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Luis Iván Cuende: Bitcoin Blockchain Entrepreneur

Stephanie Overby

Luis Iván Cuende must have been born an entrepreneur.

Having no access to capital, equipment, or collaborators in his native Oviedo, Spain, he gravitated toward the first thing he could get his three-year-old hands on: one of his software-engineer father’s old computers. By age 12, Cuende had released his own distribution of Linux. At 15, he won the HackNow award for technical talent. And he spent his 17th year as advisor to Neelie Kroes, then the European Commissioner for
Digital Agenda.

Today the 20-year-old is CTO of Stampery, a San Mateo, California, based startup he co-founded in 2015, which leverages the Bitcoin blockchain—the shared public ledger that records and secures Bitcoin currency transactions—to provide instant data notarization and document certification.

“I’ve always wanted to create things; that’s what makes me happy,” says Cuende, who splits his time between Silicon Valley and Madrid. “I got started with software because it’s easy to launch a product without the need for a factory or millions of dollars.”

Cuende started to see how he could make a big impact when he was working with Kroes, whom he views as one of the most disruptive politicians he’s met.

“She has one of the freshest minds I have seen in the policy world full of suits,” he says.

After his time as one of her advisors was up, he attempted to launch several businesses, which ultimately led to Stampery.

Know Your Strengths— and Weaknesses

Four years ago, Cuende developed Cardwee, an application that enabled companies to provide customer loyalty points via Apple Passbook. “It was the very first one, and it was a very good product,” he says. “But I knew nothing about business or marketing.”

The lesson? You can build a solid product, but if you don’t know how to sell it, you will fail. “It’s the second most important thing in business,” Cuende says. “I’m good at giving talks to big groups, but I’m very bad at selling in person to big clients. My co-founders have much more experience in that, and I try to learn from them.”

Setbacks Aren’t Failures

Cuende met his Stampery collaborators, CMO Tommaso Prennushi, a former marketing executive, and CEO Daniele Levi, a former professional DJ and a cryptography enthusiast, in 2012 at the annual tech festival Campus Party, held that year in Berlin. “We were the only ones talking about Bitcoin in Spain at the time,” Cuende says.

The trio developed several Bitcoin-related products that went nowhere. At one point, they tried to get a banking license to create a Bitcoin exchange, but that required “millions of dollars we didn’t have,” Cuende says.

They tried a new tack, coming up with the idea for Stampery. Cuende calls it “the most obvious” noncurrency application for the Bitcoin blockchain—and one they could develop at low cost. Cuende and his team built Stampery and then secured US$600,000 in pre-seed funding, led by Draper & Associates, in November 2015.

An Upside to Risk

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Stomper provides legally binding proof of a digital document’s existence, integrity and ownership.

Most successful new business ideas are obvious, Cuende believes. “It’s not that you see something that other people don’t. It’s just that the idea involves risk. It’s something that people are afraid of today.”

A few years ago, most people associated Bitcoin only with nefarious activity, he says. But the blockchain technology underpinning Bitcoin fascinated Cuende and his partners. The blockchain maintains a verifiable chronological record of Bitcoin transactions, and it resists tampering by involving a network of thousands of independently managed computers in securing every update.

“Having an immutable ledger enables you to do a lot of stuff that couldn’t be done otherwise,” says Cuende. This includes transferring electronic money without needing a bank or other institution to guarantee it. “But why should we limit that ledger to storing only financial transactions? Anything that needs to be recorded in order to have a reliable proof of its existence, integrity, and even ownership can benefit from it. It’s the first time in the history of the computer that digital information isn’t necessarily modifiable and destroyable.”

Cuende saw only one downside: “The blockchain is very bad at transaction volume.” Cuende and his co-founders wanted to be able to certify millions of documents at a time; the Bitcoin network was capable of 2.5 per second. That’s where their technological innovation came in. Stampery is designed to stamp billions of data sets per second. In addition, the technology can work with any blockchain network, not just Bitcoin’s.

That makes using the Bitcoin blockchain low risk. “If it goes down tomorrow—which is highly improbable—we can migrate to another blockchain,” he says. In addition, the legally binding proofs that Stampery generates to certify the existence, integrity, and ownership of documents will remain valid and accessible even if Stampery disappears, because it leverages a decentralized technology.

Disrupting Decades of  Paper Pushing

Stampery currently has 1,500 customers, who fall into three user groups: creators protecting their intellectual property, attorneys certifying documents, and software developers. Lawyers saw the value immediately.

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“They need to create proofs from a lot of their digital documents on a daily basis. Then we have creators who use it to prove that they were the first creating a music track, art, even a process,” Cuende says. “What we’re seeing lately is a lot of enterprise interest for areas such as document management and systems security.”

Users can stamp up to 10 files monthly and access 1 gigabyte of encrypted storage for free, or pay $9.99 to stamp 1,000 files and access 50 gigabytes of storage. The product integrates with Dropbox cloud storage. Customers can also use Stampery to certify that e-mails were sent and received.

“You can make a deal via e-mail, click a button, and certify it with no one else involved but the two parties,” says Cuende.

Stampery is focusing on nonregulated use cases to establish the market and raise awareness. But the company is also actively lobbying regulators “to see the value of storing truth in a ledger that is mathematically secure,” says Cuende, particularly in Europe where notaries play a bigger role in the economy than they do in the United States.

Pen and paper have been the standard for hundreds of years. Human notaries are not “immutable, but they have these notebooks in which they establish truth, which is recognized by the state. Obtaining this type of trust involves cost and liability,” Cuende says. “Replacing it with math could save billions for whole industries.” Early in 2016, Stampery made a deal with the Estonian government to enable everyone with an e-Residency ID to use the system to certify and notarize personal and business documents.

Learn When to Step Up

Now that one of Cuende’s co-creations has become a full-blown business, he is adapting to 10-hour workdays and managing a growing team. “Being able to attract talent is super important to me. It’s great to work with very bright people that are better than me in many things,” says Cuende. “My days have gotten crazier. There’s pressure from everywhere,” he says. “It’s a challenge. But I love that feeling.” D!

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Connected Cars Rev Up For A Revolution [VIDEO]

Michael Zipf

Every two years, almost a million car enthusiasts flock to the Frankfurt International Motor Show (IAA), the world’s largest automotive trade fair, to enjoy the legendary spectacle of automakers rolling out their latest models to an accompaniment of flashing lights, throbbing bass beats, and stylishly dressed dancers.

While the giant exhibition halls on the ground Couple buying a car --- Image by © Don Mason/Blend Images/Corbisfloor echo to the sound of visitors jostling to examine paint work and leather, sleek sports cars, people carriers, electric vehicles, and the ubiquitous SUVs, the atmosphere in the New Mobility World exhibition on the first floor is altogether calmer. Nevertheless, this is where pressing issues about the future of mobility are being discussed.

The exhibitors here include Samsung, IBM, Deutsche Telekom, and – making its debut appearance – SAP. Awake to the far-reaching revolution that lies ahead of the automotive sector, these IT companies are in Frankfurt to showcase ways in which information technology is already making it possible to connect today’s highly digitized vehicles with each other, with their drivers, and with the technological infrastructure around them.

Revved up for a revolution

Chris Urmson considers the convergence of vehicles and IT to be “the most exciting development of our age.” Speaking in Frankfurt, Urmson, who heads up Google’s driverless car program, described the number of people killed on America’s roads every year – 36,000 – as “unacceptable” and stressed that his company’s intensive research into autonomous vehicles was aimed at improving road safety.

Robert Wolcott, Professor of Innovation Management and Corporate Entrepreneurship at Northwestern University’s Kellogg School of Management, spoke of “a new industrial revolution” whose impact would be “on a par with that of the railroads in the 19th century.”

So it’s no surprise that the IT sector is steering its focus toward the automotive industry.

At the IAA’s Smart City Forum, SAP has teamed up with various cities to present solutions designed to put an end to the daily traffic gridlock. And, to judge by the figures below, their capabilities are sorely needed:

  • By 2050, around 70% of the global population will be living in cities.
  • The number of cars on the planet is set to almost double by 2030.
  • Experts predict that the volume of freight traffic on Europe’s roads will increase 80% by 2025.
  • On average, a car driver in Germany spends 36 hours stuck in traffic jams every year.

Smart cities for a better quality of life

Smart Traffic Control enables cities to optimize traffic-light controls and free up additional car lanes during the rush hour to alleviate congestion, while data collected by RFID chips, sensors, cameras, and induction loops is used to compile congestion profiles and monitor real-time traffic issues. The Chinese city of Nanjing, which is home to 8 million people, has chosen to adopt smart traffic control technology to crunch the 20 billion data points captured in the city every year to produce actionable information for predictively responding to traffic congestion. And the software even learns as it goes along. In June of this year, the city signed a Custom Development Project with SAP. Currently, the SAP HANA platform helps Nanging analyze the data generated by its 10,000 taxis. The plan is for other modes of transportation to provide data in the future too.

“Smart traffic is one of the hottest topics for the world’s ever-expanding cities,” says Norbert Koppenhagen from the SAP Innovation Center Network, who is also at the IAA to showcase SAP’s cooperation with the German city of Darmstadt, near Frankfurt. “If we can keep the traffic flowing, we’ll make city-dwellers’ lives a whole lot more livable.”

The SAP Vehicle Insights cloud application links vehicular data with sensor data to provide actionable insight into driver behavior patterns and efficiency. The software helps logistics and mobility services providers monitor live vehicle conditions and manage their services within the constraints imposed by pollution and traffic congestion. The SAP Vehicle Insights also helps fleet operators manage their fleets optimally.

City App is another innovation being showcased in Frankfurt. Developed in collaboration with the German city of Nuremberg, this app features crowdsourcing functions that allow citizens to report defects and damage in their immediate vicinity. Algorithms assimilate these reports with data about factors such as traffic density in the affected city zone to help municipal authorities optimize their response.

There is also considerable buzz around TwoGo, the mobile app that lets employees at enterprises, institutions, and municipal authorities link up and share their daily commute to the office. “This is an exciting time for TwoGo,” says Alexander Machold, a member of the TwoGo business development team. “We’ve got vehicle manufacturers, parking garage operators, local authorities, and government ministries all looking into how TwoGo could help them cut costs and develop new business models.” What’s more, he says, the app sometimes opens the door to cross-selling opportunities for other SAP solutions.

“The number of connected cars on our roads is growing; more and more vehicles are being outfitted with sensors; and even driverless cars are becoming a genuine possibility. All in all, this is a great opportunity for us to transform cities, industries, and businesses sustainably to create a better future,” says Stephan Brand, Vice President, PI Analytics Applications, Products and Innovation at SAP.

The Internet has changed the way we buy cars, while mobile technology is changing what we expect them to do. Learn more about The Hyperconnected Car.

This story also appeared in the SAP Business Trends community.

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How One Business Approach Can Save The Environment – And Bring $4.5 Trillion To The World Economy

Shelly Dutton

Despite reports of a turbulent global economy, the World Bank delivered some great news recently. For the first time in history, extreme poverty (people living on less than $1.90 each day) worldwide is set to fall to below 10%. Considering that this rate has declined from 37.1% in 1990 to 9.6% in 2015, it is hopeful that one-third of the global population will participate the middle class by 2030.

For all industries, this growth will bring new challenges and pressures when meeting unprecedented demand in an environment of dwindling – if not already scarce – resources. First of all, gold, silver, indium, iridium, tungsten, and many other vital resources could be depleted in as little as five years. And because current manufacturing methods create massive waste, about 80% of $3.2 trillion material value is lost irrecoverably each year in the consumer products industry alone.

This new reality is forcing companies to rethink our current, linear “take-make-dispose” approach to designing, producing, delivering, and selling products and services. According to Dan Wellers, Digital Futures lead for SAP, “If the economy is not sustainable, we are in trouble. And in the case of the linear economy, it is not sustainable because it inherently wastes resources that are becoming scarce. Right now, most serious businesspeople think sustainability is in conflict with earning a profit and becoming wealthy. True sustainability, economic sustainability, is exactly the opposite. With this mindset, it becomes strategic to support practices that support a circular economy in the long run.”

The circular economy: Good for business, good for the environment

What if your business practices and operation can help save our planet? Would you do it? Now, what if I said that this one business approach could put $4.5 trillion up for grabs?

By taking a more restorative and regenerative approach, every company can redesign the future of the environment, the economy, and their overall business. “Made possible by the digital economy, forward-thinking businesses are choosing to embrace this value to intentionally reimagine the economy around how we use resources,” observed Wellers. “By slowing down the depletion of resources and possibly even rejuvenating them, early adopters of circular practices have created business models that are profitable, and therefore sustainable. And they are starting to scale.”

In addition to making good financial sense, there’s another reason the circular economy is a sound business practice: Your customers. In his blog 99 Mind-Blowing Ways the Digital Economy Is Changing the Future of Business, Vivek Bapat revealed that 68% of consumers are interested in companies that bring social and environmental change. More important, 84% of global consumers actively seek out socially and environmentally responsible brands and are willing to switch brands associated with those causes.

Five ways your business can take advantage of the circular economy

As the circular economy proves, business and economic growth does not need to happen at the cost of the environment and public health and safety. As everyone searches for an answer to job creation, economic development, and environmental safety, we are in an economic era primed for change.

Wellers states, “Thanks to the exponential growth and power of digital technology, circular business models are becoming profitable. As a result, businesses are scaling their wealth by investing in new economic growth strategies.”

What are these strategies? Here are five business models that can enable companies to unlock the economic benefits of the circular economy, as stated in Accenture’s report Circular Advantage: Innovative Business Models and Technologies that Create Value:

  1. Circular supplies: Deliver fully renewable, recyclable, and biodegradable resource inputs that underpin circular production and consumption systems.
  2. Recovery of resources: Eliminate material leakage and maximize the economic value of product return flows.
  3. Extension of product life: Extend the life cycle of products and assets. Regain the value of your resources by maintaining and improving them by repairing, upgrading, remanufacturing, or remarketing products.
  4. Sharing platforms: Promote a platform for collaboration among product users as individuals or organizations.
  5. Product as a service: Provide an alternative to the traditional model of “buy and own.” Allow products to be shared by many customers through a lease or pay-for-use arrangement.

To learn more about the circular economy, check out Dan Wellers’ blog “4 Ways The Digital Economy Is Circular.”

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Data Lakes: Deep Insights

Timo Elliott, John Schitka, Michael Eacrett, and Carolyn Marsan

Dan McCaffrey has an ambitious goal: solving the world’s looming food shortage.

As vice president of data and analytics at The Climate Corporation (Climate), which is a subsidiary of Monsanto, McCaffrey leads a team of data scientists and engineers who are building an information platform that collects massive amounts of agricultural data and applies machine-learning techniques to discover new patterns. These analyses are then used to help farmers optimize their planting.

“By 2050, the world is going to have too many people at the current rate of growth. And with shrinking amounts of farmland, we must find more efficient ways to feed them. So science is needed to help solve these things,” McCaffrey explains. “That’s what excites me.”

“The deeper we can go into providing recommendations on farming practices, the more value we can offer the farmer,” McCaffrey adds.

But to deliver that insight, Climate needs data—and lots of it. That means using remote sensing and other techniques to map every field in the United States and then combining that information with climate data, soil observations, and weather data. Climate’s analysts can then produce a massive data store that they can query for insights.

Meanwhile, precision tractors stream data into Climate’s digital agriculture platform, which farmers can then access from iPads through easy data flow and visualizations. They gain insights that help them optimize their seeding rates, soil health, and fertility applications. The overall goal is to increase crop yields, which in turn boosts a farmer’s margins.

Climate is at the forefront of a push toward deriving valuable business insight from Big Data that isn’t just big, but vast. Companies of all types—from agriculture through transportation and financial services to retail—are tapping into massive repositories of data known as data lakes. They hope to discover correlations that they can exploit to expand product offerings, enhance efficiency, drive profitability, and discover new business models they never knew existed.

The internet democratized access to data and information for billions of people around the world. Ironically, however, access to data within businesses has traditionally been limited to a chosen few—until now. Today’s advances in memory, storage, and data tools make it possible for companies both large and small to cost effectively gather and retain a huge amount of data, both structured (such as data in fields in a spreadsheet or database) and unstructured (such as e-mails or social media posts). They can then allow anyone in the business to access this massive data lake and rapidly gather insights.

It’s not that companies couldn’t do this before; they just couldn’t do it cost effectively and without a lengthy development effort by the IT department. With today’s massive data stores, line-of-business executives can generate queries themselves and quickly churn out results—and they are increasingly doing so in real time. Data lakes have democratized both the access to data and its role in business strategy.

Indeed, data lakes move data from being a tactical tool for implementing a business strategy to being a foundation for developing that strategy through a scientific-style model of experimental thinking, queries, and correlations. In the past, companies’ curiosity was limited by the expense of storing data for the long term. Now companies can keep data for as long as it’s needed. And that means companies can continue to ask important questions as they arise, enabling them to future-proof their strategies.

Prescriptive Farming

Climate’s McCaffrey has many questions to answer on behalf of farmers. Climate provides several types of analytics to farmers including descriptive services, which are metrics about the farm and its operations, and predictive services related to weather and soil fertility. But eventually the company hopes to provide prescriptive services, helping farmers address all the many decisions they make each year to achieve the best outcome at the end of the season. Data lakes will provide the answers that enable Climate to follow through on its strategy.

Behind the scenes at Climate is a deep-science data lake that provides insights, such as predicting the fertility of a plot of land by combining many data sets to create accurate models. These models allow Climate to give farmers customized recommendations based on how their farm is performing.

“Machine learning really starts to work when you have the breadth of data sets from tillage to soil to weather, planting, harvest, and pesticide spray,” McCaffrey says. “The more data sets we can bring in, the better machine learning works.”

The deep-science infrastructure already has terabytes of data but is poised for significant growth as it handles a flood of measurements from field-based sensors.

“That’s really scaling up now, and that’s what’s also giving us an advantage in our ability to really personalize our advice to farmers at a deeper level because of the information we’re getting from sensor data,” McCaffrey says. “As we roll that out, our scale is going to increase by several magnitudes.”

Also on the horizon is more real-time data analytics. Currently, Climate receives real-time data from its application that streams data from the tractor’s cab, but most of its analytics applications are run nightly or even seasonally.

In August 2016, Climate expanded its platform to third-party developers so other innovators can also contribute data, such as drone-captured data or imagery, to the deep-science lake.

“That helps us in a lot of ways, in that we can get more data to help the grower,” McCaffrey says. “It’s the machine learning that allows us to find the insights in all of the data. Machine learning allows us to take mathematical shortcuts as long as you’ve got enough data and enough breadth of data.”

Predictive Maintenance

Growth is essential for U.S. railroads, which reinvest a significant portion of their revenues in maintenance and improvements to their track systems, locomotives, rail cars, terminals, and technology. With an eye on growing its business while also keeping its costs down, CSX, a transportation company based in Jacksonville, Florida, is adopting a strategy to make its freight trains more reliable.

In the past, CSX maintained its fleet of locomotives through regularly scheduled maintenance activities, which prevent failures in most locomotives as they transport freight from shipper to receiver. To achieve even higher reliability, CSX is tapping into a data lake to power predictive analytics applications that will improve maintenance activities and prevent more failures from occurring.

Beyond improving customer satisfaction and raising revenue, CSX’s new strategy also has major cost implications. Trains are expensive assets, and it’s critical for railroads to drive up utilization, limit unplanned downtime, and prevent catastrophic failures to keep the costs of those assets down.

That’s why CSX is putting all the data related to the performance and maintenance of its locomotives into a massive data store.

“We are then applying predictive analytics—or, more specifically, machine-learning algorithms—on top of that information that we are collecting to look for failure signatures that can be used to predict failures and prescribe maintenance activities,” says Michael Hendrix, technical director for analytics at CSX. “We’re really looking to better manage our fleet and the maintenance activities that go into that so we can run a more efficient network and utilize our assets more effectively.”

“In the past we would have to buy a special storage device to store large quantities of data, and we’d have to determine cost benefits to see if it was worth it,” says Donna Crutchfield, assistant vice president of information architecture and strategy at CSX. “So we were either letting the data die naturally, or we were only storing the data that was determined to be the most important at the time. But today, with the new technologies like data lakes, we’re able to store and utilize more of this data.”

CSX can now combine many different data types, such as sensor data from across the rail network and other systems that measure movement of its cars, and it can look for correlations across information that wasn’t previously analyzed together.

One of the larger data sets that CSX is capturing comprises the findings of its “wheel health detectors” across the network. These devices capture different signals about the bearings in the wheels, as well as the health of the wheels in terms of impact, sound, and heat.

“That volume of data is pretty significant, and what we would typically do is just look for signals that told us whether the wheel was bad and if we needed to set the car aside for repair. We would only keep the raw data for 10 days because of the volume and then purge everything but the alerts,” Hendrix says.

With its data lake, CSX can keep the wheel data for as long as it likes. “Now we’re starting to capture that data on a daily basis so we can start applying more machine-learning algorithms and predictive models across a larger history,” Hendrix says. “By having the full data set, we can better look for trends and patterns that will tell us if something is going to fail.”

Another key ingredient in CSX’s data set is locomotive oil. By analyzing oil samples, CSX is developing better predictions of locomotive failure. “We’ve been able to determine when a locomotive would fail and predict it far enough in advance so we could send it down for maintenance and prevent it from failing while in use,” Crutchfield says.

“Between the locomotives, the tracks, and the freight cars, we will be looking at various ways to predict those failures and prevent them so we can improve our asset allocation. Then we won’t need as many assets,” she explains. “It’s like an airport. If a plane has a failure and it’s due to connect at another airport, all the passengers have to be reassigned. A failure affects the system like dominoes. It’s a similar case with a railroad. Any failure along the road affects our operations. Fewer failures mean more asset utilization. The more optimized the network is, the better we can service the customer.”

Detecting Fraud Through Correlations

Traditionally, business strategy has been a very conscious practice, presumed to emanate mainly from the minds of experienced executives, daring entrepreneurs, or high-priced consultants. But data lakes take strategy out of that rarefied realm and put it in the environment where just about everything in business seems to be going these days: math—specifically, the correlations that emerge from applying a mathematical algorithm to huge masses of data.

The Financial Industry Regulatory Authority (FINRA), a nonprofit group that regulates broker behavior in the United States, used to rely on the experience of its employees to come up with strategies for combating fraud and insider trading. It still does that, but now FINRA has added a data lake to find patterns that a human might never see.

Overall, FINRA processes over five petabytes of transaction data from multiple sources every day. By switching from traditional database and storage technology to a data lake, FINRA was able to set up a self-service process that allows analysts to query data themselves without involving the IT department; search times dropped from several hours to 90 seconds.

While traditional databases were good at defining relationships with data, such as tracking all the transactions from a particular customer, the new data lake configurations help users identify relationships that they didn’t know existed.

Leveraging its data lake, FINRA creates an environment for curiosity, empowering its data experts to search for suspicious patterns of fraud, marketing manipulation, and compliance. As a result, FINRA was able to hand out 373 fines totaling US$134.4 million in 2016, a new record for the agency, according to Law360.

Data Lakes Don’t End Complexity for IT

Though data lakes make access to data and analysis easier for the business, they don’t necessarily make the CIO’s life a bed of roses. Implementations can be complex, and companies rarely want to walk away from investments they’ve already made in data analysis technologies, such as data warehouses.

“There have been so many millions of dollars going to data warehousing over the last two decades. The idea that you’re just going to move it all into a data lake isn’t going to happen,” says Mike Ferguson, managing director of Intelligent Business Strategies, a UK analyst firm. “It’s just not compelling enough of a business case.” But Ferguson does see data lake efficiencies freeing up the capacity of data warehouses to enable more query, reporting, and analysis.

Data lakes also don’t free companies from the need to clean up and manage data as part of the process required to gain these useful insights. “The data comes in very raw, and it needs to be treated,” says James Curtis, senior analyst for data platforms and analytics at 451 Research. “It has to be prepped and cleaned and ready.”

Companies must have strong data governance processes, as well. Customers are increasingly concerned about privacy, and rules for data usage and compliance have become stricter in some areas of the globe, such as the European Union.

Companies must create data usage policies, then, that clearly define who can access, distribute, change, delete, or otherwise manipulate all that data. Companies must also make sure that the data they collect comes from a legitimate source.

Many companies are responding by hiring chief data officers (CDOs) to ensure that as more employees gain access to data, they use it effectively and responsibly. Indeed, research company Gartner predicts that 90% of large companies will have a CDO by 2019.

Data lakes can be configured in a variety of ways: centralized or distributed, with storage on premise or in the cloud or both. Some companies have more than one data lake implementation.

“A lot of my clients try their best to go centralized for obvious reasons. It’s much simpler to manage and to gather your data in one place,” says Ferguson. “But they’re often plagued somewhere down the line with much more added complexity and realize that in many cases the data lake has to be distributed to manage data across multiple data stores.”

Meanwhile, the massive capacities of data lakes mean that data that once flowed through a manageable spigot is now blasting at companies through a fire hose.

“We’re now dealing with data coming out at extreme velocity or in very large volumes,” Ferguson says. “The idea that people can manually keep pace with the number of data sources that are coming into the enterprise—it’s just not realistic any more. We have to find ways to take complexity away, and that tends to mean that we should automate. The expectation is that the information management software, like an information catalog for example, can help a company accelerate the onboarding of data and automatically classify it, profile it, organize it, and make it easy to find.”

Beyond the technical issues, IT and the business must also make important decisions about how data lakes will be managed and who will own the data, among other things (see How to Avoid Drowning in the Lake).

How to Avoid Drowning in the Lake

The benefits of data lakes can be squandered if you don’t manage the implementation and data ownership carefully.

Deploying and managing a massive data store is a big challenge. Here’s how to address some of the most common issues that companies face:

Determine the ROI. Developing a data lake is not a trivial undertaking. You need a good business case, and you need a measurable ROI. Most importantly, you need initial questions that can be answered by the data, which will prove its value.

Find data owners. As devices with sensors proliferate across the organization, the issue of data ownership becomes more important.

Have a plan for data retention. Companies used to have to cull data because it was too expensive to store. Now companies can become data hoarders. How long do you store it? Do you keep it forever?

Manage descriptive data. Software that allows you to tag all the data in one or multiple data lakes and keep it up-to-date is not mature yet. We still need tools to bring the metadata together to support self-service and to automate metadata to speed up the preparation, integration, and analysis of data.

Develop data curation skills. There is a huge skills gap for data repository development. But many people will jump at the chance to learn these new skills if companies are willing to pay for training and certification.

Be agile enough to take advantage of the findings. It used to be that you put in a request to the IT department for data and had to wait six months for an answer. Now, you get the answer immediately. Companies must be agile to take advantage of the insights.

Secure the data. Besides the perennial issues of hacking and breaches, a lot of data lakes software is open source and less secure than typical enterprise-class software.

Measure the quality of data. Different users can work with varying levels of quality in their data. For example, data scientists working with a huge number of data points might not need completely accurate data, because they can use machine learning to cluster data or discard outlying data as needed. However, a financial analyst might need the data to be completely correct.

Avoid creating new silos. Data lakes should work with existing data architectures, such as data warehouses and data marts.

From Data Queries to New Business Models

The ability of data lakes to uncover previously hidden data correlations can massively impact any part of the business. For example, in the past, a large soft drink maker used to stock its vending machines based on local bottlers’ and delivery people’s experience and gut instincts. Today, using vast amounts of data collected from sensors in the vending machines, the company can essentially treat each machine like a retail store, optimizing the drink selection by time of day, location, and other factors. Doing this kind of predictive analysis was possible before data lakes came along, but it wasn’t practical or economical at the individual machine level because the amount of data required for accurate predictions was simply too large.

The next step is for companies to use the insights gathered from their massive data stores not just to become more efficient and profitable in their existing lines of business but also to actually change their business models.

For example, product companies could shield themselves from the harsh light of comparison shopping by offering the use of their products as a service, with sensors on those products sending the company a constant stream of data about when they need to be repaired or replaced. Customers are spared the hassle of dealing with worn-out products, and companies are protected from competition as long as customers receive the features, price, and the level of service they expect. Further, companies can continuously gather and analyze data about customers’ usage patterns and equipment performance to find ways to lower costs and develop new services.

Data for All

Given the tremendous amount of hype that has surrounded Big Data for years now, it’s tempting to dismiss data lakes as a small step forward in an already familiar technology realm. But it’s not the technology that matters as much as what it enables organizations to do. By making data available to anyone who needs it, for as long as they need it, data lakes are a powerful lever for innovation and disruption across industries.

“Companies that do not actively invest in data lakes will truly be left behind,” says Anita Raj, principal growth hacker at DataRPM, which sells predictive maintenance applications to manufacturers that want to take advantage of these massive data stores. “So it’s just the option of disrupt or be disrupted.” D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Timo Elliott is Vice President, Global Innovation Evangelist, at SAP.

John Schitka is Senior Director, Solution Marketing, Big Data Analytics, at SAP.

Michael Eacrett is Vice President, Product Management, Big Data, Enterprise Information Management, and SAP Vora, at SAP.

Carolyn Marsan is a freelance writer who focuses on business and technology topics.

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About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in articles such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

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Artificial Intelligence: The Future Of Oil And Gas

Anoop Srivastava

Oil prices have fallen dramatically over last few years, forcing some major oil companies to take drastic actions such as layoffs, cutting investments and budgets, and more. Shell, for example, shelved its plan to invest in Qatar, Aramco put on hold its deep-water exploration in the Red Sea, Schlumberger fired a few thousand employees, and the list goes on…

In view of falling oil prices and the resulting squeeze on cash flows, the oil and gas industry has been challenged to adapt and optimize its performance to remain profitable while maintaining a long-term investment and operating outlook. Currently, oil and gas companies find it difficult to maintain the same level of investment in exploration and production as when crude prices were at their peak. Operations in the oil and gas industry today means balancing a dizzying array of trade-offs in the drive for competitive advantage while maximizing return on investment.

The result is a dire need to optimize performance and optimize the cost of production per barrel. Companies have many optimization opportunities once they start using the massive data being generated by oil fields. Oil and gas companies can turn this crisis into an opportunity by leveraging technological innovations like artificial intelligence to build a foundation for long-term success. If volatility in oil prices is the new norm, the push for “value over volume” is the key to success going forward.

Using AI tools, upstream oil and gas companies can shift their approach from production at all costs to producing in context. They will need to do profit and loss management at the well level to optimize the production cost per barrel. To do this, they must integrate all aspects of production management, collect the data for analysis and forecasting, and leverage artificial intelligence to optimize operations.

When remote sensors are connected to wireless networks, data can be collected and centrally analyzed from any location. According to the consulting firm McKinsey, the oil and gas supply chain stands to gain $50 billion in savings and increased profit by adopting AI. As an example, using AI algorithms to more accurately sift through signals and noise in seismic data can decrease dry wellhead development by 10 percent.

How oil and gas can leverage artificial intelligence

1. Planning and forecasting

On a macro scale, deep machine learning can help increase awareness of macroeconomic trends to drive investment decisions in exploration and production. Economic conditions and even weather patterns can be considered to determine where investments should take place as well as intensity of production.

2. Eliminate costly risks in drilling

Drilling is an expensive and risky investment, and applying AI in the operational planning and execution stages can significantly improve well planning, real-time drilling optimization, frictional drag estimation, and well cleaning predictions. Additionally, geoscientists can better assess variables such as the rate of penetration (ROP) improvement, well integrity, operational troubleshooting, drilling equipment condition recognition, real-time drilling risk recognition, and operational decision-making.

When drilling, machine-learning software takes into consideration a plethora of factors, such as seismic vibrations, thermal gradients, and strata permeability, along with more traditional data such as pressure differentials. AI can help optimize drilling operations by driving decisions such as direction and speed in real time, and it can predict failure of equipment such as semi-submersible pumps (ESPs) to reduce unplanned downtime and equipment costs.

3. Well reservoir facility management

Wells, reservoirs, and facility management includes integration of multiple disciplines: reservoir engineering, geology, production technology, petro physics, operations, and seismic interpretation. AI can help to create tools that allow asset teams to build professional understanding and identify opportunities to improve operational performance.

AI techniques can also be applied in other activities such as reservoir characterization, modeling and     field surveillance. Fuzzy logic, artificial neural networks and expert systems are used extensively across the industry to accurately characterize reservoirs in order to attain optimum production level.

Today, AI systems form the backbone of digital oil field (DOF) concepts and implementations. However, there is still great potential for new ways to optimize field development and production costs, prolong field life, and increase the recovery factor.

4. Predictive maintenance

Today, artificial intelligence is taking the industry by storm. AI-powered software and sensor hardware enables us to use very large amounts of data to gain real-time responses on the best future course of action. With predictive analytics and cognitive security, for example, oil and gas companies can operate equipment safely and securely while receiving recommendations on how to avoid future equipment failure or mediate potential security breaches.

5. Oil and gas well surveying and inspections

Drones have been part of the oil and gas industry since 2013, when ConocoPhillips used the Boeing ScanEagle drone in trials in the Chukchi Sea.  In June 2014, the Federal Aviation Administration (FAA) issued the first commercial permit for drone use over United States soil to BP, allowing the company to survey pipelines, roads, and equipment in Prudhoe Bay, Alaska. In January, Sky-Futures completed the first drone inspection in the Gulf of Mexico.

While drones are primarily used in the midstream sector, they can be applied to almost every aspect of the industry, including land surveying and mapping, well and pipeline inspections, and security. Technology is being developed to enable drones to detect early methane leaks. In addition, one day, drones could be used to find oil and gas reservoirs underlying remote uninhabited regions, from the comfort of a warm office.

6. Remote logistics

As logistics to offshore locations is always a challenge, AI-enhanced drones can be used to deliver materials to remote offshore locations.

Current adoption of AI

Chevron is currently using AI to identify new well locations and simulation candidates in California. By using AI software to analyze the company’s large collection of historical well performance data, the company is drilling in better locations and has seen production rise 30% over conventional methods. Chevron is also using predictive models to analyze the performance of thousands of pieces of rotating equipment to detect failures before they occur. By addressing problems before they become critical, Chevron has avoided unplanned shutdowns and lowered repair expenses. Increased production and lower costs have translated to more profit per well.

Future journey

Today’s oil and gas industry has been transformed by two industry downturns in one decade. Although adoption of new hard technology such as directional drilling and hydraulic fracturing (fracking) has helped, the oil and gas industry needs to continue to innovate in today’s low-price market to survive. AI has the potential to differentiate companies that thrive and those that are left behind.

The promise of AI is already being realized in the oil and gas industry. Early adopters are taking advantage of their position  to get a head start on the competition and protect their assets. The industry has always leveraged technology to adapt to change, and early adopters have always benefited the most. As competition in the oil and gas industry continues to heat up, companies cannot afford to be left behind. For those that understand and seize the opportunities inherent in adopting cognitive technologies, the future looks bright.

For more insight on advanced technology in the energy sector, see How Digital Transformation Is Refueling The Energy Industry.

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Anoop Srivastava

About Anoop Srivastava

Anoop Srivastava is Senior Director of the Energy and Natural Resources Industries at SAP Value Engineering in Middle East and North Africa. He advises clients on their digital transformation strategies and helps them align their business strategy with IT strategy leveraging digital technology innovations such as the Internet of Things, Big Data, Advanced Analytics, Cloud etc. He has 21+ years of work experience spanning across Oil& Gas Industry, Business Consulting, Industry Value Advisory and Digital Transformation.