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Digital Transformation, Part 6: Examples Of Digital Transformation Done Right

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Every business should jump at the opportunity to improve and transform, especially when there are great rewards to reap, and when not doing so could be harmful.  The never-ending advancement of technology presents enterprises with countless promising opportunities for every aspect of their business.

If you had the chance to double the number of customers or make your employees twice as efficient, wouldn’t you take it?

Digital transformation is not something to be left for the future.  In fact, quite a number of businesses have already begun their successful digital transformations.  For any doubtful readers that remain, let me convince you of the benefits of digital transformation with three outstanding examples, for which we had the pleasure to co-innovate: Marriott, Nespresso, and T-Mobile USA.

Marriott

Marriott has done an excellent job using technology for learning, engagement, and sharing across its 15 brands. Rather than letting its large size hinder any changes, Marriott focused on tapping into the value of its employees. It accomplished this by adopting a collaboration solution to highlight best practices, accelerate decision-making processes, collect proposals, and rank ideas. This solution ensures that innovations and information don’t fall through the cracks and can instead be shared across the enterprise.

This fundamental aspect of digital transformation involves networks and collaboration

The core of Marriott’s efforts lies in sharing. This fundamental aspect of digital transformation involves networks and collaboration (see Digital Transformation, Part 3: the Building Blocks). To make this transformation comprehensive, key executives at Marriott came up with a shared vision centered on collaboration and mobile (see Digital Transformation, Part 4: The Role of Leadership). Next, Marriott adopted Work Patterns to bring the sharing of best practices into its company culture. This ultimately improves Marriott’s business practices and increases customer satisfaction.

Sharing also extends beyond the company. Through collaboration with vendors on project requirements, Marriott has reduced the time of issue resolution. Having successfully adopted these measures, Marriott is agile and ahead, ready to handle future challenges.

Nespresso

Our next example is also my favorite, given the amount of coffee I consume. Nespresso provides another illustration of digital transformation done right.

Nespresso’s overarching goal is the same as it has always been: to provide customers with the perfect coffee experience. What has changed is Nespresso’s incorporation of technology to achieve this, allowing for an even greater focus on the experience aspect of its products.

The desire to win new customers, gain a deeper understanding of its customers, and manage complex buying processes triggered Nespresso’s transformation.

The desire to win new customers, gain a deeper understanding of its customers, and manage complex buying processes triggered Nespresso’s transformation. Its initiatives are supported by a modern customer engagement solution based in the cloud, complete with network capability. Its cloud solution serves as an innovation platform with a full-fledged sales solution capable of handling the entire buying cycle: pricing, quotes, and orders. With the help of these networks, Nespresso pulls information about its customers from external sources like social media. Nespresso now can also engage directly with its customers, a market that the industry calls “a segment of one.”

Nespresso’s digital initiatives have proven fruitful. Benefits include greater penetration into new markets, higher sales and user adoption, better sales productivity, and better visibility across the entire engagement cycle. Business processes are faster than ever thanks to its new capabilities, with delivery and innovation cycles now as short as four weeks.

Most importantly, Nespresso has a single, simplified, and optimized view of its customers across all channels (an omnichannel) and systems, irrespective of connection method. It encompasses customers who engage via the website, via mobile, at an airport vending machine, or those who plan to meet George Clooney in a flagship store:

T-Mobile US

Like our other two examples, T-Mobile’s digital transformation has been fueled by its customer-centric vision. With over 50 million customers, this approach certainly makes sense. To achieve this, T-Mobile has pursued digital transformation across the board in IT, marketing, customer service, and sales.

Let’s look at these departments one by one, starting with the transformation of T-Mobile’s IT department. T-Mobile’s CIO is spearheading the change with T-Mobile’s vision and cost-effectiveness goals. The IT department now works faster and smarter, with an end-to-end application testing environment that cuts the time and labor necessary for testing processes.

Automation plays a big role in this, with over 83% of test scripts now automated. This frees up time and energy for T-Mobile’s IT experts to tackle the most difficult test scripts. Consistent and streamlined testing methods also boost efficiency and lower chances of error. Finally, T-Mobile rounded off its IT transformation by appointing experts for every step of software development.

Of course, T-Mobile didn’t stop its transformation with just IT. It’s gone on to change its marketing, customer service, and sales for the better.

One fundamental change affecting these departments is T-Mobile’s decision to make access to data on its 50 million customers available to all staff. Now, the customer experience is personalized with targeted offers and content drawn from high-speed analysis of each customer’s attributes.

For example, marketing campaigns designed by outside companies are easier than ever. Using a CRM-integrated media management network, outside marketing agencies log on to see what T-Mobile’s marketing department needs. After creating the campaign, these outside agencies simply upload it onto the same system for T-Mobile’s marketing and legal departments to okay. It doesn’t end there – the campaign is then sent to the brand library for future use. Other benefits include shortened training times (three months to four days!), a 15% increase in customer engagement productivity, and faster resolution times. Now, T-Mobile has the ability to act before a customer is lost.

Such widespread change has seen widespread results.  T-Mobile now enjoys a 50% reduction in transaction costs and better collaboration within and beyond its company.

Takeaway

Now that we’ve seen what some companies are doing for their digital transformation, it’s time to think about what your business could and should be doing, too.

These examples have hopefully provided an inspiration of the benefits of digital transformation when it’s done right. Every business is different, but every business can and must digitally transform. Only then will you enjoy new capabilities and significant business improvements while avoiding disruption and pulling ahead of the competition.

Up next: The outlook for digital transformation

Follow us via @SAPCloud and @SDenecken.

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The Future Of Supplier Collaboration: 9 Things CPOs Want Their Managers To Know Now

Sundar Kamak

As a sourcing or procurement manager, you may think there’s nothing new about supplier collaboration. Your chief procurement officer (CPO) most likely disagrees.
Forward-thinking CPOs acknowledge the benefit of supplier partnerships. They not only value collaboration, but require a revolution in how their buying organization conducts its business and operations. “Procurement must start looking to suppliers for inspiration and new capability, stop prescribing specifications and start tapping into the expertise of suppliers,” writes David Rae in Procurement Leaders. The CEO expects it of your CPO, and your CPO expects it of you. For sourcing managers, this can be a lot of pressure.

Here are nine things your CPO wants you to know about how supplier collaboration is changing – and why it matters to your company’s future and your own future.

1. The need for supplier collaboration in procurement is greater than ever

Over half (65%) of procurement practitioners say procurement at their company is becoming more collaborative with suppliers, according to The Future of Procurement, Making Collaboration Pay Off, by Oxford Economics. Why? Because the pace of business has increased exponentially, and businesses must be able to respond to new market demands with agility and innovation. In this climate, buyers are relying on suppliers more than ever before. And buyers aren’t collaborating with suppliers merely as providers of materials and goods, but as strategic partners that can help create products that are competitive differentiators.

Supplier collaboration itself isn’t new. What’s new is that it’s taken on a much greater urgency and importance.

2. You’re probably not realizing the full collective power of your supplier relationships

Supplier collaboration has always been a function of maintaining a delicate balance between demand and supply. For the most part, the primary focus of the supplier relationship is ensuring the right materials are available at the right time and location. However, sourcing managers with a narrow focus on delivery are missing out on one of the greatest advantages of forging collaborative supplier partnerships: an opportunity to drive synergies that are otherwise perceived as impossible within the confines of the business. The game-changer is when you drive those synergies with thousands, not hundreds of suppliers. Look at the Apple Store as a prime example of collaboration en masse. Without the apps, the iPhone is just another ordinary phone!

3. Collaboration comes in more than one flavor

Suppliers don’t just collaborate with you to provide a critical component or service. They also work with your engineers to help ensure costs are optimized from the buyer’s perspective as well as the supplier’s side. They may even take over the provisioning of an entire end-to-end solution. Or co-design with your R&D team through joint research and development. These forms of collaboration aren’t new, but they are becoming more common and more critical. And they are becoming more impactful, because once you start extending any of these collaboration models to more and more suppliers, your capabilities as a business increase by orders of magnitude. If one good supplier can enable your company to build its brand, expand its reach, and establish its position as a market leader – imagine what’s possible when you work collaboratively with hundreds or thousands of suppliers.

4. Keeping product sustainability top of mind pays off

Facing increasing demand for sustainable products and production, companies are relying on suppliers to answer this new market requirement.

As a sourcing manager, you may need to go outside your comfort zone to think about new, innovative ways to collaborate for achieving sustainability. Recently, I heard from an acquaintance who is a CPO of a leading services company. His organization is currently collaborating with one of the largest suppliers in the world to adhere to regulatory mandates and consumer demand for “lean and green” lightbulbs. Although this approach was interesting to me, what really struck me was his observation on how this co-innovation with the supplier is spawning cost and resource optimization and the delivery of competitive products. As reported by Andrew Winston in The Harvard Business Review, Target and Walmart partnered to launch the Personal Care Sustainability Summit last year. So even competitors are collaborating with each other and with their suppliers in the name of sustainability.

5. Co-marketing is a win-win

Look at your list of suppliers. Does anyone have a brand that is bigger than your company’s? Believe it or not, almost all of us do. So why not seize the opportunity to raise your and your supplier’s brand profile in the marketplace?

Take Intel, for example. The laptop you’re working on right now may very well have an “Intel inside” sticker on it. That’s co-marketing at work. Consistently ranked as one of the world’s top 100 most valuable brands by Millward Brown Optimor, this largest supplier of microprocessors is world-renowned for its technology and innovation. For many companies that buy supplies from Intel, the decision to co-market is a strategic approach to convey that the product is reliable and provides real value for their computing needs.

6. Suppliers get to choose their customers, too

Increased competition for high-performing suppliers is changing the way procurement operates, say 58% of procurement executives in the Oxford Economics study. Buyers have a responsibility to the supplier – and to their CEO – to be a customer of choice. When the economy is going well, you might be able to dictate the supplier’s goods and services – and sometimes even the service delivery model. When times get tough (and they can very quickly), suppliers will typically reevaluate your organization’s needs to see whether they can continue service in a fiscally responsible manner. To secure suppliers’ attention in favorable and challenging economic conditions, your organization should establish collaborative and mutually productive partnerships with them.

7. Suppliers can help simplify operations

Cost optimization will always be one of your performance metrics; however, that is only one small part of the entire puzzle. What will help your organization get noticed is leveraging the supplier relationship to innovate new and better ways of managing the product line and operating the business while balancing risk and cost optimization. Ask yourself: Which functions are no longer needed? Can they be outsourced to a supplier that can perform them better? What can be automated?

8. Suppliers have a better grasp of your sourcing categories than you do

Understand your category like never before so that your organization can realize the full potential of its supplier investments while delivering products that are consistent and of high quality. How? By leveraging the wisdom of your suppliers. To be blunt: they know more than you do. Tap into that knowledge to gain a solid understanding of the product, market category, suppliers’ capabilities, and shifting dynamics in the industry, If a buyer does not understand these areas deeply, no amount of collaboration will empower a supplier to help your company innovate as well as optimize costs and resources.

9. Remember that there’s something in it for you as well

All of us want to do strategic, impactful work. Sourcing managers with aspirations of becoming CPOs should move beyond writing contracts and pushing PO requests by building strategic procurement skill sets. For example, a working knowledge in analytics allows you to choose suppliers that can shape the market and help a product succeed – and can catch the eye of the senior leadership team.

Sundar Kamak is global vice president of solutions marketing at Ariba, an SAP company.

For more on supplier collaboration, read Making Collaboration Pay Off, part of a series on the Future of Procurement, by Oxford Economics.

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Sundar Kamak

About Sundar Kamak

Sundar Kamak is the Vice President of Products & Innovation at SAP Ariba. He is an accomplished Solutions Marketing and Product Management Execuive with 15 + year's broad experience in product strategy, positioning, SaaS, Freemium offering, go-to-market planning and execution.

Transform Or Die: What Will You Do In The Digital Economy?

Scott Feldman and Puneet Suppal

By now, most executives are keenly aware that the digital economy can be either an opportunity or a threat. The question is not whether they should engage their business in it. Rather, it’s how to unleash the power of digital technology while maintaining a healthy business, leveraging existing IT investments, and innovating without disrupting themselves.

Yet most of those executives are shying away Businesspeople in a Meeting --- Image by © Monalyn Gracia/Corbisfrom such a challenge. According to a recent study by MIT Sloan and Capgemini, only 15% of CEOs are executing a digital strategy, even though 90% agree that the digital economy will impact their industry. As these businesses ignore this reality, early adopters of digital transformation are achieving 9% higher revenue creation, 26% greater impact on profitability, and 12% more market valuation.

Why aren’t more leaders willing to transform their business and seize the opportunity of our hyperconnected world? The answer is as simple as human nature. Innately, humans are uncomfortable with the notion of change. We even find comfort in stability and predictability. Unfortunately, the digital economy is none of these – it’s fast and always evolving.

Digital transformation is no longer an option – it’s the imperative

At this moment, we are witnessing an explosion of connections, data, and innovations. And even though this hyperconnectivity has changed the game, customers are radically changing the rules – demanding simple, seamless, and personalized experiences at every touch point.

Billions of people are using social and digital communities to provide services, share insights, and engage in commerce. All the while, new channels for engaging with customers are created, and new ways for making better use of resources are emerging. It is these communities that allow companies to not only give customers what they want, but also align efforts across the business network to maximize value potential.

To seize the opportunities ahead, businesses must go beyond sensors, Big Data, analytics, and social media. More important, they need to reinvent themselves in a manner that is compatible with an increasingly digital world and its inhabitants (a.k.a. your consumers).

Here are a few companies that understand the importance of digital transformation – and are reaping the rewards:

  1. Under Armour:  No longer is this widely popular athletic brand just selling shoes and apparel. They are connecting 38 million people on a digital platform. By focusing on this services side of the business, Under Armour is poised to become a lifestyle advisor and health consultant, using his product side as the enabler.
  1. Port of Hamburg: Europe’s second-largest port is keeping carrier trucks and ships productive around the clock. By fusing facility, weather, and traffic conditions with vehicle availability and shipment schedules, the Port increased container handling capacity by 178% without expanding its physical space.
  1. Haier Asia: This top-ranking multinational consumer electronics and home appliances company decided to disrupt itself before someone else did. The company used a two-prong approach to digital transformation to create a service-based model to seize the potential of changing consumer behaviors and accelerate product development. 
  1. Uber: This startup darling is more than just a taxi service. It is transforming how urban logistics operates through a technology trifecta: Big Data, cloud, and mobile.
  1. American Society of Clinical Oncologists (ASCO): Even nonprofits can benefit from digital transformation. ASCO is transforming care for cancer patients worldwide by consolidating patient information with its CancerLinQ. By unlocking knowledge and value from the 97% of cancer patients who are not involved in clinical trials, healthcare providers can drive better, more data-driven decision making and outcomes.

It’s time to take action 

During the SAP Executive Technology Summit at SAP TechEd on October 19–20, an elite group of CIOs, CTOs, and corporate executives will gather to discuss the challenges of digital transformation and how they can solve them. With the freedom of open, candid, and interactive discussions led by SAP Board Members and senior technology leadership, delegates will exchange ideas on how to get on the right path while leveraging their existing technology infrastructure.

Stay tuned for exclusive insights from this invitation-only event in our next blog!
Scott Feldman is Global Head of the SAP HANA Customer Community at SAP. Connect with him on Twitter @sfeldman0.

Puneet Suppal drives Solution Strategy and Adoption (Customer Innovation & IoT) at SAP Labs. Connect with him on Twitter @puneetsuppal.

 

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About Scott Feldman and Puneet Suppal

Scott Feldman is the Head of SAP HANA International Customer Community. Puneet Suppal is the Customer Co-Innovation & Solution Adoption Executive at SAP.

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.