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Digital Transformation: Reimagining The World, Industry By Industry

Pat Bakey

The world as we know it is continually changing, and one of the fundamental drivers is digital transformation. Person by person, company by company, and industry by industry, a new reality is evolving.

The global economy is undergoing a digital transformation as well, and it’s happening at breakneck speed. Consequently, established business models no longer work, and previously successful business networks are rapidly disintegrating while industry boundaries evaporate. New, powerful players are emerging and shaking up the status quo as products get smart and consumers get even smarter.

What does that mean to the everyday person like you and me? It means imagining the world differently—because we must, and because we can.

Re-imagining industry

To see how the world can be imagined, let’s look at the agricultural industry—one that we can relate to because we all need food to survive.

One of the ambitious objectives of the United Nations Sustainable Development Goals (SDGs) is to eliminate hunger by 2030. However, with an estimated 9 billion people living on earth by 2050, this goal will not be possible unless we start re-imagining how food is produced today. In fact, a report from the Food and Agriculture Organization of the United Nations says that to feed the entire world population in 2050, food production must increase by 70%.

That means that the soybean farmer in Iowa as well as the cashew farmer in Africa must do things differently. And they can, thanks to digital transformation and new business models, such as precision farming, which combines a variety of technologies to enable farmers to increase production, optimize investments, and maximize returns.

Feeding the world is an attainable reality

For the agricultural industry—which consists of more than one billion workers worldwide—precision farming is a bold step. But now, farmers in even the most remote parts of the world can maximize yields like never before. They can also minimize irrigation, labor, and energy usage while intelligently using fertilizers, herbicides, and pesticides that may cause harm to the environment. They can produce better food, more economically and more efficiently.

It’s advancements like this that will end world hunger. In fact, the International Food and Policy Research Institute recently reported that agricultural technologies could increase global crop yields by as much as 67% percent while cutting food prices nearly in half by 2050.

Precision farming in action

Big Data, mobile, supply chain, and cloud technologies are key enablers for precision farming. Here are a few examples of how these tools are helping farmers around the globe.

  • Gaining new insights. Farmers are using Big Data from the Precision Agriculture Hub, which connects the world’s biggest agricultural businesses, farmers, and suppliers to farm smarter. Through technology solutions and the supply chain and network of F4FAgriculture, farmers can gain insights on which crops to plant where and when. They can also learn what pesticides and fertilizers to use; how upcoming weather patterns will affect their crops; and where the best market prices are. With this critical data, they can maximize their yields, optimize sales, and help feed more people.
  • Learning new ways to farm. The African Cashew Initiative works to help over 300,000 small-scale farmers increase cashew productivity and income in five African countries (Benin, Burkina Faso, Côte d’Ivoire, Ghana, and Mozambique). By offering training programs, materials, and access to mobile business applications, these farmers are learning the best way to bring their product to market too. They can more efficiently forecast and plan, connect to the best buyers, and implement advanced marketing strategies.
  • Increasing sustainability. In northern Ghana, the StarShea Network is helping rural women learn more efficient ways to harvest and process shea nuts and butter. The network, with more than 15,000 members, provides information technology, education, and microfinancing to its members so they can conduct business independently and sustainably. For instance, through mobile technology, these women have access to the current market prices so they can sell their products competitively to global customers. They also have the technology to scan personalized barcode labels on each shea nut sack to track individual production and storage details. 

SAP is helping the world re-imagine itself

The vision and purpose of SAP is to help the world run better and improve people’s lives. We are committed to accelerating our customers’ digital transformation and we challenge them to reimagine their operational processes, business models, and the way they interact with the world.

We are also committed to the United Nations SDGs, including improving the health of the world by ending hunger – because we must, and we can.

To learn more about precision farming initiatives from SAP, visit here.

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Pat Bakey

About Pat Bakey

Pat Bakey is the president of Industry Cloud for SAP. He is responsible for the industry cloud footprint, which covers 25 industries globally, the finance and extended supply chain lines of business and the go-to-market execution of SAP Business Suite 4 SAP HANA (SAP S/4HANA). By offering prescriptive cloud road maps by industry and lines of business, the Industry Cloud organization serves every customer in every cloud model (private, public, and hybrid), for any business size, anywhere in the world, enabling SAP’s customers to approach their digital transformation through an industry lens.

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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Derek Klobucher

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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Timo Elliott

About Timo Elliott

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

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The CIO’s Cheat Sheet For Digital Transformation

Richard Howells

You didn’t sign up for this, but your company needs you—desperately.

As CIO, you figured you’d merely lead your IT department. You’d purchase equipment and create new systems. You’d implement policies and procedures around device usage. You’d protect your enterprise from dangerous cyberattacks.

But with new, groundbreaking technologies emerging every day—from the Internet of Things (IoT) to machine learning—your role within the organization has changed. In fact, it’s growing in importance. You’re expected to be more strategic. Your colleagues now view you as an influencer and change-maker. You’re looked upon to be a driving force at your enterprise—one who can successfully guide your company into the future.

The first step in making this transition from IT leader to company leader is to team up with others in the C-suite—specifically the COO—to drive digital transformation.

Increase CIO-COO collaboration and prepare your enterprise for the digital age

The precise roles and responsibilities of a COO are difficult to pin down. They often vary from company to company. But two things about the position are generally true:

  1. The COO is second in command to the chairman or CEO of an organization.
  2. The COO is tasked with ensuring a company’s operations are running at an optimal level.

In other words, the COO role is vitally important. And as technology continues to become more and more essential to a company’s short- and long-term success, it’s crucial for the COO to establish a close working relationship with the CIO. After all, the latest innovations—which today’s CIOs are responsible for adopting and managing—will unquestionably aid an organization’s operational improvements, no matter their industry.

Take manufacturing, for instance. The primary duty of a manufacturer’s COO is to create the perfect production process—one that minimizes cost and maximizes yield. To achieve this, the COO must ensure asset availability, balance efficiency with agility, and merge planning and scheduling with execution. This requires using a solution that provides real-time visibility. It involves harnessing the power of sensor data and connectivity. It encompasses capitalizing on analytics capabilities that enable businesses to be predictive rather than reactive.

And there’s one particular platform that makes all of this—and more—possible.

Experience the sheer power of IoT

In a recent white paper, Realizing IoT’s Value — Connecting Things to People and Processes, IDC referred to IoT as “a powerful disruptive platform that can enhance business processes, improve operational and overall business performance, and, more importantly, enable those innovative business models desperately needed to succeed in the digital economy.”

According to IDC research:

  • 80% of manufacturers are familiar or very familiar with the concept of IoT.
  • 70% view IoT as extremely or very important.
  • 90% have plans to invest in IoT within the next 12 to 24 months.
  • 30% already have one or more IoT initiatives in place.

So while most manufacturers appear to be on the same page about the importance and urgency of adopting IoT technology, there are stark differences in the kind of value they believe it can provide.

Nearly one-quarter (22%) of companies view IoT as tactical, meaning it can solve specific business challenges. Nearly 60%, however, see IoT as strategic. These organizations believe the technology can help them gain competitive advantages by enhancing the current products and services they provide, reducing costs, and improving productivity.

One thing all businesses can agree on is that IoT is essential to spurring enterprise-wide digital transformation—particularly as it pertains to reimagining business processes and products.

Innovate your organization’s business processes

Companies are constantly on the lookout for ways to run their operations smarter. In recent years, IoT has emerged as one of the most formidable methods for achieving this. It paves the way for increasing connectivity and business intelligence.

So what’s the endgame to all of this? Process automation.

While fully automated business processes remain a pipe dream for many companies, plenty of manufacturers are already making great strides in transforming their existing business processes with IoT.

Here are just a few ways IoT is enabling process improvements:

  • Predictive maintenance: IoT offers manufacturers real-time visibility into the condition of an asset or piece of equipment through wired or wireless sensors. By taking a proactive rather than reactive approach to maintenance, businesses can reduce asset/equipment downtown, minimize repair costs, and increase employee productivity.
  • Real-time scheduling: IoT technology empowers manufacturers to evaluate current demand and capacity availability in the moment. This allows businesses to continuously modify production schedules, resulting in higher throughput levels, lower unit costs, and greater customer satisfaction.
  • Environmental resource management and planning: IoT-enabled sensors provide manufacturers with the ability to capture and analyze energy use. By applying cognitive technology across the enterprise, companies can take the proper steps to reduce energy consumption and promote more sustainable environmental practices.

Develop and deliver innovative products

Creating smarter business processes isn’t enough for companies today. They must aspire to develop more intelligent products, too. This capability can help modern-day enterprises provide greater value to consumers, increase revenue, and separate themselves from the competition.

IoT is tailor-made for helping businesses build innovative products. With greater connectivity between organizations and goods, manufacturers can go beyond merely producing products to producing products and selling as-a-service add-ons.

Here are few ways manufacturers are creating smarter products and experiencing greater business success with IoT:

  • Remote management: IoT enables businesses to continuously monitor the health of their products. With remote management, organizations can identify problems, implement corrective actions, and increase customer satisfaction.
  • Quality feedback loop: IoT-connected products keep design and service teams loaded with useful data. Based on the information they collect, manufacturers can continue to refine products and prevent potential product recalls.
  • Product as a service: IoT technology presents organizations with myriad revenue-generating opportunities. Selling as-a-service add-ons with products allows manufacturers to take advantage of more continuous revenue streams throughout product life cycles.

Forget best practices—embrace next practices

When it comes to a company’s digital transformation, the buck stops with its CIO. After all, the CIO is responsible for adopting and managing the cutting-edge innovations that enable organizations to fuel business growth and stay competitive.

But to achieve this, CIOs need to forget about best practices and instead embrace next practices.

IDC describes next practices as “innovative processes that enable businesses to remain successful in the evolving industry landscape and at the same time prepares them for future challenges and disruptions as the scale of innovation speeds up.”

Today, there’s no better way for a company to stay innovative and competitive than by adopting game-changing IoT technology.

Want to learn more? Download the IDC white paper.

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Richard Howells

About Richard Howells

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