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Enterprise Mobility Takes CEPSA’s Digital Strategy From Good To Groundbreaking

Joaquin Reyes

All too often, companies make the avoidable mistake of maintaining the status quo, turning a potentially great market leader into an also-ran industry follower. As one of the largest petroleum companies in the world, CEPSA is well aware of the risk of remaining complacent with market success.

Today, CEPSA is an integrated energy group that is present in all phases of the petroleum value chain – from exploration, production, refining, and transportation to marketing, trading, cogeneration, and commercialization of energy and high-value raw material. Each area demands the best processes to support more than 11,000 professionals scattered across approximately 40 companies in Europe, Asia, Africa, and Latin America. We refine over 35% of Spain’s production capacity and 2.7% of the European Union’s.

Whenever our customers and partners demand better knowledge and visibility into our operations, their orders, and changing conditions, we make a point to fulfill that requirement quickly. Lately, my team and I have improved our ability to respond to these requests by simplifying and reinventing business processes in the cloud. But even though our businesses, organizations, data sources, and processes are firmly integrated on one platform, CEPSA still needed to further digitally transform to provide a user experience that is not only context-based, real-time, and simple, but also consistently accurate and accessible anytime, anywhere.

Enterprise mobility intensifies digital evolution

After we had evolved our enterprise resource planning system into a fully consolidated solution running on a single in-memory computing platform, we decided to incorporate enterprise mobility into our digital plan. This approach allowed us to improve the user experience throughout our financial and logistical processes by designing, deploying, and rolling out three native iOS apps. Best of all, we leveraged our existing deployment of a cloud-based, in-memory platform to deliver these apps within one week by the use of the SAP Cloud Platform SDK for iOS.

  1. CEPSA Inbox – The primary intent behind the development of this customer-centric app is to accelerate the process of approving an order request, verifying a customer’s credit limit, and generating the transport order. The app was designed to provide customers with a list of their pending, processed, and completed orders as well as details about a particular purchase. We added functionality that enables customers to generate a PDF of an order and update or reject a pending request. Also, the app takes the information from every order in a customer’s profile to create a forecast visualization to help predict future delivery needs.
  1. CEPSA Order – After we realized the value of our customer app, it became apparent that our service stations could benefit from the same level of visibility and access to information. CEPSA Order provides a new simple user experience to reorder fuel and other items needed to run the stations. Like CEPSA Inbox, this app gives station owners historical insight into their pending, processed, and completed orders. Users can generate a PDF of an order to keep for their records and a graphical analysis to visualize a future forecast of petroleum demand and delivery needs.
  1. CEPSA Pay – Increasingly adopted by our customers, this mobile payment app drives seamless continuity of the purchase experience. The app integrates the Internet of Things, the geolocation function of mobile devices, existing mobile payment interfaces (such as Apple Pay), and loyalty rewards data.

For our service stations, this is a great opportunity to streamline the point of purchase, removing the traditional cash register from the equation. Customers are even finding that purchasing petroleum is much simpler and more convenient. More important, the back office is tracking sales and revenue growth in real time while evaluating this captured information to address emerging financial risks, sales opportunities, and shifts in customer behavior.

Combining the cloud and mobile apps drives speed, efficiency, and simplification

It is often said that digital transformation turns good companies into great companies. However, I believe that such a transition requires technology investments that combine meaningful user experiences with high-quality, smart applications. For us, the answer is to connect our cloud platform with mobile apps that is a best-of-breed enterprise usability design created by SAP and Apple to deliver real-time information and power exceptional experiences from the back office to the customer.

For more insight on the cloud and digital transformation, see How Cloud Technology Brings New Opportunities For Businesses.

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Joaquin Reyes

About Joaquin Reyes

Appointed CIO of CEPSA in 2003, Joaquín Reyes has been working actively to implement an IT strategy contributing to reaching CEPSA corporate objectives in all business areas. Deployment of global digital processes and shared services are key strategies to achieve consistent business integration and maximum efficiency. Joaquín started his professional career at Empresarios Agrupados and Gibbs & Hill, working as a safety engineer at nuclear power plants, followed with a position in the Operations Research Department of CEPSA. Facing the deregulation of the market in Spain in 1992, he took on the responsibility of IT manager of the new service station network to build its IT strategy and implement the solutions required to support the new retail activities.

Innovation Without Boundaries: Why The Cloud Matters

Michael Haws

Is it possible to innovate without boundaries?

Of course – if you are using the cloud. An actual cloud doesn’t have any boundaries. It’s fluid. But more important, it can provide the much-needed precipitation that brings nature to life. So it is with cloud technology – but it’s your ideas that can grow and transform your business.USA --- Clouds, Heaven --- Image by © Ocean/Corbis

Running your business in the cloud is no longer just a consideration during a typical use-case exercise. Business executives are now faced with making decisions on solutions that go beyond previous limitations with cloud computing. Selecting the latest tools to address a business process gap is now less about features and more about functionality.

It doesn’t matter whether your organization is experienced with cloud solutions or new to the concept. Cloud technology is quickly becoming a core part of addressing the needs of a growing business.

5 considerations when planning your journey to the cloud

How can your organization define its successful path to the cloud? Here are five things you should consider when investigating whether a move to the cloud is right for you.

1. Understanding the cloud is great, but putting it into action is another thing.

For most CIOs, putting a cloud strategy on paper is new territory. Cloud computing is taking on new realms: Pure managed services to software-as-a-service (SaaS). Just as legacy computing had different flavors, so does cloud technology.

2. There is more than one way to innovate in the cloud.

Alignment with an open cloud reference architecture can help your CIO deliver on the promises of the cloud while using a stair-step approach to cloud adoption – from on-premise to hybrid to full cloud computing. Some companies find their own path by constantly reevaluating their needs and shifting their focus when necessary – making the move from running a data center to delivering real value to stakeholders, for example.

3. The cloud can help accelerate processes and lower cost.

By recognizing unprecedented growth, your organization can embark on a path to significant transformation that powers greater agility and competitiveness. Choose a solution set that best meets your needs, and implement and support it moving forward. By leveraging the cloud to support the chosen solution, ongoing maintenance, training, and system issues becomes the cloud provider’s responsibility. And for you, this offers the freedom to focus on the core business.

4. You can lock down your infrastructure and ensure more efficient processes.

Do you use a traditional reporting engine against a large relational database to generate a sequential batched report to close your books at quarter’s end? If so, you’re not alone. Sure, a new solution with new technology may be an obvious improvement. But how valuable to your board will you become when you reduce the financial closing process by 1–3 days? That’s the beauty of the cloud: You can accelerate the deployment of your chosen solution and realize ROI quickly – even before the next full reporting period.

5. The cloud opens the door to new opportunity in a secure environment.

For many companies, moving to the cloud may seem impossible due to the time and effort needed to train workers and hire resources with the right skill sets. Plus, if you are a startup in a rural location, it may not be as easy to attract the right talent as it is for your Silicon Valley counterparts. The cloud allows your business to secure your infrastructure as well as recruit and onboard those hard-to-find resources by applying a managed services contract to run your cloud model

The cloud means many things to different people. What’s your path?

With SAP HANA Enterprise Cloud service, you can navigate the best path to building, running, and operating your own cloud when running critical business processes. Find out how SAP HANA Enterprise Cloud can deliver the speed and resources necessary to quickly validate and realize solid ROI.

Check out the video below or visit us at www.sap.com/services-support/svc/in-memory-computing/hana-consulting/enterprise-cloud-services/index.html.

Connect with us on Twitter: @SAPServices

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Michael Haws

About Michael Haws

Michael Haws is the Vice President of HANA Enterprise Cloud at SAP. His specialties include Enterprise Resource Planning Software & Services, Onshore, Nearshore, Offshore--Application, Infrastructure and Business Process Outsourcing.

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Consumers And Providers: Two Halves Of The Hybrid Cloud Equation

Marty McCormick

Long gone are the days of CIOs and IT managers freely spending money to move their 02 Jun 2012 --- Young creatives having lunch and conversation. --- Image by © Hero/Corbisexisting systems to the cloud without any real business justification just to be part of the latest hype. As cloud deployments are becoming more prevalent, IT leaders are now tasked with proving the tangible benefits of adopting a cloud strategy from an operational, efficiency, and cost perspective. At the same time, they must balance their end users’ increasing demand for access to more data from an ever-expanding list of public cloud sources.

Lately, public cloud systems have become part of IT landscapes both in the form of multi-tenant systems, such as software-as-a-service (SaaS) offerings and data consumption applications such as Twitter. Along with the integration of applications and data outside of the corporate domain, new architectures have been spawned, requiring real-time and seamless integration points.  As shown in the figure below, these hybrid clouds – loosely defined as the integration of data from systems in both public and private clouds in a unified fashion – are the foundation of this new IT architecture.

hybridCloudImage

Not only has the hybrid cloud changed a company’s approach to deploying new software, but it has also changed the way software is developed and sold from a provider’s perspective.

The provider perspective: Unifying development and operations

Thanks to the hybrid cloud approach, system administrators and developers are sitting side by side in an agile development model known as Development and Operations (DevOps). By increasing collaboration, communication, innovation, and problem resolution, development teams can closely collaborate with system administrators and provide a continuous feedback loop of both sides of the agile methodology.

For example, operations teams can provide feedback on reported software bugs, software support issues, and new feature requests to development teams in real time. Likewise, development teams develop and test new applications with support and maintainability as a key pillar in design.
After seeing the advantages realized by cloud providers that have embraced this approach long ago, other companies that have traditionally separated these two areas are now adopting the DevOps model.

The consumer perspective: Moving to the cloud on its own terms

From the standpoint of the corporate consumer, hybrid cloud deployments bring a number of advantages to an IT organization. Specifically, the hybrid approach allows companies to move some application functionality to the cloud at their own pace.
Many applications naturally lend themselves to public cloud domains given their application and data requirements. For most companies, HR, indirect procurement, travel, and CRM systems are the first to be deployed in a public cloud. This approach eliminates the requirement for building and operating these applications in house while allowing IT areas to take advantage of new features and technologies much faster.

However, there is one challenge consumers need to overcome: The lack of capabilities needed to extend these applications and meet business requirements when the standard offering is often insufficient. Unfortunately, this tempts organizations to create extensive custom applications that replicate information across a variety of systems to meet end user requirements. This development work can offset the cost benefits of the initial cloud application, especially when you consider the upgrades and support required to maintain the application.

What this all means to everyone involved in the hybrid cloud

Given these two perspectives, on-premise software providers are transforming themselves so they can meet the ever-evolving demands of today’s information consumer. In particular, they are preparing for these unique challenges facing customers and creating a smooth journey to a hybrid cloud.

Take SAP, for example. By adopting a DevOps model to break down a huge internal barrier and allowing tighter collaboration, the company has delivered a simpler approach to hybrid cloud deployments through the SAP HANA Cloud Platform for extending applications and SAP HANA Enterprise Cloud for hosting solutions.

Find out how these two innovations can help you implement a robust and secure hybrid cloud solution:
SAP HANA Cloud Platform
SAP HANA Enterprise Cloud

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Marty McCormick

About Marty McCormick

Marty McCormick is the Lead Technical Architect, Managed Cloud Delivery, at SAP. He is experienced in a wide range of SAP solutions, including SAP Netweaver SAP Portal, SAP CRM, SAP SRM, SAP MDM, SAP BI, and SAP ERP.

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.