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Amid today’s digital era, many travel companies are still living in the “analog” world of developing relationships with customers. The “mass production-mass market” mindset that emerged out of industrial revolution has unfortunately survived through the advent and maturity of the World Wide Web.

Mass market mentality combined with lack of a data strategy heavily emphasizes products and services, putting the customer last. Most companies don’t go beyond mass communication with consumers, which results in one-way conversations.

Today’s customer is well-informed, leveraging social media to learn more about the product, seek recommendations/opinions, and provide product feedback. If companies have not leveraged data or digital capabilities to re-imagine the purchase path and inspire customers to buy their product or service, they are well behind the curve. PepsiCo is one company that has been successful in rethinking the role of the customer in a brand. LAY’S s “Do us a Flavor” campaign resulted in customers creating and voting for new flavors.

We are seeing a changing mindset in the travel and transportation industry. Uber and Airbnb are disrupting business models through customer-centric design, shared economy, and simplicity. To improve based on customer feedback, Airbnb's CEO used Twitter to ask users for product feedback.

Thanks to IoT, digital cloud technologies, and the declining cost of storage, data is collected from everywhere – internal systems, transactional systems, external (social media) sources, and devices.

However, in many cases we are seeing that data is not leveraged for better customization:

  • Data is not consolidated and lives in silos.
  • Only “structured” data is looked at.

Customers are to provide more personalization – offering the right product at the right time in the right situation. Customers expect the level of personalization provided by Netflix and Amazon in almost every only interaction, regardless of whether the company is in the retail, entertainment, or travel industry. When they experience features, such as a recommendation engine, they get to what they need faster and company revenues increase in turn.

If you notice one thing that’s common between an Uber, Airbnb, or Amazon for that matter, it is the platform. Platforms are becoming the core of the digital economy, and they enable enterprises to provide curated personalized experiences for their customers.

Every large enterprise is building a platform (Enterprise API) to encapsulate core business logic that can be served to upstream channels like mobile, websites, call center, and kiosks. These APIs get opened for external business consumption to build new business models and partnerships. Using Data Lake technology, these enterprises are able to build a data management platform that can aggregate, build, and enrich customer/consumer data. By applying big data technologies, machine learning, and advanced analytics, enterprises can build a 360-degree view of the customer.

This results in a customer-centric strategy. Customers expect to be put first and are demanding from travel companies. If they perceive that their airline or hotel chain of choice is behind the curve in personalization, they just might take their loyalty elsewhere. The cost of acquiring a new customer is at least four times more than keeping an existing customer. This, combined with the increase in revenue from up-selling and cross-selling that personalization provides, should put technology initiatives that increase personalization at the top of the list for any travel company.

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2018 Analyst IIoT Predictions

Each year we like to go inside FreeWave and ask our team what the Industrial IoT forecast looks like for the upcoming year. Throughout 2017 we were hard at work developing some of our industry-leading Edge intelligence and industrial Wi-Fi products, so this year, instead of looking inward, we decided to take a peek around the world at 2018 IIoT predictions from some of the leading experts.

Network World

Based off a Forrester report, three immediate trends spring to the forefront: specialization, security, and Edge infrastructure. Taking a bird’s eye view, as the market proliferates, many Industrial IoT providers will no longer need to be a one-size-fits-all solution, instead being able to double down on proprietary technology that has a highly specific and specialized purpose. Edge Infrastructure, already one of the hottest sectors of IoT, will possibly determine the future of big data and predictive analytics, in turn driving machine learning and beyond. And then, of course, there is the security element.

As the domains of Operational Technology (OT) and Information Technology (IT) converge, the traditionally more vulnerable standards and practices of OT will take on more of an IT flavor, incorporating more hardened cybersecurity elements as IT managers (with security ALWAYS on their minds) take on more prominent roles in industrial operations and implement the next generation of IoT-ready devices and systems.

IDC

In early November, IDC put together a list of 10 predictions for IIoT covering myriad facets of the industry, including:

  • As much as a 25 percent increase in security spending
  • 10 percent growth in IoT sensors on Blockchain distributed ledgers
  • In three years more than $1 trillion of enterprise IoT project investments will be built on net new technology spending

These are interesting predictions and fall in line with the general trend of the industry over the last five years. But there was one prediction that caught our eye:

  • “By 2020, IT spend on Edge Infrastructure will reach up to 18 percent of the total spend on IoT Infrastructure, driven by deployments of converged IT/OT systems that reduce the time to value of data collected from their connected devices.”

Essentially, IDC is predicting that in two years Edge intelligence will use nearly 20 percent of the industry’s total IoT spend. This Edge intelligence will be driven by IT/OT convergence that enables faster data transmission via Fog Computing, enabling predictive analytics and real-time data monitoring. This is a significant note, as many companies are focused almost exclusively on figuring out how to transmit data from the Edge in usable packets.

Maciej Kranz, vice president of strategic innovation at Cisco

Kranz wrote the book on IoT (literally, check it out: Building the Internet of Things), and he tends to view it from more of a business standpoint. However, as more companies attempt to jump into the IoT fray, taking a strong – and long – business perspective could be the difference between success and failure.

In his ten predictions, Kranz finds similar footing with many analysts and thought leaders (paraphrasing):

  • IoT will become the key security domain as organizations ‘finally begin to take IoT security seriously.’
  • IoT will revolutionize data analytics as technology shifts to dynamic or real-time analytics and streaming data using AI and machine learning
  • The focus of IoT will move from driving efficiency to creating new business value as companies use IoT to create new value propositions: in manufacturing mass customization, and more mass personalization.

To us, however, the most interesting prediction offered up by Kranz has to do with standardization:

  • “We will see an industry-wide, accelerated move to open standards, open architectures and interoperability.”

At FreeWave, we have been huge proponents of opening up architectures to make the creation of IIoT software applications easier and more accessible to critical industries. Currently, many IIoT software needs require sophisticated and complex development chops. But, with the rise of NODE Red – and with the growth of language agnostic hardware – development and interoperability opportunities are opening up for everyone.

2018 could be a watershed year for the Industrial IoT. We highlighted three analyst and thought leader predictions here, but many carried the same tenor: security, analytics and proliferation will drive the growth of the industry over the next few years.

We’d love to hear from the community as well: what predictions do you have for IIoT in 2018?

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By capturing real time inventory data from vending machines, smart shelves and other instrumented sources of retail data, you learn customer preferences which let you quickly manipulate product mix to increase sales.

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Can the Public Internet Secure Our Digital Assets?

There is a lot of talk, and, indeed, hype, these days about the internet of things. But what is often overlooked is that the internet of things is also an internet of shared services and shared data. What’s more, we are becoming too heavily reliant on public internet connectivity to underpin innovative new services.

Take this as an example. Back in April, Ford Motor Company, Starbucks and Amazon announced and demonstrated an alliance that would allow a consumer to use Alexa to order and pay for their usual coffee selection from their car. Simply saying, “Alexa: ask Starbucks to start my order,” would trigger the sequence of events required to enable you to drive to the pickup point and collect your already-paid-for coffee with no waiting in line.

Making that transaction happen behind the scenes involves a complex integration of the business processes of all the companies involved. Let’s be clear: this is about data protection. For this series of transactions to be successfully handled, they must be able to share customer payment data, manage identity and authentication, and match personal accounts to customer profiles.

Because all of that critical data can be manipulated, changed or stolen, cyberattacks pose significant data protection risks for nearly any entity anywhere. The ambition of some of these consumer innovations makes an assumption that the “secure” network underpinning this ecosystem for the transfer of all that valuable personal data is the public internet. And that’s the point – it’s not secure.

As we’ve talked about previously on Syniverse's blog Synergy, the public internet poses a systemic risk to businesses and to confidential data. In short, when we are dealing on a large scale with highly sensitive data, the level of protection available today for data that, at any point, touches the public internet is substantially inadequate.

And this alliance between Ford and Starbucks is just one example of the type of innovation, across many different industry and consumer sectors, that we can expect to see a lot of in the very near future. These services will connect organizations that are sharing data and information about businesses and about consumers – about their purchase history, their preferences and requirements, and also about their likely future needs. This is potentially a very convenient and desired service from a consumer’s point of view, but at what cost?

We need security of connectivity, security from outside interference and the security of encrypted transfer and protection for our personal and financial data. And we need to be able to verify the protection of that data at all times by ensuring attribution and identity – both concepts we’ll explore more deeply in an upcoming blog post. And that’s a level of security that the public internet simply cannot provide.

Last month, an internet-based global ransomware attack took down systems and services all over the world – affecting sensitive personal healthcare data in the U.K. in particular.

Whether it is personal health records, financial records, data about the movement of freight in a supply chain, or variations in energy production and consumption, these are digital assets. Businesses, institutions and government bodies all over the world have billions of digital assets that must be constantly sent to and from different parties. And those assets require the type of high-level data protection that is not currently possible because of the systemic risk posed by the insecure public internet.

As mentioned in my last blog post on Synergy, there is an alternative. Some companies using private IP networks were able to carry on regardless throughout the high-profile cyberattacks that have been capturing headlines in the last year. That’s because those companies were not reliant on the public internet. Instead, they were all using what we are beginning to term “Triple-A” networks on which you can specify the speed and capacity of your Access to the network while guaranteeing the Availability of your connection. What’s more, on a Triple-A network, Attribution is securely controlled, so you know who and what is accessing your network and the level of authority granted both to the device accessing the network and to its user.

The public internet cannot provide or compete with a Triple-A level of security, and nor should we expect it to. It cannot live up to the stringent data protection requirements necessary for today’s critical digital assets. We cannot remain content that so much infrastructure, from banking, to transport and to power supplies, relies on a network with so many known vulnerabilities. And we must consider whether we want to carry on developing an industrial internet of things and consumer services on a public network.

We will continue to explore these issues on this blog, to highlight different approaches, and examine the requirements of the secure networks of the future. And in the process, we’ll take a look at the work being done to build more networks with a Triple-A approach.

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How Can You Cope With The Rise Of Dark Data

At this point, everyone has heard about what big data analytics can do for marketing, research, and internal productivity. However, the data only about 20% of all data created is collected and analyzed. The other 80% is known as dark data, or data that collected but not analyzed or made to be searchable. So, what is the purpose of this data, and why is it taking up terabytes worth of storage space on servers around the world?

Examples of Dark Data

  • Media: Audio, video and image files oftentimes will not be indexed, making them difficult to gain insights from. Contents of these media files, such as the people in the recording or dialogue within a video, will remain locked within the file itself.

  • Social Data: Social media analytics have improved drastically over the last few years. However, data can only be gathered from a user’s point of entry to their exit point. If a potential customer follows a link on Facebook, then send the visited website to five friends in a group chat, the firm will not realize their advertisement had 6 touchpoints, not just the one.

  • Search Histories: For many companies, especially in the financial service, healthcare, and energy industries, regulations are a constant concern. As legal compliance standards change, firms worry that they will end up deleting something valuable.

As analytics and automation improve, more dark data is beginning to be dragged out into the light. AI, for example, is getting far better at speech recognition. This allows media files to be automatically tagged with metadata and audio files to be transcribed in real time. Social data is also starting to be tracked with far better accuracy. In doing so, companies will be able to better understand their customers, their interests, and their buying habits. This will allow marketers to create limited, targeted ads based on a customers location that bring in more revenue while reducing cost.

The explosion of data we are currently seeing is only the tip of the big data iceberg. As IoT and wearable devices continue their integration into our daily lives, the amount of data we produce will only grow. Companies are looking to get ahead of the curve and ensure they can gain as much insight from this data as possible. If these firms do not have a plan to create actionable insights from this currently dark data, they ultimately could fall behind and lose out to competitors with a bigger focus on analytics.

The original story was published on ELEKS Trends Blog, visit to get more insights. 

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An Open and Dangerous Place

Let’s just say it: The public internet is great, but it’s an unfit, wide-open place to try to conduct confidential business.

More and more, the public nature of the internet is causing business and government leaders to lose sleep. The global ransomware attacks this year that crippled infrastructure and businesses across Europe clearly shows the concern is not only justified but also growing.

As a result, internet and privacy regulations, like GDPR and PSD2, are front and center as governments around the world increasingly look at the web and how it’s being used. This is creating competing and contradictory objectives.

On the one hand, governments want to protect consumer privacy and data; on the other, they want to be able to monitor what certain folks are up to on the internet. And in both cases, they can at least claim to be looking to protect people.

Regardless of the difficulty of the task, there is no doubt the big governments are circling and considering their options.

Speaking in Mexico in June, Germany Chancellor Angela Merkel touted the need for global digital rules, like those that exist for financial markets, and that those rules need to be enforceable through bodies like the World Trade Organization.

From a business perspective, I can applaud the ambition, but it does seem a little like trying to control the uncontrollable. The truth is that the public internet has come to resemble the old Wild West. It is an increasingly dangerous place to do business, with more than its fair share of rustlers, hustlers, and bandits to keep at bay.

The public internet connects the world and nearly all its citizens. When it comes to connecting businesses, national infrastructures, and governments themselves, trying to regulate the Wild West of the public internet simply isn’t an option. Instead, it’s time to take a step back and look for something different.

We believe organizations that want to conduct business, transfer data, monitor equipment and control operations globally – with certainty, security and privacy – should not be relying on the public internet. The sheer number of access points and endpoints creates an attack surface that is simply too wide to protect, especially with the increased trending of fog and edge networks that we’ve discussed on previous Syniverse blog posts.

Just last week, the online gaming store CEX was hacked. In an instant, around two million customers found their personal information and financial data had been exposed. Consumers in America, the U.K. and Australia are among those affected. As I said, the public internet presents an ever-widening attack surface.

Recently on the Syniverse blog, we’ve been talking about the need to develop private, closed networks where businesses, national utilities and governments can truly control not just access, but activity. Networks that are always on and ones where the owners always know who is on them and what they are doing. Networks that are private and built for an exact purpose, not public and adaptable.

Trying to apply or bolt on rules, regulations and security processes after the fact is never the best approach.  Especially if you are trying to apply them to a service that is omnipresent and open to anybody 24/7.

When we look at the public internet, we see fake actors, state actors, hackers and fraudsters roaming relatively freely. We see an environment where the efforts to police that state might raise as many issues as they solve.

Instead, it’s time for global businesses to build a new world. It’s time to leave the old Wild West and settle somewhere safer. It’s time to circle the wagons around a network built for purpose. That is the future.

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Digital transformation is no longer a buzzword. No industry is left behind. 
We’re currently in a new wave of  digital transformation with new technologies, processes, business models and opportunities popping up in the market faster than we can blink & think.
All the companies are using  big datamachine learningcloud, smart devices and  Internet of Things to achieve digital. But these are just means or vehicle to achieve it whereas you need a human to drive it.
It is easy to get wrapped by technology but without considering human element the transformation process will fail.
CEOs are taking a digital-first approach to change the  culture of organizations. This shift starts at the top and requires complete employee buy-in to achieve success.
Digital transformation can’t thrive unless your organization has a culture that’s willing and able to embrace it. Organization-wide adoption requires teams to change their attitude, automate the processes, shift their thinking and reject the status quo.
People are engaged by people. Productive and satisfied employees who like their work, go out of the way to satisfy customers. 
How to get this human element on your side in Digital Transformation?
·        Know your customers – customers are not just records but they are also humans, know their  behaviors, their motivations, what they like, dislike and their desires
·        Engage with employees – elaborate on what is in it for me, people need to know what is the change and how it will benefit them
·        Focus on human collaboration, learning, and innovation for digital which yields better ideas, better results
Digitization is by no means de-humanization. It is 20% technology but 80% human touch. Without a strong involvement and without taking the human element into account on all levels, digital projects are going to fail.

The best results will occur when technology and humans collaborate to create an entire ecosystem, which technology alone cannot achieve.

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The other day we were discussing and debating on a solution to be designed to meet the sensing needs for access, temperature and humidity for some devices with form part of a networking infrastructure ecosystem. The idea was to build a IoT based system for monitoring and control.

The design discussions veered around the ability to collect data from the sensors and the types of short range communication protocols which could be deployed .Questions and clarification were raised if we were compliant to use short range communication protocols in sensitive areas as customer Data Centres which are like owned and  that they may be custodians of data of their end customers .

The hidden perils of data acquisition and data ownership reared its head which needed to be addressed as we moved forward .

The data which is acquired by sensors is essentially Machine Generated Data (MGD) .This post will  dwell on the subject of data ownership of MGD as follows :

  1. Sensors ( Data Acquisition and Communication )
  2. Machine Generated Data
  3. The Lifecycle of the MGD and the Ownership Paradigm
  4. Who should be the owner of the MGD?
  5. Sensors (Data Acquisition and Communication):

In the IoT ecosystem, the physical computing frontier is managed by the Sensors .Sensors essentially include three fundamental functions:

  • The act of sensing and acquiring the data
  • Communication of the data through appropriate protocols to communicate their readings to internet cloud services for further aggregation and trend analysis
  • The activity is energised by power supply,

The additional functions would include processing/system management and user interface

The Digital Computing part comprises the IoT application. This   is determined by the types of sensors, cloud connectivity, power sources, and (optionally) user interface used in an IoT sensor device. The following diagram showcases the primacy of sensors in a typical IoT Ecosystem.

When making physical measurements such as temperature, strain, or pressure, we need a sensor to convert the physical properties into an electrical signal, usually voltage. Then, the signal must be converted to the proper amplitude and filtered for noise before being digitized, displayed, stored, or used to make a decision. Data-acquisition systems use ADCs (analog-to-digital converters) to digitize the signals with adequate signal conditioning.

Sensor data communication to the cloud can be done in multiple ways from wireline to wireless communication of various complexities. While wire line communication has some important benefits (such as reliability, privacy, and power delivery over the same wires), wireless communication is the technology that is the key catalyst in the majority of IoT applications that were not previously practical with wired systems. Reliability, channel security, long range, low power consumption, ease of use, and low cost are now reaching new levels, previously thought infeasible

Some examples of recently popular IoT wireless communication types: Wi-Fi, Bluetooth Low Energy (aka Smart), Zigbee (and other mesh 802.15.4 variants), cellular, LPWA (Low-Power, Wide-Area network variants: Ingenu, LoRaWAN, Sigfox, NB-LTE, Weightless), and Iridium satellite.

  1. Machine Generated Data (MGD)  :

Sensor data is the integral component of the increasing reality of the Internet of Things (IoT) environment. With IpV6 , anything can be outfitted with a unique ip address with  the capacity to transfer data over a network. Sensor data  is essentially Machine Generated Data . MGD is that is produced entirely by devices / machines though an event or observation.

Here we would define human-generated data, what is recorded is the direct result of human choices. Examples are buying on the web, making an inquiry, filling in a form , making payments with corresponding updates on database. We would not consider the ownership of this data in the post and would be limiting our post to MGD.

  1. The journey of the MCD and the Ownership Paradigm:

The different phases exist in the typical  journey of Machine Generated Data .

Capture and Acquisition of Data– This is a machine or a device based function through signal reception.

Processing and Synthesis of the Data – This is a function which ensures enrichment and integration of Data

Publication of the Data – This is done by expert systems and analysts who work on exception management , triggers and trends .

Usage of Data – The action which need to be taken on the processed and reported information is used by the end user .

Archival and Purging of Data – This function is essentially done by the data maintenance team with supervision.

Now let us dwell on the Ownership Paradigms .They range from the origination of data , adding value to the data through make over , monetising of data through insights generated. Interestingly, let us explore if there is any conclusive method for determining how ownership should be assigned. A number of players may be involved in the journey of the data (e.g. the user, hardware manufacturer, application developer, provider of database architecture and the purchaser of data, each having an equal lay of the claim in different stages of this journey )

  1. Who should be the owner of MGD :

Let me share the multiple and conflicting views  :

  1. The owner of the device which records Data .In essence, the owner of machine-generated data(MGD), is the entity who holds title to the device that recordw the data. In other words, the entity that owns the IoT device also owns the data produced by that device.

But there could be a  lack of clarity if the device is leased rather than owned.. When real-world constructs such as lease holdings of (say servers) come into play, it indeed gets complex and even murky.

  1. Who should be the owner of MGD :

Let me share the multiple and conflicting views  :

The owner of the device which records Data .In essence, the owner of machine-generated data(MGD), is the entity who holds title to the device that recordw the data. In other words, the entity that owns the IoT device also owns the data produced by that device.

But there could be a  lack of clarity if the device is leased rather than owned.. When real-world constructs such as lease holdings of (say servers) come into play, it indeed gets complex and even murky.

The owner is the user of the Data :The other dimension is data may be owned by one party and controlled by another. Possession of data does not necessarily equate to title. Through possession there is control. Title is ownership. Referred to as usage rights, each time data sets are copied, recopied and transmitted, control of the data follows it. There could be cases where the owner of the device could be the user of the data.

 The maker of the Database who essentially invests in aggregating, processing and making the data usable is the owner of the Data :This has a number of buyers of this paradigm . The owner of a smart thermostat does not, for example, own the data about how he uses it. The only thing that is ‘ownable’ is an aggregation or collection of such data provided there has been a relevant investment in carrying out that aggregation or collection (the individual user is very unlikely to have made that investment). The owner here could be the Home automation company . The value which could be generated though this investment could be producing market intelligence , exploiting the insights form data to build market presence and differentiation ,

The purchaser of Data could be the owner of the Data: An auto insurance company could buy the  vehicle generated data ( from the makers of automobiles )  and could design a product for  targeted offerings to specific market segments based on say driving behaviour patterns  and  demographics  .This may not be as easy as this seems – refer the url  :  http://joebarkai.com/who-owns-car-data/ which states that the owner of the vehicle and not the maker of the car owns the data collected from the electronic data recorder .

The value chain of who owns the data can be a complex one with multiple claimants . As one aggregates more sources it just gets more complicated. A good example is in the making of smart cities. The sources of data can be from multiple layers and operational areas . City authorities would be making the effort to make use of the data in areas of waste management , traffic congestion , air pollution etc . So does the city authority own the data?

My personal take is , if someone in the MGD value chain  is making the data usable for  a larger good , and  in the process may monetize the data to cover the investments , that entity deserves to  be the owner of the data  as that is where value is generated .


Posted on August 14, 2017

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Why Data Visualization Matters Now?

Data Visualization is not new, it has been around in various forms for more than thousands of years. 
Ancient Egyptians used symbolic paintings, drawn on walls & pottery, to tell timeless stories of their culture for generations to come.
Human brain understands the information via pictures more easily than writing sentences, essays, spreadsheets etc. You must have seen traffic symbols while driving…why do they have only 1 picture instead of writing a whole sentence like school ahead, deer crossing or narrow bridge? Because you as driver can grasp the image faster while keeping your eyes on the road.
Over last 25 years technology has given us popular methods like line, bar, and pie charts showing company progress in different forms, which still dominate the boardrooms.
Data visualization has become a fundamental discipline as it enables more and more businesses and decision makers to see  big data and  analytics presented visually. It helps identify the exact area that needs attention or improvement than leaving it to the leaders to interpret as they want.
Until recently making sense of all of that raw data was too daunting for most, but recent computing developments have created new tools like Tableau, Qlik with striking visual techniques, especially for use online, including the use of animations.
There is a wealth of information hiding in the data in your database that is just waiting to be discovered. Even historical complicated data collected from disparate sources start to make sense when shown pictorially.  Data Scientists do a fantastic job of analyzing this data using  machine learning, finding relationship but communicating the story to others is the last milestone.
In today's  Digital age, we as consumers generate tons of data every day and businesses want to use that for  hyper-personalization, sending right offers to us by collecting, storing & analyzing this data. Data Visualization is the necessary ingredient to bring power of this big data to mainstream.
It is hard to tell how the data behaves in the data table. Only when we apply visualization via graphs or charts, we get a clear picture how the data behaves. 
Data visualization allows us to quickly interpret the data and adjust different variables to see their effect and technology is increasingly making it easier for us to do so. 
The best data visualizations are ones that expose something new about the underlying patterns and relationships contained within the data. Data Visualization brings multiple advantages such as showing the big picture quickly with simplicity for further action.
Finally as they say “A picture is worth a thousand words” and it is much important when you are trying to show the relationships within the data.
Data is the new oil, but it is crude, and cannot really be used unless it is refined with visualization to bring the new gold nuggets
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Go Digital or Die - What will you chose?

Just before 2007, we didn't have access to smartphones like iPhone or social media apps like Instagram, Whatsapp and even email was much more limited only to desktops. 
Zoom in to Today -  Digital Transformation has revolutionized everything we do. It has been one of the hottest topics for every business. It’s a subject which keeps the CEOs awake. 
Today it is Digital or Die.
Digital is happening fast and forcefully, whether businesses are ready for it or not. You can’t hide from it. There is a possibility that five of out ten businesses like Blockbuster, Kodak and Borders that will become  digital dinosaur because of their lack of ability to adapt.
Going digital is not about moving to a specific technology like  Cloud or  Big DataAnalytics but it is really about  accommodating a change of how technology enables business. Billions of people across the world are attached to a global high-speed, real-time Internet. 
There are over 7+ billion mobile connections worldwide. In couple of years, Millennials will make up half of the working population. They expect highly personalized products and services, they want instant-gratification and they are omni-channel, online anytime, anyplace and any device. Using  Mobile firstas your strategy to go digital is no-brainer.
As technology becomes an increasing part of our everyday lives, it also becomes a vital part of business strategy to become more efficient in customer service and disrupt the market with exemplary  customer experience.
Business models are changing, from products to services and have to have a sharp focus of extraordinary customer experience with digital, like Apple. To transform to digital, companies must place customer experience at the center of digital strategy.
Customers really want access to support via digital channels without the intervention of customer reps, unless they don’t find what they are looking for at the first point of contact or something goes wrong with the product which needs to be fixed quickly.
Burberry was one of the first players to turn their fashion shows into digital happenings. The company used the buzz around the events to lure its customer base, interact with and strengthen relationships with customers, and attract new ones.
Nike had moved on from a sports apparel company to fitness driven personalized  wearables like FuelBand manufacturer.
Apple, Disney, Nordstrom and Nestle are just a handful of the household names that have mastered digital.
It’s a never-ending program of improvement. As important as the technologies and channels, are the employee training and mastering the skill set that empowers them to thrive in this more integrated and ‘digital first’ environment. 
Working from home is adopted by many organizations and moving to cloud based systems enables your employees to do that more effectively. They can access all relevant work content and more. 
Digital should not be bolt-on to home grown age old systems but must be central theme for every touch point to customer and internal processes.
Every company is a technology company today. The pace of digital is rising exponentially, making it very difficult to be the leaders in market. Your thereat is not your traditional competitor but someone who comes up with innovative ideas to steal your  customers.
As Charles Darvin once said - It is not the strongest of the species that survives, nor the most intelligent that survives, it is the one that is the most adaptable to change

It is Digital or Die. You are an easy prey if you don’t change.

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The digital revolution has created significant opportunities and threats for every industry. Companies that cannot or do not make significant changes faster to their business model in response to a disruption are unlikely to  survive
It is extremely important to do digital maturity assessment before embarking on  digital transformation.
Digital leaders must respond to the clear and present threat of digital disruption by transforming their businesses. They must embed digital capabilities into the very heart of their business, making digital a core competency, not a bolt-on. Creating lasting transformative digital capabilities requires you to build a  customer-centric culture within your organization.
This requires new capabilities that organizations need to acquire and develop which include disruptive technologies like  Big Data, AnalyticsInternet of Things, newer business models.
Digital maturity model measures readiness of the organization to attain higher value in digital  customer engagement, digital operations or digital services. It helps in incremental adoption of digital technologies and processes to drive competitive strategies, greater operationally agility and respond to rapidly changing market conditions.
Business can use the maturity model to define the roadmap, measuring progress on the milestones.
The levels of maturity can be defined as per multiple reports available and

adopt the ones which makes more sense to your business.

·     Level 1 : Project based solutions are developed for a particular problem, no integration to home grown systems, unaware of risks and opportunities
·     Level 2 : Departmentalized projects but still not known to organization, little integration
·     Level 3 : Solutions are shared between the departments for a common business problem, better integration
·     Level 4 : Organization wide efforts of digital, highly integrated, adaptive culture for  fail fast  and improve
·     Level 5 : Driven by CXOs, customer centric and complete transformation changes happen to organization
Here are the 7 categories on which business should ask questions to all the stakeholders to gauge the maturity of Digital Transformation and identify the improvement and priorities.
1.   Strategy & Roadmap - how the business operates or transforms to increase its competitive advantage through digital initiatives which are embedded within the overall business strategy
2.   Customer – Are you providing experience to customers on their preferred channels, online, offline, anytime on any device
3.   Technology – Relevant tools and technologies to make data available across all the systems
4.   Culture – Do you have the organization structure and culture to drive the digital top down
5.   Operations – Digitizing & automating the processes to enhance business efficiency and effectiveness.
6.   Partners – Are you utilizing right partners to augment your expertise
7.   Innovation – How employees are encouraged to bring the continuous innovation to how they serve the customers
Finally you know when you are digital transformed?
·             When there is nobody having “Digital” in their title
·             There is no marketing focused on digital within the organization
·             There is no separate digital strategy than company’s business strategy
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Augmented reality (AR) means amplified reality with graphics, sounds, haptic feedback and smell to the natural world as it exists. Virtual objects and information are displayed on top of the physical world, will make its way to our phones.
Just like the  Internet of Things &  Big dataAnalytics, augmented reality is going mainstream.Search engines are already expanding on image search, allowing you to point your camera at something and search for information based on what the lens takes in.
Both video games and cell phones are driving & exploiting the development of augmented reality. Everyone from tourists to someone looking for the closest McDonalds can now benefit from the ability to place computer-generated graphics in their field of vision.
Unlike  Virtual Reality, which creates a totally artificial environment like you are on the top of Eiffel tower or looking at Taj Mahal right now from your living room couch, augmented reality uses the existing environment and overlays new information on top of it.
Pokemon Go released in 2016 was the most successful game to use AR to superimpose Pokemon on physical background and all children and adults were mad chasing them in real world.
Recent innovation, Heads-Up Display (HUDs) glass with AR superimpose crystal-clear driving directions on top of the real world so you can easily navigate without taking your eyes off the road. It’s like Pokemon Go but all the adorable monsters have been replaced by driving directions.
Digital Marketing will get a boost with AR.  A new augmented reality campaign from Pepsi Max have stunned people in London, giving experiences like a prowling tiger, a meteor crashing, an alien tentacle grabbing people on the street, the bus stop window serves as a scarily realistic screen to bring these scenarios to life.
With AR, you can view your living room on a smartphone and see how virtual furniture would fit into the real world and decide what is good to buy.
Artificial Intelligence has brought virtual assistants like Siri, Alexa, Cortana, Google to life but AR can put a face to it and beef up the experience. Microsoft Hololens is currently leading the AR headset race. 
There are several industries that will benefit from AR applications, including healthcare, tourism and entertainment. However, it is  retailers who are the ones to use it more. With AR, your retail website is brought to life with a 360° online presentation of your store. In-store, augmented reality can easily display information and other visuals on packaged items with a simple image scan.
Lego’s “Digital Box” Provides Customers with an Interactive 3D Digital Experience. Aside from kiosks in stores, soon they will have mobile devices to be equipped with the capability to instantly bring up relevant information about any product in real-time.
Fashion retailer Forever 21 had put up a giant billboard which features a model walking in front of an image of the crowd below. The model occasionally leans over, and pluck someone out of the crowd. Sometimes, she drops them in her bag and happily walks off.
French cosmetic super chain Sephora is one of the leaders in AR marketing area. Their mobile apps & AR mirrors allow people to see how clothing, jewelry, and accessories look on them.
Augmented Reality cleverly blurs the line between the digital and the real by way of specially designed apps and unique visual ‘markers’ to intuitively visualise 3D virtual forms in physical realms.
We are still in the very early days of AR, and all of the future possibilities are difficult to imagine at this point. As this technology advances and gets more affordable, it will be easier for businesses to take advantage of it. AR helps to bridge the divide between the  digital and offline world.
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With its growing prevalence, the Internet of Things is ushering in a new form of ecommerce – the Commerce of Things, where everyday objects are internet connected and capable of initiating a series of purchases on their own. This new way of buying and selling online is radically changing traditional ecommerce rules and creating a new set of challenges for companies. In this new world of commerce, the product sale is no longer just a transaction; it’s the beginning of an ongoing relationship between brands and customers. Successful online brands are focused on nurturing this relationship – and taking deliberate steps to turn transactional customers into loyal members. 

There is a subtle but critical difference between a repeat customer and a member. Understanding this difference is the key to succeeding in an environment that is swiftly becoming a hyper-connected network of consumers who value the access and amenities that come with membership.

How do you build these relationships?

1.)   Create lasting relationships to make members out of customers. Members share the experience and the story of the brand, rather than just execute a basic business transaction or product purchase. For years, Disney, where everything is a show and employees are cast members, has stood by the adage “Be Our Guest,” calling to their customers in a more intimate, personable way. Cable companies refer to their customers as “subscribers;” LinkedIn has always called users “members.”

To move customers from “transaction to membership” on a relationship continuum, companies must provide extra, incremental value that replaces pure monetary benefits with more intangible rewards of being, in Disney’s case, a guest.

2.)   Use data and metrics to strengthen relationships. Once a company starts to grow its base of members, a whole new set of metrics becomes the benchmark for evaluating the customer relationship.

Asking one simple question, “What is a subscriber’s actual usage?” can yield revelations regarding whether someone is a transactional customer or an invested member. For example, January is the peak season for signing new members at fitness centers around the country. Are those who sign up then really members? If they are not actually getting personal value out of their membership, then the relationship remains transactional and fleeting at best.

Good data is powerful. If the data shows customers are not acting like members, then a company can follow up to discern the true nature of the relationship and figure out how it can become more valuable to the customer. This creates a win for both the customer and the company. 

Delta Airlines’ SkyMiles program, for example, makes great use of data to cut through barriers that could otherwise prevent strong relationships from developing. When members call in, the automated phone system quickly recognizes callers based on their phone numbers, addresses them by name and asks about recent or upcoming trips.

Personalizing interactions, continually making improvements and utilizing customer insights are key in this new, Commerce of Things world. Taking these steps can help transform transactional customers into loyal members – and take an online business to the next level.

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Digital Transformation is a phenomenon that every company must deal with and it is a reality. It is a top priority for boardroom executives. Most companies know that digital transformation is vital to survival.
Customers are demanding new instant experiences, partners want more visibility & greater access, and employees want greater convenience and work from anywhere.
Many companies are claiming that they have started it but those initiatives are isolated or tactical.  If not executed properly the only result is failure.
As you will look at weather reports, travel times, flight connections, hotel reviews before going on holiday journey, similarly you will need a road map for navigation from point A to point B.
The digital roadmap has 3 main leavers:
·        Strategy: to make it completely clear how digital transformation support overall business strategy,  define 30,60,90 days & beyond plan for measurements
·        Technology:  what are the tools and technologies you will need to go from current state to future state –  big dataanalyticsmobilityIoTcloud, microservices etc, dedicated hardware, software innovation labs, standards, guidelines, security
·        Processes & People: who are the leaders to drive the digital, what is the organization structure, operational integration of all processes, how to change to customer centric culture, training to employees and empower them
It is all about starting with  baby steps, gaining trust from business by delivering quick value and celebrating and marketing the successes to generate internal buzz.
The roadmap begins with a digital vision, mission & assessment of the digital maturity of your business today. Once the assessment and vision are completed, then next step it becomes possible to identify the systemic gaps that need to be filled. Then those steps can be built into the roadmap.
Here are the broad milestones of a successful digital transformation roadmap:
·        Boardroom/Senior management buy in, decision to go Digital and drive it across organization
·         Cultural alignment & commitment to Digital from board of directors to entry level employees
·        Identify and assess the current state of the organization on Digital
·        Put Customer first - Prepare customer  journey maps to identify all the touch points with organization
·        Find out pain areas at each touch point and respective stakeholders involved who can correct them
·        Prioritize and break them in small projects to adopt  fail fast approach. If anything did not work, just accept the failure, publish the learnings and move on.
·        Seek partners to help you in your journey, who take shared risk and shared rewards
·        Deploy agile implementation approach for quick results
·        Market your successes to whole world and repeat the process for next pain area
Transformation programs may be massive and take place over multiple years, but understanding the ROI for each phase helps keep a multi-year journey on track. With a structured approach, all of the moving parts can be managed and progress sustained throughout this journey.
Finally, you know when you are digital transformed?
·        When there is nobody having “digital” in their title
·        There is no separate digital strategy than company’s business strategy
·        There are no posters or marketing focused on digital within the organization
Enterprises that adapt, evolve and exploit this new digital reality will thrive, while those that do not, will be lost to the sands of time like  Dinosaur.
Read more…
Since many embedded devices are deployed outside of the standard enterprise security perimeter, it is critical that security be included in the device itself. Ultimately, some combination of hardware and software may be required. Building protection into the device itself provides a critical security layer whatever options are used. Security must be considered early in the design of a new device or system.
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What are Digital Twins?

Digital Transformation has brought in all the new concepts and technologies at the hands of consumers and businesses alike.
Digital Twin is one of them. It is a virtual image of your machine or asset, maintained throughout thelife cycle and easily accessible at any time. It involves  internet of things connected devices generating real time data in  Big Data platform.  This data is further analyzed in the  cloud.
With a digital twin, machine manufacturers are able to use the power of digitalization to achieve improved efficiency and quality. This approach helps ensure optimized machine design and smooth operation.
Today, machine intelligence and connectivity to the cloud allows a huge potential of digital twin technology for companies in a variety of industries
Digital Twin allows the asset operator to predict precisely when maintenance will be required based on the unique conditions, experienced by that particular asset.
GE has built a digital wind farm collecting data from turbine sensors, which uses big data and the Industrial Internet to drive down the cost of renewable electricity.
Here are the several advantages of Digital Twin technology:
·        Explore the impact of various design alternatives
·        Do simulations and testing to ensure that product designs will meet requirement
·        Understand how a projected change to a manufacturing process might impact costs or schedule
·        see the current operating status along with any recent alarms and maintenance performed on a machine
·        be instructed on how to perform proper maintenance procedures, for the specific problem they’re addressing
·        Preventing the failure, or anticipating it and doing the required  maintenancebefore failure occurs, can shorten outages
Digital twins give airlines a better idea of what happens when a jet flies through a flock of birds, or through dust storms in hot environments.
The digital twin, combined with advanced analytical tools and  machine learning, will provide a platform that changes the traditional way of how we look at the analysis of asset’s condition and performance.

It will enable a new generation of advanced  predictive analytics.
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Manufacturers seek quantifiable ROI before making leap to IIoT implementation

By now, most manufacturers have heard of the promise of the Industrial Internet of Things (IIoT).

In this bold new future of manufacturing, newly installed sensors will collect previously unavailable data on equipment, parts, inventory and even personnel that will then be shared with existing systems in an interconnected “smart” system where machines learn from other machines and executives can analyze reports based on the accumulated data.

By doing so, manufacturers can stamp out inefficiencies, eliminate bottlenecks and ultimately streamline operations to become more competitive and profitable.

However, despite the tremendous potential, there is a palpable hesitation by some in the industry to jump into the deep end of the IIoT pool.

When asked, this hesitation stems from one primary concern: If we invest in IIoT, what specific ROI can we expect and when? How will it streamline my process such that it translates into greater efficiencies and actual revenue in the short and long term?

Although it may come as a surprise, the potential return can actually be identified and quantified prior to any implementation. Furthermore, implementations can be scalable for those that want to start with “baby steps.”

In many cases, this is being facilitated by a new breed of managed service providers dedicated to IIoT that have the expertise to conduct in-plant evaluations that pinpoint a specific, achievable ROI.

These managed service providers can then implement and manage all aspects from end-to-end so manufacturers can focus on core competencies and not becoming IIoT experts. Like their IT counterparts, this can often be done on a monthly fee schedule that minimizes, or eliminates, up-front capital investment costs.


DEFINING IIOT

Despite all the fanfare for the Internet of Things, the truth is many manufacturers still have a less-than-complete understanding of what it is and how it applies to industry.

While it might appear complicated from the outside looking in, IIoT is merely a logical extension of the increasing automation and connectivity that has been a part of the plant environment for decades.

In fact, in some ways many of the component parts and pieces required already exist in a plant or are collected by more manual methods.

However, a core principle of the Industrial “Internet of Things” is to vastly supplement and improve upon the data collected through the integration of sensors in items such as products, equipment, and containers that are integral parts of the process.

In many cases, these sensors provide a tremendous wealth of critical information required to increase efficiency and streamline operations.

Armed with this new information, IIoT then seeks to facilitate machine-to-machine intelligence and interaction so that the system can learn to become more efficient based on the available data points and traffic patterns. In this way, the proverbial “left hand” now knows what the “right hand” is doing.

In addition, the mass of data collected can then be turned into reports that can be analyzed by top executives and operations personnel to provide further insights on ways to increase operational savings and revenue opportunities.

In manufacturing, the net result can impact quality control, predictive maintenance, supply chain traceability and efficiency, sustainable and green practices and even customer service.


BRINGING IT ALL TOGETHER

The difficulty, however, comes from bridging the gap between “here” and “there.”

Organizations need to do more than just collect data; it must be turned into actionable insights that increase productivity, generate savings, or uncover new income streams.

For Pacesetter, a national processor and distributor of flat rolled steel that operates processing facilities in Atlanta, Chicago and Houston, IIoT holds great promise.

“At Pacesetter, there are so many ways we can use sensors to streamline our operation, says CEO Aviva Leebow Wolmer. “I believe we need to be constantly investigating new technologies and figuring out how to integrate them into our business.”

Pacesetter has always been a trendsetter in the industry. Despite offering a commodity product, the company often takes an active role in helping its customers identify ways to streamline operations as well.

The company is currently working with Industrial Intelligence, a managed service provider that offers full, turnkey end-to-end installed IIoT solutions, to install sensors in each of its facilities to increase efficiency by using dashboards that allow management to view information in real time.

“Having access to real-time data from the sensors and being able to log in and see it to figure out the answer to a problem or question so you can make a better decision – that type of access is incredible,” says Leebow Wolmer.

She also appreciates the perspective that an outsider can bring to the table.

“Industrial Intelligence is in so many different manufacturing plants in a given year and they see different things,” explains Leebow Wolmer. “They see what works, what doesn’t, and can provide a better overall solution not just from the IIoT perspective but even best practices.”

For Pacesetter, the move to IIoT has already yielded significant returns.

In a recently completed project, Industrial Intelligence installed sensors designed to track production schedules throughout the plant. The information revealed two bottlenecks: one in which coils were not immediately ready for processing – slowing production – and another where the skids on which they are placed for shipping were often not ready.

By making the status of both coil and skids available for real time monitoring and alerting key personnel when production slowed, Pacesetter was able to push the production schedule through the existing ERP system.

This increased productivity at the Atlanta plant by 30%. Similar implementations in the other two facilities yielded similar increases in productivity.


TAKING THE FIRST STEP

According to Darren Tessitore, COO of Industrial Intelligence, the process of examining the possible ROI begins with a factory walk-through with trained expertise in manufacturing process improvement and IoT engineers that understand the back-end technologies.

A detailed analysis is then prepared, outlining the scope of the recommended IIoT implementation, exact areas and opportunities for improvement and the location of new sensors.

“The analysis gives us the ability to build the ROI,” says Tessitore. “We’re going to know exactly how much money this will make by making the changes. This takes much of the risk out of it so executives are not guessing how it might help.”

Once completed, a company like Industrial Intelligence can then provide a turnkey, end-to-end-solution.

According to Tessitore, this covers the entire gamut: all hardware and software, station monitors, etc.; the building of real-time alerts, reports & analytics; training management on how to use data points to increase profits; and even continuously monitoring and improving the system as needed.

“Unless you’re a huge company, you really don’t have somebody who can come in and guide you and create a cost effective solution to help you compete with the larger players in the space,” says Pacesetter’s Leebow Wolmer. “I think that’s what Industrial Intelligence offers that can’t be created on your own.”

“It’s not a one-size-fits-all approach,” she adds. “They have some things that can give you a little bit of IIoT or they can take an entire factory to a whole new level. By doing this they can be cost effective for a variety of sizes of organizations.”

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For quite some time, the term “machine learning” and “deep learning” seeped its way to the business language, especially when it is related to Artificial Intelligence (AI), analytics and Big Data. Frankly, the approach directed to AI which provides a great promise with regard to creating self-teaching and autonomous systems that can revolutionize various industries. 

What is Machine Learning (ML)?

One of the subfield of AL is machine learning. Here the basic principle is that machine, collect data and they learn it for themselves. No doubt, this is the most awesome tool of the business’s Artificial Intelligence kit. One of the interesting advantages of the ML is that you can easily apply the training and knowledge received from analyzing huge data set to perform various functions and excelling at them like speech recognition, facial recognition, translation, object recognition, and various other tasks.    

Compared to the hand-coding a given software tool filled with specific instructions which can be used for completing the task, the ML provides a suitable system to understand the pattern by itself and make the required predictions.

What is Deep Learning?

Frankly, a subset of the ML is called as deep learning. Here one utilizes ML techniques for solving various real-life issues, and this is possible by accessing the neural networks which easily help in stimulating the decision-making of human beings. In addition, deep learning is kind of expensive and one will need extensive data sets to train. This is because there are various number of parameters that one might need to have an understanding, possible by learning about the algorithm. Thus, this can be present at the initial stages and create various kinds of false-positives.

To have a fair understanding, let’s check how deep learning algorithm can be used for understanding how a cat looks. So, a huge amount of data set of pictures is used for underlying the basic details which separates the cat from other like panther, cheetah, fox etc.

How Machine Learning And Deep Learning Affects Job

There is a kind of hysteria of doom-and gloom surrounding the machine learning AI. The majority of it is all about how people will be out of work, as there are quite successful stories where machines were able to carry out specific job-related works and bought about extensive results in it.  

Indeed it has become a huge paranoia, but it turns out that machine learning only performs tasks, and not the job. Of course, many tasks constitute a job but ML programs are not much flexible.

However, it doesn’t mean that both machine learning and deep learning will not affect your job, as they have already done and will simply continue to do so. Most importantly, whether it will be a benefit or threat will depend on how you are going to react when you identify it.

No doubt, there are quite a lot of reasons on how white-collar jobs can be a great invitation for deep learning and other related technologies. There are various experts who feel that the professional impact which AI and deep learning along with other automated technologies can drastically affect the work force count.

Conclusion

In short, there have been certain reactions or changes with regard to how machine learning and deep learning brings. It has drastically reduced the role of various professionals who are considered as knowledge gatekeepers. Plus, there has been a positive trend towards proactive and reactive services. 

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