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The Bimodal IT And Finance Line Of Business: Reliability In Fast-Changing Times

Christoph Himmel

Digital transformation gets faster every day, and no industry across the globe has been unaffected. Because this ongoing disruption of business models has been enabled mainly by technological innovation, organizations are forced to reimagine the role of their IT functions to stay competitive and relevant.

Specifically, key trends like the Internet of Things (IoT), robotic process automation (RPA), blockchain, artificial intelligence (AI), and virtual reality (VR) provide disruptive approaches. Unlike enterprises that are born digital, traditional companies don’t have the luxury of starting with a clean slate; they must build an architecture designed for the digital enterprise on a legacy foundation.

Gartner urges CIOs to craft a more nuanced IT strategy that is both highly standardized and highly flexible. The analyst firm proposed the concept of the two-speed IT, and recommends that, to maintain business-critical IT operations, strategic planning for an IT department should include a fast track that allows some projects to be implemented quickly.

Using the bimodal approach is a great tool to support a focus on new products, new experiences, and getting things out the door quickly, and there is evidence that this concept is already being widely adopted. As an example, a recent study in Switzerland by SwissQ Consulting showed that 50% of survey participants have established a bimodal IT or planning to do so.

The dangers of creating separate organizations

One of the major pitfalls of applying the concept is to distinguish between a slow and a fast IT – not only in terms of IT systems, but also for skills and culture – and even worse, to create separate organizations. It is common to start new kind of businesses with spinoffs, or at least with a separate organizational unit, to avoid overloading the entrepreneurs with mature, longstanding processes and administrative requirements from the old business. Silverstone Edge, an Australian consulting company, describes – as an example – the evolving complexity of required organizational models throughout the digital service transformation.

However, these approaches to distinct responsibilities and processes have a high risk of undermining all processes of evolving businesses, which still have dependencies and require an even more integrated architecture. As digital transformation brings the customer into sharper focus, many innovators must deal with customer-centricity. This focus is not limited to customer service, web shops, sales optimization, and marketing processes. A perfect front end cannot deliver customer satisfaction when service delivery is not managed to the same level of perfection.

Gartner’s view of the two modes is shown on the following chart and clearly demonstrates that integration is key.


Mode 1 ensures a reliable backbone, while Mode 2 allows highly adaptable and ideally innovative capabilities.

Most interpretations show that core finance processes fall into Mode 1 as the best known “systems of record” to secure funding and governance. But is this the whole truth?

Flexibility to support market dynamics

At least when considering rapidly changing organizational approaches like spinoffs, co-innovation, or collaborations, the backbone must be flexible enough to support the market dynamics. This challenge is one of the major criticisms published by Forrester Research. Interpretation of the stability of Mode 1 as fixed and carved-in-stone processes is therefore the wrong direction. Still, the backbone has to provide flexibility for M&A activities such as changing, merging, and splitting legal entities, yet still be reliable and compliant to act as a platform to deliver live business insights. Trust in the system and hence the numbers is key.

And as Gartner’s Mode 1 shows an arrow into the “systems of innovation” area, this is true for finance as well. The finance function in the digital age has an important presence in the front office. For instance, the finance operations process area with real-time receivables and payables enables immediate reaction to collection efforts and collaboration with business partners. “Live finance” data also supports all sales-related activities with meaningful insights about individual customer or evaluation of the company’s own risk profile while adding each new opportunity or deal.

So, Mode 1 and Mode 2 do not need to be reflected by organizations, but continuous organizational reengineering must be supported by those two modes. The challenge for the IT department is then to provide a flexible, yet reliable backbone with a separately managed, highly integrated front-office innovation platform.

The model company approach

The “model company” approach is one concept that can help. A model company approach helps customers simplify, accelerate, and enable digital transformation by providing a framework based on broad business process expertise and project experience. In particular for finance, the model company approach provides a jump-start to get a new business up and running within a short time frame – for example, a spinoff that requires only simple processes to start.

Learn more
For more information about the model company approach, please comment below, contact me at SAP, or click on this link.

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About Christoph Himmel

Christoph Himmel is the service portfolio manager for finance in the Global Service and Support team at SAP. He has more than 17 years of experience in implementing SAP Finance solutions, developing new customer scenarios (particularly in B2C), and designing services towards finance customer audiences. He has a PhD in Science and an MBA.

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.

Designing The Transformed CFO

Marina Trusa

There was once a time when the rapid march of technology was a concern only for the IT function. But in the era of digital transformation, no function is safe from change.

For finance, digital transformation doesn’t only mean comprehending the transformation of the organization as a whole, but also grappling with how technology is transforming finance itself.

Amid this period of change, the role of the CFO endures. But what exactly will that role look like once the finance function and the organization it supports have been fundamentally transformed?

Marina Trusa has spent her recent years grappling with the impact of technology on finance from inside technology-driven organizations, including Oracle and the Commonwealth Bank of Australia (CBA), and now as the general manager of finance at the international currency transfer provider OFX (formerly, OzForex).

She says working at an a high-growth, digitally oriented company gives her the opportunity to design a true technology-driven finance function. And it has led her to rethink some of the fundamentals of the function offers.

“Historically, finance was always seen more as compliance and accounting,” Trusa says. “And that still has a place – having control and statutory reporting is of paramount importance. But more and more, I see finance leaders now being appointed who come from a background of decision support and with a much more commercial stance.”

Hence she sees her own role as understanding not just the business itself and how it makes money, but how to put the right tools in place to predict those behaviors for business cases or investment development. It is a topic she will be speaking on at the inaugural Finance Innovation and Tech Fest taking place in Sydney on September 11 and 12, 2017.

“For this, I have to understand the historical behaviors of our customers and their drivers, the costs linked to that, the macro elements, and then model all of this and be able to advise,” Trusa says.

“And that is all part of the finance function. So it is less and less about somebody just inputting the data and just crunching numbers. A lot of software can do that for you. It is all about how you interpret information, and how you can draw the insights that the management can go and act on.”

For example, knowing how much it costs to acquire a customer is simply a precursor to asking whether that money was worth spending. That requires an understanding of predictive analytics to determine how much that customer is going to generate in the future.

And that means having a fundamental appreciation of technology.

Hence Trusa has taken an active interest in her organization’s IT infrastructure and architecture, including how data is captured.

“So yes, I am general manager of finance,” she says. “But the role is a mix of strategy, finance, and data analytics, with IT and digital as well.

“This is behavioral, but it is also technical. When I do development plans with the teams, there are very specific things that they need to be learning and training in.”

Training the next generation

Trusa is living this transformation firsthand, and she believes it is not being fully taught to the next generation. Hence, she and two collaborators are developing a unit for the University of Sydney business school’s Master of Management course on how technology is transforming all roles across the business, including the finance function.

“Before I joined CBA, I underestimated the extent to which I would need to be across the IT element of technology,” Trusa says. “And even now, with the prevalence of Tableau and all of the tools finance is using, it is only now that the universities and the educators are catching up with this.”

One of the concepts behind the unit is to think about the skills that someone will need to be relevant in finance in the next five years, and how to build resilience into systems that increases the business’ competitive advantage and allows it to operate in an agile environment.

“It was not something I read in a book that told me I really had to understand data and analytics tools or understand how architects are going to be building and structuring the data so that you can answer business-related questions,” Trusa says. “With the students, they need to be thinking about it now, because when they come into the finance function in the next two to three years, a lot of things will be automated.”

Trusa is keen to reassure prospective students that her course won’t involve a deep dive into technical concepts such as architecture. Rather, it will build on fundamental concepts, such as systems and product development and how ideas are prototyped, as well as concepts around data and analytics, cybersecurity, blockchain, and machine learning. It will also examine how roles within the business are changing and being disrupted and how communications are changing.

“The whole idea of the course will be showing how IT became a part of the everyday life that we can’t ignore,” Trusa says. “IT is reflective of every element of the business nowadays. It not only defines competitiveness; it defines the efficiency of your support functions, it defines the product you are developing, and it also defines agility.”

Beyond the technical skill set

Agility also applies to the mindsets of the people she wants to hire into OFX. While an accounting or finance degree remains vital, there are other attributes, such as flexibility, lateral thinking, and problem-solving, that are rising in importance.

“The type of people I hire in the finance function is very critical, because they have to be able to not only easily respond to changing business demands, but also to be able to learn pretty quickly on the go,” Trusa says. “You can learn this – it is not something that is unattainable.”

Her thinking is directly reflected in the changes Trusa is implementing now within OFX, including investments in automation to reduce reliance on manual work.

“So there will be minimal number-crunching and data entries, there will be hardly any Excel,” Trusa says. “And the reason is to not only help the business to run and understand cost. It is all about me partnering with the business and advising the CEO to say, ‘these are the options you have, these are the levers you can pull in certain market scenarios, and if you tweak a few things, this is how you will be ready.”

“And without automating things and doing it in the software, there is no way to do that. That is the new role of finance.”

Join the upcoming Webcast along with leading Guest Finance Speakers from Jetstar, SAP & EY, to hear insights from the latest SAP & Oxford Economics CFO Study on “Characteristics of a Highly Effective Finance Function.” Register now.

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Data Lakes: Deep Insights

Timo Elliott, John Schitka, Michael Eacrett, and Carolyn Marsan

Dan McCaffrey has an ambitious goal: solving the world’s looming food shortage.

As vice president of data and analytics at The Climate Corporation (Climate), which is a subsidiary of Monsanto, McCaffrey leads a team of data scientists and engineers who are building an information platform that collects massive amounts of agricultural data and applies machine-learning techniques to discover new patterns. These analyses are then used to help farmers optimize their planting.

“By 2050, the world is going to have too many people at the current rate of growth. And with shrinking amounts of farmland, we must find more efficient ways to feed them. So science is needed to help solve these things,” McCaffrey explains. “That’s what excites me.”

“The deeper we can go into providing recommendations on farming practices, the more value we can offer the farmer,” McCaffrey adds.

But to deliver that insight, Climate needs data—and lots of it. That means using remote sensing and other techniques to map every field in the United States and then combining that information with climate data, soil observations, and weather data. Climate’s analysts can then produce a massive data store that they can query for insights.

Meanwhile, precision tractors stream data into Climate’s digital agriculture platform, which farmers can then access from iPads through easy data flow and visualizations. They gain insights that help them optimize their seeding rates, soil health, and fertility applications. The overall goal is to increase crop yields, which in turn boosts a farmer’s margins.

Climate is at the forefront of a push toward deriving valuable business insight from Big Data that isn’t just big, but vast. Companies of all types—from agriculture through transportation and financial services to retail—are tapping into massive repositories of data known as data lakes. They hope to discover correlations that they can exploit to expand product offerings, enhance efficiency, drive profitability, and discover new business models they never knew existed.

The internet democratized access to data and information for billions of people around the world. Ironically, however, access to data within businesses has traditionally been limited to a chosen few—until now. Today’s advances in memory, storage, and data tools make it possible for companies both large and small to cost effectively gather and retain a huge amount of data, both structured (such as data in fields in a spreadsheet or database) and unstructured (such as e-mails or social media posts). They can then allow anyone in the business to access this massive data lake and rapidly gather insights.

It’s not that companies couldn’t do this before; they just couldn’t do it cost effectively and without a lengthy development effort by the IT department. With today’s massive data stores, line-of-business executives can generate queries themselves and quickly churn out results—and they are increasingly doing so in real time. Data lakes have democratized both the access to data and its role in business strategy.

Indeed, data lakes move data from being a tactical tool for implementing a business strategy to being a foundation for developing that strategy through a scientific-style model of experimental thinking, queries, and correlations. In the past, companies’ curiosity was limited by the expense of storing data for the long term. Now companies can keep data for as long as it’s needed. And that means companies can continue to ask important questions as they arise, enabling them to future-proof their strategies.

Prescriptive Farming

Climate’s McCaffrey has many questions to answer on behalf of farmers. Climate provides several types of analytics to farmers including descriptive services, which are metrics about the farm and its operations, and predictive services related to weather and soil fertility. But eventually the company hopes to provide prescriptive services, helping farmers address all the many decisions they make each year to achieve the best outcome at the end of the season. Data lakes will provide the answers that enable Climate to follow through on its strategy.

Behind the scenes at Climate is a deep-science data lake that provides insights, such as predicting the fertility of a plot of land by combining many data sets to create accurate models. These models allow Climate to give farmers customized recommendations based on how their farm is performing.

“Machine learning really starts to work when you have the breadth of data sets from tillage to soil to weather, planting, harvest, and pesticide spray,” McCaffrey says. “The more data sets we can bring in, the better machine learning works.”

The deep-science infrastructure already has terabytes of data but is poised for significant growth as it handles a flood of measurements from field-based sensors.

“That’s really scaling up now, and that’s what’s also giving us an advantage in our ability to really personalize our advice to farmers at a deeper level because of the information we’re getting from sensor data,” McCaffrey says. “As we roll that out, our scale is going to increase by several magnitudes.”

Also on the horizon is more real-time data analytics. Currently, Climate receives real-time data from its application that streams data from the tractor’s cab, but most of its analytics applications are run nightly or even seasonally.

In August 2016, Climate expanded its platform to third-party developers so other innovators can also contribute data, such as drone-captured data or imagery, to the deep-science lake.

“That helps us in a lot of ways, in that we can get more data to help the grower,” McCaffrey says. “It’s the machine learning that allows us to find the insights in all of the data. Machine learning allows us to take mathematical shortcuts as long as you’ve got enough data and enough breadth of data.”

Predictive Maintenance

Growth is essential for U.S. railroads, which reinvest a significant portion of their revenues in maintenance and improvements to their track systems, locomotives, rail cars, terminals, and technology. With an eye on growing its business while also keeping its costs down, CSX, a transportation company based in Jacksonville, Florida, is adopting a strategy to make its freight trains more reliable.

In the past, CSX maintained its fleet of locomotives through regularly scheduled maintenance activities, which prevent failures in most locomotives as they transport freight from shipper to receiver. To achieve even higher reliability, CSX is tapping into a data lake to power predictive analytics applications that will improve maintenance activities and prevent more failures from occurring.

Beyond improving customer satisfaction and raising revenue, CSX’s new strategy also has major cost implications. Trains are expensive assets, and it’s critical for railroads to drive up utilization, limit unplanned downtime, and prevent catastrophic failures to keep the costs of those assets down.

That’s why CSX is putting all the data related to the performance and maintenance of its locomotives into a massive data store.

“We are then applying predictive analytics—or, more specifically, machine-learning algorithms—on top of that information that we are collecting to look for failure signatures that can be used to predict failures and prescribe maintenance activities,” says Michael Hendrix, technical director for analytics at CSX. “We’re really looking to better manage our fleet and the maintenance activities that go into that so we can run a more efficient network and utilize our assets more effectively.”

“In the past we would have to buy a special storage device to store large quantities of data, and we’d have to determine cost benefits to see if it was worth it,” says Donna Crutchfield, assistant vice president of information architecture and strategy at CSX. “So we were either letting the data die naturally, or we were only storing the data that was determined to be the most important at the time. But today, with the new technologies like data lakes, we’re able to store and utilize more of this data.”

CSX can now combine many different data types, such as sensor data from across the rail network and other systems that measure movement of its cars, and it can look for correlations across information that wasn’t previously analyzed together.

One of the larger data sets that CSX is capturing comprises the findings of its “wheel health detectors” across the network. These devices capture different signals about the bearings in the wheels, as well as the health of the wheels in terms of impact, sound, and heat.

“That volume of data is pretty significant, and what we would typically do is just look for signals that told us whether the wheel was bad and if we needed to set the car aside for repair. We would only keep the raw data for 10 days because of the volume and then purge everything but the alerts,” Hendrix says.

With its data lake, CSX can keep the wheel data for as long as it likes. “Now we’re starting to capture that data on a daily basis so we can start applying more machine-learning algorithms and predictive models across a larger history,” Hendrix says. “By having the full data set, we can better look for trends and patterns that will tell us if something is going to fail.”

Another key ingredient in CSX’s data set is locomotive oil. By analyzing oil samples, CSX is developing better predictions of locomotive failure. “We’ve been able to determine when a locomotive would fail and predict it far enough in advance so we could send it down for maintenance and prevent it from failing while in use,” Crutchfield says.

“Between the locomotives, the tracks, and the freight cars, we will be looking at various ways to predict those failures and prevent them so we can improve our asset allocation. Then we won’t need as many assets,” she explains. “It’s like an airport. If a plane has a failure and it’s due to connect at another airport, all the passengers have to be reassigned. A failure affects the system like dominoes. It’s a similar case with a railroad. Any failure along the road affects our operations. Fewer failures mean more asset utilization. The more optimized the network is, the better we can service the customer.”

Detecting Fraud Through Correlations

Traditionally, business strategy has been a very conscious practice, presumed to emanate mainly from the minds of experienced executives, daring entrepreneurs, or high-priced consultants. But data lakes take strategy out of that rarefied realm and put it in the environment where just about everything in business seems to be going these days: math—specifically, the correlations that emerge from applying a mathematical algorithm to huge masses of data.

The Financial Industry Regulatory Authority (FINRA), a nonprofit group that regulates broker behavior in the United States, used to rely on the experience of its employees to come up with strategies for combating fraud and insider trading. It still does that, but now FINRA has added a data lake to find patterns that a human might never see.

Overall, FINRA processes over five petabytes of transaction data from multiple sources every day. By switching from traditional database and storage technology to a data lake, FINRA was able to set up a self-service process that allows analysts to query data themselves without involving the IT department; search times dropped from several hours to 90 seconds.

While traditional databases were good at defining relationships with data, such as tracking all the transactions from a particular customer, the new data lake configurations help users identify relationships that they didn’t know existed.

Leveraging its data lake, FINRA creates an environment for curiosity, empowering its data experts to search for suspicious patterns of fraud, marketing manipulation, and compliance. As a result, FINRA was able to hand out 373 fines totaling US$134.4 million in 2016, a new record for the agency, according to Law360.

Data Lakes Don’t End Complexity for IT

Though data lakes make access to data and analysis easier for the business, they don’t necessarily make the CIO’s life a bed of roses. Implementations can be complex, and companies rarely want to walk away from investments they’ve already made in data analysis technologies, such as data warehouses.

“There have been so many millions of dollars going to data warehousing over the last two decades. The idea that you’re just going to move it all into a data lake isn’t going to happen,” says Mike Ferguson, managing director of Intelligent Business Strategies, a UK analyst firm. “It’s just not compelling enough of a business case.” But Ferguson does see data lake efficiencies freeing up the capacity of data warehouses to enable more query, reporting, and analysis.

Data lakes also don’t free companies from the need to clean up and manage data as part of the process required to gain these useful insights. “The data comes in very raw, and it needs to be treated,” says James Curtis, senior analyst for data platforms and analytics at 451 Research. “It has to be prepped and cleaned and ready.”

Companies must have strong data governance processes, as well. Customers are increasingly concerned about privacy, and rules for data usage and compliance have become stricter in some areas of the globe, such as the European Union.

Companies must create data usage policies, then, that clearly define who can access, distribute, change, delete, or otherwise manipulate all that data. Companies must also make sure that the data they collect comes from a legitimate source.

Many companies are responding by hiring chief data officers (CDOs) to ensure that as more employees gain access to data, they use it effectively and responsibly. Indeed, research company Gartner predicts that 90% of large companies will have a CDO by 2019.

Data lakes can be configured in a variety of ways: centralized or distributed, with storage on premise or in the cloud or both. Some companies have more than one data lake implementation.

“A lot of my clients try their best to go centralized for obvious reasons. It’s much simpler to manage and to gather your data in one place,” says Ferguson. “But they’re often plagued somewhere down the line with much more added complexity and realize that in many cases the data lake has to be distributed to manage data across multiple data stores.”

Meanwhile, the massive capacities of data lakes mean that data that once flowed through a manageable spigot is now blasting at companies through a fire hose.

“We’re now dealing with data coming out at extreme velocity or in very large volumes,” Ferguson says. “The idea that people can manually keep pace with the number of data sources that are coming into the enterprise—it’s just not realistic any more. We have to find ways to take complexity away, and that tends to mean that we should automate. The expectation is that the information management software, like an information catalog for example, can help a company accelerate the onboarding of data and automatically classify it, profile it, organize it, and make it easy to find.”

Beyond the technical issues, IT and the business must also make important decisions about how data lakes will be managed and who will own the data, among other things (see How to Avoid Drowning in the Lake).

How to Avoid Drowning in the Lake

The benefits of data lakes can be squandered if you don’t manage the implementation and data ownership carefully.

Deploying and managing a massive data store is a big challenge. Here’s how to address some of the most common issues that companies face:

Determine the ROI. Developing a data lake is not a trivial undertaking. You need a good business case, and you need a measurable ROI. Most importantly, you need initial questions that can be answered by the data, which will prove its value.

Find data owners. As devices with sensors proliferate across the organization, the issue of data ownership becomes more important.

Have a plan for data retention. Companies used to have to cull data because it was too expensive to store. Now companies can become data hoarders. How long do you store it? Do you keep it forever?

Manage descriptive data. Software that allows you to tag all the data in one or multiple data lakes and keep it up-to-date is not mature yet. We still need tools to bring the metadata together to support self-service and to automate metadata to speed up the preparation, integration, and analysis of data.

Develop data curation skills. There is a huge skills gap for data repository development. But many people will jump at the chance to learn these new skills if companies are willing to pay for training and certification.

Be agile enough to take advantage of the findings. It used to be that you put in a request to the IT department for data and had to wait six months for an answer. Now, you get the answer immediately. Companies must be agile to take advantage of the insights.

Secure the data. Besides the perennial issues of hacking and breaches, a lot of data lakes software is open source and less secure than typical enterprise-class software.

Measure the quality of data. Different users can work with varying levels of quality in their data. For example, data scientists working with a huge number of data points might not need completely accurate data, because they can use machine learning to cluster data or discard outlying data as needed. However, a financial analyst might need the data to be completely correct.

Avoid creating new silos. Data lakes should work with existing data architectures, such as data warehouses and data marts.

From Data Queries to New Business Models

The ability of data lakes to uncover previously hidden data correlations can massively impact any part of the business. For example, in the past, a large soft drink maker used to stock its vending machines based on local bottlers’ and delivery people’s experience and gut instincts. Today, using vast amounts of data collected from sensors in the vending machines, the company can essentially treat each machine like a retail store, optimizing the drink selection by time of day, location, and other factors. Doing this kind of predictive analysis was possible before data lakes came along, but it wasn’t practical or economical at the individual machine level because the amount of data required for accurate predictions was simply too large.

The next step is for companies to use the insights gathered from their massive data stores not just to become more efficient and profitable in their existing lines of business but also to actually change their business models.

For example, product companies could shield themselves from the harsh light of comparison shopping by offering the use of their products as a service, with sensors on those products sending the company a constant stream of data about when they need to be repaired or replaced. Customers are spared the hassle of dealing with worn-out products, and companies are protected from competition as long as customers receive the features, price, and the level of service they expect. Further, companies can continuously gather and analyze data about customers’ usage patterns and equipment performance to find ways to lower costs and develop new services.

Data for All

Given the tremendous amount of hype that has surrounded Big Data for years now, it’s tempting to dismiss data lakes as a small step forward in an already familiar technology realm. But it’s not the technology that matters as much as what it enables organizations to do. By making data available to anyone who needs it, for as long as they need it, data lakes are a powerful lever for innovation and disruption across industries.

“Companies that do not actively invest in data lakes will truly be left behind,” says Anita Raj, principal growth hacker at DataRPM, which sells predictive maintenance applications to manufacturers that want to take advantage of these massive data stores. “So it’s just the option of disrupt or be disrupted.” D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Timo Elliott is Vice President, Global Innovation Evangelist, at SAP.

John Schitka is Senior Director, Solution Marketing, Big Data Analytics, at SAP.

Michael Eacrett is Vice President, Product Management, Big Data, Enterprise Information Management, and SAP Vora, at SAP.

Carolyn Marsan is a freelance writer who focuses on business and technology topics.

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About Timo Elliott

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

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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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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.