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Three Strategies For Driving Digitalization Across Your Business

Richard Howells

Business and IT leaders are always looking for new ways to serve and delight customers. With the emergence of Internet of Things (IoT) devices and data – along with associated technologies such as cloud computing, predictive analytics, artificial intelligence, and machine learning – companies can achieve these goals through digitalization.

Organizations now have access to unparalleled amounts of data across all areas of the business and can converge operational and information technologies to make products and processes smarter.

But success in this endeavor requires three strategies:

  1. A customer-centric view that delivers a 360-degree customer experience throughout the engagement cycle
  1. A distributed manufacturing process that leverages technologies such as automation, robotics, and 3D printing to serve a segment of one
  1. A network economy of assets, manufacturing, logistics, partners, and people

360-degree customer experience

Customer expectations are driving a need for new business models. Companies can no longer simply sell products. They must take care of their customers throughout the entire engagement cycle.

My son and I recently saw a display of vinyl records. My son asked, “What are they?” I explained that records were popular before CDs. My son asked, “What are CDs?” He has grown up in an era of subscription-based streaming music and no longer has to bother with physical items.

In fact, there are more and more subscription-based, pay-as-you-go, and consumption-based models across industries. To support these models, companies need to design, deliver, track, and maintain products and services differently. Among other things, they need predictive maintenance to keep products up and running, or else they won’t satisfy customers and make money.

Likewise, understanding the customer environment can help predict what customers want. If you operate a retail store, for example, is it in a rural or urban neighborhood? Is it close to a school or an aging population? Are there local special events? What’s the weather forecast? Combining traditional point-of-sale (POS) data with information from IoT sensors and smart products can improve sales forecasts and make sure the right products are in the right place at the right time.

This all needs to be backed up with omnichannel logistics. My son recently went online to order sneakers. Within five minutes he had designed sneakers with his team colors and his number on the side. He clicked “place order,” then discovered that they would take six weeks to deliver. The company lost the sale. There’s no point in enabling ordering that takes minutes if you can’t deliver on the customer promise. Companies must now be able to deliver on the same day or even within the same hour, not only to the retail store but also to the customer’s door. They must be able to deliver both full truckloads to one location and single items to many locations.

Distributed manufacturing process

A desire to get closer to customers through individualized products and services, combined with pressures from new geopolitical realities, is driving companies to rethink their outsourced manufacturing strategies and consider local distributed manufacturing. Similarly, the need for agile manufacturing to produce a lot size of one is driving companies to move from continuous mass production to configurable production cells.

What’s more, to make manufacturing more efficient and economical, manufacturers are increasing their use of automation and robotics. They’re also using 3D printing to reduce inventory carrying costs and enable more rapid prototyping and customization.

As assets become smarter and more connected, companies can design smarter manufacturing processes and create smarter products. They can gain better visibility into equipment performance to improve usage and uptime. And they can better configure and automate manufacturing processes to improve business agility.

Network economy

When it comes to digitalization, the notion that “the whole is greater than the sum of its parts” couldn’t be truer. From cross-department collaboration in functions like sales and operations planning (S&OP), to cross-company collaboration in design, sourcing, and manufacturing, to “collaboration” among smart products and assets, digitalized functions must work together.

Now, cross-industry business models are emerging to leverage data from smart assets to drive processes across sectors. For example, sensors on tractors can measure soil moisture to trigger an order to a chemical plant for an appropriate formulation of fertilizer. Sensors in trucks can likewise drive preventive maintenance, rerouting of shipments, insurance claims, and the redesign of vehicle parts.

The explosion of connected things and data enables the transformation of processes, products, and business models – and is driving the need for truly digitalized business. Embracing 360-degree customer experience, distributed manufacturing, and a network economy are three strategies that will get you there.

Want to learn more about driving digitalization across your business? Join us for the can’t-miss conference, SAP Leonardo Live, July 11 and 12 at the Kap Europa Congress Center in Frankfurt, Germany. The event will bring together a vibrant global community of up to 1,500 IoT, manufacturing, supply chain, R&D, and operations decision makers, influencers, analysts, and media. Learn firsthand from more than 50 SAP customer showcases how to connect IoT and core business processes to achieve digital transformation.

This article originally appeared on the Huffington Post.  

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About Richard Howells

Richard Howells is a Vice President at SAP responsible for the positioning, messaging, AR , PR and go-to market activities for the SAP Supply Chain solutions.

Will The Collaborative Economy Completely Reimagine Tomorrow's Big Business?

Daniel Newman

Today, the largest car rental and hospitality companies are Uber and Airbnb, respectively. What do they have in common? Let’s see — neither of them own physical possessions associated with their service, and both have turned a non-performing asset into an incredible revenue source.

Don’t be surprised, because this is the new model for doing business. People want to rent instead of own, and at the same time, they want to monetize whatever they have in excess. This is the core of the sharing economy. The concept of earning money by sharing may have existed before, but not at such a large scale. From renting rooms to rides to clothes to parking spaces to just about anything else you can imagine, the sharing economy is rethinking how businesses are growing.

What’s driving the collaborative economy?

The sharing economy, or the collaborative economy, as it’s also called, is “an economic model where technologies enable people to get what they need from each other—rather than from centralized institutions,” explains Jeremiah Owyang, business analyst and founder of Crowd Companies, a collaborative economy platform. This means you could rent someone’s living room for a day or two, ride someone else’s bike for a couple of hours, or even take someone’s pet out for a walk—all for a rental fee.

Even a few years ago, this sort of a thing was unthinkable. When Airbnb launched in 2008, many people were skeptical, as the whole idea seemed not only irrational, but totally stupid. I mean, why would anyone want to spend the night in a stranger’s room and sleep on an air bed, right? Well, turns out many people did! Airbnb moved from spare rooms to luxury condos, villas, and even castles and private islands in more than 30,000 cities across 190 countries, and rentals reached a staggering 15 million plus last year.

What is driving this trend? Millennials definitely play a role. Their love for everything on-demand, plus their frugal mindset, makes them ideal for the sharing economy. But the sharing economy is attractive to consumers across a wide demographic, as it only makes sense.

How collaborative economy is reshaping the future of businesses

Until recently, collaborative-economy startups like Uber and Airbnb were looked upon as threats. Disuptors to any marketplace are usually threatening, so this isn’t surprising. Established businesses that were accustomed to the way things had always been did (and still do) rail against companies like Uber or AirBnB, yet consumers seem to love them. And that’s what matters. Uber has faced many harsh criticisms, yet it continues to provide more than a million rides a month.

We are living in an era of consumer-driven enterprise, where consumers are at the helm. Perhaps this is the biggest reason why the collaborative economy is here to stay. No matter what industry, companies are trying to bring customers to the fore. A collaborative business model allows customers to call the shots. A great example is the cloud, which relies on resource sharing and allows users to scale up or down according to their needs.

Today, traditional businesses are participating in a collaborative economy in different ways. Some are acquiring startups. General Motors, for example, invested $3 million to acquire RelayRides, a peer-to-peer car sharing service. Others are entering into partnerships like Marriott, which partnered with LiquidSpace, an online platform to book flexible workspaces. Other brands, like GE, BMW, Walgreens, and Pepsi are also stepping into the collaborative-economy space and holding the hands of startups instead of competing with them.

Changes in the workplace

Remote work and telecommuting has taken off as companies become more comfortable with the idea of people working outside their offices, and cloud technology is enabling that. Now, let’s look at the scenario from the lens of the sharing economy. With companies looking to find temporary resources that can meet the fast-changing demands of the business, freelancers could replace a large chunk of full-time professionals in future. Why? Because at the heart of this disruptive practice lies the concept of sharing human resources.

As companies set out to temporarily use the services of people to meet short- and medium-term goals, it’s going to completely change the way we build companies. Also, as we have seen through the growth of companies like Airbnb and Uber, it’s going to change the deliverables that companies provide. With demand changing and technology proliferating at breakneck speed, it’s not just important that businesses start to see and adopt this change; it’s imperative because companies that over-commit to any one thing will find themselves obsolete.

When it comes to workplaces, so much is happening today that it’s impossible to predict where things are ultimately headed. But one thing is for sure: The collaborative economy is not going anywhere as long as our priorities are built around better, faster, more efficient and cost-effective.

Want more insight on today’s sharing economy? see Collaborative Economy: It’s Real And It’s Disrupting Enterprises.

This article was originally seen on Ricoh Blog.

The post Will the Collaborative Economy Completely Reimagine Tomorrows Big Business appeared first on Millennial CEO.

Photo Credit: Pedrolu33 via Compfight cc

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About Daniel Newman

Daniel Newman serves as the Co-Founder and CEO of EC3, a quickly growing hosted IT and Communication service provider. Prior to this role Daniel has held several prominent leadership roles including serving as CEO of United Visual. Parent company to United Visual Systems, United Visual Productions, and United GlobalComm; a family of companies focused on Visual Communications and Audio Visual Technologies. Daniel is also widely published and active in the Social Media Community. He is the Author of Amazon Best Selling Business Book "The Millennial CEO." Daniel also Co-Founded the Global online Community 12 Most and was recognized by the Huffington Post as one of the 100 Business and Leadership Accounts to Follow on Twitter. Newman is an Adjunct Professor of Management at North Central College. He attained his undergraduate degree in Marketing at Northern Illinois University and an Executive MBA from North Central College in Naperville, IL. Newman currently resides in Aurora, Illinois with his wife (Lisa) and his two daughters (Hailey 9, Avery 5). A Chicago native all of his life, Newman is an avid golfer, a fitness fan, and a classically trained pianist

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.