Regulatory requirements exist to promote the controlled sharing of information, while protecting the privacy and/or security of the information. Regulations for each type of information have their own set of rules, policies, and guidelines. Cloud Service Providers (CSP) are faced with increasing demand for services at decreasing prices. Demonstrating and maintaining compliance with regulations is a nontrivial task and doing so against numerous sets of regulatory requirements can be daunting task. CSPs need a foundation that provides a uniform, non-repetitive view across all the requirements.

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Successful businesses have mastered how to manage their network and leverage it as a platform to engage staff and customers. Increased dependency, combined with the need for pervasive connectivity, greater insights and control over behavior, is driving organisations to embrace a cloud-managed network (CMN) model.

A CMN uses a software as a service (SaaS) model to securely deliver ease-of-use and simplicity for control and analytics of on premise devices being deployed over geographically-dispersed organisations. At the same time, a CMN can maintain physical and software-based security for the critical network switching and wireless infrastructure located on premise.

The rapid evolution of wireless LANs

In a relatively short period of time, wireless LANs have seen tremendous progress. From the first IEEE 802.11b wireless standard introduced in the late 90s to today’s 802.11ac Wave 2 standard, we have seen major increases in throughput as access points (APs) continue to evolve. These advancements are paving the way for increases in wireless performance and promising a future of even higher speeds.

The latest generation of on-premise wireless is built with strong security enablement between wireless clients and APs, as well as secure management, control and data plane traffic between APs and the controller. For larger installations, an independent and centralised management system can be deployed to manage multiple controllers and their associated APs, as well as the wired network. Today’s wireless architecture is well-suited for the CMN – a merger of wireless with the SaaS model.

The ins and outs of a cloud-managed network

CMNs bring the benefits of the cloud to enterprise networking, delivering easy to use, cost effective wired and wireless networks that are centrally managed and controlled over the web. CMNs offer a full-fledged alternative to the on premise managed network. With a CMN, the management and control plane traffic is hosted from the cloud while data plane traffic and the devices (APs, switches, etc.) stay on premise.

CMNs are built using a multi-tenant architecture for the cloud resident controller type functionality. Physical and virtual cloud based network resources adapt to network expansion or contraction based on individual customer demand. Smaller locations and remote locations lacking sufficient onsite IT to provide support for the more traditional on premise-managed solutions can reap large benefits by using the CMN approach.

Benefits of a cloud-managed network

The goal of a CMN is to simplify deployment, management and control over network infrastructure devices, such as wireless APs and switches, and provide operational savings. With the management platform residing securely in the cloud, the need to purchase, deploy, maintain, power, secure and locate network appliances on premise is eliminated, reducing CapEx. OpEx is also reduced as IT no longer needs to worry about on premise network management platforms, software updates or sending IT resources to remote locations for installations and troubleshooting.

There are many other benefits to using a CMN for your business. On premise wireless controllers are not required in the CMN model where the data-plane and control plane are distributed to the AP’s at each site, while the management plane is maintained in the cloud.

Easy to deploy, use and centrally manage, CNMs are a good option for geographically dispersed organisations with multiple locations. Software updates are pushed out from the cloud to the on premise access points and switches, reducing risk and easing the burden on IT. Cloud-managed wireless provides elasticity and instant scalability and network expansion is pay as you grow so you buy only what you need.

Evaluating cloud-managed solutions

A cloud-managed network can be a great total cost (TCO) saver, however at some point the cloud-managed networks TCO may start to exceed that of on premise solution TCO, so it’s crucial to run the numbers to ensure an optimal outcome. Carefully compare the expected product life of on premise managed verses cloud-managed deployments. The TCO should include the number of CMN devices, subscription fees, size and requirements of your organisation, trained IT staff available, user requirements and integration with existing infrastructure, applications and users. It is also worth noting that larger, geographically dispersed organizations might find that the benefits of functionality like ease-of-use, zero touch device deployment and management provide more advantages than just looking at the device TCO of an on premise solution.

Additionally, you might want to start with a CMN and move to an on premise managed solution as your business grows and requirements change, so make sure that the equipment you select has the flexibility to move from a cloud to on premise without a rip and replace.

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• Big Data & business analytics software worldwide revenues will grow from nearly $122B in 2015 to more than $187B in 2019, an increase of more than 50% over the five-year forecast period.
• The market for prescriptive analytics software is estimated to grow from approximately $415M in 2014 to $1.1B in 2019, attaining a 22% CAGR.
• By 2020, predictive and prescriptive analytics will attract 40% of enterprises’ net new investment in business intelligence and analytics.

Making enterprises more customer-centric, sharpening focus on key initiatives that lead to entering new markets and creating new business models, and improving operational performance are three dominant factors driving analytics, Big Data, and business intelligence (BI) investments today. Unleashing the insights hidden in unstructured data is providing enterprises with the potential to compete and improve in areas they had limited visibility into before. Examples of these areas include the complexity of B2B selling and service relationships,  healthcare services, and maintenance, repair, and overhaul (MRO) of complex machinery.

Presented below are a roundup of recent analytics and big data forecasts and market estimates:

• Global revenue in the business intelligence (BI) and analytics market is forecast to reach $16.9B in 2016, an increase of 5.2% from 2015. Gartner predicts that the BI and analytics market is in the final stages of a multiyear shift from IT-led, system-of-record reporting to business-led, self-service analytics. Source: Gartner Says Worldwide Business Intelligence and Analytics Market to Reach $16.9 Billion in 2016.

• Big data and business analytics worldwide revenues will grow from nearly $122B in 2015 to more than $187B in 2019, an increase of more than 50% over the five-year forecast period. The industries that present the largest revenue opportunities are Discrete Manufacturing ($22.8B in 2019), Banking ($22.1B), and Process Manufacturing ($16.4B). Source: Worldwide Big Data and Business Analytics Revenues Forecast to Reach $187 Billion in 2019, According to IDC.

• The global big data market will grow from $18.3B in 2014 to $92.2B by 2026, representing a compound annual growth rate of 14.4 percent. Wikibon predicts significant growth in all four sub-segments of big data software through 2026. Data management (14% CAGR), core technologies such as Hadoop, Spark and streaming analytics (24% CAGR), databases (18% CAGR) and big data applications, analytics and tools (23% CAGR) are the four fastest growing sub-segments according to Wikibon. Source: Wikibon forecasts Big Data market to hit $92.2B by 2026.

• In 2015, the Global Analytics and Business Intelligence applications market grew 4% to approach nearly $11.6B in license, maintenance and subscription revenues with SAP maintaining market leadership. SAP led the marketing with 10% market share and $1.2B in Analytics and Business Intelligence (BI) product revenues, riding on a 23% jump in license, maintenance, and subscription revenues. SAS Institute was No. 2 achieving 9% share; IBM was the third at 8%, and Oracle and Microsoft were fourth and fifth place with 7% and 5%, respectively. Source: Apps Run The World: Top 10 Analytics and BI Software Vendors and Market Forecast 2015-2020.

• Through 2020, Spending on Cloud-Based Big Data and Analytics (BDA) Technology Will Grow 4.5x Faster Than Spending for On-Premises Solutions. IDC’s FutureScape: Worldwide Big Data and Analytics 2016 Predictions (PDF, 13 , no opt-in, courtesy of Cloudera) define ten predictions that define the future of big data and analytics (BDA). IDC also predicts that by 2020, 50% of all business analytics software will incorporate prescriptive analytics built on cognitive computing functionality. The Source: IDC FutureScape: Worldwide Big Data and Analytics 2016 Predictions courtesy of Cloudera

• The Total Data market is expected to nearly double in size, growing from $69.6B in revenue in 2015 to $132.3B in 2020. The specific market segments included in 451 Research’s analysis are operational databases, analytic databases, reporting and analytics, data management, performance management, event/stream processing, distributed data grid/cache, Hadoop, and search-based data platforms and analytics. Source: Total Data market expected to reach $132bn by 2020; 451 Research, June 14, 2016.

• U.S. and International Operations (29%) and Enterprises (27%) lead the adoption of Big Data globally. Midmarket firms have the fastest growth rate of Big Data initiatives and programs in their organizations between 2014 and 2015.  Source: International Institute For Analytics. Advanced Analytics and Big Data Adoption Report, 2016.(free, opt-in reqd.)

• Improving customer relationships (55%) and making the business more data-focused (53%) are the top two business goals or objectives driving investments in data-driven initiatives today. 78% of enterprises agree that collection and analysis of Big Data have the potential to change fundamentally the way they do business over the next 1 to 3 years. Source: IDG Enterprise 2016 Data & Analytics Research, July 5, 2016.

• Venture capital (VC) investment in Big Data accelerated quickly at the beginning of the year with DataDog ($94M), BloomReach ($56M), Qubole ($30M), PlaceIQ ($25M) and others receiving funding. Big Data startups received $6.64B in venture capital investment in 2015, 11% of total tech VC.  M&A activity has remained moderate (FirstMark noted 35 acquisitions since their latest landscape was published last year). Source: Matt Turck’s blog post, Is Big Data Still a Thing? (The 2016 Big Data Landscape).

• Oracle (16.4%), SAP (13.1%), IBM (10.3%), Microsoft (9.1%) and SAS (6.1%) are the market share leaders in business analytics through 2015. During the last year, IDC also found the on-premises portion of the overall market contracted by 1.4%, while the public cloud services revenue grew 26.5%. Public cloud portion of the market now represents 17% of the market. Source: IDC, Worldwide Business Analytics Software Market Shares, 2015: Healthy Demand Despite Currency Exchange Rate Headwinds (PDF, free, courtesy of SAS).

• IDC forecasts global spending on cognitive systems will reach nearly $31.3 billion in 2019 with a five-year compound annual growth rate (CAGR) of 55%. More than 40% of all cognitive systems spending throughout the forecast will go to software, which includes both cognitive applications (i.e., text and rich media analytics, tagging, searching, machine learning, categorization, clustering, hypothesis generation, question answering, visualization, filtering, alerting, and navigation). Also included in the forecasts are cognitive software platforms, which enable the development of intelligent, advisory, and cognitively enabled solutions.  Source:  Worldwide Spending on Cognitive Systems Forecast to Soar to More Than $31 Billion in 2019, According to a New IDC Spending Guide.

• The market for prescriptive analytics software is estimated to grow from approximately $415M in 2014 to $1.1B in 2019, attaining a 22% CAGR. 10% of organizations currently have some form of prescriptive analytics. The portion of organizations adopting prescriptive analytics is forecasted to grow to 35% by 2020, and the bulk of new adoption will be from large organizations in mature economies. Source: Gartner Forecast Snapshot: Prescriptive Analytics, Worldwide, 2016; 5 February 2016 (client access reqd).

• The global business intelligence and analytics software market is expected to increase from $17.9B in 2014 to $26.78B in 2019, attaining a CAGR of 8.4%. Banking, financial services, insurance, retail, IT, and telecom will account for the largest percentage of the analytics and BI market. Source: Blog post on Marketresearch.com, The Business Intelligence and Analytics Software Market.

•  By 2020, predictive and prescriptive analytics will attract 40% of enterprises’ net new investment in business intelligence and analytics. By 2020, only 50% of chief analytics officers will have successfully created a narrative that links financial objectives to business intelligence and analytics initiatives and investments. Source: Gartner, 100 Data and Analytics Predictions Through 2020 Published: 24 March 2016 ID: G00301430 Analyst(s): Douglas Laney | Ankush Jain (client access reqd).

• Big Data Analytics & Hadoop Market accounted for $8.48B in 2015 and is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 to 2022. The rise of big data analytics and rapid growth in consumer data capture and taxonomy techniques are a few of the many factors fueling market growth. Source: Stratistics Market Research Consulting (PDF, opt-in, payment reqd). 

The hype around data breaches in the health industry may seem commonplace and cause complacency. Last year, it was Anthem and Premera Blue Cross suffering attacks affecting nearly 90 million people combined. Among others, last month it was Banner Health – a nationwide health system based in Arizona – which reported a cyberattack affecting 3.7 million patients and customers.
This month the U.S. Department of Health and Human Services’ Office for Civil Rights (OCR) levied a $5.5M fine on Advocate Health Care Network, the highest penalty to date on a health care organization having a data breach that caused a violation of HIPAA. The fine is in addition to the other costs and damages facing Advocate Health Care Network.

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The mere abundance of cloud computing possibilities and developments would be enough to make it the future of business. However, there is much more to it.
When one first starts scratching the surface of what cloud computing is and what its business applications can be, it soon becomes very obvious that there are innumerable layers to it. Even those who are passionate about the subject learn new things and discover new avenues on a daily basis. If they have the time, on an hourly basis.

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The explosion of cloud onto the enterprise scene has, literally, revolutionized how businesses across the size spectrum do business, yet there’s a price tag tucked into this cloud’s silver lining that smart decision-makers should pay heed to.
This is the era of what I like to call the consumerization of enterprise software. It’s bringing tremendous benefit to the business world, but it’s also bringing a great deal of change, particularly to the understanding of how business software adoption and use is done, and enterprises of all sizes need to familiarize themselves with best practices-understanding to leverage the power of the cloud.

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As cloud adoption continues to transform business, today’s global enterprises are challenged with managing a growing amount of information living outside of the data center. The rapid adoption of IoT and increasingly mobile workforce are exacerbating the problem. Ensuring secure data sharing and efficient backup poses capacity and bandwidth considerations as well as policy and regulatory compliance issues.

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While some existing apps allow the Doctors to write using their stylus pen, what is needed is an integrated solution that includes handwriting recognition, voice , face recognition and other cognitive intelligence as part of health care applications.

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Computer-aided design (CAD) and computer-aided engineering (CAE) often use extremely large files, which make remote working and collaboration difficult to achieve. Most businesses are able to work remotely with laptops, smartphones and tablet PCs, but the processing power required to create, store and share CAD files over a wide area network is often prohibitive. Yet with the power of the internet they can work remotely and collaboratively.

The key challenge, because the files they send to their colleagues and CAD partners across the globe are so large, is about how their IT teams mitigate the effects of latency in wide area networks (WAN) to enable them to work uninterrupted by slow network connections. Strained WAN resources aren’t the only issue that concerns them. They need to deploy remote access control technology to protect their data as it flows across the internet, and from cloud to cloud, to ensure that only authorised individuals can work on any given CAD or CAE projects.

In essence, the internet and more recently cloud computing has become an enabler of remotely situated design, manufacturing, construction and engineering teams. Not only can they share their skills, knowledge and expertise, but also their data. Cloud can handle the peaks in storage and computing demand, but organisations must be able to get it up and down quickly in order to benefit from the potential cost efficiencies and the infrastructure agility that it can offer.

“With the ubiquitous access to the internet it is now possible to gather data from every part of the world and bring it back to a central hub for analysis, and you can design an aircraft or a car in one country while manufacturing it in another”, says David Trossell, CEO and CTO of data acceleration company, Bridgeworks. He points out that this means a huge amount of data is constantly being moved around and that all of the data is logged.

It’s about time

The Engineer says in its June article, ‘It’s About Time – Evolving Network Standards for the Industrial IoT’, “The Industrial Internet of Things (IIoT) promises a world of smarter, hyper-connected devices and infrastructure where electrical grids, manufacturing machines, and transportation systems are outfitted with embedded sensing, processing, control and analysis capabilities.”

The article recognises that latency can still be a problem, and it claims that:

“Much of today’s network infrastructure is not equipped to handle such time-sensitive data. Many industrial systems and networks were designed according to the Purdue model for control hierarchy in which multiple, rigid bus layers are created and optimised to meet the requirements for specific tasks. Each layer has varying levels of latency, bandwidth and quality of service, making interoperability challenging, and the timely transfer of critical data virtually impossible. In addition, today’s proprietary Ethernet derivatives have limited bandwidth and require modified hardware.”

The article adds: “Once networked together, they’ll create a smart system of systems that shares data between devices, across the enterprise and in the cloud. These systems will generate incredible amounts of data, such as the condition monitoring solution for the Victoria Line of the London Underground rail system, which yields 32 terabytes of data every day. This Big Analog Data will be analysed and processed to drive informed business decisions that will ultimately improve safety, uptime and operational efficiency.”

Commenting on the article, and specifically about the London Underground example, Trossell says: “Everything is real-time, and so the question has to be: How can we get the data back as fast as possible to analyse it and to inform the appropriate people. Some elements of this task may be in-house first for a quick exception analysis?” Some of this data may then be pushed to the cloud for further in-depth analysis by comparing present data with historical data to see whether anything can be learnt or improved from a maintenance and service perspective. With an unimpeded network, big data analysis from a wide range of data sources is possible, adding the ability to gain insights that were once not so easy to obtain.

From IoT to IIoT

Trossell thinks that the broader expression of the Internet of Things (IoT) is just one of the current buzzwords that everyone for a variety of reasons is getting excited about. “Most people think of this as their connected fridge, the smart meter for their utilities or the ability to control their heating system at home, but with the ever increasing diversity and the decreasing cost of sensors for industrial use, the term takes on a new level of sophistication and volume when applied to industry”, he explains.

In industry, IoT gives birth to IIoT, which involves monitoring the performance of complex machinery such as gas turbines, aircraft, ships, electrical grids and oil rigs. So it’s not just about a diversely spread group of CAD engineers working collaboratively across the globe. “IIoT has never been so diverse and in depth with vast amounts of data being created every second. To put this in perspective, each Airbus A350 test fight can received measurements from 60,000 separate sensors”, he claims. That’s a phenomenal amount of data that needs to be transmitted, backed-up, stored, and at some point it needs to be analysed in real-time in order to have any value.

“An example of this is a company that has developed a system where the aircraft technician can download the data from the black box flight recorder, send it over the internet where it is analysed with artificial intelligence for anomalies exceptions and then passed to an expert for investigation”, he explains. The benefit is that this approach can engage several experts, for example from an air transport safety board and involve manufacturers, across the globe to find out, unusual pilot activity, or sensor data can be collated to enable an airline to reduce maintenance and unplanned outages and possible safety implications  to a minimum therefore improving availability and profitability.

“However, just like the consumer internet, moving vast amount of data across the internet has it challenges – especially when the data may be half the world away”, he warns. These challenges include increased network latency due to the teams working at a distance over a WAN, and potential security breaches. He adds: “Moving files around between various data silos can be inhibitive even over a LAN – the cost of 10Gb networks are dropping considerably, but with WANs the problem is about moving data over distance because of latency.”

Yet in spite of the gremlins posed by security threats and network latency, there are many companies around the world that are established virtually thanks to the internet. They are often specialists in their chosen disciplines, and each of them can add a bit to the whole picture, but Trossell believes it’s no good to collect or generate data if you can’t use it to encourage and enable the collaboration of globally dispersed multi-disciplinary teams, to allow for innovation and for the creation of efficiencies. The data – including sensor data – must get to the right people at the right time if it is to add any value, but latency can prevent this from happening and latency can turn invaluable data into redundant and out of date data that adds nothing of worth or merit. 

Being smart

Companies investing in IIoT and remote working therefore need to protect their businesses by investing in solutions that can mitigate the impact of network latency while enabling data to be securely sent at velocity between the various data users and analysers. With smart systems, the challenges can be harder to overcome because in a traditional Purdue system data flows up and down the model, but Trossell says that smart systems and IIoT data tends to flow in all directions like a web. Being smart is also about mitigating latency and reducing the potential threats to data. With this in mind, Trossell offers five top tips that could ensure that your company gains the most from its data:

• Remember that the two biggest killers of performance in WAN is packet loss and latency. When you have them together then you will suffer massive performance hits.
• Adding bandwidth to your WAN will not necessarily increase performance, but it will increase costs!
• Marshal and consolidate data if possible rather than allowing lots of individual streams as this is a more effective use of WAN bandwidth.
• Use a product such as PORTrockIT to accelerate data transmissions, and use applications that pre-compress and encrypt data before it is sent to the WAN.

In essence, by mitigating latency and improving data security, industrial organisations can maximise the potential of the industrial internet. With the growing amount of sensor data, and the growing need to work collaboratively with remote teams across the world, these challenges are going to become more and more prevalent and obstinate. Industrial organisations therefore need to act today to ensure they protect their businesses well into the future, to enable them to participate in the industrial internet of things, and to allow them to benefit from real-time big data analysis right now. In other words, there is no point in using smart technology if you aren’t being smart with it too.