Tag Archives: Challenges

Expert Interview (Part 2): Elise Roy on Human Centered Design and Overcoming Challenges with Big Data

In case you missed Part 1, read here!

Recently, while Elise was working with NPR, they discussed the fact that episodes of NPR programs posted online did not provide captions. While these shows generally have an article associated with them or a transcript of the conversation, Elise pointed out that NPR might be filtering out a significant portion of the population who might have hearing loss but are still able to appreciate an audio-centered show. Or, those who were completely deaf who liked the pacing captions brought and a less cluttered visual experience.

Expert Interview Part 2 Elise Roy on Human Centered Design and Overcoming Challenges with Big Data banner Expert Interview (Part 2): Elise Roy on Human Centered Design and Overcoming Challenges with Big Data

Because of their conversation, NPR has a better understanding of an entire market they might be missing out on.

Her way of problem-solving is catching on.

“A couple years ago I was telling people about human centered design, they had no idea what I was talking about,” Elise says. “But now they’re starting to recognize the value it provides businesses and starting to see how they can create more targeted responsive solutions.”

Big Data plays an important role in creating more customer-centric solutions. It allows organizations to better understand how to react to the human experience and build more personalized and customized experiences and identify patterns that otherwise might have been difficult to see.

Currently, one of the biggest struggles with integrating the perspective of people with disabilities is that there are such a wide variety of disabilities– it can be challenging to design with each one in mind.

Elise says Big Data can help overcome those challenges.

There are already products on the market that benefit individuals with disabilities that use the power of Big Data and the Internet of Things.

For instance, there are companies developing doorbell home security solutions that alert users to motion and allow them to monitor the door remotely– an ideal solution for individuals with mobility problems. Innovation like this and others including the Roomba or self-driving cars not only make it easier for people with disabilities to live independently but are also products that the general population enjoys as well.

In order to continue to bring innovations like these to market, it will be essential that Big Data be paired with human centered design methods.

“This is because big data can easily be influenced by bias,” Elise says. “For example, we could only collect certain kinds of data and be missing out on a key thing that would get uncovered through the human centered design process during the observation phase.”

Recently, Microsoft hired several experts in bias reduction in Artificial Intelligence when they recognized their AI applications were biased in the sense that they were designed around the beliefs of those who were designing them rather than the people who were going to experience their applications.

Moving forward, Elise believes there needs to be symbiosis between Big Data and the human aspect of design.

Elise’s consulting business is still in its infancy, but she’s excited about potential impact on innovation that of looking at innovation through the lens of the disabled offers for businesses.

“There’s a lot of people who have gotten back to me and said it’s really impacted how they’re thinking about things,” Elise says.

We also have a new eBook focused on Strategies for Improving Big Data Quality available for download. Take a look!

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Common Security Challenges on the Mainframe and IBM i Platforms

Security on the mainframe and IBM i platform (and all of IT) is at the forefront of CIO/CTO minds today. Various compliance laws and regulations, most recently GDPR, has forced these leaders to focus on how safe and secure their data is – as well as the ability to have security team visibility into this data.

There are various areas to consider with respect to security. I’ll touch on each of them briefly here:

Controlling access to system and data

Are the people entrusted with access to data the only ones who are accessing this data? Regulations such as GDPR require that access to protected data be limited to those that need it and only for periods of time where that access is required. How are organizations ensuring that nobody else is able to get at this data? Can the organization prove that it is monitoring all access and has governance in place to determine who has access, when, and for how long?

Limiting activity granted to user profiles

The days where entire teams and groups of people are granted wide open access to all systems has come to an end. Having necessary security in place means that only those that need access to systems are granted such access and that few are granted this access on an ongoing basis.

Allowing access to those that don’t need it or allowing it for too long shows that the organization has inadequate controls and may not be protecting private data.

mainframe and ibm i security challenges banner Common Security Challenges on the Mainframe and IBM i Platforms

Tracking database and system activity

Just controlling access to data and limiting user activity isn’t enough to prove the organization is doing everything possible to protect the infrastructure and the data. The organization must track all system and database activity and be prepared to react when there are users and activities present that aren’t expected or thought to be allowed or necessary.

Reporting on security violations

GDPR and other regulations/laws prescribe timeframes on what must be reported and when, but it’s best practice to have a plan in place to report on security violations and breaches that have been found. Many organizations have suffered lost revenue, fines, and lost reputation because of breaches that were either not caught or not reported in a timely manner.

Ensuring compliance with regulations

Not complying with security regulations is a quick path to being out of business. Large fines (to the organizations and to the leaders personally) can cause irrevocable damage to the business financially and to the reputation to the business. It’s also the right way to do business, especially today when so much private data exists and can be exploited in ways that can do harm to the individual.

Ensuring compliance requires a plan as well as the tools to capture the information necessary to monitor the infrastructure for breaches and potential breaches.

Protecting data via encryption, masking, scrambling, etc.

Putting encryption and other techniques into software solutions that look at, keep, and transport data is a big step towards protecting the data and show that the organization is serious about doing what’s necessary to protect their customers and their private data. CIO’s and other leaders should be examining all of the ways that data is transported, stored, viewed, and used and ensure that the data is thoroughly protected throughout its lifecycle.

Visibility of data

Finally, let’s address the visibility of all of this key security data. Organizations have invested a lot of money in various platforms that help make their business money, but all of them store critical system and security logs in various ways. Mainframes store data in logs, typically accessible with tools only used by mainframe system programmers. IBM i systems have data in journals that are, again, only accessible natively by IBM i experts.

But most organizations have centralized teams now who are looking at security data and how successful can those teams be if they are missing data from vital platforms like the mainframe and IBM i?

Many organizations have standardized on Splunk as a place to receive and view all kinds of data, security data included. That means that in order for the security people to be successful, they only need to know how to work with the data in Splunk, not all the various platforms and technologies that capture and provide the data.

Ironstream and our new Ironstream for IBM i take data from the mainframe and IBM i systems and forward that crucial data to Splunk and other SIEM consoles for viewing, alerting, and analysis. Having that vital data to hand means that organizations have complete visibility into these environments without the need for costly monitoring systems or for specialized, scarce, and costly expertise. Make sure to register for our upcoming webcast: Ironstream for IBM i – Enabling Splunk Insight into Key Security and Operational Metrics.

Download the Next-Gen Operational Intelligence checklist to discover what you need to start monitoring on your mainframe.

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Curves Ahead: Auto Industry Faces Connected Car Challenges

In 2013, several UK supermarket chains discovered that products they were selling as beef were actually made at least partly—and in some cases, entirely—from horsemeat. The resulting uproar led to a series of product recalls, prompted stricter food testing, and spurred the European food industry to take a closer look at how unlabeled or mislabeled ingredients were finding their way into the food chain.

By 2020, a scandal like this will be eminently preventable.

The separation between bovine and equine will become immutable with Internet of Things (IoT) sensors, which will track the provenance and identity of every animal from stall to store, adding the data to a blockchain that anyone can check but no one can alter.

Food processing companies will be able to use that blockchain to confirm and label the contents of their products accordingly—down to the specific farms and animals represented in every individual package. That level of detail may be too much information for shoppers, but they will at least be able to trust that their meatballs come from the appropriate species.

The Spine of Digitalization

Q118 CoverFeature img1 spine Curves Ahead: Auto Industry Faces Connected Car Challenges

Keeping food safer and more traceable is just the beginning, however. Improvements in the supply chain, which have been incremental for decades despite billions of dollars of technology investments, are about to go exponential. Emerging technologies are converging to transform the supply chain from tactical to strategic, from an easily replicable commodity to a new source of competitive differentiation.

You may already be thinking about how to take advantage of blockchain technology, which makes data and transactions immutable, transparent, and verifiable (see “What Is Blockchain and How Does It Work?”). That will be a powerful tool to boost supply chain speed and efficiency—always a worthy goal, but hardly a disruptive one.

However, if you think of blockchain as the spine of digitalization and technologies such as AI, the IoT, 3D printing, autonomous vehicles, and drones as the limbs, you have a powerful supply chain body that can leapfrog ahead of its competition.

What Is Blockchain and How Does It Work?

Here’s why blockchain technology is critical to transforming the supply chain.

Blockchain is essentially a sequential, distributed ledger of transactions that is constantly updated on a global network of computers. The ownership and history of a transaction is embedded in the blockchain at the transaction’s earliest stages and verified at every subsequent stage.

A blockchain network uses vast amounts of computing power to encrypt the ledger as it’s being written. This makes it possible for every computer in the network to verify the transactions safely and transparently. The more organizations that participate in the ledger, the more complex and secure the encryption becomes, making it increasingly tamperproof.

Why does blockchain matter for the supply chain?

  • It enables the safe exchange of value without a central verifying partner, which makes transactions faster and less expensive.
  • It dramatically simplifies recordkeeping by establishing a single, authoritative view of the truth across all parties.
  • It builds a secure, immutable history and chain of custody as different parties handle the items being shipped, and it updates the relevant documentation.
  • By doing these things, blockchain allows companies to create smart contracts based on programmable business logic, which can execute themselves autonomously and thereby save time and money by reducing friction and intermediaries.

Hints of the Future

Q118 CoverFeature img2 future Curves Ahead: Auto Industry Faces Connected Car ChallengesIn the mid-1990s, when the World Wide Web was in its infancy, we had no idea that the internet would become so large and pervasive, nor that we’d find a way to carry it all in our pockets on small slabs of glass.

But we could tell that it had vast potential.

Today, with the combination of emerging technologies that promise to turbocharge digital transformation, we’re just beginning to see how we might turn the supply chain into a source of competitive advantage (see “What’s the Magic Combination?”).

What’s the Magic Combination?

Those who focus on blockchain in isolation will miss out on a much bigger supply chain opportunity.

Many experts believe emerging technologies will work with blockchain to digitalize the supply chain and create new business models:

  • Blockchain will provide the foundation of automated trust for all parties in the supply chain.
  • The IoT will link objects—from tiny devices to large machines—and generate data about status, locations, and transactions that will be recorded on the blockchain.
  • 3D printing will extend the supply chain to the customer’s doorstep with hyperlocal manufacturing of parts and products with IoT sensors built into the items and/or their packaging. Every manufactured object will be smart, connected, and able to communicate so that it can be tracked and traced as needed.
  • Big Data management tools will process all the information streaming in around the clock from IoT sensors.
  • AI and machine learning will analyze this enormous amount of data to reveal patterns and enable true predictability in every area of the supply chain.

Combining these technologies with powerful analytics tools to predict trends will make lack of visibility into the supply chain a thing of the past. Organizations will be able to examine a single machine across its entire lifecycle and identify areas where they can improve performance and increase return on investment. They’ll be able to follow and monitor every component of a product, from design through delivery and service. They’ll be able to trigger and track automated actions between and among partners and customers to provide customized transactions in real time based on real data.

After decades of talk about markets of one, companies will finally have the power to create them—at scale and profitably.

Amazon, for example, is becoming as much a logistics company as a retailer. Its ordering and delivery systems are so streamlined that its customers can launch and complete a same-day transaction with a push of a single IP-enabled button or a word to its ever-attentive AI device, Alexa. And this level of experimentation and innovation is bubbling up across industries.

Consider manufacturing, where the IoT is transforming automation inside already highly automated factories. Machine-to-machine communication is enabling robots to set up, provision, and unload equipment quickly and accurately with minimal human intervention. Meanwhile, sensors across the factory floor are already capable of gathering such information as how often each machine needs maintenance or how much raw material to order given current production trends.

Once they harvest enough data, businesses will be able to feed it through machine learning algorithms to identify trends that forecast future outcomes. At that point, the supply chain will start to become both automated and predictive. We’ll begin to see business models that include proactively scheduling maintenance, replacing parts just before they’re likely to break, and automatically ordering materials and initiating customer shipments.

Italian train operator Trenitalia, for example, has put IoT sensors on its locomotives and passenger cars and is using analytics and in-memory computing to gauge the health of its trains in real time, according to an article in Computer Weekly. “It is now possible to affordably collect huge amounts of data from hundreds of sensors in a single train, analyse that data in real time and detect problems before they actually happen,” Trenitalia’s CIO Danilo Gismondi told Computer Weekly.

The project, which is scheduled to be completed in 2018, will change Trenitalia’s business model, allowing it to schedule more trips and make each one more profitable. The railway company will be able to better plan parts inventories and determine which lines are consistently performing poorly and need upgrades. The new system will save €100 million a year, according to ARC Advisory Group.

New business models continue to evolve as 3D printers become more sophisticated and affordable, making it possible to move the end of the supply chain closer to the customer. Companies can design parts and products in materials ranging from carbon fiber to chocolate and then print those items in their warehouse, at a conveniently located third-party vendor, or even on the client’s premises.

In addition to minimizing their shipping expenses and reducing fulfillment time, companies will be able to offer more personalized or customized items affordably in small quantities. For example, clothing retailer Ministry of Supply recently installed a 3D printer at its Boston store that enables it to make an article of clothing to a customer’s specifications in under 90 minutes, according to an article in Forbes.

This kind of highly distributed manufacturing has potential across many industries. It could even create a market for secure manufacturing for highly regulated sectors, allowing a manufacturer to transmit encrypted templates to printers in tightly protected locations, for example.

Meanwhile, organizations are investigating ways of using blockchain technology to authenticate, track and trace, automate, and otherwise manage transactions and interactions, both internally and within their vendor and customer networks. The ability to collect data, record it on the blockchain for immediate verification, and make that trustworthy data available for any application delivers indisputable value in any business context. The supply chain will be no exception.

Q118 CoverFeature img3 cheers ver2 Curves Ahead: Auto Industry Faces Connected Car Challenges

Blockchain Is the Change Driver

The supply chain is configured as we know it today because it’s impossible to create a contract that accounts for every possible contingency. Consider cross-border financial transfers, which are so complex and must meet so many regulations that they require a tremendous number of intermediaries to plug the gaps: lawyers, accountants, customer service reps, warehouse operators, bankers, and more. By reducing that complexity, blockchain technology makes intermediaries less necessary—a transformation that is revolutionary even when measured only in cost savings.

“If you’re selling 100 items a minute, 24 hours a day, reducing the cost of the supply chain by just $ 1 per item saves you more than $ 52.5 million a year,” notes Dirk Lonser, SAP go-to-market leader at DXC Technology, an IT services company. “By replacing manual processes and multiple peer-to-peer connections through fax or e-mail with a single medium where everyone can exchange verified information instantaneously, blockchain will boost profit margins exponentially without raising prices or even increasing individual productivity.”

But the potential for blockchain extends far beyond cost cutting and streamlining, says Irfan Khan, CEO of supply chain management consulting and systems integration firm Bristlecone, a Mahindra Group company. It will give companies ways to differentiate.

“Blockchain will let enterprises more accurately trace faulty parts or products from end users back to factories for recalls,” Khan says. “It will streamline supplier onboarding, contracting, and management by creating an integrated platform that the company’s entire network can access in real time. It will give vendors secure, transparent visibility into inventory 24×7. And at a time when counterfeiting is a real concern in multiple industries, it will make it easy for both retailers and customers to check product authenticity.”

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials. Although the key parts of the process remain the same as in today’s analog supply chain, performing them electronically with blockchain technology shortens each stage from hours or days to seconds while eliminating reams of wasteful paperwork. With goods moving that quickly, companies have ample room for designing new business models around manufacturing, service, and delivery.

Q118 CoverFeature img4 roadblock Curves Ahead: Auto Industry Faces Connected Car Challenges

Challenges on the Path to Adoption

For all this to work, however, the data on the blockchain must be correct from the beginning. The pills, produce, or parts on the delivery truck need to be the same as the items listed on the manifest at the loading dock. Every use case assumes that the data is accurate—and that will only happen when everything that’s manufactured is smart, connected, and able to self-verify automatically with the help of machine learning tuned to detect errors and potential fraud.

Companies are already seeing the possibilities of applying this bundle of emerging technologies to the supply chain. IDC projects that by 2021, at least 25% of Forbes Global 2000 (G2000) companies will use blockchain services as a foundation for digital trust at scale; 30% of top global manufacturers and retailers will do so by 2020. IDC also predicts that by 2020, up to 10% of pilot and production blockchain-distributed ledgers will incorporate data from IoT sensors.

Despite IDC’s optimism, though, the biggest barrier to adoption is the early stage level of enterprise use cases, particularly around blockchain. Currently, the sole significant enterprise blockchain production system is the virtual currency Bitcoin, which has unfortunately been tainted by its associations with speculation, dubious financial transactions, and the so-called dark web.

The technology is still in a sufficiently early stage that there’s significant uncertainty about its ability to handle the massive amounts of data a global enterprise supply chain generates daily. Never mind that it’s completely unregulated, with no global standard. There’s also a critical global shortage of experts who can explain emerging technologies like blockchain, the IoT, and machine learning to nontechnology industries and educate organizations in how the technologies can improve their supply chain processes. Finally, there is concern about how blockchain’s complex algorithms gobble computing power—and electricity (see “Blockchain Blackouts”).

Blockchain Blackouts

Q118 CoverFeature img5 blackout Curves Ahead: Auto Industry Faces Connected Car ChallengesBlockchain is a power glutton. Can technology mediate the issue?

A major concern today is the enormous carbon footprint of the networks creating and solving the algorithmic problems that keep blockchains secure. Although virtual currency enthusiasts claim the problem is overstated, Michael Reed, head of blockchain technology for Intel, has been widely quoted as saying that the energy demands of blockchains are a significant drain on the world’s electricity resources.

Indeed, Wired magazine has estimated that by July 2019, the Bitcoin network alone will require more energy than the entire United States currently uses and that by February 2020 it will use as much electricity as the entire world does today.

Still, computing power is becoming more energy efficient by the day and sticking with paperwork will become too slow, so experts—Intel’s Reed among them—consider this a solvable problem.

“We don’t know yet what the market will adopt. In a decade, it might be status quo or best practice, or it could be the next Betamax, a great technology for which there was no demand,” Lonser says. “Even highly regulated industries that need greater transparency in the entire supply chain are moving fairly slowly.”

Blockchain will require acceptance by a critical mass of companies, governments, and other organizations before it displaces paper documentation. It’s a chicken-and-egg issue: multiple companies need to adopt these technologies at the same time so they can build a blockchain to exchange information, yet getting multiple companies to do anything simultaneously is a challenge. Some early initiatives are already underway, though:

  • A London-based startup called Everledger is using blockchain and IoT technology to track the provenance, ownership, and lifecycles of valuable assets. The company began by tracking diamonds from mine to jewelry using roughly 200 different characteristics, with a goal of stopping both the demand for and the supply of “conflict diamonds”—diamonds mined in war zones and sold to finance insurgencies. It has since expanded to cover wine, artwork, and other high-value items to prevent fraud and verify authenticity.
  • In September 2017, SAP announced the creation of its SAP Leonardo Blockchain Co-Innovation program, a group of 27 enterprise customers interested in co-innovating around blockchain and creating business buy-in. The diverse group of participants includes management and technology services companies Capgemini and Deloitte, cosmetics company Natura Cosméticos S.A., and Moog Inc., a manufacturer of precision motion control systems.
  • Two of Europe’s largest shipping ports—Rotterdam and Antwerp—are working on blockchain projects to streamline interaction with port customers. The Antwerp terminal authority says eliminating paperwork could cut the costs of container transport by as much as 50%.
  • The Chinese online shopping behemoth Alibaba is experimenting with blockchain to verify the authenticity of food products and catch counterfeits before they endanger people’s health and lives.
  • Technology and transportation executives have teamed up to create the Blockchain in Transport Alliance (BiTA), a forum for developing blockchain standards and education for the freight industry.

It’s likely that the first blockchain-based enterprise supply chain use case will emerge in the next year among companies that see it as an opportunity to bolster their legal compliance and improve business processes. Once that happens, expect others to follow.

Q118 CoverFeature img7 milk Curves Ahead: Auto Industry Faces Connected Car Challenges

Customers Will Expect Change

It’s only a matter of time before the supply chain becomes a competitive driver. The question for today’s enterprises is how to prepare for the shift. Customers are going to expect constant, granular visibility into their transactions and faster, more customized service every step of the way. Organizations will need to be ready to meet those expectations.

If organizations have manual business processes that could never be automated before, now is the time to see if it’s possible. Organizations that have made initial investments in emerging technologies are looking at how their pilot projects are paying off and where they might extend to the supply chain. They are starting to think creatively about how to combine technologies to offer a product, service, or business model not possible before.

A manufacturer will load a self-driving truck with a 3D printer capable of creating a customer’s ordered item en route to delivering it. A vendor will capture the market for a socially responsible product by allowing its customers to track the product’s production and verify that none of its subcontractors use slave labor. And a supermarket chain will win over customers by persuading them that their choice of supermarket is also a choice between being certain of what’s in their food and simply hoping that what’s on the label matches what’s inside.

At that point, a smart supply chain won’t just be a competitive edge. It will become a competitive necessity. D!


About the Authors

Gil Perez is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

Tom Raftery is Global Vice President, Futurist, and Internet of Things Evangelist, at SAP.

Hans Thalbauer is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

Dan Wellers is Global Lead, Digital Futures, at SAP.

Fawn Fitter is a freelance writer specializing in business and technology.

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

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Data Integration Challenges in a Siloed World

Modernizing your infrastructure and operations means breaking down “silos” — including those that hamper your data integration processes. Here’s a look at the silos that typically stand in the way of data integration, and what businesses can do to tear them down.

“Breaking down silos” is lingo that you’ll hear if you follow the DevOps movement. Part of the point of DevOps is to eliminate the barriers that typically prevent different types of IT staff — such as the development and the IT Ops teams — from collaborating with each other.

Data Integration Challenges in a Siloed World banner Data Integration Challenges in a Siloed World

According to the DevOps mantra, everyone should work in close coordination, rather than having each team operate in its own silo. Silos stifle innovation, make automation difficult and lead to the loss of important information as data is transferred between teams.

Silos and Data Integration

articulated male 818202 960 720 600x Data Integration Challenges in a Siloed World

Although the DevOps movement focuses primarily on software development and delivery, rather than data operations, the value of tearing down silos is not limited to the world of DevOps.

The same concept can be applied to data integration operations — especially if you embrace the DataOps mantra, which extends DevOps thinking into the world of data management.

After all, the typical business’s data operations tend to be “siloed” for a number of reasons:

  • Businesses have many discrete sources of data, ranging from server and network logs to website logs, digital transactions records and perhaps even ink-and-paper files. Because each type of data originates from a different source, building a single process for integrating all of the data into a common pipeline can be challenging.
  • Different teams within the organization tend to produce different types of information, and they may not share it with each other. For example, your marketing department might store data related to customer engagement in a recent offline ad campaign. That data might be able to provide insights to website designers, who could use it to determine how best to engage customers online. But chances are that your marketing team and web design team don’t communicate much, or share data with each other on a routine basis.
  • Modern IT infrastructure tends to be quite diverse. Your businesses may use a combination of on-premise and cloud servers, with multiple operating systems, Web servers and so on in the mix. Each part of your infrastructure produces logs and other types of data in its own format, making integration hard.
  • Young data and historical data are usually stored in different locations. You probably archive historical data after a certain period, for example, possibly off-site. In contrast, real-time data and near-real-time data may remain in their original data sources. This also leads to data silos because data is stored in different places depending on its age.

How do you destroy these silos? The short answer is data integration.

bigstock Data Integration and Database  192826513 600x Data Integration Challenges in a Siloed World

Data integration refers to the process of collecting data from disparate sources and turning it into actionable information. Data integration typically involves data aggregation, data transformations, and data visualizations.

The end goal of data integration is to turn data into information that can deliver meaningful insights to people reviewing it, without forcing them to think too hard in order to find those insights.

If your data remains siloed, data integration is nigh impossible. You can’t achieve easy, obvious insights if you have to look in multiple places to find data, or if complementary data produced by different teams or machines is not combined together.

Nor can you integrate data effectively if your organization is siloed. Everyone should be able to view collective data insights in the same place and at the same time, so that they can also communicate in the same place and at the same time.

Conclusion

In short, your data, and the teams that work with it are probably spread across disparate locations. In other words, they are siloed.

Deriving maximum value from your data requires breaking down those silos through data integration.

For more Big Data insights, check out our recent webcast, 2018 Big Data Trends: Liberate, Integrate, and Trust Your Data, to see what every business needs to know in the upcoming year.

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Challenges For Sustainable Digital Ethics

In 2013, several UK supermarket chains discovered that products they were selling as beef were actually made at least partly—and in some cases, entirely—from horsemeat. The resulting uproar led to a series of product recalls, prompted stricter food testing, and spurred the European food industry to take a closer look at how unlabeled or mislabeled ingredients were finding their way into the food chain.

By 2020, a scandal like this will be eminently preventable.

The separation between bovine and equine will become immutable with Internet of Things (IoT) sensors, which will track the provenance and identity of every animal from stall to store, adding the data to a blockchain that anyone can check but no one can alter.

Food processing companies will be able to use that blockchain to confirm and label the contents of their products accordingly—down to the specific farms and animals represented in every individual package. That level of detail may be too much information for shoppers, but they will at least be able to trust that their meatballs come from the appropriate species.

The Spine of Digitalization

Q118 CoverFeature img1 spine Challenges For Sustainable Digital Ethics

Keeping food safer and more traceable is just the beginning, however. Improvements in the supply chain, which have been incremental for decades despite billions of dollars of technology investments, are about to go exponential. Emerging technologies are converging to transform the supply chain from tactical to strategic, from an easily replicable commodity to a new source of competitive differentiation.

You may already be thinking about how to take advantage of blockchain technology, which makes data and transactions immutable, transparent, and verifiable (see “What Is Blockchain and How Does It Work?”). That will be a powerful tool to boost supply chain speed and efficiency—always a worthy goal, but hardly a disruptive one.

However, if you think of blockchain as the spine of digitalization and technologies such as AI, the IoT, 3D printing, autonomous vehicles, and drones as the limbs, you have a powerful supply chain body that can leapfrog ahead of its competition.

What Is Blockchain and How Does It Work?

Here’s why blockchain technology is critical to transforming the supply chain.

Blockchain is essentially a sequential, distributed ledger of transactions that is constantly updated on a global network of computers. The ownership and history of a transaction is embedded in the blockchain at the transaction’s earliest stages and verified at every subsequent stage.

A blockchain network uses vast amounts of computing power to encrypt the ledger as it’s being written. This makes it possible for every computer in the network to verify the transactions safely and transparently. The more organizations that participate in the ledger, the more complex and secure the encryption becomes, making it increasingly tamperproof.

Why does blockchain matter for the supply chain?

  • It enables the safe exchange of value without a central verifying partner, which makes transactions faster and less expensive.
  • It dramatically simplifies recordkeeping by establishing a single, authoritative view of the truth across all parties.
  • It builds a secure, immutable history and chain of custody as different parties handle the items being shipped, and it updates the relevant documentation.
  • By doing these things, blockchain allows companies to create smart contracts based on programmable business logic, which can execute themselves autonomously and thereby save time and money by reducing friction and intermediaries.

Hints of the Future

Q118 CoverFeature img2 future Challenges For Sustainable Digital EthicsIn the mid-1990s, when the World Wide Web was in its infancy, we had no idea that the internet would become so large and pervasive, nor that we’d find a way to carry it all in our pockets on small slabs of glass.

But we could tell that it had vast potential.

Today, with the combination of emerging technologies that promise to turbocharge digital transformation, we’re just beginning to see how we might turn the supply chain into a source of competitive advantage (see “What’s the Magic Combination?”).

What’s the Magic Combination?

Those who focus on blockchain in isolation will miss out on a much bigger supply chain opportunity.

Many experts believe emerging technologies will work with blockchain to digitalize the supply chain and create new business models:

  • Blockchain will provide the foundation of automated trust for all parties in the supply chain.
  • The IoT will link objects—from tiny devices to large machines—and generate data about status, locations, and transactions that will be recorded on the blockchain.
  • 3D printing will extend the supply chain to the customer’s doorstep with hyperlocal manufacturing of parts and products with IoT sensors built into the items and/or their packaging. Every manufactured object will be smart, connected, and able to communicate so that it can be tracked and traced as needed.
  • Big Data management tools will process all the information streaming in around the clock from IoT sensors.
  • AI and machine learning will analyze this enormous amount of data to reveal patterns and enable true predictability in every area of the supply chain.

Combining these technologies with powerful analytics tools to predict trends will make lack of visibility into the supply chain a thing of the past. Organizations will be able to examine a single machine across its entire lifecycle and identify areas where they can improve performance and increase return on investment. They’ll be able to follow and monitor every component of a product, from design through delivery and service. They’ll be able to trigger and track automated actions between and among partners and customers to provide customized transactions in real time based on real data.

After decades of talk about markets of one, companies will finally have the power to create them—at scale and profitably.

Amazon, for example, is becoming as much a logistics company as a retailer. Its ordering and delivery systems are so streamlined that its customers can launch and complete a same-day transaction with a push of a single IP-enabled button or a word to its ever-attentive AI device, Alexa. And this level of experimentation and innovation is bubbling up across industries.

Consider manufacturing, where the IoT is transforming automation inside already highly automated factories. Machine-to-machine communication is enabling robots to set up, provision, and unload equipment quickly and accurately with minimal human intervention. Meanwhile, sensors across the factory floor are already capable of gathering such information as how often each machine needs maintenance or how much raw material to order given current production trends.

Once they harvest enough data, businesses will be able to feed it through machine learning algorithms to identify trends that forecast future outcomes. At that point, the supply chain will start to become both automated and predictive. We’ll begin to see business models that include proactively scheduling maintenance, replacing parts just before they’re likely to break, and automatically ordering materials and initiating customer shipments.

Italian train operator Trenitalia, for example, has put IoT sensors on its locomotives and passenger cars and is using analytics and in-memory computing to gauge the health of its trains in real time, according to an article in Computer Weekly. “It is now possible to affordably collect huge amounts of data from hundreds of sensors in a single train, analyse that data in real time and detect problems before they actually happen,” Trenitalia’s CIO Danilo Gismondi told Computer Weekly.

The project, which is scheduled to be completed in 2018, will change Trenitalia’s business model, allowing it to schedule more trips and make each one more profitable. The railway company will be able to better plan parts inventories and determine which lines are consistently performing poorly and need upgrades. The new system will save €100 million a year, according to ARC Advisory Group.

New business models continue to evolve as 3D printers become more sophisticated and affordable, making it possible to move the end of the supply chain closer to the customer. Companies can design parts and products in materials ranging from carbon fiber to chocolate and then print those items in their warehouse, at a conveniently located third-party vendor, or even on the client’s premises.

In addition to minimizing their shipping expenses and reducing fulfillment time, companies will be able to offer more personalized or customized items affordably in small quantities. For example, clothing retailer Ministry of Supply recently installed a 3D printer at its Boston store that enables it to make an article of clothing to a customer’s specifications in under 90 minutes, according to an article in Forbes.

This kind of highly distributed manufacturing has potential across many industries. It could even create a market for secure manufacturing for highly regulated sectors, allowing a manufacturer to transmit encrypted templates to printers in tightly protected locations, for example.

Meanwhile, organizations are investigating ways of using blockchain technology to authenticate, track and trace, automate, and otherwise manage transactions and interactions, both internally and within their vendor and customer networks. The ability to collect data, record it on the blockchain for immediate verification, and make that trustworthy data available for any application delivers indisputable value in any business context. The supply chain will be no exception.

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Blockchain Is the Change Driver

The supply chain is configured as we know it today because it’s impossible to create a contract that accounts for every possible contingency. Consider cross-border financial transfers, which are so complex and must meet so many regulations that they require a tremendous number of intermediaries to plug the gaps: lawyers, accountants, customer service reps, warehouse operators, bankers, and more. By reducing that complexity, blockchain technology makes intermediaries less necessary—a transformation that is revolutionary even when measured only in cost savings.

“If you’re selling 100 items a minute, 24 hours a day, reducing the cost of the supply chain by just $ 1 per item saves you more than $ 52.5 million a year,” notes Dirk Lonser, SAP go-to-market leader at DXC Technology, an IT services company. “By replacing manual processes and multiple peer-to-peer connections through fax or e-mail with a single medium where everyone can exchange verified information instantaneously, blockchain will boost profit margins exponentially without raising prices or even increasing individual productivity.”

But the potential for blockchain extends far beyond cost cutting and streamlining, says Irfan Khan, CEO of supply chain management consulting and systems integration firm Bristlecone, a Mahindra Group company. It will give companies ways to differentiate.

“Blockchain will let enterprises more accurately trace faulty parts or products from end users back to factories for recalls,” Khan says. “It will streamline supplier onboarding, contracting, and management by creating an integrated platform that the company’s entire network can access in real time. It will give vendors secure, transparent visibility into inventory 24×7. And at a time when counterfeiting is a real concern in multiple industries, it will make it easy for both retailers and customers to check product authenticity.”

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials. Although the key parts of the process remain the same as in today’s analog supply chain, performing them electronically with blockchain technology shortens each stage from hours or days to seconds while eliminating reams of wasteful paperwork. With goods moving that quickly, companies have ample room for designing new business models around manufacturing, service, and delivery.

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Challenges on the Path to Adoption

For all this to work, however, the data on the blockchain must be correct from the beginning. The pills, produce, or parts on the delivery truck need to be the same as the items listed on the manifest at the loading dock. Every use case assumes that the data is accurate—and that will only happen when everything that’s manufactured is smart, connected, and able to self-verify automatically with the help of machine learning tuned to detect errors and potential fraud.

Companies are already seeing the possibilities of applying this bundle of emerging technologies to the supply chain. IDC projects that by 2021, at least 25% of Forbes Global 2000 (G2000) companies will use blockchain services as a foundation for digital trust at scale; 30% of top global manufacturers and retailers will do so by 2020. IDC also predicts that by 2020, up to 10% of pilot and production blockchain-distributed ledgers will incorporate data from IoT sensors.

Despite IDC’s optimism, though, the biggest barrier to adoption is the early stage level of enterprise use cases, particularly around blockchain. Currently, the sole significant enterprise blockchain production system is the virtual currency Bitcoin, which has unfortunately been tainted by its associations with speculation, dubious financial transactions, and the so-called dark web.

The technology is still in a sufficiently early stage that there’s significant uncertainty about its ability to handle the massive amounts of data a global enterprise supply chain generates daily. Never mind that it’s completely unregulated, with no global standard. There’s also a critical global shortage of experts who can explain emerging technologies like blockchain, the IoT, and machine learning to nontechnology industries and educate organizations in how the technologies can improve their supply chain processes. Finally, there is concern about how blockchain’s complex algorithms gobble computing power—and electricity (see “Blockchain Blackouts”).

Blockchain Blackouts

Q118 CoverFeature img5 blackout Challenges For Sustainable Digital EthicsBlockchain is a power glutton. Can technology mediate the issue?

A major concern today is the enormous carbon footprint of the networks creating and solving the algorithmic problems that keep blockchains secure. Although virtual currency enthusiasts claim the problem is overstated, Michael Reed, head of blockchain technology for Intel, has been widely quoted as saying that the energy demands of blockchains are a significant drain on the world’s electricity resources.

Indeed, Wired magazine has estimated that by July 2019, the Bitcoin network alone will require more energy than the entire United States currently uses and that by February 2020 it will use as much electricity as the entire world does today.

Still, computing power is becoming more energy efficient by the day and sticking with paperwork will become too slow, so experts—Intel’s Reed among them—consider this a solvable problem.

“We don’t know yet what the market will adopt. In a decade, it might be status quo or best practice, or it could be the next Betamax, a great technology for which there was no demand,” Lonser says. “Even highly regulated industries that need greater transparency in the entire supply chain are moving fairly slowly.”

Blockchain will require acceptance by a critical mass of companies, governments, and other organizations before it displaces paper documentation. It’s a chicken-and-egg issue: multiple companies need to adopt these technologies at the same time so they can build a blockchain to exchange information, yet getting multiple companies to do anything simultaneously is a challenge. Some early initiatives are already underway, though:

  • A London-based startup called Everledger is using blockchain and IoT technology to track the provenance, ownership, and lifecycles of valuable assets. The company began by tracking diamonds from mine to jewelry using roughly 200 different characteristics, with a goal of stopping both the demand for and the supply of “conflict diamonds”—diamonds mined in war zones and sold to finance insurgencies. It has since expanded to cover wine, artwork, and other high-value items to prevent fraud and verify authenticity.
  • In September 2017, SAP announced the creation of its SAP Leonardo Blockchain Co-Innovation program, a group of 27 enterprise customers interested in co-innovating around blockchain and creating business buy-in. The diverse group of participants includes management and technology services companies Capgemini and Deloitte, cosmetics company Natura Cosméticos S.A., and Moog Inc., a manufacturer of precision motion control systems.
  • Two of Europe’s largest shipping ports—Rotterdam and Antwerp—are working on blockchain projects to streamline interaction with port customers. The Antwerp terminal authority says eliminating paperwork could cut the costs of container transport by as much as 50%.
  • The Chinese online shopping behemoth Alibaba is experimenting with blockchain to verify the authenticity of food products and catch counterfeits before they endanger people’s health and lives.
  • Technology and transportation executives have teamed up to create the Blockchain in Transport Alliance (BiTA), a forum for developing blockchain standards and education for the freight industry.

It’s likely that the first blockchain-based enterprise supply chain use case will emerge in the next year among companies that see it as an opportunity to bolster their legal compliance and improve business processes. Once that happens, expect others to follow.

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Customers Will Expect Change

It’s only a matter of time before the supply chain becomes a competitive driver. The question for today’s enterprises is how to prepare for the shift. Customers are going to expect constant, granular visibility into their transactions and faster, more customized service every step of the way. Organizations will need to be ready to meet those expectations.

If organizations have manual business processes that could never be automated before, now is the time to see if it’s possible. Organizations that have made initial investments in emerging technologies are looking at how their pilot projects are paying off and where they might extend to the supply chain. They are starting to think creatively about how to combine technologies to offer a product, service, or business model not possible before.

A manufacturer will load a self-driving truck with a 3D printer capable of creating a customer’s ordered item en route to delivering it. A vendor will capture the market for a socially responsible product by allowing its customers to track the product’s production and verify that none of its subcontractors use slave labor. And a supermarket chain will win over customers by persuading them that their choice of supermarket is also a choice between being certain of what’s in their food and simply hoping that what’s on the label matches what’s inside.

At that point, a smart supply chain won’t just be a competitive edge. It will become a competitive necessity. D!


About the Authors

Gil Perez is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

Tom Raftery is Global Vice President, Futurist, and Internet of Things Evangelist, at SAP.

Hans Thalbauer is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

Dan Wellers is Global Lead, Digital Futures, at SAP.

Fawn Fitter is a freelance writer specializing in business and technology.

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

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Expert Interview (Part 2): James Kobielus on Reasons for Data Scientist Insomnia including Neural Network Development Challenges

In the first half of our two-part conversation with Wikibon lead analyst James Kobielus (@jameskobielus), he discussed the incredible impact of machine learning in helping organizations make better business decisions and be more productive. In today’s Part 2, he addresses what aspects of machine learning should be keeping data scientists up at night. (Hint: neural networks)

Several Challenges Involved with Developing Neural Networks

Developing these algorithms is not without its challenges, Kobielus says.

The first major challenge is finding data.

Algorithms can’t do magic unless they’ve been “trained.” And in order to train them, the algorithms require fresh data. But acquiring this training data set is a big hurdle for developers.

For eCommerce sites, this is less of a problem – they have their own data in the form of transaction histories, site visits and customer information that can be used to train the model and determine how predictive it is.

blog banner 2018 Big Data Trends eBook Expert Interview (Part 2): James Kobielus on Reasons for Data Scientist Insomnia including Neural Network Development Challenges

But the process of amassing those training data sets when you don’t have data is trickier – developers have to rely upon commercial data sets that they’ve purchased or open source data sets.

After getting the training data, which might come from a dozen different sources, the next challenge is aggregating it so the data can be harmonized with a common set of variables. Another challenge is having the ability to cleanse data to make sure it’s free of contradictions and inconsistencies. All this takes time and resources in the form of databases, storage, processing and data engineers. This process is expensive but essential. (For more on this, read Uniting Data Quality and Data Integration)

Third, organizations need data scientists, who are expensive resources. They need to find enough people to manage the whole process – from building to training to evaluating to governing.

“Finding the right people with the right skills, recruiting the right people is absolutely essential,” Kobielus says.

Before jumping into machine learning, organizations should also make sure it makes sense for your business strategies.

Industries like finance and marketing have made a clear case for themselves in implementing Big Data. In the case of finance, it allows them to do high-level analysis to detect things like fraud. And in marketing, for instance, CMOs, found it useful to develop algorithms that allowed them  to conduct sentiment analysis on social media.

There are a lot of uses for it to be sure, Kobielus says, but there are methods for deriving insights from data that don’t involve neural networks. It’s up to the business to determine whether using neural networks is overkill for their purposes.

“It’s not the only way to skin these cats,” he says.

If you already have the tools in place, then it probably makes sense to keep using them. Or, if you find traditional tools can’t address needs like transcription or facial recognition, then it probably makes sense to go to a newer form of machine learning.

What Should Really Be Keeping Data Scientists Up at Night 

While those in the tech industry might be fretting over whether AI will displace the gainfully employed or that there’s a skills deficit in the field, Kobielus has other worries related to data science.

For one, the algorithms used for machine learning and AI are really complex and they drive so many decisions and processes in our lives.

“What if something goes wrong? What if a self-driving vehicle crashes? What if the algorithm does something nefarious in your bank account? How can society mitigate the risks,” Kobielus asks.

When there’s a negative outcome, the question asked is who’s responsible. The person who wrote the algorithm? The data engineer? The business analyst who defined the features?

These are the questions that should keep data scientists, businesses, and lawyers up at night. And the answers aren’t clear-cut.

In order to start answering some of these questions, there needs to be algorithmic transparency, so that there can be algorithmic accountability.

Ultimately, everyone is responsible for the outcome.

There’s a huge legal gray area when it comes to machine learning because the models used are probabilistic and you can’t predict every single execution path for a given probabilistic application built on ML.

blog kobielus quote3 Expert Interview (Part 2): James Kobielus on Reasons for Data Scientist Insomnia including Neural Network Development Challenges

“There’s a limit beyond which you can anticipate the particular action of a particular algorithm at a particular time,” Kobielus says.

For algorithmic accountability, there need to be audit trails. But an audit log for any given application has the potential to be larger than all the databases on Earth. Not just that, but how would you roll it up into a coherent narrative to hand to a jury?

“Algorithmic accountability should keep people up at night,” he says.

Just as he said concerns about automation are overblown, Kobielus says it’s also unnecessary to worry that there aren’t enough skilled data scientists working today.

Data science is getting easier.

Back in the 80s, developers had to know underlying protocols like HTTP, but today nobody needs to worry about the protocol plumbing anymore. It will be the same for machine learning, Kobielus says. Increasingly, the underlying data is being abstracted away by higher-level tools that are more user friendly.

“More and more, these things can be done by average knowledge workers, and it will be executed by underlying structure,” he says.

Does Kobielus worry about the job security of data scientists then? Not really. He believes data science automation tools will allow data scientists to do less with more and hopefully to allow them to develop their skills in more challenging and creative realms.

For 5 key trends to watch for in the next 12 months, check out our new report: 2018 Big Data Trends: Liberate, Integrate & Trust

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The Four Challenges to Growth – and How Buster + Punch is Tackling Them

Posted by David Turner, Senior Marketing Director, EMEA, Oracle NetSuite

When Martin Preen was brought in by the founders of Buster + Punch his task was expressed simply, if a challenging one to deliver. As CEO, his mission was to help build the “world’s first home fashion label” and plot a path to international expansion.

BP The Four Challenges to Growth – and How Buster + Punch is Tackling ThemLaunched in an East London garage by industrial designer Massimo ‘Buster’ Minale, Buster + Punch creates cutting edge products, ranging from lighting and home hardware to custom motorcycles. Since 2013, it has become a “cult-favourite interior design brand.”

Critical acclaim was followed by financial success. Turnover has doubled every year and it now has operations in seven office locations across three continents. But growth hasn’t been straight forward. Like any start-up, it has to manage the multiple complexities that come with international expansion, an increase in personnel and a change in expectations.

Preen outlined the four key challenges to growth and how to tackle them:

1. Managing cash

Preen, a former investment banker, said: “Most people who do a start-up know that you’ve got to keep an eye on the cash or you’re screwed.” In other words, cash is king. But managing the cash is not just about reconciling the bank account – it’s about having a complete understanding of the supply chain, payment terms and many other aspects of business finance. Understanding requires visibility.

2. Reading the data

“If cash is king, data is, too,” said Preen. To manage cash requires reading the data. “One of the big challenges for us was that while in London we had a system that was just okay; in the US we had nothing; and in Asia we just had spreadsheets. Trying to pull something together to see where we were took an age,” explained Preen. “If you haven’t got that visibility about where you are, it’s exceptionally difficult to make really, really good decisions.”

Earlier this year, Preen and his team selected and implemented NetSuite OneWorld to give it a real-time view across its operations from manufacturing and warehouses to supply chain and customer service. The implementation of OneWorld – which supports 190 currencies, more than 20 languages, automated tax calculation and reporting in more than 100 countries, and customer transactions in more than 200 countries – means Buster + Punch can more accurately forecast international product demand, scaling its manufacturing and delivery processes accordingly.

3. People and operational control

As a start-up grows, the culture begins to change. “The core people at the beginning know each other, they do everything together and they know what they have got to do,” explained Preen. He calls those early recruits “X-Men of business,” people with a clear major talent but put in a position to multitask at a stage where getting the job done is more important than defining precise roles and responsibilities. As the business expands, however, this ad hoc approach becomes incompatible with long-term growth. While the founding members of the team try and hold on to the old way of doing things, “the new people don’t quite know what … their remit is.” It is adapting to this new “business-as-usual” that is most difficult for employees and business leaders alike.

Preen said NetSuite’s capabilities have the ability to “make us smarter.” It means using workflow features to define the team’s roles and responsibilities. It means being able to manage all operations on a single cloud platform. And it means being able to draw on data from manufacturing plants in Asia, physical locations in Sweden and the UK and retail channels across the world. Where lack of visibility made it difficult for the company to make the good decisions, this new found global clarity is game changing.

“We had a whole hairball infrastructure of systems and spreadsheets across various sites,” recalled Preen. “In order to bring that together we wanted to look at a technology that would run in all of our different locations and be able to consolidate that data across three legal entities as well. And we came across NetSuite. We loved the dashboard views and the workflow reporting.”

4. Keeping it fun

In pursuit of business discipline, organisations can lose the spark of ingenuity that made them successful in the first place. “The challenge for me,” said Preen, “is how do we put a level of control into the structure to allow the business to grow but also keep it fun?” For fun, read product innovation and what Preen called, “involving ourselves in things that are a little bit of rock and roll.” This includes motorcycle customisation, joining forces with Rolls Royce to create a contemporary version of the Edison light bulb and an association with the Q Awards, not just as a sponsor but as the maker of the awards themselves.

Learn more about NetSuite for the retail industry.

Posted on Mon, December 11, 2017
by NetSuite filed under

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Modelling Deposit Price Elasticity: Challenges and Approach

This is the third in a series of blogs on deposit pricing, focusing on price elasticity modelling approaches and challenges.

The goal of any deposit price optimization solution is to make data-driven pricing decisions to manage portfolio balances and trade these off against the associated costs. These solutions should allow a pricing manager to prepare and run what-if analyses to assess the impact of pricing strategies, competitor price actions or movements in central bank base rates.

Fundamental to these solutions are price-elasticity models that capture and predict customer behavior as a response to pricing and other non-price factors. In this blog, we discuss the challenges and solution approaches for the development of robust price-elasticity models.

Price Response Signal

Price sensitivity can be measured with regards to product rate, market ranking, competitor rates or even interest paid to other products in the portfolio. The modelling challenge is not only to measure price sensitivity accurately, but to capture as much richness in pricing behavior as possible, e.g., variations in price sensitivity across different segments.

For example, ultra-low bank base rates have become the new normal in the US and Europe and it can be a challenge to isolate the price signal. There may be limited price variation in the modelling period or else one-time shocks caused by the presence of non-price related factors (such as Brexit) that drive balance flows. Previous experience of price sensitivity models across different markets, interest rate environments and transformation of price-related variables provides an anchor to avoid misdiagnosis of the price signal.

Cannibalization

In order to truly understand the impact of pricing decisions, the flows between individual products and segments must be understood. This allows the prediction of the balance distribution by product and requires a tried and tested process for inferring balance flows. Understanding and predicting balance flows across products in turn allows pricing managers to assess the impact of cannibalization on pricing decisions and overall portfolio revenue.

Data Availability & Granularity

One of the biggest challenges for the development of price sensitivity models is data availability. The development of overly complex models with insufficient data results in the often-cited adage of garbage-in, garbage-out, so it is important to ensure that the modelling approach is appropriate to the data available.

Finer granularity, where more modelling segments are used, introduces richer pricing behavior and allows greater insights into pricing decisions. This must be balanced with the need for sufficient deposits within each segment to ensure a statistically significant price signal can be measured.

Another consideration concerns the model resolution: An established deposit portfolio with relatively stable balances might only re-price a few times a year and a monthly granularity is sufficient. On the other hand, a smaller bank that needs to make shorter-term funding decisions might buy balances through the introduction of market-leading rates. This type of pricing action typically occurs more frequently and would therefore need a weekly granularity.

Modelling Methodologies

Depending on a bank’s objectives and the availability of data, there are a number of modelling approaches that might be considered.

Deposit Price Elasticity Modelling Approaches FICO Modelling Deposit Price Elasticity: Challenges and Approach

Model Management

It is vitally important that all stakeholders from the pricing analyst to senior management have confidence in pricing models. The best way to achieve this is to ensure full transparency of the models, methodology and the factors that drive predictions.

With a full understanding of model drivers, the business can justify why particular pricing decisions are made to internal stakeholders and also answer any challenges posed by external regulators that a “black box” solution could not.

An ongoing model management process should monitor model performance against established accuracy thresholds to guide model recalibration and redevelopment. This ensures that models respond to changes in the market and provides a mechanism whereby the impact of recent pricing decisions feed back into the price sensitivity models.

Conclusion

In order to develop deposit price sensitivity models, careful consideration is needed to evaluate an organization’s requirements and mitigate some of the challenges discussed here. The deployment of such models offers substantial improvements on approaches that rely exclusively on expert judgment. It allows banks to more effectively manage their deposit portfolio, better understand their customer behavior and preferences, and identify new revenue opportunities. It is also a critical component on the journey towards full price optimization where banks derive all the benefits that data driven models have to offer.

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Overcome 3 Challenges Faced by Professional Services Firms with a Specific CRM Solution

CRM Blog Overcome 3 Challenges Faced by Professional Services Firms with a Specific CRM Solution

The flexibility of a CRM solution such as Microsoft Dynamics 365 allows you to turn it into the heart of your organization’s operations. You can then use it as the entryway from which all your activities can be completed. It can also integrate a solution specifically designed for your industry, allowing you to complete all your tasks from a single interface that’s intuitive, familiar and user-friendly. With a solution that incorporates their specific business rules and needs, professional services firms can overcome several of the challenges they encounter daily.

1. Have only one version of the truth across your organization

One of the challenges that many professional services firms must face is the utilization of non-integrated applications and Excel spreadsheets. Result: resources don’t always have access to the latest information or the most recent version of a document, which can lead to discrepancies regarding facts or activity status. By centralizing all your information with a specific solution integrated to your CRM system, not only do you ensure access for resources across your entire organization, you also offer them complete visibility on all aspects of your business.

As such, your mandates, invoices and WIPs, as well as customer activities and all communications exchanged with them, can be accessed from a single source updated in real time. The associates have increased visibility on their mandates and their clients, as well as on their complete history and profitability. By having all this data on hand, they can also better serve their clients, exceed their expectations and ensure their satisfaction.

2. Shorten your invoicing cycle

Maintain the balance between finances and operations by leveraging the flexibility of your CRM system and the control of your ERP solution. With a complete, bidirectional integration, information is transferred from one system to the other in real time. This not only avoids constant back-and-forth communications between your associates and the accounting team, but it also eliminates manual data entry and reduces the risk of errors and double entries.

Various invoice templates can be used and combined from the CRM, then transferred to the accounting system. This allows you to create invoices that meet all your clients’ criteria and that fit the requirements of the different services offered by your organization, while also adapting them to your organization’s branding. Lastly, having access to your financial data in real time means that you can invoice faster. This way, you improve your cash flow while also reducing the risk of write-offs and bad debts.

3. Optimize the utilization and productivity rates of your resources

By using workflows to automate certain process, you can facilitate your associates’ tasks and activities. For example, they can be notified to approve the budget when a new mandate is created. Among the specific functionalities of our solution, a resource planning tool also makes it possible to view easily the resources that are currently assigned and those that are available to work on new mandates. This tool thus makes it possible to optimize resource utilization and better plan hiring. Lastly, the solution allows you to budget the potential for billable hours per resource per month and to track them throughout the year.

One last challenge that many professional services firms must face is the difficulty of staying on top of technological trends to meet the constantly evolving needs of their clients. Scalable and flexible, the Microsoft Dynamics 365 platform makes it possible to catch up on this technological lag, while a specific solution such as JOVACO’s accounting solution improves efficiency and performance across your entire organization.

By JOVACO Solutions, Microsoft Dynamics 365 specialist in Quebec

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Expert Interview (Part 2): Robert Corace of SoftServe Discusses Digital Transformation Challenges

In Part 1, Robert talked about how critical importance of digital transformation for organizations. In part two, highlights the results of recent research on digital transformation with a focus on the common challenges organizations face. He also provides some examples of  innovative strategies that companies such as Netflix and Amazon are using to tackle these digital transformation challenges.

What are the most common frustration or challenges your clients are coming to you with to solve? How do you help them?

As our recent research showed, security is the chief concern and the biggest challenge to solve. As I already mentioned, data mining and analytics is also a struggle for many, as well as experience design and organizational inflexibility.

On a higher, more strategic level, though, many companies understand that they need to transform, but they lack clear vision into what areas they need to focus on, where to start, and how to move forward with their transformative initiatives in the fastest and most efficient way. That’s why we have SoftServe Labs, in fact, to help our clients with research and proof-of-concept before they make large investments.

I wouldn’t describe these as purely challenges, though, as these companies also stand to gain a lot. Digital asset management, Cloud computing, mobile technologies, and the Internet of Things (IoT) approached as a part of digital transformation efforts can bring a lot of benefits to consumer facing operations, retail, the finance and banking sector, and many others.

What are some of the digital transformation challenges facing organizations today in harnessing their data?

Numerous security breaches and hacking attacks serve as a proof that we haven’t yet solved security challenges facing all businesses, small and large. Privacy is also a big concern, especially when it comes to access to personal data in healthcare, education, state and government organizations, etc.

blog digital transformation Expert Interview (Part 2): Robert Corace of SoftServe Discusses Digital Transformation Challenges

Data security is one of the common digital transformation challenges facing businesses today.

Another aspect of it is legacy software that cannot handle the amounts of data that require daily processing, and it can’t be all substituted within a couple of days due to financial and resource strains it would put upon the organizations. Artificial Intelligence (AI), though hugely promising, is not yet at that stage when it can automate decision making for truly impactful processes, beyond initial analysis. However, it can facilitate and speed them up considerably.

Related: The Future of Artificial Intelligence in Sales and Account Planning

It also is very important to remember that harnessing data is not an end in itself, but rather a means to help organizations achieve their business and strategic goals. And the consumer – a human being– is at the heart of all of it. So, no purely technical solution, no matter how powerful or innovative, will bring true value if it’s not applied correctly or as a part of a well-thought and comprehensive strategy.

What organizations do you feel have been especially forward-thinking and/or innovative at leveraging their data to solve? What can we learn from them to solve our own digital transformation challenges?

Well, when it comes to leveraging data and personalization, giants like Google and Netflix immediately come to mind. It’s interesting how thoroughly analyzing data and making the right predictions, Netflix managed to reduce the range of content available on their platform while improving customer satisfaction.

And look how Amazon is using data from different sensors and machine learning to disrupt the grocery business with their “Amazon Go” retail store.

When it comes to attracting new customers, which is also a challenge for traditional companies, I like the example of L’inizio Pizza Bar in New York. Their manager decided to attract Pokémon Go players to the place, and he spent just $ 10 to have Pokémon characters lured to his restaurant. The business went up by 75 percent. So, it’s never about technology or software only, it’s about innovative thinking and human ingenuity.

How can organizations manage their data assets more efficiently and effectively? What should their data management strategies include?

With the “Internet of Everything” and connected everything blurring the concepts of office and home devices as well as working hours and workplace, data assets need to be secure and protected and accessible from a variety of different devices, in different formats and easily searchable.

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For some organizations – most likely in the government sector, finance, and insurance, etc. – it will require switching to intranet to secure their assets from any unauthorized access or potential loss of information. For others, where remote access from any place, any time is a higher priority, omni-channel and compatibility will be the key focus. The challenges here include the already discussed legacy software and integration issues.

According to IDC research, by 2022 almost all data – 93 percent – in the digital universe will be unstructured. It will also, most likely be content in different formats, including audio and video files, images, interactive content, etc. Not only will this require greater storage and processing capacity, it also means that this data will need to be easily searchable and user friendly if we want it to be used versus stored.

When it comes to customer-facing content, another requirement is consistency across various channels. On the whole, when it comes to data, the current leaders in asset management are platform providers. With these platforms, instead of building their own solutions from scratch, which is a costly and time-consuming approach, businesses can quickly customize and scale a ready-made solution, adding and discarding additional features depending on their current needs.

What are some of the most exciting Big Data trends or innovations you’re following right now? Why do they interest you?

SoftServe’s 2016 Big Data survey showed 62 percent of organizations expect to implement machine learning by 2018, so apparently machine learning and Artificial Intelligence are huge Big Data trends we’re following right now. Chatbots as a customer-facing form of AI technology have gained momentum and are quickly becoming an area of huge interest for all kinds of user support activities.

But from a high-level perspective it’s nothing new, really. Once again, it’s all focused around building a better, different experience for a consumer, so machine learning, AI and chatbots are in fact just new(ish), possibly more effective ways to achieve the same goal: leveraging data to improve customer experience and stay relevant in an increasingly competitive marketplace.

For more on challenges driving digital transformations, download the eBook “Hadoop Perspectives for 2017” which offers an in-depth look at the results of Syncsort’s annual Hadoop survey, including five trends to watch for in 2017.

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