P for Prescriptive Analytics & Predictive Analytics

Prescriptive Analytics is an advanced analytics technology that can provide recommendations to decision makers and help them achieve business goals by solving complicated optimization problems.
Prescriptive analytics helps organizations make better decisions by optimizing trade-offs between business goals, such as costs or customer service, while considering predictions, rules, and constraints on available resources, to recommend the best course of action, whether you want to decide on a configuration, a design, a plan, or a schedule.

 

With the flood of data available to businesses regarding their supply chain these days, companies are turning to analytics solutions to extract meaning from the huge volumes of data to help improve decision making

Companies that are attempting to optimize their S&OP efforts need capabilities to analyze historical data, forecast what might happen in the future. The promise of doing it right and becoming a data driven organization is great. Huge ROI’s can be enjoyed as evidenced by companies that have optimized their supply chain, lowered operating costs, increased revenues, or improved their customer service and product mix.
Looking at all the analytic options can be a daunting task. However, luckily these analytic options can be categorized at a high level into three distinct types. No one type of analytic is better than another, and in fact, they co-exist with, and complement each other. In order for a business have a holistic view of the market and how a company competes efficiently within that market requires a robust analytic environment which includes:

Descriptive Analytics, which use data aggregation and data mining to provide insight into the past and answer: “What has happened?”

Predictive Analytics, which use statistical models and forecasts techniques to understand the future and answer: “What could happen?”

Prescriptive Analytics, which use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?”

Descriptive Analytics: Insight into the past

Descriptive analysis or statistics does exactly what the name implies they “Describe”, or summarize raw data and make it something that is interpretable by humans. They are analytics that describe the past. The past refers to any point of time that an event has occurred, whether it is one minute ago, or one year ago. Descriptive analytics are useful because they allow us to learn from past behaviors, and understand how they might influence future outcomes.
The vast majority of the statistics we use fall into this category. (Think basic arithmetic like sums, averages, percent changes). Usually, the underlying data is a count, or aggregate of a filtered column of data to which basic math is applied. For all practical purposes, there are an infinite number of these statistics. Descriptive statistics are useful to show things like, total stock in inventory, average dollars spent per customer and Year over year change in sales. Common examples of descriptive analytics are reports that provide historical insights regarding the company’s production, financials, operations, sales, finance, inventory and customers.

Use Descriptive Analytics when you need to understand at an aggregate level what is going on in your company, and when you want to summarize and describe different aspects of your business.

Predictive Analytics: Understanding the future

Predictive analytics has its roots in the ability to “Predict” what might happen. These analytics are about understanding the future. Predictive analytics provides companies with actionable insights based on data. Predictive analytics provide estimates about the likelihood of a future outcome. It is important to remember that no statistical algorithm can “predict” the future with 100% certainty. Companies use these statistics to forecast what might happen in the future. This is because the foundation of predictive analytics is based on probabilities.

These statistics try to take the data that you have, and fill in the missing data with best guesses. They combine historical data found in ERP, CRM, HR and POS systems to identify patterns in the data and apply statistical models and algorithms to capture relationships between various data sets. Companies use Predictive statistics and analytics anytime they want to look into the future. Predictive analytics can be used throughout the organization, from forecasting customer behavior and purchasing patterns to identifying trends in sales activities. They also help forecast demand for inputs from the supply chain, operations and inventory.

One common application most people are familiar with is the use of predictive analytics to produce a credit score. These scores are used by financial services to determine the probability of customers making future credit payments on time. Typical business uses include, understanding how sales might close at the end of the year, predicting what items customers will purchase together, or forecasting inventory levels based upon a myriad of variables.

Use Predictive Analytics any time you need to know something about the future, or fill in the information that you do not have.

Prescriptive Analytics: Advise on possible outcomes

The relatively new field of prescriptive analytics allows users to “prescribe” a number of different possible actions to and guide them towards a solution. In a nut-shell, these analytics are all about providing advice. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.

These analytics go beyond descriptive and predictive analytics by recommending one or more possible courses of action. Essentially they predict multiple futures and allow companies to assess a number of possible outcomes based upon their actions. Prescriptive analytics use a combination of techniques and tools such as business rules, algorithms, machine learning and computational modelling procedures. These techniques are applied against input from many different data sets including historical and transactional data, real-time data feeds, and big data.

Prescriptive analytics are relatively complex to administer, and most companies are not yet using them in their daily course of business. When implemented correctly, they can have a large impact on how businesses make decisions, and on the company’s bottom line. Larger companies are successfully using prescriptive analytics to optimize production, scheduling and inventory in the supply chain to make sure that are delivering the right products at the right time and optimizing the customer experience.

Use Prescriptive Analytics anytime you need to provide users with advice on what action to take.

 

References:

https://www.ibm.com/analytics/data-science/predictive-analytics

https://halobi.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/

 

 

O for Orchestration Cloud

Orchestration describes automated arrangement, coordination, and management of complex computer systems, and services. It is often discussed as having an inherent intelligence or even implicitly autonomic control, but those are largely aspirations or analogies rather than technical descriptions.   
 
In most situations, cloud automation describes a task or function accomplished without human intervention. Cloud orchestration describes the arranging and coordination of automated tasks, ultimately resulting in a consolidated process or workflow.  

 
Cloud Orchestrator is a cloud management platform that automates provisioning of cloud services using policy-based tools. It enables you to configure, provision, deploy development environments, integrate service management—and add management, monitoring, back-up and security—in minutes.

Cloud orchestration is the end-to-end automation of the deployment of services in a cloud environment. More specifically, it is the automated arrangement, coordination, and management of complex computer systems, middleware, and services—all of which helps to accelerate the delivery of IT services while reducing costs. It is used to manage cloud infrastructure, which supplies and assigns required cloud resources to the customer like the creation of VMs, allocation of storage capacity, management of network resources, and granting access to cloud software. By using the appropriate orchestration mechanisms, users can deploy and start using services on servers or on any cloud platforms.
There are three aspects to cloud orchestration:

• Resource orchestration, where resources are allocated
• Workload orchestration, where workloads are shared between the resources
• Service orchestration, where services are deployed on servers or cloud environments

Many people think that orchestration and automation are the same thing, but orchestration is actually more complex. Automation usually focuses on a single task, while orchestration deals with the end-to-end process, including management of all related services, taking care of high availability (HA), post deployment, failure recovery, scaling, and more. Automation is usually discussed in the context of specific tasks, whereas orchestration refers to the automation of processes and workflows. Basically, orchestration automates the automation—specifically, the order that tasks take place across specific machines, especially where there are diverse dependencies. 

Why should you choose orchestration?

The manual process of setting up an environment involves multiple steps. Using an orchestrator tool, it's easy to quickly configure, provision, deploy, and develop environments, integrate service management, monitoring, backup, and security services—and all of these steps are repeatable.
Another advantage to orchestration is that it enables you to make your products available on a wider variety of cloud environments, which enables users to deploy them more easily. As a result, you can increase your products' exposure to a wider audience, and potentially expand revenue opportunities for your company.

Reference:
https://www.ibm.com/developerworks/cloud/library/cl-cloud-orchestration-technologies-trs/index.html
https://cloudify.co/product/
 

N for Natural Language Processing!

What is natural language processing?

 

Natural-language processing (NLP) is a field of computer science, artificial intelligence concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language data.

 

 

 

Natural language generation (NLG) is the natural language processing task of generating natural language from a machine representation system such as a knowledge base or a logical form. ... It could be said an NLG system is like a translator that converts data into a natural language representation.

 

In computing, natural language refers to a human language such as English, Russian, German, or Japanese as distinct from the typically artificial command or programming language with which one usually talks to a computer. The term usually refers to a written language but might also apply to spoken language.

 

Natural language processing (NLP) can be defined as the ability of a machine to analyze, understand, and generate human speech. ... These languages were constructed to communicate instructions to machines. Because computers operate on artificial languages, they are unable to understand natural language.

 

Text and other unstructured content from sources like social media posts, articles and blogs is filled with insights that might help your business. The problem is that it’s difficult to parse unstructured text to see trends. For example, are people making positive or negative comments about my product since it was released?

 You can’t easily quantify and understand social media because it’s a big mass of unstructured text. Because the information can’t be mined, in many cases the data just sits there, unusable. Your data has always contained business-critical answers for your business. The problem was you couldn’t easily find the answers through all the noise. But with sophisticated natural language processing (NLP) software, you can. NLP allows the user to extract key metadata from their text, including entities, relations, concepts, sentiment, and emotion. Now your program can understand the complexities of human language to uncover meaningful insights.

Natural Language Understanding builds on the legacy of its predecessor AlchemyLanguage. It remains a leader in text analytics and offers a few key improvements. Natural Language Understanding features the familiar AlchemyLanguage capabilities through a simplified API. It was re-architected to run natively on Bluemix and is software as a service (SaaS).
Natural language processing capabilities for advanced text analytics
Natural Language Understanding includes the core functions of AlchemyLanguage along with some improvements and consolidation. Here are a few key differentiating features:

Sentiment and emotion

Natural Language Understanding returns both overall sentiment and emotion for a document and targeted sentiment and emotion towards keywords in the text. For example, while a customer review may have an overall negative sentiment, particular keywords in the review may have a positive tone, which allows a deeper analysis of the text.

Custom models

Using Natural Language Understanding, you can adapt entity and relationship extraction with custom models for specific uses. With IBM Watson Knowledge Studio, subject matter experts can collaborate on the creation of custom models without having to write a single line of code. These models can then be easily deployed to Natural Language Understanding to identify industry and domain-specific entities and relationships in unstructured text.
New pricing and free forever plan

Natural Language Understanding has improved pricing and a free forever plan. The pricing structure has been simplified. It is based on just the types of metadata that need to be extracted and the amount of data analyzed. The new pricing is particularly attractive if you’re deploying custom models from Watson Knowledge Studio. It’s easy to experiment with the service because you can deploy and use one custom model with the Natural Language Understanding free forever plan and because Watson Knowledge Studio has a free plan as well, you don’t have to pay anything to start customizing
Natural Language Understanding.

You can easily track and manage your usage of Natural Language Understanding with the Bluemix usage dashboard. By setting up email spending notifications, you can be alerted to the spending thresholds you specify, so you can find out when you’ve reached 80%, 90% and 100% spending. You also can edit each spending notification as your needs change.

Languages
Natural Language Understanding has broad language coverage. It can understand text in nine different languages, including English, French, German, Spanish, Portuguese, Italian, Russian, Arabic and Swedish. You can customize Natural Language Understanding’s ability to detect entities and relationships with Watson Knowledge Studio in English, Arabic, Brazilian Portuguese, French, German, Italian, Japanese, Korean and Spanish. (IBM is adding more languages all the time, so check back soon if your favorite language isn’t supported today.)

   References:

https://www.ibm.com/blogs/watson/2017/04/watson-natural-language-understanding-advanced-text-analytics/

 https://developer.ibm.com/dwblog/2017/create-custom-nlu-annotation-without-writing-line-code/

 

M for Machine Learning

Machine learning has taken some massive strides forward in the past few years, even emerging to assist and enhance Google’s core search engine algorithm. But again, we’ve only seen it in a limited range of applications.

We expect to see machine learning updates emerge across the board, entering almost any type of consumer application you can think of, from offering better recommended products based on prior purchase history to gradually improving the user experience of an analytic app. It won’t be long before machine learning becomes a kind of “new normal,” with people expecting this type of artificial intelligence as a component of every form of technology.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Some machine learning methods

Machine learning algorithms are often categorized as supervised or unsupervised.

• Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
• In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
• Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiringunlabeled data generally doesn’t require additional resources.
• Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

IBM Watson Machine Learning-   A Quick Overview
  
What is Watson Machine Learning? 

Use your own data to create, train, and deploy machine learning and deep learning models. Leverage an automated, collaborative workflow to grow intelligent business applications easily and with more confidence.
 

Watson Machine Learning features

• Machine and deep learning

Create different models and compare the results. Run automated experiments and self-learning models with an integrated Watson Machine Learning engine.

• Open source technologies

Use the same Jupyter notebooks you know and love, with Python, R, and Scala. Jump-start your R experience with a free, open-source RStudio tool. Scale on demand with Apache Spark and create while you learn.

• Easy visualizations

No programming required! Create machine learning models using visual modeling tools and quickly identify patterns, gain insights, and make decisions faster. Choose from IBM tools such as PixieDust and Brunel.

Watson Machine Learning benefits

All-in-one-   Do your work in one place, without ever leaving the site.

Connect to your data-  Connect to more than 30 types of data stores as part of IBM Cloud.

Community help-   Harness the power of shared data sets, notebooks, articles, and more.
 

 

References:
http://www.expertsystem.com/machine-learning-definition/
https://www.ibm.com/cloud/machine-learning

Leadership In Energy And Environmental Design (LEED)

Leadership in Energy and Environmental Design (LEED) is a set of rating systems for evaluating the design and environmental performance of buildings, homes and neighborhoods. Devised by the United States Green Building Council, the system provides specifications to projects for environmentally friendly actions both during the construction and use of the building. LEED was started to appreciate, drive and accelerate green building practices.

Leadership in Energy and Environmental Design is a voluntary program and provides a framework for green buildings that can identify and implement measurable design, construction, maintenance and operations solutions. One salient feature of LEED is its possible adaptation by different building types. It measures nine key areas:

• Regional priority
• Sustainable sites
• Innovations in design
• Materials & resources
• Energy & atmosphere
• Water efficiency
• Indoor environmental quality
• Awareness & education
• Locations & linkages

The credit-based system sets identical criteria for every project to qualify and highly encourages sustainable design. It causes the building operators and owners to be more environmentally responsible and also use the resources more efficiently. LEED provides certification to quality building projects which satisfy the requirements. The certifications provided by LEED are categorized as Certified, Silver, Gold and Platinum, with more points awarded to higher energy efficiency.

There are many benefits of obtaining LEED certification in projects. Firstly, it projects a positive image of the project to the community. Green building practices can help in significant cost and energy savings. It promotes clean and renewable energy.

The indoor air quality and daylight provides better quality of life to those in the buildings. Indirectly, this helps in increasing the productivity of the people in the building, home or neighborhood. Many cities and states are providing tax benefits for green buildings and some U.S. government agencies are also adopting LEED or similar standards, with some ranking the minimum qualification as equivalent to LEED Silver certification.

 

 

H for Hadoop & Humanized Big Data. (visual, empathetic, qualitative)

What is Hadoop ?

 

Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.

 

 Why is Hadoop important?

• Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things (IoT), that's a key consideration.
  
• Computing power. Hadoop's distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have.
  
• Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
• Flexibility. Unlike traditional relational databases, you don’t have to preprocess data before storing it. You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos.
• Low cost. The open-source framework is free and uses commodity hardware to store large quantities of data.
• Scalability. You can easily grow your system to handle more data simply by adding nodes. Little administration is required.

 How it works?

 

Doug Cutting, Mike Cafarella and team took the solution provided by Google and started an Open Source Project called HADOOP in 2005 and Doug named it after his son's toy elephant. Now Apache Hadoop is a registered trademark of the Apache Software Foundation.

Hadoop runs applications using the MapReduce algorithm, where the data is processed in parallel on different CPU nodes. In short, Hadoop framework is capable enough to develop applications capable of running on clusters of computers and they could perform complete statistical analysis for a huge amounts of data.

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

Hadoop Architecture

Hadoop framework includes following four modules:

• Hadoop Common: These are Java libraries and utilities required by other Hadoop modules. These libraries provides filesystem and OS level abstractions and contains the necessary Java files and scripts required to start Hadoop.
• Hadoop YARN: This is a framework for job scheduling and cluster resource management.
• Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.
• Hadoop MapReduce: This is YARN-based system for parallel processing of large data sets.
  
  
  How Does Hadoop Work?
  

  Stage 1

  A user/application can submit a job to the Hadoop (a hadoop job client) for required process by specifying the following items:
  
• The location of the input and output files in the distributed file system.
• The java classes in the form of jar file containing the implementation of map and reduce functions.
• The job configuration by setting different parameters specific to the job.

Stage 2

The Hadoop job client then submits the job (jar/executable etc) and configuration to the JobTracker which then assumes the responsibility of distributing the software/configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client.

Stage 3

The TaskTrackers on different nodes execute the task as per MapReduce implementation and output of the reduce function is stored into the output files on the file system.

Advantages of Hadoop

• Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores.
• Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather Hadoop library itself has been designed to detect and handle failures at the application layer.
• Servers can be added or removed from the cluster dynamically and Hadoop continues to operate without interruption.
• Another big advantage of Hadoop is that apart from being open source, it is compatible on all the platforms since it is Java based
  

References:

https://www.tutorialspoint.com/hadoop/hadoop_hdfs_overview.htm

https://www.sas.com/en_in/insights/big-data/hadoop.html

 

 

Fun Fact: "Hadoop” was the name of a yellow toy elephant owned by the son of one of its inventors.

 

Just Enough Operating System (JeOS)

Just Enough Operating System (JeOS) is a tech design concept in which a leaner version of an operating system (OS) replaces the full version to run on a specific device or hardware setup. The term represents a sea change in the way engineers address the necessity of an OS to a given hardware design. It promotes the idea that "less is more," in terms of operating systems that should be installed in a technology product.

JeOS designers have to consider a given OS' kernel or core, as well as tools and utilities required to create a custom OS that enables faster operations and less required installation memory. The use of JeOS designs often corresponds to a virtual appliance method, where a virtual machine (VM) image runs on a given platform.

There also is a tendency for open source operating systems to lead the pack, in terms of JeOS development, even though this model is being adopted by other major licensed operating systems.

An Ideal Platform for Today's Agile Environments

Based on enterprise favorite SUSE Linux Enterprise Server, JeOS provides an ideal platform for today’s agile environments whether it is private cloud or virtualized data center. JeOS (pronounced /jo͞os/, just like “juice”) is a perfect minimized host OS for deployment of container applications, private cloud images, and simplification of IT operations. In your enterprise or data center, take advantage of several different flavors of JeOS—KVM/Xen Fully Virtualized, Xen Paravirtualized, Microsoft Hyper-V, VMware, or OpenStack Cloud.
 
In today's private clouds and virtualized datacenter, fast deployment is critical to achieve operational efficiency. How can you quickly fire up an image in your virtual data center or private cloud without worrying about the certifications? How can you be sure hundreds of virtual machine images are standardized in configuration for efficiency?

SUSE Linux Enterprise Server JeOS (Just Enough Operating System) is a slimmed down form factor of SUSE Linux Enterprise Server that is ready to run in virtualization environment and cloud. With SUSE Linux Enterprise Server JeOS, you can choose the right sized SUSE Linux Enterprise Server option to fit your needs.


Just enough OS that's ready-to-run

 

JeOS trims unnecessary components down to keep the size small (about 300 MB). It is designed specifically for virtualization environment such as VMware, Hyper-V, KVM, and Xen. It is ready-to-run in your hypervisors to save your time in deployment and configuration.
 

No need to re-certify

  If you are certified on SUSE Linux Enterprise Server today, you do not need to re-certify for JeOS. JeOS is built on the same code base as SUSE Linux Enterprise Server. There's no extra effort required to get the same enterprise-grade, mission-critical performance, stability and services in a smaller package.

Minimized Container Host OS

JeOS leverages the strong foundation of SUSE Linux Enterprise Server to deliver an exceptionally agile and fast development platform. Deploy applications such as with containers faster than ever using JeOS, without compromising on the enterprise quality SUSE Linux foundation.
 

Private Cloud Image

As an administrator, use JeOS to create ready to run virtual images for demonstration, testing, or production.

Simplify IT Operations

JeOS provides a minimal system, a “Silver Image”, for further customization to suit your business needs. You can use the Silver Image as a base to build “Gold Images” faster and with accuracy.

 Reference:

 https://www.suse.com
Image:  Linkedin
https://www.linkedin.com/pulse/suse-linux-enterprise-12-service-pack-1-adhi-lim/

Internet of Things - Connecting Devices to Human Value

We can define the Internet of Things as the next stage in the Internet as some do, whereby things and objects with sensors and actuators are connected to the Internet so they can gather, send and get data, leading to smarter solutions and in some cases also act upon data.

That’s how most of us see it. Wearable s are connected and enable us to send and receive data, vehicles get connected, home appliances, industrial assets, street lights, you name it. However, that’s just part of the story that looks at the what, rather than the why and how.

We define the Internet of Things as a network of connected devices with 

1) unique identifiers in the form of an IP address which 

2) have embedded technologies or are equipped with technologies that enable them to sense, 

gather data and communicate about the environment in which they reside and/or themselves.

The potential and reality of the Internet of Things does not lie in the ability to connect IoT-enabled objects nor in the embedded technologies and electronics such as sensors, actuators and connectivity capabilities. It resides in the ways the IoT is used to leverage the insights from data, automate, digitize, digitalize, optimize and in more mature stages transform processes, business models and even industries in a scope of digital transformation.

There are 7 crucial Internet of Things characteristics:

 

1. Connectivity. This doesn’t need much further explanation. Devices, sensors, they need to be connected: to an item, to eachother, actuators, a process and to ‘the Internet’ or another network.
2. Things. Anything that can be tagged or connected as such as it’s designed to be connected. From sensors and household appliances to tagged livestock. Devices can contain sensors or sensing materials can be attached to devices and items.
3. Data. Data is the glue of the Internet of Things, the first step towards action and intelligence.
4. Communication. Devices get connected so they can communicate data and this data can be analyzed.
5. Intelligence. The aspect of intelligence as in the sensing capabilities in IoT devices and the intelligence gathered from data analytics (also artificial intelligence).
6. Action. The consequence of intelligence. This can be manual action, action based upon debates regarding phenomena (for instance in climate change decisions) and automation, often the most important piece.
7. Ecosystem. The place of the Internet of Things from a perspective of other technologies, communities, goals and the picture in which the Internet of Things fits. The Internet of Everything dimension, the platform dimension and the need for solid partnerships.

The Internet of Things (IoT) is growing rapidly, with 127 new devices connecting to the Internet every second. Although many new applications target consumers, including smart-home systems and connected cars, others help companies optimize operations ranging from manufacturing to customer segmentation. As IoT expands, companies’ connectivity expenditures will rise by about 15 percent annually through 2022. To capture this growth, connectivity providers will extend their coverage and investigate innovative technologies, including low-power, wide-area networks (LPWANs).
Such shifts could have major repercussions for companies that sell IoT devices or services. For many years, they relied on country leads to select connectivity providers, and the default choice was often the largest regional or local player. A few also asked systems integrators for provider recommendations, often with similar results. But as IoT becomes more important to the bottom line, companies must reassess their connectivity needs and make more nuanced decisions that consider global coverage, intelligent-switching capabilities, service delivery, pricing, security, and IoT expertise.

Strong intelligent-switching capabilities

Many providers are investigating two intelligent-switching technologies, both of which are relatively new. The first, intelligent mobile switching, enables IoT devices to shift seamlessly from one MNO or MVNO to another. It is still uncommon for IoT devices to have this ability. The second technology, intelligent platform switching, lets devices transition among unlicensed, cellular, and mobile platforms depending on their data-transmission requirements and other factors. No IoT devices are yet capable of platform switching, but some companies are increasing their investment in this area.

Mobile switching can take various forms. Some IoT players enable this capability through multiple international mobile subscriber identity (multi-IMSI) technology, which allows a single subscriber-identity module (SIM) card to be assigned numerous local numbers, including those for different countries (Exhibit 1). This tactic keeps roaming charges lower than those obtained through bilateral agreements with other providers. Since multi-IMSI networks are still not widely available, most companies cannot take advantage of them and still incur roaming charges.

Embedded universal integrated-circuit cards (eUICCs), an emerging SIM technology, may eventually represent a better solution than multi-IMSI for mobile switching in IoT. Each eUICC hosts profiles of multiple MNOs that users can remotely add or remove on demand, potentially giving them more control over roaming costs and quality than multi-ISMI technology.

Customized pricing

Most IoT connectivity providers offer multiple pricing plans with different data limits and other features—one plan might have low set-up fees and high overage charges while a second offers the opposite. When evaluating their options, most companies choose a standard plan, rather than requesting a customized offering, because they lack insight into their connectivity needs and usage patterns. Without this information, they often pay for unnecessary features, such as a data-volume allowance that far exceeds their requirements.
The creation of a customized pricing plan may seem daunting, but a simple approach can help. As a first step, companies should determine how employees are using IoT devices within their organization, as well as how customers are using their IoT-enabled products. During this analysis, they should focus on their most important use cases, which can relate to internal operations, customer needs, or both. Companies can then classify their organization into one of three categories based on data needs (low, medium, or high). Roaming and connectivity requirements, as well as the need for overage protection

Emphasis on security

As IoT implementation increases, so will threats from hackers. When companies are trying to determine how well connectivity providers can combat such intrusions, they should focus on three areas: infrastructure, endpoint security, and encryption techniques.

Infrastructure

Most providers offer strong network-design measures and process-design protection, including traffic separation and access management, but there may be important differentiators related to technology-design protection, including firewalls. Companies should also gauge how quickly providers can respond to hacker intrusions.

Endpoint security

Most IoT players are reluctant to require device authentication, a process in which a machine’s credentials are compared to those on an authorized list to determine if it has permission to access the system. But the cybersecurity threats to IoT may require them to reconsider this stance. For instance, they might decide to ask device users to enter passwords before connecting a device to IoT, and would thus need providers who can support this capability. IoT device manufacturers must also fortify their systems through signature detection (determining that a device is infected and communicating with hackers) or by looking for traffic anomalies. The ability to spot traffic aberrations in real time could give providers a great advantage.

Encryption standards

Cryptography—the process of transforming plain text into encrypted text—is essential to protecting the integrity of data transmitted over IoT and keeping them confidential. But companies should keep in mind that all encryption processes are not created equal when evaluating providers. For example, they should seek providers with encryption methods that allow for agility—in other words, those with base algorithms that can easily adapt and evolve in response to an attack. In addition, companies should ensure that providers follow best practices for cryptography. Consider issues related to crypto keys—the algorithms that encrypt text. If a provider uses a system-wide crypto key, hackers that unlock the code could breach its entire organization.
While many connectivity providers are strong in one or two of these areas, few offer comprehensive security solutions that incorporate all three defenses. Unless they step up their game, IoT players will need to contact cybersecurity specialists for additional protection.

IoT connectivity expertise

As IoT connectivity requirements increase in complexity, and as options continue to multiply, companies will need providers who can advise them about the best solutions and potential partnerships. These providers may include both start-ups specializing in IoT and established players in the mobile sphere.
As discussed, companies appreciate contracts that include tailored pricing based on data usage and roaming. But the best providers will take customization beyond that by looking at each customer’s top use cases and considering their specific requirements—for instance, the typical frequency of data transfer and reliability needs. With this information, they can identify the best connectivity solutions.

 

References:  https://www.youtube.com/watch?v=uEsKZGOxNKw  Internet of Things Simplified

https://www.mckinsey.com/industries/high-tech/our-insights/unlocking-value-from-iot-connectivity-six-considerations--for-choosing-a-provider

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