Ben Woo takes exception to a Gartner analyst saying that Big Data is now in the “Trough of Disillusionment”. The nub of his argument is that where you are on the hype curve depends on how the hype curve plays out in your domain.
I think the hype curve is an interesting idea, but it's intended to remind us about the psychology of change, rather than necessarily guiding our investment choices. Also, the trough of disillusionment on one hype curve may create an opportunity for interest in another innovation. We need to remember that hype is about inflating expectations, and allow for both boosterism and disappointment in our technology decisions. Neuralytics
“Haven’t heard of Audit Data Standards? It’s not surprising; it is a relatively new idea developed by the AICPA’s Assurance Services Executive Committee’s Emerging Assurance Technologies Task Force (try and say that three times fast).”
There's your first problem, guys! I say this with affection. Better just to say “AICPA’s Audit Data Standards”. Annex yourselves a little authority. GBQ
Advocates of data standards are nice people. They want what's best for everyone they work with and for. Their message about standards is sometimes passionately made, but its essence is a sober one of business benefits. And we're talking about benefits that quickly become self-evident.
So I'm interested to learn the FDA is telling the pharma industry that if they don't use the standard data formats for drug studies, they won't get approval for their drugs. This is the stick, not the carrot. Is it a good approach? It's blunt. Frankly, the FDA have the power, so they can use it. But would they really deny Americans an effective medication just because of an apparently technical issue?
This is important. The FDA's stance demonstrates that data standards are not just a “technical issue”. If a company's submitted data does not conform with the standard, it can't be correctly evaluated. Without the standard, the science falls apart. Without the standard, the agency can't fulfil its mandate. The standard has a pivotal role in the approval process. It could really be a matter of life and death.
The FDA's approval power is rarely encountered in business, where the penalties for failing to use standards come from a more diffuse source – the market. In business, if we fail to use standards, we're punished by partners and abandoned by customers. Our productivity suffers and our operational costs are higher than those of our competitors. We're locked out of new opportunities.
Those of us in business can't look to an authority figure like the FDA to impose best practice. It's our responsibility to see the consequences of non-use of standards. Study Data Formats
Version 2.0 of the Universal Business Language (UBL) was released in 2006. UBL V2.1 came out in November 2013.
Sounds like a long wait for an upgrade, right? Wrong. You may want your anti-virus software to be updating every few days, but standards are only updated as and when they need to be. The stability of UBL over these seven years is a mark of success.
UBL is an XML standard for objects like invoices and purchase orders. Danish public sector organizations have been using it for invoicing since 2005, continuing to save 100m euros in labor costs every year. UBL has a lot of traction in Europe and has also spawned the e-freight standard, which is getting global attention.
We hear less about XML these days because it's become part of the business background. Good standards do good work – unnoticed, uncelebrated – saving time and money year after year. OASIS
Henrik Liliendahl Sørensen notices that many business applications lack fields for social identities. In other words, they can't capture details like Twitter handles. This is a replay of the old problem that arose when people started to have business and home phones, and then cellphones, and then Skype names, and so on.
The growth in social identities demonstrates that data standards must always develop to keep in line with business demands. We can't restrict this kind of data growth, and we can't benefit from it unless we cater for it structurally. Year 2000
The talk about organizations appointing Chief Data Officers (CDOs) might suggest people are taking data and its exploitation more seriously these days. And they are. But there's a long way to go, and a few symbolic appointments will not do the trick.
Looking at myths about big data, Rachel Clinton at Smart Vision Europe picks out a salient point: “Gartner research based on case studies of hundreds of companies estimates that in 2016 85% of fortune 500 companies will still not be able to exploit big data for competitive advantage.” That's a stunning conclusion, given all the coverage big data has had over the last several years. Predictions aren't the same as fate. It's possible to use a prediction as a wake-up call.
Think of the millenium bug: some people say it was all hype, because the world didn't come to an end. But the disaster didn't happen because people took heed of the warning, and fixed their systems. Successful data exploitation isn't in the gift of a CDO. It won't be provided by some silver bullet system. It's going to take the combined efforts of everyone in the business. This is the knowledge era. We're in a knowledge economy. This means there's a knowledge dimension to every activity we undertake. In turn, this means everyone in the enterprise has responsibility for data – its meaning, quality, and usability. Myths About Big Data
With organizations producing and consuming more and more data, and employees demanding access via new devices, many IT departments are struggling to balance sharing with security.
One of the major problems is the proliferation of user identities. Getting a “single view of the employee” across all the applications they use and the assets which have been issues to the is a real headache. Also, it's hard to make sure their access to applications is revoked and devices are reclaimed or wiped when they leave the organization.
Kurt Johnson has the insight that while this effect may be being caused by the growth of data, it can also be managed with big data techniques. Organizations need to use analytics to study identity activity in real time.
The people we used to call “users” – ie humanity – are now inside the system. They are actors within the information flow of the business. Data standards need to cover people, their roles and their relationships to each other and every relevant element of the business. Should this perspective develop from Security or HR? I believe it's a strategic issue which ought to be addressed by your information governance function. Taming Identity Data
Blogger Marco Mirandola has a fun way of pointing out that you don't need to use much of a standard to get benefits from it. He says that as a kid at the beach in Italy, he used to watch young Italian men managing to engage with female German tourists “with just few words of German, and frankly even poorly pronounced”
The 80/20 rule has a strong effect in the standards world. There are truly great benefits to be gained just by using the core elements of a widely used standard. And increasingly, using the core common vocabulary of the industry you're in is the only meaningful way to start participating. Standards
Here's something to ponder: “[Healthcare industry providers and payers are spending an enormous amount oftime and resources normalizing data and layering on analytics, but no one really knows what they need to know.” Healthcare isn't the only industry looking to analytics for insight and innovation, and the conundrum about not knowing what you need to know affects everyone.
But I think people are beginning to appreciate that great insights are not just going to emerge by themselves from even the biggest, baddest collection of data. We need to be asking questions of our data – even if they're not the right questions from the get-go.
This is the objective, a scientific approach. Astronomers don't just measure a load of star light and then expect the data to mold itself into truths about the universe. They come up with theories, and test them against the data.
That's what businesses need to do. If you're working with, say, health data, then first you need a data standard which ensures you're analyzing the data properly – comparing apples with apples. Then you need to frame some theories. Off the top of my head: If the Emergency Room is busy on a Friday night, it will be quiet on a Monday morning. This is based on the assumption that some percentage of accidents is related to the onset of the weekend. We could chew through the data and find the theory isn't true in one hospital – perhaps because Monday morning is short-staffed or people have realized you can't get seen at the weekend, so they wait for Monday.
That's not an exciting example – but it could be part of a chain of investigation and process redesign that eventually saves lives. It could easily be a dumb question, or a question that doesn't give as high a return as some other question. But how long does it take to frame and process such questions? Literally, seconds. Let's not wait around for lightning to strike. Let's standardize the data, and start the queries.HDM
Identifying stakeholders is usually mentioned as an important part of any change program, or any project that impacts a diverse range of people. Standards come under both these descriptions. Implementing standards is about improving the way work is done and enabling people or organizations to work together. So stakeholder identification must be an important priority.
Except, I submit, everybody is potentially a standards stakeholder. User groups for standards are almost by definition large. Standards also encourage the growth of the population using them. This means standards movements have to include very large numbers of people.
Is this possible? In order to make standards development practical, we tend to use a kind of representative democracy. We draft interested experts from across the domain to bring their knowledge, champion their colleagues, and constructively critique each other's viewpoints. When it comes to standards deployment, we often have to recruit a new set of representatives or at least augment the current representatives with additional leaders.
We therefore have two sets of stakeholders: Those who are invested in the creation of a good, robust set of standards, and those that realize the undoubted benefits for standards. Ultimately both groups are motivated by the impulses that standards users will share: The desire to make life better, save money, grow the business, and compete.
My friend, Celent's Jamie Bisker suggests that incremental process improvement in insurance is reaching the land of diminishing returns. He sees the future as lying in individualization, and grouping for risk management rather than sales or functional reasons.
I'm not sure that process improvement will ever end. We go through phases of development where we think we've almost reached the high point of what is humanly (or technologically) possible. These periods always turn out to be short – terminated by new challenges, new opportunities and new ways of seeing the world. He writes an engaging piece. take a look.
My latest book presents the challenges members face when adopting industry standards as well as the opportunities that come as a result. It features my discussions with many people over many years and follows the foundation I set in my first book "The Business Information Revolution".
Industry standards are never adopted in a vacuum. They become part and parcel of all the trials and tribulations managers face in their day to day work. ACORD Standards are always part of a larger software development project that brings along people, priorities and politics. Adopting industry standards isn't simple, but the benefits far outweigh the problems of building and maintaining proprietary alternatives.
I trust that you will appreciate my frankness, identify with some of the challenges and learn from what others have done to pave the way.
Previous Book
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