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Could Big Data Have Saved Ancient Civilizations?

David Jonker

The burning of the Ancient Library of Alexandria has come to symbolize the tragedy of irretrievably losing valuable cultural information and knowledge. The Egyptian center of scholarship was one of the largest and most important libraries of the ancient world, standing from its construction in the 3rd century BC until the Romans conquered Egypt in 30 BC.

In those days, much of the known world’s recorded knowledge was kept in the library. Today, our information is decentralized across huge regions, and thanks to digital technology, there is a lot more of it. So much more, in fact, that it’s been theorized that every last papyrus scroll in the vast Library of Alexandria could now fit onto an ordinary flash drive.

Ninety percent of the world’s data has been created in the last two years. According to the documentary The Human Face of Big Data (shown worldwide as part of SAP’s Our Digital Future film series), the typical person in the Western world is now exposed to as much data in one day as someone in the 15th century would have seen in their entire life.

For convenience, we call this phenomenon Big Data. We won’t always call it that. The term denotes something still new and exciting. Since the recent explosion of data generation, there hasn’t yet been time to scrape the surface of its potential to inform decisions.

There is such an abundance of data that we don’t yet know if it will actually hinder us more than help us. We could be suffering from debilitating information overload. Big Data and digital technology are moving us into totally uncharted territory. We’re just beginning to understand how we can use all that data to improve the world and human lives.

Rather than just feeling smug about its ability to help us live better, we have to also be wary of following a path of self-destruction. We can’t simply dismiss doomsday believers as “negative” or “gloomy” thinkers.

The Library of Alexandria was the ancient world’s attempt at Big Data, making full use of the technology of the day. You can be sure the great minds of the day were scrutinizing the library’s “data” to make intellectual connections, further human knowledge, and preserve and advance their civilization.

Little did they know at the time, their efforts were in vain; that the conquering Roman Empire would undo centuries of work invested into the library’s body of knowledge. Little did the Romans know, emperors’ squabbles and decadence would eventually lead to their downfall. Every ancient civilization collapsed for some reason or another (though never just one reason).

The world of today, however, is very different. We are in an age of independent states, rather than empires, and because of modern technology we are fast moving towards a global civilization. The fall of our global civilization would be a terminal disaster for the entire world.

The Human Face of Big Data spends some time discussing how we can get out of the problems we’ve made. Climate change, overpopulation, conflict over finite resources, invasive species, nuclear instability – it would be foolish to think any of these problems or threats will solve themselves or never occur.

As we tackle these problems with the help of Big Data, can we dare to dream that our global civilization will be the first that doesn’t unwittingly destroy itself? Can we ever be sure our actions won’t lead to our collapse? To begin answering these questions, let’s look back at a few ancient civilizations (chiefly by way of Jared Diamond’s excellent book Collapse), consider how they collapsed, and think about how Big Data might have saved them…

Greenland Norse Vikings

In AD 984, Vikings settled in Greenland, and by 1450 they had died out. They inadvertently caused soil erosion and deforestation, which meant they weren’t able to make the charcoal they needed to support themselves as an Iron Age society. Dwindling trade with neighboring mother country Norway didn’t help the Vikings either, nor did their hostile relationship with the Eskimos with whom they shared Greenland. Those Eskimos may have blocked Norse access to the outer fjords, which they depended on for seals.

What if the Vikings had the Big Data we have today? They might have built a vast sensor network feeding into a database system that measured how much deforestation they could safely carry out. They might have used insight from data to figure out how to share fjord access with the Eskimos harmoniously, while mapping an efficient trade route around the sea ice separating Norway and Greenland.

Easter Islanders

Out of hundreds of islands in the Pacific Ocean, none has suffered a case of deforestation as severe as that which destroyed the civilization on Easter Island in the 1600s. Jared Diamond has called it “the clearest example of a society that destroyed itself by over-exploiting its own resources.” A combination of environmental factors led to the deforestation, but on such a small island, how could the Easter Islanders not have seen what they were doing? Diamond asked, “what did they say when they were cutting down the last palm tree?” In the future, people might be asking the same about us.

With Big Data, the civilization might have been able to identify and address problems caused by volcanic activity, latitude, rainfall patterns, and the lack of “continental dust from Asia” that protects the Pacific islands by restoring soil fertility.

Indus Valley Civilization

The largest of the early urban civilizations, the Indus once covered more than a million square kilometers and may have accounted for 10% of the world’s population. After a period of stability and great technological advancement, when the civilization’s rivers flooded adequately to support farming, climate change caused the floods to dry up, and the cities had to be abandoned.

We can’t imagine that happening in today’s developed cities, in an age where the likes of Buenos Aries have sensor networks monitoring and controlling the flow and pressure of their entire water supply. Even if natural climate change did make a region uninhabitable over time, we’d surely have the foresight to know it was coming.

If Big Data is to save our global civilization, achieving something our ancestors didn’t, it will depend on more than just data. If Big Data really does have the potential to solve civilization-ending problems, our fate will depend on humans acting on insights.

The most urgent problems facing today’s global civilization are of our own making. There are many things we still don’t understand, and many things we haven’t started or stopped doing to solve these problems. Big Data will be crucial in helping us change that. Unlike ancient civilizations, digital technology can ensure our future.

Get more insight on how cities are using data to build a better future in Smarter Cities: Future Metropolis and Societal Impact.

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David Jonker

About David Jonker

David leads the global marketing team for the SAP BusinessObjects Predictive Analytics business, a machine learning platform for both analysts and data scientists. He manages the innovation lab creating data analytics showcases with customers, partners, and not-for-profit organizations.

Saving The World’s Treasure: Can Technology Stop Pirates?

Simon Davies

When you hear the word pirates, you might think of eye patches, crosses on maps, parrots on shoulders, or all of the above in a Disney movie. Despite the fact that the fictional kind of pirate has thoroughly permeated the common consciousness, Somali pirates have been a real threat to international shipping since the early 21st century.

We need some great plans to combat these sea thieves. In this day and age, technology seems to be the best answer.

Pirates are a real threat to the global economy

Yep, they’re real. On June 23rd, 2017, a group of armed pirates hijacked a Thai oil tanker and drained the vessel of 1.5 million litres of diesel fuel.

Studies have shown that more than 80% of the world’s trade is transported by sea, which means that our economy is highly reliant on the shipping trade. It’s no mystery why pirates seem eager to hijack merchant’s vessels and claim the “treasure” on board. Maritime piracy is a big problem that can take a sizeable cut from the world’s economy—$6 billion, to be exact.

Satellite imagery provides eyes from above

Maritime tracking using the latest in satellite technology is the new solution on the horizon, according to Earth observation experts Earth-i, who have discussed how satellite data helps prevent maritime piracy. With high-resolution images, it’s possible to track ship and vessel movements to ensure safe passage for passenger and cargo boats across the world’s seas.

Monitoring, observation, and tracking technology has been used to surveil trade at sea with precision and reliability. Pirates can disable terrestrial AIS (Automatic Identification Systems), which are used to track ships and vessels, but the same cannot be said for satellite AIS.

Companies using satellite AIS also benefit from the tech’s capability of providing coverage for the most remote parts of the Earth and sea 24/7.

Unmanned stealth vessels can take on pirates remotely

A self-made millionaire has taken the treacherous seas’ biggest problem into his own hands by inventing a one-of-a-kind high-tech stealth boat.

The Ghost is a seaborne combat vessel made by Greg Sancoff’s startup, Juliet Marine. It’s called “Ghost” because it’s “virtually invisible to sonar and radar detection through its aluminium and stainless steel construction.”

Sancoff said that although the boat can function as a speedboat and attack ships for Navy SEALS, it is best suited for fighting pirates. Gas turbines are used for the engine, and the ship rides above the water on robotically stabilised pontoons, making the vessel steady on rough seas.

Inside the high-tech vessel, the battleship is controlled by an array of computer screens, but Greg Sancoff said that the anti-pirate machine can be modified for unmanned operations, potentially making the Ghost moniker even more fitting.

Unmanned stealth vessels may be an effective way to combat pirates, but they are expensive. That expense may be justified if Juliet Marine is accurate in saying that two Ghosts, which would cost $20 million, could protect thousands of square miles.

Hardware for tackling pirates head-on isn’t always effective

Large budgets have already been spent on hardware for directly tackling pirates, with less than impressive results.

The long-range acoustic device (LRAD) uses a pain-inducing sound beam that has been used to drive pirates away, and the ADS (active denial system) transmits a narrow beam of electromagnetic energy to heat the skin without causing permanent damage. The wave can be used to penetrate beneath the skin and cause an unbearable burning sensation, forcing pirates to jump overboard.

Unfortunately, as Wired discussed in an article titled “Sonic, Pain Weapons All Wrong for Pirate Fight,” these options are severely limited. The LRAD can be rendered completely useless by pirates’ firearms; guns such as the AK-47 out-range the non-lethal sonic weapon. Meanwhile, the ADS, costing $3 million, is known to have harmed the people using it.

Evasion remains the best defense

Despite the proliferation of high-tech solutions for taking on pirates, HowStuffWorks maintains that the best defense against pirates can be low-tech: “The best defense against a pirate attack is evasion.” They have recommended that crews encountering pirates should fire flares, sound their alarms, call for help, and warn other ships in the area when encountering pirates. They should then commit to outmaneuvring the pirates.

With the ability to monitor the progress and route of vessels, shipping companies using satellite systems to track their ships will find avoiding pirates far easier. This informed-evasion not only keeps the monitored crews safe; it can also provide greater security for all vessels at sea. If shipping companies share their insights (gained through satellite data) with the relevant authorities, they will always be one step ahead. Removing the element of surprise from the arsenal of pirates, which is arguably one of their best weapons, could help prevent maritime piracy for good.

For more on technology’s role in security, see Ransomware Attack Highlights Need For Comprehensive Cybersecurity.

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Simon Davies

About Simon Davies

Simon Davies is a London-based freelance writer with an interest in startup culture, issues, and solutions. He works explores new markets and disruptive technologies and communicates those recent developments to a wide, public audience. Simon is also a contributor at socialbarrel.com, socialnomics.net, and tech.co. Follow Simon @simontheodavies on Twitter.

Why Youth Will Determine ASEAN’s Success In The Digital Revolution

Michael Zipf

Don’t let the European Union and all of its troubles fool you. At least one regional integration has a lot to celebrate: The Association of Southeast Asian Nations (ASEAN) turns 50 this year.

Indonesia, Malaysia, Philippines, Thailand, and Singapore founded the association in 1967 to facilitate economic and political collaboration among its members and to accelerate economic growth and social progress. Five more countries joined ASEAN in the 1980s and 1990s, helping to make it an economic powerhouse with a combined GDP of US$2.5 trillion. (If it were a single country, ASEAN would be the seventh-largest economy in the world.)

But ASEAN is in the middle of the same digital revolution as the rest of the world. Technologies such as artificial intelligence, robotics, machine learning, 3D printing, autonomous vehicles, and nanotechnology, along with accelerating progress in genetics, automation, and materials science, are shaking up the planet’s economies.

ASEAN taking over as the world’s factory

We are at “an inflection point in the history of our economies and societies because of digitization,” according to economists Erik Brynjolfsson and Andrew McAfee in their latest book, The Second Machine Age. “It’s an inflection point in the right direction – bounty instead of scarcity, freedom instead of constraint – but one that will bring with it some difficult challenges and choices … The choices we make from now on will determine what kind of world that is.”

Brynjolfsson and McAfee are optimistic about the future, but they argue that technology may “leave a lot of people, organizations, and institutions behind.” They point out that especially workers with only “ordinary skills and abilities to offer” will suffer since “computers, robots, and other digital technologies are acquiring these skills and abilities at an extraordinary rate.”

The low cost of labor in Indonesia, Laos, Myanmar, and Vietnam is a competitive advantage for multinational firms – and for ASEAN. Experts from ANZ Bank believe “Southeast Asia will take up China’s mantle of the world’s factory over the next 10 to 15 years.”

Preparing the next generation

Much of what lies ahead for ASEAN will depend on how the younger generations will handle digitalization – and the challenges described in the 17 United Nations Sustainable Development Goals. Almost half of the region’s population will be younger than 30 by 2020. It will be a young, diverse, and digitally savvy population – so ASEAN could be in a good position to benefit from the digital transformation.

But getting into that good position will not be easy. Youths need to be prepared for the digital economy and be sensitized for what’s needed to ensure prosperity in a sustainable way. The challenges ahead are so fundamental that ASEAN will need help from all its stakeholders: public and private sectors, academia, and civil society.

Global partnership for sustainable development

The ASEAN Foundation is rolling out three initiatives in 2017 to address ASEAN’s economic, environmental, and societal issues. The projects are based on three focus areas:

  • Education: The data analytics competition “ASEAN Data Science Explorers“ (ADSE) is already underway. University students across all 10 ASEAN member states from any discipline are invited to deliver data-driven insights for ASEAN across six U.N. Sustainable Development Goals cloud-based analytics.
  • Volunteerism: In collaboration with the United Nations Volunteers (UNV) program and the German Federal Ministry for Economic Cooperation and Development, ASEAN Secretariat, ASEAN Foundation, SAP, and other partners have launched the Youth Volunteering Innovation Challenge (YVIC) in ASEAN. Under the theme “Impact ASEAN,” the initiative supports young volunteers throughout the ASEAN countries in their journey to catalyze youth-led innovation for social impact and sustainable development.
  • Entrepreneurship: Through social sabbatical programs, employees from companies such as SAP are supporting social impact intermediaries through mentoring and pro-bono consulting. The program, in association with several partners, will impact close to 20 social enterprises across Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Singapore, Thailand, and Vietnam in 2017.

These and other collaborative efforts will help many of ASEAN’s young people excel alongside Brynjolfsson and McAfee’s “computers, robots, and other digital technologies.” Beyond justifying the two economists’ optimism, these efforts will help equip youth in the 10 ASEAN countries with the skills they need to meet the United Nations Sustainable Development Goals – and to thrive in the digital economy.

Applying artificial intelligence (AI) to complex decisions has clear benefits. But it also increasingly means automating ethical choices that can alter human lives. Learn more about how scientists are Teaching Machines Right from Wrong.

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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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Timo Elliott

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.