Intelligent Data Handling is the Success Mantra for IoT

Ample Opportunities wait for Sellers and Buyers of Big Data Solutions in association with IoT

By 2020, IoT will encompass 50 billion devices connected over the internet and that will generate a data of 40 zetabytes through communication. The communication will cover about 25% of human to machine communication and 75% of machine to machine communication.

This represents a massive amount of unstructured data with the potential to ultimately provide very insightful and informative content that will help companies make more informed decisions. This data generated using IoT is considered as one of the next revolutionary areas in technology and industry.

In new research available exclusively through a partnership with TelecomEngine.com, Mind Commerce reveals the ‘Role of Big Data in IoT’. Mind Commerce spoke to companies and understand how these companies will be approaching to Big Data in IoT. IoT has brought diversified companies under one umbrella. The companies who were serving particular clients in their vertical in silo are now expanding and merging with each other to share strengths and overlap weaknesses.

IoT is the Next Digital Living

Internet of Things (IoT) is an advanced concept of interconnecting devices, systems and services beyond Machine to Machine (M2M) communication. It is an interconnection between embedded devices within internet infrastructure.

It is a future of communication through embedded and connected devices with unique identity for personalized, group and business use. IoT covers almost each and every physical device in nearly all fields.

IoT is beyond machine to machine (M2M) communication and provides advanced connectivity of devices, systems and services. It covers variety of protocols, domains and applications. IoT is the next step in automation in almost every field including home automation, vehicle automation and automation of healthcare devices over IP networks. It also enables advanced applications like smart-grid.

Internet of Things or IoT is a new way of technology life that is supposed to acquire most of the digital living of the planet.

Big Data and Analytics Business runs on Data and so does IoT

Use of Big Data and Analytics in IoT will generate a billion dollar business among the consumer and industrial sectors. Data analytics and insights generated from the data on real time basis are helping companies to understand consumer behavior and usage pattern of consumers, machinery and devices. The Big Data in IoT is such a powerful tool if used correctly will help companies to establish how to go ahead in business using data generated by their very own assets.

Billions of devices, such as sensors and actuators, embedded into machines, vehicles, tools, wearables and in buildings will be capturing data on various events such as shopping activity, utility usage, measuring advertisement response, in industrial sectors it provides data related to parcel delivery, status of goods in transport, smart grids to access the consumption pattern and usage history of certain region, weather forecasts, traffic conditions, unscheduled and schedule maintenance of vehicle, aviation delays and timings, downloads and usage of applications, and much more.

Big Data processing in IoT is different than conventional Big Data Solutions

Advancements in data distribution systems have made it easier to access large scale log processing and data mining. However, these systems are designed to access and analyze data generated by humans. In IoT data is generated from machines and machines generate log data day and night and they will generate large quantity of reports and run statistical calculations to provide insights on the real time basis for further actionable.

The statistical data generated by IoT will be different than

conventional methods of push and ad hoc to pull and event based.

Big Data in IoT will require more robust, agile and scalable platforms, analytical tools and data storage systems than conventional Big Data Infrastructure. The data generation is many times faster. Generation of data is of high volume and it will come in many forms and thus there is need of developing new platforms and systems. Companies are developing Java based lightweight data interchange platform and DDS helping in real time data processing and light weight protocols.

Most existing log formats have weak log procedures and it was complex to write expressions to parse, extract and load data from logs. Interfaces that are used today show rigidity and stiffness in many ways. Some of them such as protocol buffer require templates and versioning while JSON does not have centralized templates. The rigid interfaces are easier to use but make it difficult to adapt in case of rapidly evolving data. Whereas flexible interfaces are adaptive to evolving data but they are harder to manage.

IoT uses various data formats which are usually binary formats or compressed textual formats. And the data generated from each sensor lands in a raw format. Data processing platform such as Hadoop with MAPReduce as a primary processing paradigm convert data downstream to more sophisticated formats such as column oriented DBMS.

Hadoop supports various types of workloads generated in IoT.  Sensors generate on the go events and low latency queries, thus there is a need for the platform that will support streaming against scalable semi structured data. Hadoop offers Hadoop Distributed File System (HDFS – an append-only file system) to persist data. Message queues such as Apache Kafka can be used to buffer and feed the data into stream processing systems such as Apache Storm or to leverage the stream part of generic engines such as Apache Spark. Specialized architectures are used to combine historical data residing in HDFS with a newly generated incoming data.

There are certain limitations in current use of Hadoop architecture especially concerning its storage layer HDFS. Hadoop is unmatched when dealing with raw data however it demonstrates limitations in supporting wide array of workloads. In storage layer, HDFS provides only a flat namespace, making real world applications to use separate and dedicated clusters for different workloads.

Big Data in IoT deals with machines and data generated by machines is continuous 24 hrs. a day and it differs in speed, rawness and structure. Therefore the data management capabilities differ than usual data management and processing occurring in Big Data software and systems.

Customers of Big Data in IoT will need Answers for their Challenges

There will a challenge in front of Big Data consultants and service providers to develop new set of applications and tools for Non-IT companies to capitalize on their data generated through IoT. These companies may not bother what is an underlying platform but will focus more on business benefits they will get from investments in Big Data analytics. Companies want to know what exactly could solve in terms of revenue and ROI.

Another issue that a company needs to solve with its internal management is centralized data collection and distribution of data in restricted manner to all departments involved in decision making. In most of the companies departments work in silos and therefore the data generated also remains in silo. Every department has its own set of vendors and own set of infrastructure which makes integration between the departments a complex task. Some functions of each department are either outsourced or contracted to a third party in such cases accessing data is a challenge.

After assessing data, compilation and making a productive output from the data is a daunting task where it should serve a company holistically. These tasks become complex when a data is compiled from various parts of the globe. Therefore, Big Data Analytics in IoT that looks simple actually is a very complex process in multivendor environment.

Big Data in IoT will need Scalable Infrastructure

Data generated in IoT is directly dependent on the number of things or devices deployed in consumer or industrial segment. Thus, data generated by things is always increasing and IT teams need to be alert on adding more storage infrastructure to cope up with increasing need.

For using a full-fledged commercial application of IoT a company will need to capture and store all the incoming sensor data for historical references. The volume of the data generated will depend on the number of sensors deployed in the vicinity.  The data from single sensor could be not more than some MBs per year however; cumulative data from all deployed sensors can scale up and reach in gigabytes or petabytes depending upon the speed of deployment.

There are many options available including private or share cloud storage space in physical, virtual or software defined data centers that are available globally.

Non-IT companies use datacenters and storage for a structured database that they are operating for the years and store database generated from their field reports, quality reports, financial statements and other functional database such as HR, Services, sales etc. However the quantity of the data was not too large or too unpredictable. However, the quantity of the data that will be generated with IoT will be huge and unpredictable thus causing a state of confusion for the small IT team that is not habitual to such a massive transformation.

Data Security and Privacy will be a Big Concern

Data collected either from consumer IoT or Industrial IoT needs to go through privacy and data security checks. As data is open to internet the risk increases even more.

Companies need to be cautious while using personal information such as buying pattern, credit information, financial information, usage pattern, security controls, remote monitoring and data derived from security access. There are many different forms of data such as audio, video and text that can be private for a person will be captured and thus it makes more concerns from the users try not to go for IoT.

There should be a mechanism developed to make it clear to the user what data will be used and for what purpose and also it will need to sign consent forms. These activities may put a control on what data to access and the purpose of insights driven through big data and analytics may remain incomplete.

Big Data in IoT will help Many Industry Segments to grow their Business

IoT and Big Data are for everyone including individuals and industries. However there are certain industries that have found use of IoT a next step of improving business in drowning economy. Experts working in these industries have seen use of big data analytics and IoT as an opportunity to tap into unseen areas of improvements that were not explored earlier.

These industries include HVAC or Building Automation, Consumer Devices and Electronics where new ‘smart’ products are already occupying market space, Healthcare Companies that are using IoT for remote patient monitoring, e-health initiatives where big data analysis and insights will be very helpful in developing solutions, Manufacturing Sector where unscheduled maintenance, operational excellence and harmony in production are major goals achieved through IoT and Big Data, Oil and Gas industry where big data will be helping saving a lot of money through the effective use of tools and technology, Transport and Cargo industry that found a lot of scope big data brings in improving business, and Utility sector which is believed to be the most favored sector that see huge benefits in terms of smart use of utilities helping in saving natural resources, costs and wastage of utilities.

Ample Opportunities wait for Sellers and Buyers of Big Data Solutions in association with IoT

Big data brings loads of benefits to the end users in operational excellence as well as in generating new market opportunities. This will be the prime reasons for the growth of Big Data in IoT.

Largely protocols and standard frameworks in IoT will be Opensource, however there are many technology platforms, software and API development platforms, analytical and visual data representation platform, techniques of data streaming, collection, and storage lead to a complex business model as against the ideal situation of interoperability between two or more companies.

Large companies in IT are entering the business of Big Data Analytics in IoT either through acquisition or partnering with companies and startups developing various tools, platforms and APIs in Big Data.

Mind Commerce estimates that the Big Data in IoT will generate 35% more revenue in new business opportunities such as leasing machinery for various purposes, targeted marketing and personalized advertising, revenues in click advertising on mobile phones, emails and websites.

On the operational front Big Data in IoT will save about 40% of the costs that are presently are the leakages due to operational inefficiency largely in delivery of goods, manufacturing sites, and transportation. Through initiatives such as Smart Grid it will save large chunk of costs in power and other utilities for individuals, companies as well as utility suppliers.

Market Opportunity: Big Data in the Internet of Things provides analysis about the key business trends in Big Data deployments relative to IoT and assesses what companies doing in this area to generate a strong market share.

About Mind Commerce

Mind Commerce is the trusted source for research and strategic analysis focused on digital technologies and the telecommunications industry. Reports provide key trends, projections, and in-depth analysis for infrastructure, platforms, devices, applications, services, emerging business models and opportunities.