Too Big to Nail

Too Big to Nail

Shipping loves big and it understands commodities and supply. Data is just a big commodity in need of a supply chain.

Too_big_featOver the past few months shipping has given the impression that it’s locked in a media arms race to capture the crown for the world’s biggest ship.  The size of anything which can be expressed to the general public in terms of multiple soccer pitches or Mount Everests will be a guaranteed headliner, particularly if you can bolt on complex systems, the vicissitudes of Mother Nature and lifetime voyages equal to multiple trips to the moon and back.

Shipping loves big, but while the evidence suggests that building bigger ships isn’t going to deliver competitiveness or growth in the long term, there’s another big thing that could, and that’s data. Of course you can’t measure big data in terms of soccer pitches, and that’s really where the problem lies. We’re good at dealing with tangible physical assets and complexity in the maritime industry, but when it comes to digital—and most things linked to information technology—we struggle.

For a start there’s that word ‘big’. It scares people, and it does so for a very simple, but good reason. Big compared to what? In our industry the majority have very little idea exactly what data exists in their organisations and even less about what it represents in terms of volume. It’s a basic but crucial point: how do you have a conversation about leveraging big data when you aren’t sure whether what you’ve got qualifies?

But the data ignorance extends far further than volume. A recent survey undertaken by Futurenautics Research on behalf of a major maritime supplier discovered that despite the massive increases in data transfer between ships and shore, operators don’t really know what most of it is, and—aside from prioritising operational traffic over crew communications—have no idea of its comparative value.

Let’s put this in context though. Gartner recently reported that 85 per cent of Fortune 500 organisations will be unable to exploit Big Data for competitive advantage during 2015, while a recent Accenture study found that half of all executives are unclear about the business outcomes they are getting from their data analytics programs. The maritime industry may have been far slower to focus on data than others, but it isn’t alone in struggling to extract value from it.
The good news though, is that later adoption means we have the opportunity to prevent making, or amplifying, the mistakes of others. Actually for our industry talking about ‘Big’ data with all the connotations and prejudices around the term, is pretty unhelpful. Perhaps the conversation in shipping and maritime would be better served by the term ‘Enterprise Data’.

Actually for our industry talking about ‘Big’ data with all the connotations and prejudices around the term, is pretty unhelpful. Perhaps the conversation in shipping and maritime would be better served by the term ‘Enterprise Data’.

For many years businesses have been basing decisions on transactional data stored in relational databases. This Enterprise Data relates to customer interactions, business performance, computer notifications, and external events in the business environment. But that left out a whole range of other data which was previously too difficult and expensive to capture, but which has yielded some amazing results when it’s mined for useful insight. This data includes email, blogs, social media photographs and—importantly—sensor data.

As the cost of storage and the computing power necessary to collect this data has decreased more companies are looking to include this non-traditional, yet potentially very valuable data with the traditional Enterprise Data in their business intelligence analysis But as technologies to collect and manage this data have evolved rapidly, organisations have adopted them piecemeal, implementing systems which are driven by departmental priorities, not enterprise-wide requirements. The result is that companies’ data ecosystems have become overly complex and filled with data silos, making data more difficult to access, limiting the value organisations can get out of it with the result that Enterprise Data is vastly underutilised.

For companies in other industries the level of siloing and complexity is actually far higher than it is in maritime. The benefit of late adoption for maritime could be that we avoid such high levels of both. But in order to do so we have to act quickly.  The core and overriding maxim when it comes to data is that it is owned by the organisation. Already maritime is dividing up what it refers to as ‘Technical’ versus ‘Commercial’ data and by doing so it risks repeating the mistakes of other industries.

Lloyd’s Register in its recent Horizons magazine described its involvement in, and support of, a variety of what the headlines describe as Big Data projects. But read a little further and it becomes clear that this isn’t really Big Data at all.
“Everyone’s talking about ‘big data’; I think that big data is a term that’s more a concern for the business consumer,” says Lloyd’s Register CEO Richard Sadler. “What I’m talking about is machine data analytics. We’re not trying to work out the consumption habits of somebody; we’re trying to identify through an increasing amount of data the acceptable performance limits of all the complex systems that are onboard ships.”

Give Lloyd’s Register its due, Big Data is undoubtedly where it’s at, and technical data is LR’s area of expertise. But conflating the two for an uninitiated audience is risky. What Lloyd’s Register is doing, perhaps understandably, is what maritime has always done. It is approaching data through the paradigm of engineering. Because that’s all maritime knows how to do. It has even coined a term for it— ‘data-centric engineering.’

Many times in this magazine and elsewhere we’ve pointed out that in our industry the word technology is used interchangeably with engineering. That’s because up until the last decade or so, they were basically the same. But that mindset is becoming a dangerous one. It’s preventing organisations from contextualising data as an enterprise issue and it risks moving it from the IT silo where it currently resides, straight into the engineering silo, limiting its access and value to the enterprise.

The ability to interact with data is becoming a necessary skill for everyone in the organisation. But in order to do that, people need access.

The ability to interact with data is becoming a necessary skill for everyone in the organisation. But in order to do that, people need access.

One can imagine the considerable pressure organisations like Lloyd’s Register and DNV GL are coming under as they attempt to haul the industry towards its technology-enabled future. Class societies like these prepared to take the lead are already transforming the nature and the level of discourse about what the industry needs to do. We asked Tor E Svensen, CEO of DNV GL to be our Futurenaut this issue for very good reason.

But whilst on the one hand anything which gets shipping to engage with data is positive, the temptation to reinforce those Big Data prejudices is one we need to avoid. I’m not sure who the ‘business consumer’ LR refers to actually is, but it sounds suspiciously like a swipe at what many assume Big Data is all about. Yes, big grocers use Big Data in order to sell us more of what we want, when we want it and reduce time, money and effort trying to sell us what we don’t want. But what’s wrong with that? Isn’t that what every organisation should be doing?

This idea that there is good, solid, important data—like technical, engineering data—and then fluffy, second-rate data about customers and how they consume our products, is lazy and misguided. That idea represents the absolute antithesis of what Big Data is all about.

What a Big Data programme does it to take multiple streams of data and find insight and value from them. If your starting point is that proprietary data should only be used to improve the legacy systems you’re already working with then you’ve fallen at the first hurdle.

Technical data is just one area of business performance and like every other type of data, it needs to be available to, and analysed in the context of, overall enterprise goals and performance. It is making that data available in real time to the right people which is the real challenge.

The solution is to change the way we view data. In short it has to be acknowledged that data is just another commodity essential to the effective operation of the business. In the same way your people need heating, lighting, pens, chairs and lavatory paper, they need access to data. And in the same way that you have a supply chain for those other items, you need one for data.

Treating data as a supply chain allows organisations to unlock the value hidden in it. Here’s an example of how a modern data supply chain works. The supply chain begins when data is created, imported or combined with other data, then moves through a variety of other links in the chain, incrementally acquiring value. The supply chain ends with actionable, valuable business insights. Those could be new services, products, process innovations, globalisation strategies or marketing campaigns.

Managed effectively and configured properly the data supply chain enables organisations to really discover their data, leverage more data sources, and then accelerate that data to take advantage of more advanced techniques such as machine learning.

For an example of how that can boost profitability look at Booz Allen Hamilton’s project with a major airline. The company pulled together data on schedules, routes, fares, destinations and historical passenger loads and combined it with sports schedules, convention dates, school seasonality, people movement by age segment, and social media data.

“The airline had lots of BI (business intelligence) dashboards and PDF reports about each of these areas separately, but they had never combined all of that information and let machines go to work,” explains Sullivan. “The results helped the airline make adjustments to flight schedules and fares that have resulted in tens of millions of dollars in additional revenue.” According to Sullivan the core analysis took three people on a data science team seven weeks to complete, and proves his point that “letting smart people go off and stitch together lots of information can really pay off.”

The days when executives and managers asked for data to be queried somewhere else in the organisation and then sat back and waited for it to turn up are fast disappearing. As we’ve explained before, the ability to interact with data is becoming a necessary skill for everyone in the organisation. But in order to do that, people need access.

The bottom line is that Enterprise Data could be offering maritime companies competitive advantage that most are leaving on the table. But generating business insights from data takes a radically new attitude—accepting it as a commodity rather than an abstract concept best left to IT.

The tools and technologies required to build the kind of data platforms we need to support the data supply chain are becoming cheaper each year, and won’t require big capex spend. Unlocking the value in our data means enabling it to flow easily and usefully throughout the whole organisation—and in the fullness of time through the company’s ecosystem of partners including stakeholders, suppliers and customers.

Many of the banks we rescued after the financial crisis were referred to as too big to fail, but Big Data suffers from a different preconception—that it’s too big to nail. To suggest that the data universe is anything other than very complex would be entirely wrong, but the challenge is not insurmountable.

Shipping understands commodities, and it understands supply chains. And it is certainly no stranger to big. Bringing that experience to bear on Enterprise Data could be the key that unlocks some serious value.

Image credit © Getty Images


This article appeared in the January 2015 issue of Futurenautics


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