In recent days, neural networks have become a topic for discussion. But the question still needs to be solved- How can it affect our world today and tomorrow?

The global neural network market's compound annual growth rate (CAGR) is expected to be 26.7% from 2021 to 2030. This means that new areas of application for them might appear soon. The Internet of Things that is IoT, is today's most fascinating and required technological solution for business. Around 61% of companies utilize IoT platforms, and we can anticipate the integration of neural networks into enterprise IoT solutions. This anticipation raises many questions, like what gets such collaboration and how to prepare it. Can we optimize the IoT ecosystem using neural networks, and who will approach such solutions?

What do you understand by a neural network, and how is it beneficial for enterprise IoT?

An artificial neural network that is ANN is a network of artificial neurons striving to simulate the analytical mechanisms taken by the human brain. This type of artificial intelligence includes a range of algorithms that can "learn" from their own experience and improve themselves, which is very different from classical algorithms that are programmed to resolve only specific tasks. Thus, with time, the neural network will remain pertinent and keep on improving.

With the proper implementation, enterprise internet of things (EIoT) and ANN can offer the business the most valuable things: precise analytics and forecasts. In general, it is not possible to compare both. Enterprise IoT is a system that needs software for data analysis, whereas ANN is a component that needs a large amount of data to be operational. Their team naturally controls the analytical tasks; therefore, high-level business tasks are performed most effectively, reducing costs, automating processes, finding new revenue sources, etc.

In the Internet of Things ecosystem, neural networks help in two areas above all:

• Data acquisition via ANN-based machine vision
• Advanced-data analysis

If it needs significant investments to execute ANN in big data analytics solutions, neural network image processing can decrease the cost of the IoT solution. Thus, neural networks improve enterprise IoT solutions, enhance their value, and speed up global adoption.

Which solutions within enterprise IoT can be enhanced using neural networks?

IoT-based visual control

The IoT ecosystem begins with data collection. Data quality impacts the accuracy of the ultimate prediction. If you implement visual control in your production processes, neural networks can boost the quality of products by superseding outdated algorithms. Besides this, they will optimize the EIoT solution. Conventional machine vision systems are pricey as they require the highest resolution cameras to catch minor defects in a product. They come with complex specific software that fails to respond to immediate changes.

Neural networks within machine vision systems can:

• Diminish camera requirements
• Self-learn on your data
• Automate high-speed operations

Indeed, industrial cameras use large-format global shutter sensors having high sensitivity and resolution to develop the highest quality images. Nevertheless, a well-trained ANN starts to identify images with time. It allows them to reduce the technical needs for the camera and ultimately cuts the final cost of the enterprise IoT implementation. You cannot compromise the quality of images to detect small components like parts in circuit boards; however, it is manageable for printing production, completeness checking, or food packaging.

After training, neural networks use massive amounts of data to identify objects from the images. It enables you to customize the EIoT solution and train the ANN to operate specifically with your product by processing your images.

For example, convolutional neural networks are utilized actively in the healthcare industry to detect X-rays and CT scans. The outcome offered by such custom systems is more precise than conventional ones. The capability to process information at high speeds permits the automation of production processes. When the problem or defect is caught, neural networks promptly report it to the operator or launch an intelligent reaction, like automating sorting. Hence, it allows real-time detection and rejection of defective production.

An exclusive example of how ANN is utilized for edge and fog computing. As per PSA, a neural network executed in a machine vision system permits lowering the number of defects by 90% in half a year, whereas production costs are decreased by 30%. Prospective areas for ANN in IoT visual control are quality assurance, sorting, production, collecting, marking, traffic control, and ADAS.

Big data advanced analytics for enterprise IoT:

Today, neural networks allow businesses to grab advantages like predictive maintenance, new revenue flows, asset management, etc. It is possible via deep neural networks (DNN) and the deep Learning (DL) method involving multiple layers for data processing. They detect hidden data trends and valuable information from a significant dataset by employing classification, clustering, and regression. It results in effective business solutions and the facilitation of business applications.

In comparison to traditional models, DL manages with the attributes that are expected for IoT data:

1. Assess the time of taking measurements
2. Resist the high noise of the enterprise IoT data
3. Conduct accurate real-time analysis
4. Determine heterogeneous and discordant data
5. Process a large amount of data

In practice, this implies that you don't require middle solutions to deliver and sort the data in the cloud or to analyze them in real-time. For example, full-cycle metallurgical enterprises can execute one solution to analyze the variable and unstructured data from metal mining, smelting, and final manufacturing products. Airplanes generate about 800TB of data per hour, making it impossible to process it all ideally using conventional analytical systems.

Today, DNN models are successful in the following enterprise IoT applications. 

Healthcare:

Today, it has become easy to predict disease using AI-based IoT systems, and this technology is developing for further improvements. For instance, the latest invention based on the neural network can detect the risk of heart attacks by up to 94.8%. DNN is also helpful in disease detection: the spectrogram of a person's voice received using IoT devices can identify voice pathologies after DNN processing. In general, ANN-based IoT health monitoring systems' accuracy is estimated to be above 85%.

Power consumption:

DL systems in the enterprise Internet of Things have provided results in power demand prediction based on power price forecasting, consumption data, anomaly, power theft detection, and leak detection. Smart meter data analysis permits you to calculate consumption, determine the unusual usage of electricity, and predict with an accuracy of more than 95%, which will help you to adjust energy consumption.

Manufacturing:

Neural networks help to use the most demanded IoT service among manufacturers properly- predictive equipment maintenance. It was ascertained to be a workable practice for mechanical and electrical systems. This network provides accurate real-time status monitoring and predicts proper life rest. Another best example is the recognition of employee activity by taking readings and following in-depth analysis.

Transportation & Logistics:

Deep Learning has made smart transportation systems possible. It offers better traffic congestion management by processing travel time, speed, weather, and occupational parking forecasting. Analytical reports based on vehicle data help to discover dangerous driving and possible issues before the failure happens.

As we know, the previous industries generate heterogeneous data. Therefore, the potential of ANN analytics within EIoT will be unlocked for multiple complicated systems.

When to consider ANN for enterprise IoT:

Till now, research in the field of ANNs been very active, and we cannot foretell all the advantages or pitfalls these solutions will convey. No doubt, neural networks find out correlations, models, and trends better than other algorithms. The IoT ecosystem's data will become more extensive, complex, and diverse with time. So, the development of neural networks is the future of IoT.

For now, we can look into the following features of neural networks for enterprise IoT:

• They suit the IoT ecosystem architecture, substituting alternative solutions with significant advantages.
• Essential for industrial image processing.
• Progressive ANN-based data analytics gets the high-level business value of the enterprise IoT solutions – improves productivity, and exactness, boosts sales, and produces informed business decisions.
• Training the ANN requires time and expenditure but will become fully customizable.
• We cannot conclude it is an affordable solution, but the advantages are priceless if the IoT ecosystem is executed accurately.

Therefore, if you are provided with a neural network as one of the opportunities for executing your idea within the IoT ecosystem, give it a chance. You never know, this solution will become a must-have in the coming years.

With the advent of the Internet of Things, Big Data is becoming more and more important. After all, when you have devices that are constantly collecting data, you need somewhere to store it all. But the Internet of Things is not just changing the way we store data; it’s changing the way we collect and use it as well. In this blog post, we will explore how the Internet of Things is transforming Big Data. From new data sources to new ways of analyzing data, the Internet of Things is changing the Big Data landscape in a big way.

How is the Internet of Things transforming Big Data?

The Internet of Things is transforming Big Data in a number of ways. One way is by making it possible to collect more data than ever before. This is because devices that are connected to the Internet can generate a huge amount of data. This data can be used to help businesses and organizations make better decisions.

Another way the Internet of Things is transforming Big Data is by making it easier to process and analyze this data. This is because there are now many tools and technologies that can help with this. One example is machine learning, which can be used to find patterns in data.

The Internet of Things is also changing the way we think about Big Data. This is because it’s not just about collecting large amounts of data – it’s also about understanding how this data can be used to improve our lives and businesses.

The Benefits of the Internet of Things for Big Data

1. The internet of things offers a number of benefits for big data.
2. It allows for a greater volume of data to be collected and stored.
3. Also, it provides a more diverse range of data types, which can be used to create more accurate and comprehensive models.
4. It enables real-time data collection and analysis, which can help organizations make better decisions and take action more quickly.
5. It can improve the accuracy of predictions by using historical data to train predictive models.
6. Finally, the internet of things can help reduce the cost of storing and processing big data.

The Challenges of the Internet of Things for Big Data

The internet of things is transforming big data in a number of ways. One challenge is the sheer volume of data that is generated by devices and sensors. Another challenge is the variety of data formats, which can make it difficult to derive insights. Additionally, the real-time nature of data from the internet of things presents challenges for traditional big data infrastructure.

Conclusion

The Internet of Things is bringing a new level of connectivity to the world, and with it, a huge influx of data. This data is transforming how businesses operate, giving them new insights into their customers and operations.

The Internet of Things is also changing how we interact with the world around us, making our lives more convenient and efficient. With so much potential, it's no wonder that the Internet of Things is one of the most talked-about topics in the tech world today.

Last year's Cambridge Analytica scandal has developed to the point in which many different big data-related problems and strategies have surfaced "the mainstream". The fact that many independent marketing agencies and enterprises started valuing data points is indeed the starting point in regards to the usage of big data and data-related algorithm in digital marketing. Let's analyse how, after GDPR, this is still a gold mine for agencies.

What Are Data Points?

With Data Points we define those packages which combine cookies, site's preferences and searches, combined together in alphanumerical strings which are then processed by native tools by many companies who are working with data science.
Data points have been used by Cambridge Analytica to set up campaigns like the Trump campaign and the Brexit one, resulting in over 80% engagement from their facebook ads, the reason why both campaigns were extremely successful, for such delicate matters.

The Machine Learning Side Of Data

In 2018, it has been stated that there was a drastic increase in hiring Python developers in digital marketing agencies, due to the fact that many were trying to "exploit" data points to better target their ads. In the UK, which was recently elected as the European technology powerhouse, it has been pointed out how machine and deep learning have impacted agencies. In Manchester, eventually, Stephen McCance, operation director at Red Cow Media, have invested over £300.000 in data science-related strategies, leading, of course, to a far bigger awareness of the topic in Europe as a whole.

GDPR, IoT And ML: How Do They Work Together?

Once the Cambridge Analytica scandal happened, the GDPRstrategy which was in place had to add specific sections which were related to this very matter. In fact, big data gathering isn't that simple in the IoT, nowadays, as the site/app/software must state properly whether data points are being collected or stored. Even if machine learning could avoid architectures that are limiting such data collection, GDPR has strictly limited access to R algorithms (the ones, to reference, which are processing those alphanumerical strings mentioned above) when it comes to data points and cookies.

To Conclude

The Cambridge Analytica will be remembered in the future as the biggest step towards proper regulation of big data and personal data in general. Data regulation and awareness have moved massively in the last couple of years, passing from being a completely neutral field to becoming part of our day to day talks and, most importantly, business strategies.

"One day I'm in my cubicle, Steve shows up with someone I've never met before. He asks me, 'Guy, what do you think of this company Knoware?'. I said, 'Well Steve, it is a mediocre company, mediocre product, lot of drilling practises, doesn't make full use of graphics, just basic mediocrity, nothing that strategic for us.' He says to me, 'I want you to meet the CEO of Knoware.' So that's what was like working for Steve Jobs. ‘You always have to be on the ball.

A lot of water has flowed under the bridge since then. The flow of information has also changed the way we live in today’s world.

Your mark on the world begins…

Every morning when we read a newspaper having out so much information we came to know the latest happening in the world (of course in details), yeah you are right even the internet edition also. This is just a very basic example of IoT. All our Railways, Air and even sea networks are connected with the help of IoT. We can take the example of banking. It is very easy to transact any amount of money from part of the world to other with help of e-commerce. We can purchase anything online with help of debit and credit cards. This has made our lives more and more simple. People are working on the internet without really having to go outside to their workplace. IoT has changed the whole scenario. Companies can share technologies online. Even the doctors can guide the other doctors while operating on a patient with the help of Information Technology. A whole new world is coming our way. Technology is allowing us to reimagine our future transportation system. Advances in connected automation, navigation, communication, robotics, and smart cities—coupled with a surge in transportation-related data—will dramatically change how we travel and deliver goods and services. Automation in the field of transportation is everywhere. Have we as humans become an afterthought? We order service on our smartphones, we manoeuvre around in increasingly automated vehicles, we ride in driverless transport, and we will increasingly find ourselves sharing our highways and byways with drones and other unmanned craft.

1) SaaS & Bring Your Own Device

Global movements such as BYOD and SaaS, where consumerisation of IT and mobility are drastically changing the capabilities of employees and their expectations of a workspace. Building your own apps is the ideal way to mitigate the risk of BYOD and SaaS. An organisation can provide those that only allow the user to access what they need. The enter-prise’s concern is the data; the employee’s concern is the device. In the IT security world, we care about both. Now that most of the organizations started adopting BYOD in some form, it is not just their personal iPads and laptops that users are bringing into the office, they are also using the consumer apps available in their personal device for work purpose which leads to the next wave in mobility. In the very near future BYOD won’t be a ‘trend’ but a norm no one would think twice about.

2) The Emergence of Big Data

 "Big data" alluringly holds out the promise of competitive advantages to companies that can use it to unlock secrets about customers, website usage and other key elements of their business operations. Big Data now stream from daily life: from phones and credit cards and televisions and computers; from the infrastructure of cities; from sensor-equipped buildings, trains, buses, planes, bridges, and factories. It's estimated that 43 trillion gigabytes of new data will be created by the year 2020. 

3) Cloud computing: How it's transforming the role of IT

Market conditions require significant change and many organizations are using this driver as an opportunity to simplify their applications and data through rationalization and technology innovations such as Cloud Computing. Cloud is defined as any cloud service where consumers are able to access software applications over the internet. The applications are hosted in “the cloud” and can be used for a wide range of tasks for both individuals and organisations. Google, Twitter, Facebook and Flickr are all examples of SaaS, with users able to access the services via any internet enabled device. Cloud is also the fastest growing because it keeps pace with emerging and future business models than on-premise systems, the majority of which were designed for business models of the past.

The next step, moving towards virtual workspaces, can make organisations far more agile but only if those responsible for the IT (and in effect, the productivity) of the employees understand the relationship employees have with their devices and how these change throughout the day based on their personal preference – be it a smartphone for the train, a tablet device for a client meeting or a laptop for remote working at home.

4) Millions of sensitive IT services exposed to the Internet

All the more the Internet is becoming more and more important for nearly everybody as it is one of the newest and most forward-looking media and surely "the" medium of the future. These advances—in fields such as robotics, A.I., computing, synthetic biology, 3D printing, medicine, and nanomaterials—are making it possible for small teams to do what was once possible only for governments and large corporations: solve the grand challenges in education, water, food, shelter, health, and security. Technology is, today, moving faster than ever. Advances that took decades, sometime centuries, such as the development of telephones, airplanes, and the first computers, now happen in years.

The macro trends that have changed the playing field in the past 10 years have been cloud, mobility, Big Data, and social networking. An even bigger trend ahead will be the Internet of Things that will extend information technology into every aspect of our lives. IT has become more agile and responsive to the needs of the business. While cloud was considered hype just a few years ago, the cloud in its many forms, private, public, hybrid, is transforming IT into a service model. IT leaders who embraced these changes have been able to do more with less and have proven their strategic value.

According to Steve, the iPhone was originally a tablet project. Partway through the R&D process, he said, “Hmm, we can make a phone out of this.” After the launch, many people rewrote history and said that the purpose of the iPhone was to reinvent the future of telephony.

Today, technology is, moving faster than ever. The ubiquity of network connectivity and the proliferation of smart devices (such as sensors, signs, phones, tablets, lights, and drones) have created platforms upon which every enterprise can innovate. Since the past few years we have also seen countless innovations that improve our daily lives. From Internet technology to finance to genetics and beyond - we have seen technologies such as mobile, social media, smartphones, big data, predictive analytics, and cloud, among others are fundamentally different than the preceding IT-based technologies. And advances in science and technology have changed the way we communicate, our thought processes, exchange views, understand the way we relate to one another and think about what it means to be a real Innovative change maker. Perhaps one day you too can be a part of reinventing something which is new, timely, relevant and useful.

Best Regards,

Raj Kosaraju

Raj Kosaraju specializes on Cloud Computing, Data Warehousing, Business Intelligence, Supply Chain Management, Big Data & IoT.

Technologists and analysts are on a path to discovery, obtaining answers on how technology and the data collected can make our cities more efficient and cost effective. 

While IoT may be seen as another buzzword at the moment, companies like SAP, Cloud Sigma, Net Atlantic and Amazon Web Services are working to make sure that for businesses, IoT is a reality. It’s companies with this willingness to change, adopt and invent that will win the new economy. Mobile phones, online shopping, social networks, electronic communication, GPS and instrumented machinery all produce torrents of data as a by-product of their ordinary operations. Most companies want their platform to be the foundation of everything it does, whether it is with big data, data analytics, IoT or app development. The same  rub off phenomenon was emulated in Latin American countries  like Brazil, Argentina, Mexico and European countries like Brussels, Italy,  Germany, Denmark , Poland and Prague in recent times.

It is important to realize that technology is exploding before our very eyes, generating unprecedented opportunities. With easy access to cheap cloud services, smarter people came up with these platforms, and it has fundamentally changed businesses and created new ways of working. Mobile cannot be an afterthought. It needs to be integrated in everything you do and positioned at the forefront of your strategy. You have no valid reason to avoid migrating to the cloud. Cloud provides a ubiquitous, on-demand, broad network with elastic resource pooling. It’s a self-configurable, cost-effective computing and measured service. On the application side, cloud computing helps in adopting new capabilities, meeting the costs to deploy, employing viable software, and maintaining and training people on enterprise software. If enterprises want to keep pace, they need to emulate the architectures, processes and practices of these exemplary cloud providers.

One of the main factors of contributing value additions is the concept of a Smart City which is described as one that uses digital technologies or information and communication technologies to enhance the quality and performance of urban services, to reduce costs and resource consumption and to engage more effectively and actively with its citizens. We will interact and get information from these smart systems using our smartphones, watches and other wearables, and crucially, the machines will also speak to each other.The idea is to embed the advances in technology and data collection which are making the Internet of Things (IoT) a reality into the infrastructures of the environments where we live. We will interact and get information from these smart systems using our smartphones, watches and other wearables, and crucially, the machines will also speak to each other. Technologists and analysts are on a path to discovery, obtaining answers on how technology and the data collected can make our cities more efficient and cost effective. The current model adopted for IoT is to attract businesses to develop software and hardware applications in this domain. The model also encourages businesses to put their creativity to use for the greater good, making cities safer, smarter and more sustainable.

A few years ago like many others  I predicted  that Business models will be shaped by analytics, data and the cloud. Moreover, the IoT is deeply tied in with data, analytics and cloud to enable them and to improve solutions. The key goal is to ensure there is value to both customers and businesses. You can effectively put this strategy into action and build a modern data ecosystem that will transform your data into actionable insights.  

Till we meet next time...

Best,

Raj Kosaraju

CIO 

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.

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.

How edge computing is poised to jump-start the next industrial revolution.

From travel to fitness to entertainment, we now have killer apps for many things we never knew we needed. Over the past decade, we’ve witnessed tremendous improvements in terms of democratizing data and productivity across the consumer world.

Building on that, we’re entering a new era of software-defined machines that will transform productivity, products and services in the industrial world. This is the critical link which will drive new scenarios at even faster rates of innovation. By 2020, the Industrial Internet of Things (IIoT) is expected to be a $225 billion market.

To jump-start the productivity engine of IIoT, real-time response is needed at the machine-level at scale and that requires an edge-plus-cloud architecture designed specifically for the Industrial Internet. From Google maps to weather apps, we’ve been experiencing the benefits of cloud and edge computing working together in our daily lives for quite some time.

But, what is edge? Edge is the physical location that allows computing closer to the source of data. Edge computing enables data analytics to occur and resulting insights to be gleaned closer to the machines. While edge computing isn’t new, it’s beginning to take hold in the industrial sector – and the opportunity is far greater than anything we’ve seen in the consumer sector, and here’s why:

Real-time data in a real-time world: The edge is not merely a way to collect data for transmission to the cloud. We are now able to process, analyze and act upon the collected data at the edge within milliseconds. It is the gateway for optimizing industrial data. And when millions of dollars and human lives are on the line, edge computing is essential for optimizing industrial data at every aspect of an operation.

Take windfarms for example. If wind direction changes, the edge software onsite would collect and analyze this data in real-time and then communicate to the wind turbine to adjust appropriately using an edge device, such as a field agent and connected control system, and successfully capture more kinetic energy. Because the data is not sent to the cloud, the processing time is significantly faster. This increases wind turbines’ production, and ultimately distributes more clean energy to our cities, increasing the value of the renewable energy space.

Big data, big trade-offs: The harsh and remote conditions of many industrial sites make it challenging to connect and cost-effectively transmit large quantities of data in real-time. We are now able to add intelligence to machines at the edge of the network, in the plant or field. Through edge computing on the device, we’re bringing analytics capabilities closer to the machine and providing a less expensive option for optimizing asset performance.

Consider the thousands of terabytes of data from a gas turbine. Sending this data to the cloud to run advanced analytics maybe technologically possible, but certainly too cost prohibitive to do a daily basis. Through edge computing, we can capture streaming data from a turbine and use this data in real-time to prevent unplanned downtime and optimize production to extend the life of the machine.

What’s Next

Today, only 3% of data from industrial assets is useable. Connecting machines from the cloud to the edge will dramatically increase useable data by providing greater access to high powered, cost effective computing and analytics tools at the machine and plant level.

Consider the fact that for years traditional control systems were designed to keep a machine running the same way day in and day out for the lifecycle of the machine. At GE Energy Connections, we recently debuted the Industrial Internet Control System (IICS), which successfully allows machines to see, think and do and will enable machine learning at scale. To take IICS to the next level, we’re creating an ecosystem of edge offerings to accelerate widespread adoption across the industrial sector. We’re advancing this ecosystem and empowering app developers who want to play a role in driving the new industrial era. 

Currently, to add value to a software system, a developer writes the code, ports it into the legacy software stack, shuts down the devices and finally, updates it. That’s all going to change. We are working on creating an opportunity for any developer to create value-added edge applications. Customers will be able port the necessary apps to their machine without having to shut it down, just like we do on our phones today. Companies will be able to download apps for their needs and update frequently to ensure their business is running smoothly. While no one likes to run out of battery on their smart phone, an outage for a powerplant is far more costly, so the ability to port apps without shutting down devices and being able to detect issues before it occurs will be a game changer.

From wind turbines to autonomous cars, edge computing is poised to completely revolutionize our world. It’s forcing change in the way information is sent, stored and analyzed.  And there’s no sign of slowing down.

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

adopt the ones which makes more sense to your business.

Finally you know when you are digital transformed?