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From Demonetization To Democratization: Will Blockchain Help Or Hinder?

Jean Loh

There’s a major shakeup happening around the world in the use – or disuse – of cash. India demonetized its largest cash bills and started to move towards electronic currency. Other countries are following suit. In the sharing economy, for those offering services like Airbnb and Uber, there are no cash transactions at all. And even large retail chains are beginning to accept mobile payments.

What’s disrupting our relationship with paper money and driving this digital makeover?

This question was the focus of the March 29, 2017 “Coffee Break with Game Changers Radio” episode, presented by SAP and produced and moderated by Bonnie D. Graham (follow on Twitter: @SAPRadio #SAPRadio). Joining Bonnie were thought leaders Rajeev Srinivasan, adjunct professor of Innovation at the Indian Institute of Management in Bangalore; Simon Bain, CEO of SearchYourCloud.com; and Nadine Hoffmann, global solution manager for Innovation at SAP. Click to listen to the full episode.

Blockchain’s role in creating a cashless society is still up for debate

Rajeev explained how blockchain – the digital ledger in which transactions made electronically are recorded chronologically and publicly – can potentially reconstruct today’s economy. He cited demonetization in India, where blockchain can feasibly support a cashless society. “In one fell swoop, Narendra Modi, prime minister of India, removed 86% of currency notes. He did this, even though it would be a bit inconvenient, to reduce corruption, bribing, and ‘black money’ – money that’s hidden. We’ve had a history of about 5% to 10% of people actually paying tax, which means that those who are actually paying bear a huge load for all the deadbeats. And I think the prime minister also thought this would be a good way to boost the economy.”

Rajeev noted that the benefits of Modi’s decision outweigh the fear of hyperinflation because the poor will benefit faster from economic growth, rather than letting the corrupt and wealthy hoard and hide money.

Simon countered that, even though the intent is “admirable,” demonetization is hurting the wrong people. “It’s the unbanked who will get hurt in this shift. It’s the people who have worked in a cash economy because, for whatever reason, they won’t use a bank – their credit rating isn’t high enough or there is no bank near their village. What about the guy who doesn’t have a computer, who doesn’t have a smartphone? He can’t function in a cashless society.”

Simon underscored that, although studies from the World Economic Forum have revealed that cell phones are ubiquitous across all economic levels worldwide, these devices are typically “the good old-fashioned Nokia phones we all had many years ago.” In his opinion, there are other proven banking methods used in Africa, such as SIM cards, known as Inpeso, that may help India. However, he noted that these are not delivering a form of demonetization or blockchain that governments and financial services are hoping to achieve.

Nadine took the position that a cashless society actually brings the poor, as well as others without access to the technology, into the economy. “Inpeso is an example of how people are connected to the cashless society. They can take part in the financial environment, which wasn’t available to them before. Blockchain helps us secure authorization and ease the transfer of money, which makes life easier. In the long run, it allows consumers to control their data and control what they want to do to take part in the economy.”

It’s time to reconsider current financial processes

After a lively roundtable conversation about the positive potential of blockchain, Simon offered a stark reminder that, while blockchain is a mechanism for enabling and securing transactions, it won’t secure the database. “The person who’s going in there to cleanse the database can get all the information out. Most security attacks do not happen in the cloud, on the Internet, or within an external environment – they happen internally. In some instances, blockchains are going to be too small to secure. You need a vast distributed network to make it properly secure.”

Rajeev echoed Simon’s concerns, adding that blockchain’s influence goes beyond financial transactions. For example, it is also used in other business dealings such as maintaining “tender red cards,” a term referring to purchases made through the government. “The government prevents this practice in India because it’s a big scam. You can walk into a land registry office and slip somebody some money. All of a sudden, someone else’s land is your right. It’s very difficult to undo that kind of mischief.”

Nadine agreed with her co-panelists, suggesting that these challenges signal the need to seriously rethink current processes. “Blockchain may be an easy way to get people on board who are not included in the economy and enable them to make payments through contracts. But I also see that the technology cannot resolve concerns around security. We have to take a step back and look at what’s possible. It’s not as simple as finding use cases. Find your criteria, do a proper analysis, and take the next step.”

Crystal ball predictions: Will blockchain deliver as promised by 2020?

While it remains to be seen whether the world becomes a cashless society, the realities of blockchain may help to shift how banks and governments look at the financial system.

Simon predicted that while nothing much will change regarding the use of cash by 2020, he hopes that people will start taking security and privacy seriously.

Nadine expressed a more bullish and optimistic view of the future – one of change and ease. “I see more unbanked people being part of the whole environment. There will definitely be a decrease in cash transactions, increasing the potential for a cashless society. But rather than harm us, this new reality will help improve processing.”

Listen to the SAP Radio show “Money’s Digital Makeover – Part 1” on demand.

Follow SAP Finance online: @SAPFinance (Twitter)  | LinkedIn | FacebookYouTube

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Jean Loh

About Jean Loh

Jean Loh is the director, Global Audience Marketing at SAP. She is an experienced marketing and communication professional, currently responsible for developing thought leadership content that is unbiased and audience-led while addressing market challenges to illuminate and solve the unmet needs of CFOs, CIOs, and the wider global finance and IT audience.

Standardize, Centralize, And Automate Your Corporate Close

Elizabeth Milne

Part 12 in the Continuous Accounting Series

Corporate finance departments are acclimatizing to an environment of growth, mixed with uncertainty. For most enterprises, key growth measures are on the rise, from earnings to hiring. In response, CFOs are seeing it as a good time to take increased risk. Yet a deep vein of uncertainty is also running through the thoughts of many finance leaders, who are reacting to policy uncertainty around trade, domestic and international tax policy, and a changing regulatory environment.

These dueling requirements of supporting growth, yet preparing for uncertainty, are forcing corporate finance and accounting teams to spend more time planning, analyzing, and working with business partners than ever before.

The corporate close remains a significant drag on resources due to the need to corral the numbers from across the organization and consolidate the financials for reporting. In addition, there’s a greater need to deliver corporate financial and management results faster, and for global organizations to break out numbers across entities, subsidiaries, and countries in more detail for management and regulatory reporting.

So far in this blog series, we’ve talked about how local entities can close their own books faster, and tactics for dealing with intercompany reconciliation processes. Now it’s time to shine the light squarely on the corporate close. Our goal here is simple: to help accounting spend less time bogged down in the corporate close, improve controls, and increase the time available for corporate accounting to spend on tackling the dual CFO mandates of managing growth and balancing uncertainty.

Cut the wait (and risk) — reduce local entity spreadsheets

If you took a quick poll of your accounting team on where they spend their time in the close, much of it is likely spent hunting for report packages from local entities, looking for supporting documentation both sides of intercompany transactions, and locating additional disclosure information for IFRS reporting needs.

One of the biggest issues is that data from entities is often submitted using spreadsheets, or CSV extracts, which puts corporate on the back foot. Disregarding the waiting time, if you learned that, according to the ACCA, “90% of spreadsheets contain serious errors, while more than 90% of spreadsheet users are convinced that their models are error-free,” would you be quite as trusting of those emailed financials from a local entity?

The fact is, it’s simple for errors to be introduced into a spreadsheet between the local ERP, manual entry by local accounting teams, and spreadsheet calculations to create the local financial reports. With so many manual touches on the data before it gets to corporate, it’s a wonder there aren’t more issues.

While many subsidiaries are often running different accounting apps, financial consolidation and intercompany apps can help cut spreadsheets out by automating the output of local entity financials to a standard format, which leaves less room for human error. With cloud-based Web access, they can also provide corporate with the ability to log in and download the reports themselves as soon as they’re ready, rather than waiting for them to be posted to an SFTP server or arrive in email.

See where local entity close bottlenecks are

What if corporate accounting knew where the local entity bottlenecks are, instead of mostly waiting for entity reports to trickle in for the corporate close? One of the problems is that local entities often don’t provide a great deal of transparency into where they are in their own close process, so it’s often a black box to corporate. Without that visibility, it limits corporate’s ability to proactively engage with entities that are having issues with say, reconciliations, adjustments, or journal entries.

Local entities close processes are also dependent on spreadsheets, emails, or other documents, that don’t provide clear status to corporate, leading to uncertainty. Worse, there is still also a varying degree of standardization in the close processes across entities, so what one entity has well automated, may be another entities bottleneck. This varying degree of standardization creates risk for corporate.

Close management applications essentially act as the collaborative, closing overlay for corporate and local entities. Everyone can share status, where approvals are, and which step of the entity close they’re on with their peer entities, and with corporate. For corporate accounting, it reduces the risk of waiting on entity financials and enables them to be more proactive if there are issues, and for entities, it improves communication on real-time status to corporate.

Minimize corporate accounting time wasted chasing local entity detail

The reality is that corporate shared service centers spend an inordinate amount of time chasing down local entity invoices, POs, pricing, contract, details to support reconciliations, intercompany eliminations, or accounting variances. Data from local entities often arrives at a relatively summary level, and that those spreadsheets just don’t provide the detail to substantiate balances and accounting processes. An intercompany transaction, for example, really requires invoice and PO details. Yet you’ll rarely find that in the same spreadsheet as the local entities reporting package, forcing corporate accounting to go on the hunt.

Closing cockpits, intercompany accounting repository, and a degree of ERP standardization can all help by providing better self-service for corporate into local entity detail. For example, cloud invoicing and payables apps provide a greater level of self-service for corporate SSCs to log in and get the details themselves rather than waiting on entity teams.

Next step: getting consolidated

In my next blog, we’ll move from getting local entity data and details to managing the financial consolidation process, mapping local accounting structures to corporate, and performing eliminations, currency translations, and other areas to fast-track corporate financial and management reporting.

Learn how organizations are gaining instant financial insights and using them to make better decisions—both now and in the future. Register now for the 2017 Financial Excellence Forum, Oct. 10-11 in New York City.

Follow SAP Finance online: @SAPFinance (Twitter)  | LinkedIn | FacebookYouTube

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Elizabeth Milne

About Elizabeth Milne

Elizabeth Milne has over 20 years of experience improving the software solutions for multi-national, multi-billion dollar organizations. Her finance career began working at Walt Disney, then Warner Bros. in the areas of financial consolidation, budgeting, and financial reporting. She subsequently moved to the software industry and has held positions including implementation consultant and manager, account executive, pre-sales consultant, solution management team at SAP, Business Objects and Cartesis. She graduated with an Executive MBA from Northwestern University’s Kellogg Graduate School of Management. In 2014 she published her first book “Accelerated Financial Closing with SAP.” She currently manages the accounting and financial close portfolio for SAP Product Marketing. You can follow her on twitter @ElizabethEMilne

The Oft-Neglected Essential In Digital Transformation: Steering Model Redesign

Reinhold Exner

Part 1 in a series

Every executive team beginning to map out a transformational strategy – redesigning business models, offering new services, making acquisitions, going digital – needs to translate that strategy into concrete activities. And once executed as operational processes, that strategy can be determined to be effective only if the impact can be measured. For the CFO, this means instituting a steering model that maps directly to the transformation being undertaken.

Defining measurable targets

A “steering model,” in short, is a framework for operationalizing corporate strategies and objectives into measurable targets. These targets are typically measured by so-called key performance indicators (KPIs). A steering model, by nature, is a top management concern, hence it’s owned by the board, CEO, and most commonly by the CFO.

All companies have in place a steering model based on financial KPIs, including P&L, balance sheet, cash, or cost-related KPIs. However, due to past developments (like introduction of balance scorecard and learning based on economic crises), many companies have broadened their steering model beyond pure financial KPIs. One of the main reasons for doing this is to improve insights into driving factors (leading indicators) for the targeted strategic KPIs.

An opportunity to better support the business

On our consulting team at SAP, we work with companies that recognize that their steering model redesign should be not only business-driven, but also a more technology-driven opportunity to support the business in a better way. This often leads to similar requirements to validate future readiness of the current steering model.

We often hear, for example, that the steering model is “historically grown,” or that the steering model “was developed 10 years ago” and never challenged systematically to improve. Others state that they don’t get the data out of their systems, which are far too aggregated, and that the processes in place to extract the data are cumbersome. And some complain that there is no proper master data in place: “We are thinking and acting in silos.” In many of these cases, company management feels a “need for change.” But how do these questions lead to a new steering model – and how does this impact the ability to ramp up a digital transformation journey?

How the steering model drives future performance capabilities

McKinsey defines a transformation as “an intense, organization-wide program to enhance performance (an earnings improvement of 25% or more, for example) and to boost organizational health.” So a corporate transformation is a huge step from the current situation to the future state, with typically a very high level of inspiration and ambition. A transformation aims for a holistic reconfiguration of a company, such as to boost top-line growth, cost efficiency, operational excellence, and customer satisfaction.

Take, for example, a company that chooses to shift from decentralized responsibility to a model emphasizing more centralized, controlled, and managed decision-making. This change implies new centralized reporting requirements, which need to be reflected by the transformation. This is by no means a purely technical task of providing already existing reports to new users. Instead, the company needs to enable the centralized teams with the same level of insight enjoyed by those who are closer to the action. It’s a business change-management task that should be driven by company leadership – with a technical implementation, of course.

Or a company might face difficulties regarding KPI comparability due to partly unharmonized KPIs, organizational models, master data, and processes. It’s obvious that aiming for comparability, improved transparency, and reliability will lead to new process design, new setup of responsibility areas, and hence a steering model redesign. Strong top management commitment will be required to foster a significantly improved cross-departmental alignment of designing the new landscape, complemented by strong program governance.

Addressing steering model redesign from the outset

A ramp-up phase is typically the early phase of preparing for the upcoming transformation. This phase encompasses, among others, giving direction by clarifying a mandate for the transformation program. This involves sponsorship as well as stakeholder alignment and setup of a sustainable foundation like internal and external recruiting, knowledge transfer, and initial organizational change-management measures. Change management should include clear and systematic change communications and other essential onboarding activities.

During the ramp-up phase, top management needs to be very clear about the transformation objectives, including a mandate for a revision of the steering model. If this journey takes some years, it needs to be clear that by reaching the end of the transformation journey, the existing steering model must not be outdated. For this reason, it’s essential that if there are any indicators towards a new steering model, these signals are taken seriously and are sufficiently reflected in very early stages of a digital transformation journey: in the ramp-up phase.

There are many aspects to consider as you embark on this undertaking. My next blogs in this series will explore further.

Oxford Economics recently surveyed 1,500 finance executives to understand the attitudes of finance professionals toward the function’s changing requirements and challenges. Read the full study, “How Finance Leadership Pays Off: Six Ways CFOs Stay Ahead of the Pack.”

And learn how organizations are gaining instant financial insights and using them to make better decisions – both now and in the future. Register now for 2017 Financial Excellence Forum, Oct. 10-11 in New York City.

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | FacebookYouTube

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Reinhold Exner

About Reinhold Exner

Reinhold Exner is a principal business transformation consultant with SAP. He has 14 years of management experience in various controlling functions and has worked with SAP for 8 years. He supports international operating companies in optimizing their finance organization and processes leveraging SAP solutions. Reinhold holds a degree in Industrial Engineering (Dipl. Wirtschaftsingenieur) from Technical University of Karlsruhe, Germany.

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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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.