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Be The Change: Promoting Corporate Social Responsibility

Meghan M. Biro

I’m sure you’ve heard all about the millennial generation. Those 20- to 36-year-olds, pampered throughout their lives by their baby-boomer parents, have grown up to be self-absorbed, entitled narcissists, right?

Actually, this isn’t an accurate picture of millennials—and since they now represent the largest share of the American workforce, that’s good to know. Despite widely held perceptions about their supposedly “me-first” ways, these younger workers rank social responsibility as an important tenet of life and are looking to work for companies that share their sense of social responsibility.

In case you doubt the desire of millennials to align themselves with socially responsible companies, look no further than the Horizon Media’s Finger on the Pulse study, which found that 81 percent of this younger generation expect companies to make a public commitment to good corporate citizenship. Millennials also put their money where their mouth is: According to the 2015 Cone Communications Millennial CSR Study, 62 percent are willing to take a pay cut to work for a socially responsible company—a full six percentage points higher than the average response of all age groups surveyed.

The need for a CSR plan

Obviously, then, companies need to do more than just offer perks like free snacks to recruit and retain this valuable workforce segment. Having a formal corporate social responsibility (CSR) program is the key way for companies to demonstrate their commitment to the positive ideals their employees espouse. And here’s a PR bonus for you: By promoting corporate social responsibility, you’re also conveying to your customers that you care about the world outside your company’s walls.

At most companies, the HR department falls into the organizational sweet spot for managing the CSR program. As Angela Schettino of Think People Consulting observes, a company’s HR strategy links to the four components of any successful CSR initiative. First, of course, are employees, in keeping with HR’s focus on their rights and well-being, but the three other components—environment, community, and marketplace—also fall under HR’s domain.

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HR’s most appropriate role in managing a CSR plan would be to monitor its adoption and then document its successes throughout the company. In the area of energy conservation, for instance, the HR department could start by implementing a company-wide recycling program and promote earth-friendly practices like subsidizing public transit costs or encouraging employees to shut off the lights, computers, printers, and copiers during non-work hours.

Try these CSR initiatives

Here are some other ideas for HR departments and companies to consider as they implement and manage their CSR program.

  • Create a company culture compatible with CSR. As Strategic HR Inc. describes, this can start with your job advertisements and interview process. Use corporate social responsibility as a recruitment tactic, which will attract the socially responsible employees who will support and sustain your program. Perhaps even consider adding a position—chief sustainability officer—whose role would be consistent with your company’s focus on CSR.
  • Pick a cause. Look at what other successful companies are doing and see if your organization can model a similar CSR program. Starbucks, for instance, has several programs in place to promote environmental sustainability. Toms has a program called “Giving Shoes,” in which the company donates a pair of shoes to a child in need for every pair of shoes purchased. To date, the company has given away more than 70 million new pairs of shoes.
  • Allow time off for volunteering. As part of your employee engagement program, give employees a few days of volunteer time off (VTO) per fiscal year to do something meaningful in their communities.
  • Donate to a good cause.Take a cue from companies like Jersey Mike’s Subs, which has raised more than $20 million since 2010 by donating 100 percent of its sales nationwide on its annual Day of Giving. Or consider the corporate goodwill generated by Patagonia, a sustainable clothing brand that gave all $10 million from its Black Friday 2016 sales to hundreds of grassroots environmental organizations.
  • Match employee contributions. Convey to employees that “we’re all in this together” by matching their contributions to a charity of their choice. It’s a way for them to stretch their giving dollars—and for you to demonstrate firsthand that the causes they value are causes that you value as well.

Demonstrating your company’s commitment to the communities and environment in which you work isn’t just the right ethical decision, it’s good business. As Patti Dunham, MA, MBA, SPHR, SHRM-SCP, states, “…becoming socially aware and responsible helps the company’s bottom line. The impact on the organization’s public image and becoming an “employer of choice” because of these initiatives is immeasurable.”

If you haven’t already done so, consider empowering your HR department to implement and manage a corporate social responsibility program this year.

For more on the importance of bringing purpose into the workplace, see Why Companies Of The Future Need Purpose.

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About Meghan M. Biro

Meghan Biro is talent management and HR tech brand strategist, analyst, digital catalyst, author and speaker. I am the founder and CEO of TalentCulture and host of the #WorkTrends live podcast and Twitter Chat. Over my career, I have worked with early-stage ventures and global brands like Microsoft, IBM and Google, helping them recruit and empower stellar talent. I have been a guest on numerous radio shows and online forums, and has been a featured speaker at global conferences. I am the co-author of The Character-Based Leader: Instigating a Revolution of Leadership One Person at a Time, and a regular contributor at Forbes, Huffington Post, Entrepreneur and several other media outlets. I also serve on advisory boards for leading HR and technology brands.

Why Corporate Social Responsibility Could Be Your Next Strategic Priority

Derek Klobucher

When organizations do the right thing, value can extend far beyond the good deed itself. Corporate social responsibility (CSR) can help drive better business outcomes, attract like-minded partners, increase employee engagement and more.

“Just like human resources years ago … CSR is going to grow into a strategic partner in the company,” John Matthews, SAP’s global vice president of HCM LoB Business Partner, Global Customer Strategy & Business Operations, said on Changing The Game with HR last week. “Doing good is also good for business.”

CSR refers to how organizations go above and beyond to evaluate and own their environmental and social impacts. But growing into strategic partnership with other, more quantifiable lines of business would require objective CSR metrics.

Quantifying good deeds

“We’re going to see the emergence of an index that captures the corporate social responsibility agenda … the responsibility with which companies act,” Chris Johnson, senior partner at New York-based human resources consulting firm Mercer, said on Changing the Game with HR. “And the index will be a key part of how the company will be accountable to its shareholders.”

If this seems farfetched, consider that shareholders are also beginning to demand sustainability. And organizations already get rated as best places to work, on work-life balance, and many other ratings; and Mercer even sponsors the Britain’s Healthiest Company index.

Johnson predicts a CSR index within the decade.

“It could be a very public account—a transparency and public accountability thing,” Johnson said. Advocacy groups “will be able to go to those companies that are low down [on] the index, and offer them a way of clamoring up the index and demonstrating their broader responsibility to society.”

“People love to work for a corporation that is paying it forward,” Bonnie J. Addario, founder of the Bonnie J. Addario Lung Cancer Foundation, said.

But CSR-minded organizations will still want a return on investment.

Paying it forward

“Corporate social responsibility also helps the bottom line, meaning that it helps you build trust with customers, employees, as well as with your suppliers,” SAP’s Matthews said. “If you give them that guidance, that direction, and you’re clear on what matters, others will come running to you—and come running with you to help solve problems.”

One of Matthews’ “problems” is a 3,400-mile bicycle ride across the U.S. to raise awareness—and funds—for lung cancer research; he’s doing so in memory of his late mother who died of the disease. Whether the issue is healthcare, education or implementing design elements that cut costs by increasing energy efficiency, corporate social responsibility can be an effective way to increase employee engagement.

“People love to work for a corporation that is paying it forward,” Bonnie J. Addario, founder of the Bonnie J. Addario Lung Cancer Foundation, said on Changing the Game with HR. “It’s not always about money … it’s about involvement—it’s about having an emotional connection.”

“We’re going to see the emergence of an index that captures the corporate social responsibility agenda … the responsibility with which companies act,” Chris Johnson, senior partner at New York-based human resources consulting firm Mercer, said.

More than a cause

“CSR is becoming much more of a heritage asset, meaning people prefer their service efforts to leave lasting effects,” Kevin Xu, CEO of global intellectual property management company MEBO International, stated on Forbes CommunityVoice last month. “Rather than championing campaigns that make big splashes, businesses want to build and work toward causes that resonate with and get carried on by younger generations.”

These efforts can lead to new partnerships with like-minded organizations—what a wireless solutions provider’s CEO called a “return on doing good,” as opposed to a simple return on investment. And it’s a great way to build pride within the organization.

“I’ve already had 30 people from SAP from all across the world … who just heard what we were doing, and said, ‘How can I help?’” SAP’s Matthews said. “And it grows every day … so I’m very happy, fortunate, and proud to work for SAP.”

This story originally appeared on SAP’s Business TrendsClick here for a replay of this episode. And click here to learn more about Matthews’ ride. Follow Derek on Twitter@DKlobucher

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About Derek Klobucher

Derek Klobucher is a Brand Journalist, Content Marketer and Master Digital Storyteller at SAP. His responsibilities include conceiving, developing and conducting global, company-wide employee brand journalism training; managing content, promotion and strategy for social networks and online media; and mentoring SAP employees, contractors and interns to optimize blogging and social media efforts.

Diversity Is Hard Work And Requires Rethinking

Anka Wittenberg

In the third episode of the SAP Future Factor series, I sat down with Iris Bohnet, professor of public policy and behavioral economist at the Harvard Kennedy School of Government, to talk about the positive impact of diversity in the workplace for employee engagement and the business bottom line. We also talked about what it takes for companies to create an inclusive work environment.

Hint: It does not happen overnight.

Anka: As we discussed during our SAP Future Factor episode, an increasing number of companies are paying attention to diversity in the workplace.

Iris: Yes, workplaces today are much more heterogenous. Companies recognize that this heterogeneity is an asset. There is a lot of evidence that diverse teams tend to outperform homogenous teams in terms of creativity, innovation, and group processes.

Anka: There is also a strong business case for diversity and inclusion, which are linked to employee engagement. At SAP, we tracked an increase of 48 million euros on operational profit per year by increasing employee engagement by one percent alone. And, to your point, we see a huge benefit for innovation. Research has consistently shown that the more diverse your teams are, the more innovative you are. To tap into that innovative potential, we use a “design thinking” approach to the way we work at SAP, which means that we work with diverse teams to evaluate an issue from different angles and come up with out-of-the-box ideas.

Furthermore, diversity is important for a company to be able to relate to their customers – the diversity of customers needs to be reflected in the diversity of employees. However, diversity is also increasing complexity – which brings up its own set of challenges.

Iris: Certainly. Diversity is hard work. It is hard work for the exact reason that makes it an asset – bringing together a diversity of perspectives. This helps us consider issues from different angles, but can also be a trigger for debate, argument, disagreement. However, we can’t afford to forget that if we all have the same perspective, we will only move in one direction.

Many companies are implementing diversity training. Diversity training itself may not solve the problem, but it can open doors, and we can integrate technology to help us begin “redesigning” the way we work and even how we learn. From your perspective, what are some of the technologies that will have a positive impact in terms of making companies more diverse and inclusive?

Anka: Technology can be helpful in discovering and eliminating unconscious bias across the HR lifecycle. For example, software can identify biased language in job postings and suggest alternatives, so that companies can source talent more broadly. Machine learning is used to put together diverse teams. I truly believe that technology is a catalyst for change that helps us become aware of unconscious biases and create a more inclusive environment.

However, it’s important to recognize that each company faces its own unique set of challenges. The same company may even experience different challenges in different geographic regions. For example, recruitment might be an issue in one region, but in another, it may be the retention of talent. Given that, it’s critical to identify where the blind spots are, and then put clear action items behind it.

Iris: Precisely. Unfortunately, many companies and governments continue to throw money at the problem without diagnosing what is broken. It’s important to understand what the challenge is and then intervene strategically. For example, I worked with a tech company that found out that it was much less biased based on gender and race than it thought, but on the flipside, had a much bigger disciplinary bias. Blind evaluation, as we discuss in the Future Factor episode, can be helpful in terms of eliminating implicit bias in hiring.

Anka: Adopting an inclusive mindset and embracing diversity go beyond recruitment. As you mentioned earlier, we have a much more heterogeneous workforce today. For example, at SAP, we have a workforce that spans five generations. We have programs in place, such as “Autism at Work,” to recruit differently abled individuals who excel at certain tasks but may have a different way of working. This means that we need to change the norms around work to integrate individuals with different backgrounds, expectations, and working styles.

Iris: I couldn’t agree more. Previously when we talked about flexibility, it was pretty much associated with women. But now, we have a whole new generation of people with different needs, including requiring more flexible work arrangements for various reasons such as child care or elder care or simply because they want to pursue interests outside of work. It is a surprise for many companies that employees are no longer defining themselves with work.

Anka: Thank you, Iris, for being part of the SAP Future Factor series and for your support and guidance in helping SAP achieve its goal of 25% women in leadership. As you know, it was a journey over many years, but we built a strategy around the goal and now have an environment that is inclusive of the thoughts and opinions of men and women in management. This allows us to better serve an increasingly diverse customer base, attract and retain talent, and compete in the global economy.

Before we conclude, I want to congratulate you on the publication of the German edition of your book, What Works: Gender Equality by Design. It is an excellent resource for understanding organizational dynamics and design in relation to diversity and inclusion.

To watch the entire discussion between SAP chief diversity officer Anka Wittenberg and Prof. Iris Bohnet, click here

For more on digitization, work, and HR, visit Episode 1 and Episode 2 of the SAP Future Factor Web Salon, in which HR executives and thought leaders from science/academia discuss the digitization of work.

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Anka Wittenberg

About Anka Wittenberg

Anka Wittenberg is the Chief Diversity & Inclusion Officer at SAP. She is responsible for the development and implementation of SAP’s Diversity and Inclusion strategy globally, ensuring sustainable business success.

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.