Tag Archives: 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.
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.
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.
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.
In the past couple of years, we’ve seen tremendous growth in demand for Syncsort DMX-h from large enterprises looking to leverage their Mainframe data on Hadoop. Syncsort DMX-h is currently the recognized leader in bringing Mainframe data into Big Data platforms as part of Hadoop implementation.
A large percentage of these customers have come to us after a recommendation from one of the Hadoop vendors or from a Systems Integrator, the hands-on people who really get the technical challenges of this type of integration. Some people might wonder why the leaders in this industry recommend Syncsort, rather than some of the giants in Data Integration. What kind of problems trip those giants up, and what does Syncsort have under the hood that makes the hands-on Hadoop professionals recommend it?
To get an idea of the challenges that Syncsort takes on, let’s look at some Hadoop implementation examples and what technical problems they faced.
Use Case 1: How Mainframe Data Can Go Native on Hadoop
A large enterprise was masking sensitive data on the Mainframe to use in mainframe application testing. This used a lot of expensive CPU time. They were looking to save the cost of MIPS by doing the masking on Hadoop, then bringing the masked data back to the Mainframe.
DMX-h has a unique capability to move Mainframe data to be processed in Hadoop without doing any conversion. By preserving the original Mainframe EBCDIC encoding and data formats such as Packed-Decimal, DMX-h could process the files in Hadoop without any loss of precision or changes in data structure. The original COBOL copybook was used by DMX-h to understand the data, including Occurs Depending On and Redefines definitions. After masking sensitive parts on Hadoop, that same copybook still matched the data when it was moved back to the Mainframe.
Keeping the data in its original Mainframe format also helped with governance, compliance and auditing. There is no need to justify data changes when the data is simply moved to another processing system, not altered.
Use Case 2: Efficiently Combining Complex Mainframe Data with Diverse Data Sources for Big Data Analytics on Hadoop
Another common use case Syncsort DMX-h does routinely is to move Mainframe data to Hadoop so it can be combined with other data sources, and be part of analytics run in MapReduce or Spark. We’ve seen plenty of situations when a customer has tried different solutions to ingest Mainframe data but has hit roadblocks that stalled its Hadoop implementation, where DMX-h easily handles the situation.
In one example, a customer had nested Occurs Depending On clauses in their COBOL copybooks. Their existing solution was expanding each of the records to the maximum occurrence length. This was causing the size of data to blow up hugely on Hadoop, and the ingestion was painfully slow. With DMX-h, the records were kept at their intended size and the ingestion proceeded at a much faster rate. The ingestion completed in under 10 minutes, as opposed to 4 hours with the existing solution.
In another example, the customer had VSAM data with many segments. Their existing solution was reading the VSAM file once for each segment, which was taking a lot of time and using up expensive processing on the Mainframe. If a VSAM file had for instance, 5 segments, the other solution had to read that same file 5 times over. DMX-h reads VSAM only once, partitioning the data by segment ID using a field on the copybook and then splits the data into separate segments, allowing each segment to be mapped by a different copybook, for further processing.
Use Case 3: Simplifying Mainframe Data Access
We have helped users who discovered that accessing Mainframe data isn’t as easy as other data formats. For example, they might have found that their tool doesn’t handle Packed-Decimal or LOW-VALUES in COBOL, or cannot transfer data securely from the Mainframe using Connect:Direct or FTPS.
Another big challenge during Hadoop implementation is getting the COBOL copybook to match the Mainframe data. The original COBOL developers may be long gone, and there is no one around who can fix the copybook. Our Professional Services team sees that all the time, and helps enterprises correct copybook problems so the value of the data can be fully realized.
In these and other practical situations, customers have told us they chose DMX-h as the faster, easier to use, and more cost-effective solution. Mainframe sources can be accessed and processed with DMX-h with just a few mouse clicks. The simplicity makes hard problems look easy.
We’ve spent a lot of time listening to our customer’s pains and aspirations for their Mainframe data. Our 40+ years of Mainframe and Data Integration expertise combined with active involvement in the Apache Hadoop community resulted in DMX-h’s strong Mainframe Access and Integration functionality. Another thing that sets us apart is our mature set of features related to security. Our ease of integration with Kerberos, and easy handling of encrypted or compressed data make a huge difference in production implementations.
For some more specifics, read where Arnie Farrelly, VP of Global Support and Services, has recounted some of what his team has experienced when working with large enterprises trying to leverage their Mainframe data in Hadoop.
And here is a short video demonstrating how to use DMX-h to access Mainframe data and integrate it with Hadoop.
The shortage of trained data scientists remains at the top of the list of data science challenges enterprises face today, according to new research from TDWI.
“We hear constantly that the biggest challenge any organization faces in a data science environment is finding the right skills,” said Fern Halper, vice president and research director at TDWI, based in Renton, Wash., in a webcast highlighting the recent findings.
The research surveyed more than 300 enterprises on their experiences with big data and data science. The two topics are increasingly blending into one another, as organizations need workers who can make sense of the massive troves of data they’ve been collecting over the last several years.
Other common challenges cited by survey respondents included lack of clarity around who owns certain data, lack of understanding of big data tools, lack of enterprise architectures needed to harness big data, security and privacy concerns, and insufficient governance protocols.
The technology piece appeared particularly vexing. Halper said many new tools have emerged within the last few years, including Hadoop, Spark, Python and others, and enterprises are having a hard time staying on top of all these rapid developments.
“Some respondents thought there were too many technologies and a lot of hype out there,” she said. “They didn’t know what to do. Others thought things are changing so fast, and they’re not nimble enough to maintain the best architectures.”
For now, enterprises are sticking with the tools they know, in part, to address these data science challenges. About 80% of survey respondents said they currently use data warehouse tools as their primary data source. For analysis, simple query and data visualization tools top the list of most used. Over the next two years, data warehouse tools will remain prominent, but the top two technologies enterprises plan to add during that time are Hadoop and open source R.
Halper said the results show clear momentum around unstructured data querying and predictive analytics, including machine learning. At the same time, it doesn’t look like these emerging tools and practices are going to completely unseat more tried-and-true tools in the foreseeable future.
“The data warehouse isn’t going away, but it’s being supplanted by these other types of platforms and creating an ecosystem,” she said. “There’s lots of momentum around predictive analytics. It’s a hot technology, and machine learning is making it hotter.”
These days it seems that we are witnessing waves of extreme disruption rather than incremental technology change. While some tech news stories have been just so much noise, unlikely to have long-term impact, a few are important signals of much bigger, longer-term changes afoot.
From bots to blockchains, augmented realities to human-machine convergence, a number of rapidly advancing technological capabilities hit important inflection points in 2016. We looked at five important emerging technology news stories that happened this year and the trends set in motion that will have an impact for a long time to come.
Immersive experiences were one of three top-level trends identified by Gartner for 2016, and that was evident in the enormous popularity of Pokémon Go. While the hype may have come and gone, the immersive technologies that have been quietly advancing in the background for years are ready to boil over into the big time—and into the enterprise.
The free location-based augmented reality (AR) game took off shortly after Nintendo launched it in July, and it became the most downloaded app in Apple’s app store history in its first week, as reported by TechCrunch. Average daily usage of the app on Android devices in July 2016 exceeded that of the standard-bearers Snapchat, Instagram, and Facebook, according to SimilarWeb. Within two months, Pokémon Go had generated more than US$ 440 million, according to Sensor Tower.
Unlike virtual reality (VR), which immerses us in a simulated world, AR layers computer-generated information such as graphics, sound, or other data on top of our view of the real world. In the case of Pokémon Go, players venture through the physical world using a digital map to search for Pokémon characters.
The game’s instant global acceptance was a surprise. Most watching this space expected an immersive headset device like Oculus Rift or Google Cardboard to steal the headlines. But it took Pikachu and the gang to break through. Pokémon Go capitalized on a generation’s nostalgia for its childhood and harnessed the latest advancements in key AR enabling technologies such as geolocation and computer vision.
Just as mobile technologies percolated inside companies for several years before the iPhone exploded onto the market, companies have been dabbling in AR since the beginning of the decade. IKEA created an AR catalog app in 2013 to help customers visualize how their KIVIK modular sofa, for example, would look in their living rooms. Mitsubishi Electric has been perfecting an AR application, introduced in 2011, that enables homeowners to visualize its HVAC products in their homes. Newport News Shipbuilding has launched some 30 AR projects to help the company build and maintain its vessels. Tech giants including Facebook, HP, and Apple have been snapping up immersive tech startups for some time.
The overnight success of Pokémon Go will fuel interest in and understanding of all mediated reality technology—virtual and augmented. It’s created a shorthand for describing immersive reality and could launch a wave of technology consumerization the likes of which we haven’t seen since the iPhone instigated a tsunami of smartphone usage. Enterprises would be wise to figure out the role of immersive technology sooner rather than later. “AR and VR will both be the new normal within five years,” says futurist Gerd Leonhard, noting that the biggest hurdles may be mobile bandwidth availability and concerns about sensory overload. “Pokémon is an obvious opening scene only—professional use of AR and VR will explode.”
Blockchains, the decentralized digital ledgers of transactions that are processed by a distributed network, first made headlines as the foundation for new types of financial transactions beginning with Bitcoin in 2009. According to Greenwich Associates, financial and technology companies will invest an estimated $ 1 billion in blockchain technology in 2016. But, as Gartner recently pointed out, there could be even more rapid evolution and acceptance in the areas of manufacturing, government, healthcare, and education.
By the 2020s, blockchain-based systems will reduce or eliminate many points of friction for a variety of business transactions. Individuals and companies will be able to exchange a wide range of digitized or digitally represented assets and value with anyone else, according to PwC. The supervised peer-to-peer network concept “is the future,” says Leonhard.
But the most important blockchain-related news of 2016 revealed a weak link in the application of technology that is touted as an immutable record.
In theory, blockchain technology creates a highly tamper-resistant structure that makes transactions secure and verifiable through a massively distributed digital ledger. All the transactions that take place are recorded in this ledger, which lives on many computers. High-grade encryption makes it nearly impossible for someone to cheat the system.
In practice, however, blockchain-based transactions and contracts are only as good as the code that enables them.
Case in point: The DAO, one of the first major implementations of a “Decentralized Autonomous Organization” (for which the fund is named). The DAO was a crowdfunded venture capital fund using cryptocurrency for investments and run through smart contracts. The rules that govern those smart contracts, along with all financial transaction records, are maintained on the blockchain. In June, the DAO revealed that an individual exploited a vulnerability in the company’s smart contract code to take control of nearly $ 60 million worth of the company’s digital currency.
The fund’s investors voted to basically rewrite the smart contract code and roll back the transaction, in essence going against the intent of blockchain-based smart contracts, which are supposed to be irreversible once they self-execute.
The DAO’s experience confirmed one of the inherent risks of distributed ledger technology—and, in particular, the risk of running a very large fund autonomously through smart contracts based on blockchain technology. Smart contract code must be as error-free as possible. As Cornell University professor and hacker Emin Gün Sirer wrote in his blog, “writing a robust, secure smart contract requires extreme amounts of diligence. It’s more similar to writing code for a nuclear power reactor, than to writing loose web code.” Since smart contracts are intended to be executed irreversibly on the blockchain, their code should not be rewritten and improved over time, as software typically is. But since no code can ever be completely airtight, smart contracts may have to build in contingency plans for when weaknesses in their code are exploited.
Importantly, the incident was not a result of any inherent weakness in the blockchain or distributed ledger technology generally. It will not be the end of cryptocurrencies or smart contracts. And it’s leading to more consideration of editable blockchains, which proponents say would only be used in extraordinary circumstances, according to Technology Review.
Application programming interfaces (APIs), the computer codes that serve as a bridge between software applications, are not traditionally a hot topic outside of coder circles. But they are critical components in much of the consumer technology we’ve all come to rely on day-to-day.
One of the most important events in API history was the introduction of such an interface for Google Maps a decade ago. The map app was so popular that everyone wanted to incorporate its capabilities into their own systems. So Google released an API that enabled developers to connect to and use the technology without having to hack into it. The result was the launch of hundreds of inventive location-enabled apps using Google technology. Today, millions of web sites and apps use Google Maps APIs, from Allstate’s GoodHome app, which shows homeowners a personalized risk assessment of their properties, to Harley-Davidson’s Ride Planner to 7-Eleven’s app for finding the nearest Slurpee.
Ultimately, it became de rigueur for apps to open up their systems in a safe way for experimentation by others through APIs. Technology professional Kin Lane, who tracks the now enormous world of APIs, has said, “APIs bring together a unique blend of technology, business, and politics into a transparent, self-service mix that can foster innovation.”
Thus it was significant when Apple announced in June that it would open up Siri to third-party developers through an API, giving the wider world the ability to integrate Siri’s voice commands into their apps. The move came on the heels of similar decisions by Amazon, Facebook, and Microsoft, all of which have AI bots or assistants of their own. And in October, Google opened up its Google Assistant as well.
The introduction of APIs confirms that the AI technology behind these bots has matured significantly—and that a new wave of AI-based innovation is nigh.
The best way to spark that innovation is to open up AI technologies such as Siri so that coders can use them as platforms to build new apps that can more rapidly expand AI uses and capabilities. Call it the “platformication” of AI. The value will be less in the specific AI products a company introduces than in the value of the platform for innovation. And that depends on the quality of the API. The tech company that attracts the best and brightest will win. AI platforms are just beginning to emerge and the question is: Who will be the platform leader?
In June, Swiss citizens voted on a proposal to introduce a guaranteed basic income for all of its citizens, as reported by BBC News. It was the first country to take the issue to the polls, but it won’t be the last. Discussions about the impact of both automation and the advancing gig economy on individual livelihoods are happening around the world. Other countries—including the United States—are looking at solutions to the problem. Both Finland and the Netherlands have universal guaranteed income pilots planned for next year. Meanwhile, American startup incubator Y Combinator is launching an experiment to give 100 families in Oakland, California, a minimum wage for five years with no strings attached, according to Quartz.
The world is on the verge of potential job loss at a scale and speed never seen before. The Industrial Revolution was more of an evolution, happening over more than a century. The ongoing digital revolution is happening in relative hyper speed.
No one is exactly sure how increased automation and digitization will affect the world’s workforce. One 2013 study suggests as much as 47% of the U.S workforce is at risk of being replaced by machines over the next two decades, but even a conservative estimate of 10% could have a dramatic impact, not just on workers but on society as a whole.
The proposed solution in Switzerland did not pass, in part because a major political party did not introduce it, and citizens are only beginning to consider the potential implications of digitization on their incomes. What’s more, the idea of simply guaranteeing pay runs contrary to long-held notions in many societies that humans ought to earn their keep.
Whether or not state-funded support is the answer is just one of the questions that must be answered. The votes and pilots underway make it clear that governments will have to respond with some policy measures. The question is: What will those measures be? The larger impact of mass job displacement, what future employment conditions might look like, and what the responsibilities of institutions are in ensuring that we can support ourselves are among the issues that policy makers will need to address.
New business models resulting from digitization will create some new types of roles—but those will require training and perhaps continued education. And not all of those who will be displaced will be in a position to remake their careers. Just consider taxi drivers: In the United States, about 223,000 people currently earn their living behind the wheel of a hired car. The average New York livery driver is 46 years old, according to the New York City Taxi and Limousine Commission, and no formal education is required. When self-driving cars take over, those jobs will go away and the men and women who held them may not be qualified for the new positions that emerge.
As digitization dramatically changes the constructs of commerce and work, no one is quite sure how people will be impacted. But waiting to see how it all shakes out is not a winning strategy. Companies and governments today will have to experiment with potential solutions before the severity of the problem is clear. Among the questions that will have to be answered: How can we retrain large parts of the workforce? How will we support those who fall through the cracks? Will we prioritize and fund education? Technological progress and shifting work models will continue, whether or not we plan for their consequences.
In April, a young man, who was believed to have permanently lost feeling in and control over his hands and legs as the result of a devastating spine injury, became able to use his right hand and fingers again. He used technology that transmits his thoughts directly to his hand muscles, bypassing his injured spinal cord. Doctors implanted a computer chip into the quadriplegic’s brain two years ago and—with ongoing training and practice—he can now perform everyday tasks like pouring from a bottle and playing video games.
The system reconnected the man’s brain directly to his muscles—the first time that engineers have successfully bypassed the nervous system’s information superhighway, the spinal cord. It’s the medical equivalent of moving from wired to wireless computing.
The man has in essence become a cyborg, that term first coined in 1960 to describe “self-regulating human-machine systems.” Yet the beneficiary of this scientific advance himself said, “You’re not going to be looked on as, ‘Oh, I’m a cyborg now because I have this big huge prosthetic on the side of my arm.’ It’s something a lot more natural and intuitive to learn because I can see my own hand reacting.”
As described in IEEE Spectrum, the “neural-bypass system” records signals that the man generates when thinking about moving his hand, decodes those signals, and routes them to the electric sleeve around his arm to stimulate movement: “The result looks surprisingly simple and natural: When Burkhart thinks about picking up a bottle, he picks up the bottle. When he thinks about playing a chord in Guitar Hero, he plays the chord.”
What seems straightforward on the surface is powered by a sophisticated algorithm that can analyze the vast amounts of data the man’s brain produces, separating important signals from noise.
The fact that engineers have begun to unlock the complex code that controls brain-body communication opens up enormous possibilities. Neural prostheses (cochlear implants) have already reversed hearing loss. Light-sensitive chips serving as artificial retinas are showing progress in restoring vision. Other researchers are exploring computer implants that can read human thoughts directly to signal an external computer to help people speak or move in new ways. “Human and machine are converging,” says Leonhard.
The National Academy of Engineering predicts that “the intersection of engineering and neuroscience promises great advances in healthcare, manufacturing, and communication.”
Burkhart spent two years in training with the computer that has helped power his arm to get this far. It’s the result of more than a decade of development in brain-computer interfaces. And it can currently be used only in the lab; researchers are working on a system for home use. But it’s a clear indication of how quickly the lines between man and machine are blurring—and it opens the door for further computerized reanimation in many new scenarios.
This fall, Switzerland hosted its first cyborg Olympics, in which disabled patients compete using the latest assistive technologies, including robot exoskeletons and brainwave-readers. Paraplegic athletes use electrical simulation systems to compete in cycling, for example. The winners are those who can control their device the best. “Instead of celebrating the human body moving under its own power,” said a recent article in the IEEE Spectrum, “the cyborg games will celebrate the strength and ingenuity of human-machine collaborations.” D!
Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.
As the popularity of Google Maps, Snapchat and Pokémon Go has made clear, location-based technologies have revolutionized how people use mobile phones. By 2018, there will be 2.5 billion smartphone users worldwide, according to eMarketer Inc. They will be navigating to restaurants, Snapchatting their vacations, checking movies playing at the nearest cinema, RSVPing on Meetup or looking up product availability at area Walmart stores.
According to the Pew Research Center, 90% of smartphone owners use them to get information related to their location. Now, companies are starting to tap the location-based services (LBS) on consumers’ phones in order to send them relevant offers and messages. According to the Location Based Marketing Association’s (LBMA) latest trends report, 75% of marketers agree and believe that location-based marketing is an important business issue for 2016.
Location-based technologies use wireless transmission, such as between a smartphone and a beacon or Wi-Fi access point, to pinpoint a user’s location. A mobile app that has access to a phone’s location services can provide navigation as well as location-specific content, like coupons or product reviews. In fact, there are myriad uses for LBS in marketing, advertising and customer engagement.
MEPLAN GmbH, a German trade services provider, created the expoNAVIGATION app to help conference attendees find their favorite exhibitors faster. A user searches a database of exhibitors and enters a list of those he wants to visit. The app uses beacons to plot the shortest route around the floor, thus optimizing the customer’s time and, hopefully, boosting sales for exhibitors.
The Aquarium of Western Australia (AQWA), based in Hillarys, Australia, has a mobile app that guides visitors along several themed tours (like the Shipwreck Coast or Animal Extremes tours) with interactive activities for kids. Created by Apps Ppl, a developer of cloud-based mobile apps, the AQWA app is part of a larger mobile app — “Everythere” — that tourists use to research activities around Perth and to get directions.
Location is the only piece of data that lets you know where people are throughout the day so you can engage [with] them. Asif Khanpresident, Location Based Marketing Association
But it’s the ability to combine location data with other customer information collected from a mobile app or store loyalty program that has the biggest potential for personalizing how businesses engage with their customers. People often use smartphones to browse the web, make online purchases and pay at the checkout counter. That information, and more, can be accessed and used to understand the buyer’s habits and shopping preferences.
“[LBS] can tie your entire marketing strategy together,” said Asif Khan, president of the LBMA. “We’re using location to blend brick and mortar with e-commerce and digital.”
The data can also be aggregated and combined with other consumer information and used to analyze consumer behaviors and trends.
Investment in location-based technologies will rise significantly in the near future, according to Juniper Research Ltd., based in Hampshire, England. The firm expects the LBS market to jump from $ 12.2 billion in 2014 to $ 43.3 billion by 2019, with context-aware mobile services being the main driving force.
Potholes in the road to location-based technologies
Nevertheless, businesses have been cautious about adopting LBS, despite their interest. Forrester Research’s report “Make Smart Wireless Location Technology Decisions” found that just 3% of businesses surveyed were actually using beacons, while another 11% were piloting them.
This is because location-based services are actually a collection of technologies — some old, some new and others still in research and development. It’s a rapidly developing market, one that can quickly confound an unsuspecting marketer or business owner.
The most common technologies are the following:
GPS: GPS systems are commonly used for maps and other outdoor navigation. They can’t penetrate walls and aren’t accurate enough for use in small spaces, so they’re not used for indoor tracking.
Wi-Fi: Already generally offered free to customers, Wi-Fi is often used for simple tracking of store traffic. One downside is it can’t identify unique individuals if they’re using an iPhone, and accuracy can vary.
Bluetooth beacons: Used primarily for indoor navigation, beacons have an accuracy range of one to several meters, depending on the product and whether fingerprinting or triangulation techniques are also used. Available in sizes as small as a matchbook, they can be hidden behind pictures or in lights. According to Forrester’s June 2016 report “Make Smart Wireless Location Decisions,” some beacons can send only basic data and can’t accept updates, while others are more flexible. Beacons also require maintenance.
“You have to place them, manage them, change batteries in them. They’re operationally intensive,” explained Andre Kindness, principal analyst at Forrester, which estimated an annual maintenance cost of $ 240,000 for keeping beacons operational in a 1,000-square-meter store versus $ 60,000 for Wi-Fi. On the other hand, Wi-Fi nodes run $ 900 a piece, according to the same study.
Two emerging technologies are visible light, emitted via smart LED lights and potentially capable of tracking with an accuracy of a few centimeters, and ultrasound waves, which send out chirps that are picked up by a phone’s audio receiver.
Both are promising technologies, said Bruce Krulwich, chief analyst at New York-based Grizzly Analytics Ltd., which specializes in mobile technologies like location-based services and IoT. However, both have their drawbacks, as well. For visible light, businesses have to replace their lighting with smart LED lights and controllers. Meanwhile, ultrasound may trip on other ambient sounds.
With more work, however, both may achieve performance better than today’s Bluetooth-based solutions, said Krulwich.
Fear of big brother
Consumer concerns about privacy are another challenge for location-based technologies. To work well, the apps need access to the phone’s location services, and consumers can deny apps access. According to the Pew Research Center, over one-third of adults and 46% of teenagers turn off location services due to fears over privacy.
The Los Angeles County Museum of Art’s (LACMA) mobile app requires both Bluetooth and location services to be fully functional. To encourage participation, the museum pre-empted the usual terse system messages users get asking if they want to share location data with one that asks, “Would you like to receive location-based data?”
“We created it to be less intimidating,” said Tomas Garcia, digital media product developer at LACMA. He added that it’s also faster than using individual service prompts.
In fact, users will give up their location data for the right incentive. A Forrester brief, “Fuel Contextual Marketing with Location Data,” found that most phone owners would do so in exchange for benefits like discounts, a loyalty program, rewards for visiting a store or to get navigational aid in a store.
A location-based future?
Location is rapidly becoming the most valuable piece of information for consumer marketing.
Social media platforms and many mobile apps, like weather and news, already collect a user’s location information, said Khan, and they make it available in aggregate form to advertisers.
“We describe location as the cookie for the physical world. Location is the only piece of data that lets you know where people are throughout the day so you can engage [with] them,” he said.
Behavioral data, such as location, is fast becoming more important in marketing than standard demographics, said Maribel Lopez, head of mobile marketing research firm Lopez Research, based in San Francisco.
“Behavioral demographics are much more interesting,” said Lopez. “You may find that Android users do this, iOS users do that, people who are in my store 10 minutes do one thing, while those who stay much longer do another.”
But that also means marketers must think through their messages to target customers’ preferences without making them feel stalked.
“The greatest challenge will be to figure out what messages you want to send and where,” said Lopez. “It’s the most contextual engagement you can have, and people expect engagement, not a generic message or coupon.”
The Dutch banking and financial services company Rabobank had been doing predictive analytics for several years, maintaining models that projected which customers might default on their mortgage or abandon an account application midway through the process.
These were mainly built around structured data analysis, but in 2015, the analytics team started receiving more requests for models that would have to delve into unstructured data. Enter cognitive computing applications.
In a presentation at IBM’s World of Watson event in Las Vegas, Muriel Serrurier Schepper, business consultant for advanced analytics at Rabobank, said her team started seeing more demand for analytics of customer feedback and mortgage files, both of which are unstructured free-text files. This demanded a new technology — in this case, IBM’s Watson platform — but also a new approach to thinking about analytics.
Cognitive apps are more business than tech challenge
A big part of the growing excitement behind artificial intelligence (AI) and cognitive computing is the possibility of simple tools. IBM and other vendors, such as Microsoft, Amazon and Facebook, are introducing tools that promise cognitive capabilities without requiring the user to be an expert programmer or data scientist. So, in some cases, the biggest hurdles to overcome for enterprises looking to adopt cognitive tools are organizational, not technical.
At Rabobank, Serrurier Schepper helped create a group dedicated to all things AI. This group identifies use cases, selects technologies and shares information about projects throughout the organization. She said this kind of centralized approach is important in this time of rapid development of AI tools.
Around the time the group was created, Serrurier Schepper said she was seeing lines of business talking with technology vendors about AI tools. She was worried that the situation could lead to duplicated work, siloed projects and inflated expectations. The centralized approach has helped mitigate these problems by allowing people who know the technology to lead projects.
“AI is everywhere, and people think it’s so fantastic. And these companies, including IBM, come in and then you go to do a project and see that it’s not really that great yet,” Serrurier Schepper said. “You have to train a model, and it takes time.”
After building a centralized AI unit, teams should look for quick wins and then publicize their success, Serrurier Schepper said. Models may take a long time to train, but once they’re delivering strong results, sharing this with the rest of the company can help build support for future initiatives.
“We’ve been a bit under the radar at our company,” Serrurier Schepper said. “We didn’t want everyone wondering what’s going on as our models learn. But now, we’re out there telling people what we can do.”
Pick the right use case for cognitive tools
Choosing the right use cases for cognitive computing applications is also important. There is a general notion that AI software can perform just about any task. And while that may be the ultimate goal of the technology, today’s tools are a ways off from that. Enterprises need to identify business problems where the technology is competent, and that’s not always a simple proposition.
“Sometimes, it’s tough, because with most of these problems, you first have to get your hands dirty in the data before seeing if there’s any value there,” Gianluca Antonini, director of IT at Swiss Re, said in a presentation at the conference. “The business case isn’t always clear.”
If you get too hung up on ROI, you’ll never do anything. Abhijit Singhhead of the business technology group at ICICI Bank Ltd.
The Zurich-based reinsurance company has rolled out Watson-based cognitive applications in several areas, including enterprise search, claims processing and chatbots that serve as internal assistants, as well as customer service agents.
To address this, Swiss Re has set up an internal analytics consulting service within the IT department. This team sits down with lines of business to determine if they have a need for cognitive computing applications. And together, the two sides of the house shape projects. The analytics team then develops proof-of-concept projects, which may or may not succeed. Projects have to perform well in this stage before being rolled out in a wider production environment.
Businesses need to accept a certain rate of failure with AI projects if they want to reap the benefits of the technology. In a panel discussion, Abhijit Singh, head of the business technology group at ICICI Bank Ltd., said experimental projects are just as likely to turn into tools that reshape the business as they are to fail completely. Enterprises need to continue supporting these initiatives, even if the immediate payoff isn’t clear.
“If you get too hung up on ROI, you’ll never do anything,” he said.
Singh and his team recently developed a chatbot for use in customer service built around a homegrown cognitive system. He said it started as an experiment and nobody bothered projecting a return.
It’s now performing well and is an example of the kind of benefit businesses can reap if they remain open to the possibilities of cognitive computing applications. “There are things coming that are opening up huge opportunities,” he said.
With more digital work being done than ever before, the sheer volume of data that marketing departments collect is growing each and every day. As a result of this influx of data, marketers now have more opportunity to drive better outcomes for their organizations. Marketing departments need to gain deeper insights into their marketing data in order to maximize their impact and ROI. However, analysts must deal with the increasing volume and complexity of the data before they can deliver timely insights relating to important areas such as campaign, website and social analytics.
There are several challenges that analysts face, but two of the most crucial are Manual Manipulation of the data and Lack of Insight due to the flood of data. Manually hunting for and combining data is inefficient, and there is no easy way to translate the flood of marketing data into insights and action.
So, how can you, as an analyst, combat these challenges and get more value from your marketing data at the point of impact?
Join this webinar and learn how you or your analysts can:
- Quickly blend marketing data from multiple sources to deliver a comprehensive view of your marketing initiatives.
- Prepare and cleanse your data in 1/10th the time it normally takes, and automate the process for easy repeatability.
- Gain deeper insights into your marketing data by utilizing predictive analysis with no coding required.
- Visualize your data in interactive reports and dashboards that can be accessed with any browser and shared with everyone in your organization.
Join us Thursday Oct. 27th 10 am PDT
Noisy neighbors in IT are just as frustrating as they are in real life — particularly when it comes to colocation…
Step 2 of 2:
By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners. You can withdraw your consent at any time. Contact TechTarget at 275 Grove Street, Newton, MA.
and cloud. If someone has an application that consumes so many network and compute resources that it negatively affects other users’ apps, in essence, it behaves like a denial of service attack on your system. However, the last thing you want to do is to throttle back your performance, allowing the neighbor to get on with the party.
One of the differences between cloud versus colocation is that public cloud is a fully multi-tenant environment while colocation is partially shared. With cloud, everything — the server, storage and network — is shared; in colocation, although the main servers and storage, along with a portion of the networking, belongs to you, there will be a shared portion of the LAN and the wide area network (WAN) that can cause major problems.
Let’s consider the noisy neighbor issue in the cloud versus colocation.
A public cloud platform is predicated on users sharing as much as possible when it comes to resources. The user rarely has much control over the physical infrastructure, having bought into the use of logical resource “chunks.” Platform management is also controlled by the cloud provider.
A large cloud provider, such as Amazon Web Services or Microsoft Azure, is unlikely to run out of cloud resources. Effective resource elasticity should ensure that even a resource-intensive application doesn’t adversely impact other workloads around it.
However, other cloud services — particularly those based on a small number of white box servers, a cheap network switch and a network attached storage array — will run into problems as soon as they get more than one user. There aren’t enough physical resources to share; as soon as one application starts to burst, it will try to grab resources. If that burst is due to the application behaving badly — for example, if it has a memory leak or a poorly programmed re-entrant code loop — that provider is doomed. The lack of capability to share resources means that something has to give, and in a poorly managed environment, the whole platform could collapse.
In a public cloud environment, the cloud provider must be able to monitor in real time. It must have written into its agreements with users the proper procedures for a rogue workload. At minimum, this should include a rapid warning to the workload’s owner.
Avoid providers that just try to sell you a large initial instance of a logical platform so that you have more headroom to play with — this is no different than overprovisioning in the physical world.
The more established public cloud providers will also make part of that information available to all users so they can account for it with their own workload management tools. There is nothing more annoying for a systems administrator than when everything looks fine on a fully detailed set of logs on their own system only to discover that the damage is due to a neighboring user that allowed some developer to write a subroutine.
Avoid providers that just try to sell you a large initial instance of a logical platform so that you have more headroom to play with — this is no different than overprovisioning in the physical world. Look for those who can provide dedicated logical resource slices with guaranteed service level agreements. It will cost more, but having a guaranteed amount of network bandwidth could be the best guarantee against being hit by a third-party noisy neighbor.
Another difference between colocation versus cloud is that, with colocation, you own a cage, rack, cabinet or room in which you run your company’s applications and functions on your hardware. The main server-to-server network will be fully under your control. But when you need outside access, you will need to use shared infrastructure such as the data center LAN and WAN.
You also have a responsibility to make sure that you aren’t the noisy neighbor. Use tools that enable you to monitor in real time. Being able to monitor the data center LAN and WAN is key to making sure you are not a noisy neighbor. This is also a way to see if someone else is being one.
Many colocation providers will have their own systems management capabilities, from simple sys admin tools to full data center infrastructure management tools. Look for those providers who are willing to share data from these systems so that both parties are monitoring.
Look to the colocation providers who are network-agnostic, enabling the use of multiple WAN providers. If a noisy neighbor impacts your traffic, you can switch all or part of your workload over to an alternative network. With the LAN, you may need to look to dedicated, guaranteed logical bandwidth. In extreme scenarios, consider dedicated network connections from your equipment to the WAN, in addition to dedicated WAN connections.
A well-managed colocation environment is where the colocation provider monitors what is going on and provides network switching and management services. This environment also includes full facility management, which will ensure noisy neighbors do not impact your workloads.
No discussion of big data is complete without addressing mainframe data. Depending on whom you ask, about 60 to 80 percent of all the transactional data in the world is stored on mainframes. This transactional data is a gold mine of reference data that can be used to make sense of enterprise-wide data and drive your big data analytics, but getting it off the mainframe is, well, challenging. That is especially true if you need to get it off the mainframe, yet keep the mainframe data format. Here are the challenges associated with integrating mainframe data into Hadoop, while allowing organizations to work with mainframe data in Hadoop or Spark in its native format ̶ essential for maintaining data lineage and compliance.
Due to their cost-effective scalability, Hadoop and Spark have taken hold in just about every large enterprise. However, industries such as banking, insurance, and healthcare, haven’t been able to fully leverage these platforms because they have a lot of critical data on the mainframe, which can’t be altered due to regulatory mandates. However, Syncsort’s DMX-h software allows you to quickly access mainframe data unchanged. It can then be integrated with other enterprise data sources, without the need for specialized skills in either Hadoop or mainframe. By copying the data via DMX-h, you can preserve the data lineage for the purposes of governance while eliminating much of the latency often associated with these tasks. It just takes a few simple clicks to do.
Challenge: Addressing the Hadoop Connectivity Issues with the Mainframe
It’s been problematic to integrate mainframe data into Hadoop because there is no native connectivity and processing capabilities in Hadoop for mainframe data. Syncsort DMX-h solves this issue, allowing organizations to work with mainframe data in Hadoop or Spark in its native format ̶ essential for maintaining data lineage and compliance. Furthermore, it offers support for FTPS and Connect:Direct. In fact, Syncsort says their customers are telling them that with the current release of DMX-h, they have delivered a solution that will allow them to do things that were previously impossible. They say that Syncsort both simplified and secured the process of accessing and integrating mainframe data with Big Data platforms, and now help organizations with governance when loading mainframe data into Hadoop.. Since Syncsort is a contributor to both Apache Sqoop and Apache Spark open source library for accessing the mainframe, DMX-h extends these connections in order to offer additional support for file type, data type, and COBOL Copybook.
It can take a frustrating amount of time and effort to load database tables into Hadoop, primarily because developers must develop individual loads for each and every table. With DMX Data Funnel™ , you can easily ingest hundreds of DB2 tables into Hadoop, all in one single swoop. It also allows you to extract and migrate entire database schemas in a single invocation. One customer had this to say: “Syncsort’s Data Funnel utility has been a powerful tool in our Data Lake strategy. We were able to ingest into Hadoop over 800 tables from one source system … with one press of the button, all while leveraging our existing DMX-h install. Its configuration-based approach provides great flexibility from source to target. Data Funnel is a powerful data pump for our Data Lake”.
Challenge: There is Only a Limited Amount of Time to Access Mainframe Data
Access to mainframe data is limited to short periods of time in which users have to extract extremely large quantities of data. Attempting to translate and unpack the data in transit takes too much time. With Syncsort DMX-h, data can be copied from the mainframe to Hadoop, while keeping the mainframe formatting, very efficiently. After the data is in Hadoop, DMX-h is able to take advantage of the distributed resources of the clusters in order to access and integrate the data natively, without staging a translated copy.
Does Syncsort DMX-h sound like the perfect option for you? You can see this product and all of Syncsort’s Big Data solutions here.
The challenges of Office 365 eDiscovery manifest differently based on the product you’re using within that suite…
Step 2 of 2:
By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners. You can withdraw your consent at any time. Contact TechTarget at 275 Grove Street, Newton, MA.
of services. When we talk about Office 365 it’s not just one product.
Microsoft Sway is one example. It’s a brand new way of telling a story — the new PowerPoint for the 21st century. That’s all well and good, except it has a very different modality for sharing information, very little in the way of controls when it comes to sharing that information and probably not a great deal of control for compliance.
It’s important to pay attention to the products your employees are using. Your organization should carefully control how those products are being introduced to your employee population and ensure that the products have a minimum level of compliance capability built-in.
Another common Office 365 eDiscovery challenge is locating conversations in social media, and enterprise social in particular. When you look at Office 365, conversations can happen inside of SharePoint and Yammer. For eDiscovery purposes, conversations can also be comments embedded within a Word document stored in SharePoint or Yammer, or held in OneDrive. Skype for Business has a whole conversational element as well.
When faced with an eDiscovery scenario, it can be difficult for organizations to go out and find all of the content, because there are so many different places where it could be stored, and not necessarily all stored in the right way. In certain cases, the nomenclature is going to be different enough that not even search tools are going to be able to find everything you’re looking for.
Before embarking on an eDiscovery product implementation, companies should keep in mind that Microsoft has addressed some Office 365 eDiscovery concerns, but not universally across the suite of services.
This entry passed through the Full-Text RSS service – if this is your content and you’re reading it on someone else’s site, please read the FAQ at fivefilters.org/content-only/faq.php#publishers.