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Top Secrets For Ensuring A Successful Digital Transformation

Ruchi Verma

Buzzwords such as “disruption” and “uberization” seem to be on the tip of every executive’s tongue these days, but these are more than just trendy concepts. The reality is that organizations across all industries and types of business are rewriting the rules of strategy.

Business transformation is no longer just about digitization of customer experience, work, and resources, but taking customers by surprise with innovative and highly personalized offers and experiences at every opportunity available. A recent study by Massachusetts Institute of Technology and Accenture reports that companies that reach a higher level of digital maturity are 26% more profitable, grow 9% faster, and achieve 12% higher market valuations than their industry peers.

Customers trying to innovate or reform their business models are mainly driven by two strategic motivations: growth and protection.

They want to grow by becoming an always-on service provider, opening new revenue streams with IoT and connected devices, providing payment flexibility, and moving to multi-sided business models such as transacting on behalf of third parties. They also want to protect their core by providing go-to market agility and cost savings as well as automation and scaling.

Figure 1: 5 key imperatives for your monetization journey

One thing for sure is that a transformation journey starts with aligning the three primary elements involved: people, process, and technology. Following that, the biggest challenge lies in spotting new growth opportunities and being ready—i.e., being agile enough to respond quickly and able to successfully scale in the process of monetizing them.

In this blog, I will explore the imperatives from end customer’s perspective. This is important as customers are co-creating their own journeys today; they demand instant engagement and personalization. Understanding customer expectations and mapping them to the organization’s capabilities is the first step towards truly monetizing the digital transformation and aiding sustainable growth by multiplying ARPUs (average revenue per users) while keeping the customer churn low. The rapid growth of all types of devices (wearables, smartphones, tablets, good old desktops, among others) and app culture has made it imperative for service providers to remain highly contextual and consistent across multiple channels.

Top five customer expectations and associated imperatives

Figure 2: Customer expectations and associated impact on organizations

1. Visibility, predictability, usage controls, accurate charging, and responsive customer service

With the advent of the prepaid economy, customers expect full predictability on their consumption. They want to pay exactly for what they have consumed and have complete flexibility in managing the services in which they are enrolled.

This means that the “always-on” service provider must be able to create targeted subscription offers that are instantly available for quotes and orders across all channels, and that they can meter and rate real-time subscription charges and usage-based fees. These critical abilities should be backed up by strong self-care options that capture customers in one moment and allow them to navigate seamlessly – understanding that although happiness drives loyalty, simplifying processes and reducing the amount of input required from customers has an even greater impact on churn.

2. Rich, single-source, seamless services

Today’s companies, brands, and manufacturers have multiple distribution partners as the result of geographic and organizational spread. The ability to manage both channel and marketplace partners in a highly automated manner is essential for smooth operations in a distributed supply chain. It is imperative that disseminating details of revenue-sharing, apportionment, commissions, and royalties along the entire value chain be easy to apply and manage flexibly. A second imperative is to bill consumers in a rich, single-sourced invoice for any combination of consolidated products and services they may consume across complex supply chains.

3. Payment flexibility

From Venmo to Uber to Snapchat, the payment landscape is undergoing massive shifts, all of which point toward one overarching outcome: the gradual decline of “transactional uniformity.” Offering the freedom to choose a preferred payment option is a clear mandate in order to retain customers. Standard payment options such as credit or debit and online payments are expectations of the past. What today’s customers demand is flexibility in payment cycles such as prepaid, postpaid, deferred payments, and invoices per different cycles.

Offering differentiated billing and payment services targeting individual customer segments while automating payment handling from any channel is crucial. In an Ernst & Young paper on billing transformation, authors David Connolly and Rick Raisinghani point out that as a service provider’s first and most frequent touch point with its customers, billing presents an opportunity to create a positive experience and to build longstanding relationships.

Additionally, with the underlying objective of cost-savings due to automation, these prerequisites are mandatory: thorough review of customer history to avoid late or missing payments, and having an effective credit and collection management system along with a receptive payment handling and receivable system to continuously evaluate credit risk and thus lower DSO (days sales outstanding).

4. Error-free transparent billing and consolidated invoices

Often, the inability of invoicing systems to integrate multiple services and third-party charges leads to time-consuming, manual processes. Receiving complicated and unclear invoices not only frustrates customers, but it can also be directly responsible for loss in revenue.

Customers appreciate a consolidated approach as it simplifies invoice management and payment processes. Rather than having to manage multiple invoices for all orders placed with your company over the past week, month, or other time period, customers can manage and pay a single bill.

Every invoice you generate introduces additional opportunities for error, misplaced documents, and payment delays. Invoice consolidation makes sense for companies with customers that routinely place multiple orders per billing period. Some of the financial and operational AR benefits that can be achieved with a consolidated invoicing approach include more efficient receivables processes, higher customer satisfaction levels, reduced billing errors, and improved compliance and audit trails.

5. Quick and easy issue resolution

Quick, hassle-free issue resolution an important imperative for loyal and happy customers. Often, lack of process coordination between sales, service, and collections, and lack of common data model causes delays in processing customer issues. We see this often as the financial landscape of many companies was built in a highly siloed fashion, making it very difficult to obtain a 360° view on the customer financial and historical data.

Having a back-end system that integrates credit, collections, and dispute management with service processes improves agent efficiency by providing of 360-degree customer view, including interaction history, fact sheets, and detailed financial views resulting in lower TCO. Improved interactions and efficiency that focuses on customer needs supports seamless dispute handling and increases customer satisfaction.

Today, many organizations understand that the impact of billing processes are not limited to the back office. Billing affects customer satisfaction and customer retention, and a customer-focused billing strategy can create real competitive differentiation. As a common denominator for the business processes introduced by marketing, finance and accounting, customer service, and IT, billing systems run in parallel to a customer journey.

From designing monetization policies to managing and mediating sales and orders, handling collections, commissions and royalties, and analyzing consumption data to improve future pricing, an efficient billing system ticks off the imperative checklist and is instrumental in providing a truly seamless customer journey for businesses— inside and out.

For more on this topic, see Why Digital Transformation Is Not Just About Technology.

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Ruchi Verma

About Ruchi Verma

Ruchi Verma is a global market development specialist, supporting the SAP Hybris Billing solution suite, at SAP. She has wealth of experience in driving technological transformations and process improvements in retail, telecom, and banking industries. Her interest lies in analyzing technological trends and how they impact business model innovations. Based out of Paris, she is focusing on how strong billing solutions can aid monetization of digital transformation and help companies in tackling their business model transformation challenges. She holds an Master of Business Administration (M.B.A.) from HEC Paris with specialization in Marketing and Digital Transformation.

Intrigo Gains Confidence In The Cloud

Darren Roos

The public cloud services market is expected to grow 18 percent this year, according to Gartner, totaling $246.8 billion. With the research firm also predicting sizeable growth in the Middle East and North Africa, India, and the Asia/Pacific region by year’s end, it’s clear that this is a global movement that is on the rise among today’s enterprises.

Although many companies are still determining their overall cloud strategy, the question for many is not whether to implement a cloud solution, but when and how it will be carried out. Sid Nag, research director at Gartner, has cited numerous reasons for this uptick in cloud services, noting that many companies are now realizing the benefits of cloud solutions. These include greater agility and scalability, lower costs, and more opportunities for innovation—all of which are factors that fuel business growth and enable companies to keep pace within their industries.

Case in point: Intrigo Systems

One such company benefiting from the cloud is Intrigo Systems, a systems integrator and technology service provider that specializes in implementing extended supply chain solutions for its customers. Started in 2009, the company has undergone tremendous growth within the last eight years. This has resulted in a customer base of nearly 200 organizations, with revenues ranging from US$300 million to $60 billion, which are served by Intrigo’s network of more than 250 consultants worldwide.

While most of its operations are based in North America, Intrigo has been expanding its business. The company has gone from having offices in Fremont, Calif., Houston, and Dallas to establishing a presence worldwide. Today, Intrigo boasts offices not only in multiple United States cities, but in Chennai, Bangalore, Frankfurt, and Heidelberg.

Designing for the future

Intrigo’s remarkable success and growth since its formation led the company to a crossroads. Like many in its position, it needed to start looking ahead and considering what tools would be required to create a foundation that could support its aggressive global scale-up.

“We realized the only way we could do this was to bring automation and the digital transformation to our enterprise,” said Kanth Krishnan, chief customer officer of Intrigo Systems. “Another important consideration for us was how we could accomplish this efficiently, while still providing our customers and network of consultants with top-notch service and resources.”

Intrigo began drilling down into its business processes, identifying the need to eliminate redundancies and enhance its resource management capabilities. By implementing an integrated business solution, the company has been able to better manage its assets. This has improved its ability to oversee its network of consultants and satisfy the various needs of its wide-ranging customer base.

Following this, the company turned to another trouble spot: travel and expenses. As the business grew, Intrigo’s consultants began to expand their reach, traveling worldwide to deliver its services and expertise to customers, many of which operate on an international scale. Intrigo knew it was critical to establish a more organized and scalable means of tracking these items. To accomplish this, it turned to an automated expense-reporting tool, which has helped the organization better manage these processes. While these solutions have helped address specific areas of the business, there was still the issue of how best to manage the company’s operations holistically.

“We felt as if we had hit a wall,” said Krishnan. “We’d grown to such a degree that we felt limited by many of our other systems, which couldn’t deliver the services we needed from a multinational standpoint. We thought about implementing local solutions in each country, but that didn’t really make sense to us. We realized we needed a more inclusive solution and couldn’t think of any company better suited to provide it than SAP.”

Intrigo wanted to create a single platform on which it could operate as a cohesive, global entity. It also wanted the ability to manage its resources and projects more efficiently. The company hoped that a more intuitive and comprehensive system would allow it to gain better visibility into its projects, from where money and resources were spent to how different initiatives translated into outcomes for customers.

Understanding how its projects were operating was just one aspect the company’s journey. Intrigo knew it needed to put in place a system that could not only supply valuable data on how its money and employees were being allocated, but also provide insight that would help the company make proactive changes to improve its operations and lower expenses.

To accomplish these goals, Intrigo implemented a solution equipped with an advanced in-memory platform, which brings contextual analytics, digital assistance capabilities, machine learning, and a well-designed user interface to the public cloud. The solution enables companies to benefit from the latest and most innovative advances in ERP without sizeable up-front capital expenditures or extensive infrastructure changes.

The solution includes a personal assistant, which streamlines many of the tedious tasks associated with enterprise maintenance and offers insights and guidance to drive efficient collaboration and better business decisions.

A new, intelligent ERP core

In just eight weeks, the solution became the intelligent core of Intrigo’s ERP platform. The rapid, fit-to-standard implementation ensured that the company could smoothly transition its operations without any lapse in service or functions. It also integrated tightly with existing solutions to serve as a scalable, long-term platform capable of growing and changing with the company, and help it remain responsive and competitive within its market.

As a result, Intrigo has gained greater visibility into its operations, enhanced its resource management capabilities, reduced revenue leakage, built robust controls and compliance processes, and instituted an intuitive, unified platform to better oversee global business operations from end to end. This is bringing increased transparency and efficiency to every aspect of the business, from billing and timesheets to customer engagement and employee satisfaction.

For more insight on where cloud solutions are headed, see Why CFOs Are Getting Serious About The Cloud.

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Darren Roos

About Darren Roos

Darren Roos is President of SAP S/4HANA Cloud at SAP.

What's Driving The Demand For Python Programmers?

Melissa Burns

According to TIOBE index, Python is currently the fourth most popular programming language in the world. From a CIO’s viewpoint, Python is an extremely promising programming language for a number of reasons, but mostly because, in general, it allows a company to roll out products much faster than almost any other. It may be less precise than, say, Java, but writing a program in Python is often five to six times faster than writing the same program in Java. The ability to release a product earlier than a competitor and make the necessary changes and corrections faster is often crucial in this industry.

That is why the demand for Python specialists keeps growing at an incredible rate, and a company that manages to hire a team of skilled Python professionals will get an important advantage over the competition.

Yet there still seems to be a fair number of negative conceptions concerning Python within the programming community, especially among old-school coders. Many believe that Python is simplistic and not serious enough for those working professionally. Yet growing demand for Python programmers seems to be at odds with this idea. And there are very good reasons to pay attention to it. Let’s take a look at them.

1. Python is simple and convenient

Python is simple enough to be learned fairly quickly, even by those without prior experience in programming. What’s far more important, however, is that it provides a comfortable and transparent interface as well as easily readable and understandable code. This makes the programmer’s job easier and faster. For example, a Python program is normally three to five times shorter than an equivalent Java program, mainly due to Python’s built-in high-level data types and dynamic typing. In fact, Python is very similar to the simplified “pseudo-code” programmers use in prototypes of their work, the only difference being that it actually does what it says.

2. Python is a general purpose language

Today Python is used for a wide variety of tasks by companies both small and large. Google, for example, extensively applies it in most of its cloud-based solutions. If a company of this magnitude treats this language seriously, there is no reason for anybody else to look at it askance. Iflexion Python developers often apply it for automation, for Python is excellent at scripting and has a number of tools like Fabric and Ansible that allow you to automate repetitive processes.

3. Python is free and open source

Python’s pre-made binaries are freely available, and advanced programmers can just as freely get its source code. Moreover, the license allows you to modify the source code and distribute it, which makes it a great community-supported language.

4. Python is object-oriented

In practical terms, that means that Python allows you to create reusable information structures, which considerably cuts the amount of unnecessary, repetitive work. This is not just an advantage in and of itself. Since most modern programming languages are object-oriented as well, it means that by learning and using Python, you’ll encounter the same things as you would in other programming environments and languages.

5. Python has a huge standard library

Python’s standard library has over 300 modules providing ready-made tools aimed at solving a wide variety of tasks and eliminating the need to create solutions on your own. It supports many protocols and formats standard for Internet-facing apps (e.g., HTTP and MIME). And it is just the tip of an iceberg – as of May 2017, the Python Package Index contained more than 107,000 third-party packages.

6. Python has user-friendly data structures

Python uses built-in list and dictionary data structures and supports a function of dynamic high-level data typing, thus considerably decreasing the amount of support code necessary to run a program.

That’s Python in a nutshell. It may be a good idea to keep an eye on whether your primary competitors are actively looking for highly skilled Python programmers. If so, you may soon find them greatly increasing their production rates. Preparing a job offer as soon as possible is an important order of business.

Learn more about the biggest issues facing CIOs in the Top 5 CIO Blogs Of July 2017.

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Melissa Burns

About Melissa Burns

Melissa Burns is an entrepreneur and independent journalist. She spends her time writing articles, overviews, and analyses about entrepreneurship, startups, business innovations, and technology. Follow her at @melissaaburns.

Data Lakes: Deep Insights

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

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

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

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

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

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

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

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

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

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

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

Prescriptive Farming

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

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

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

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

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

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

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

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

Predictive Maintenance

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

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

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

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

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

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

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

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

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

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

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

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

Detecting Fraud Through Correlations

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

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

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

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

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

Data Lakes Don’t End Complexity for IT

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

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

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

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

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

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

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

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

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

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

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

How to Avoid Drowning in the Lake

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

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

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

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

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

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

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

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

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

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

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

From Data Queries to New Business Models

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

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

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

Data for All

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

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

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


About the Authors:

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

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

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

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

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Timo Elliott

About Timo Elliott

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

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The CIO’s Cheat Sheet For Digital Transformation

Richard Howells

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

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

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

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

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

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

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

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

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

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

Experience the sheer power of IoT

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

According to IDC research:

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

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

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

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

Innovate your organization’s business processes

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

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

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

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

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

Develop and deliver innovative products

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

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

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

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

Forget best practices—embrace next practices

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

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

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

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

Want to learn more? Download the IDC white paper.

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Richard Howells

About Richard Howells

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