toward future returns. We must reward people for the investments they make as well as the current goals they achieve.

Here's the hard part: Because we can't predict which investments will pay off and which won't, we are going to have to reward people for their intentions and commitment. If we're going to expect people to care about the future, then it's the caring we need to reward, not the potential outcome.

What I'm describing is known under another name: innovation. But if you lead with the idea of innovation, people immediately start thinking about genius levels of creativity, and amazing new technologies. That's great – but we also need everyday, commonplace attention to the care of the future. Incremental improvements, not just breakthroughs.

One of the best investments any organization can make in the future is to ensure it will be able to manage its information easily, guarding against unnecessary complexity and exploiting best practice wherever possible. There's a name for that too: standards.

Organizations must reward people whenever they opt for standards. We must be looking to reuse preexisting components and architectures in every area of the business. The world is full of great tools and techniques that not only save money and improve performance now, but also form a rich legacy for the years ahead.

Adding Meaning

Technical data standards abound in many industries. Bringing data together from across these standards is often challenging. This is because they're technical standards – they don't have a business meaning. You need to add this missing layer in the form of a common business model or what's sometimes called semantic data. In other words, you've got to overlay meaning on the data. 

But people can find it hard to agree on meanings because they come at data with different uses in mind. You will approach data differently if you're concerned with providing management information than if you're looking to connect business partners.  The ability to abstract yourself away from your own immediate purposes and see the other guy's point of view is central to making progress with a common model. More importantly, this is the same ability that lies at the heart of successful knowledge-based organizations. We live in an era when connecting with others and sharing information are more important than defending home territory.