Monday, November 20, 2017

Several Ways Big Data can Save or Destroy your Business


Nothing about Big Data is Small at all.
The internet pundits some 2 decades ago claimed the world to be becoming paperless and for all data present in the world going digital. With information piling up and computational capacities becoming increasingly heavy, the flow of data has significantly multiplied. As an outcome, for the management of this data, emerged a term called “Big data”. Big data is triggering an enormous change in the way businesses used to work. Companies are becoming heavily dependent on different tools for the management of big data.
Big data is the arrangement and organization of large volume of data. This data can be found in either structured or unstructured format. With massive information present in the market, the analysis and regulation of data get out of hands for many companies. However, big data management serves as a savior for such corporations that tend to accumulate a lot of information from different sources, for the purpose of market intelligence during the course of business. This data can be found in terabytes or petabytes.
The management of big data is the real determiner of a business’ success or failure. Proper gathering and management of information has a make or break effect on a company’s business model. It enables enterprises to search and analyze the data to find solutions to user-centric issues.
There are huge chunks of data and streams available in the market, therefore, the chances of missing on a great amount of important and critical information become high. As the need of market intelligence is increasing, the need for applications that focus on management of data services is gaining momentum too. There are many ventures taking the plunge, yet the question is if a company is actually willing to try luck in making its game better. Working with data analytics can be challenging for many companies as they require immense resources. The risks of loss are as high as the chances of gains.
Any entrepreneur can easily consider it a sign of intelligent business startup that it looks at the consistent players in the arena for muse. It is very vital for any business to look around and observe what trends the other actors in the market are following. This gives businessmen ideas to run the venture successfully and generate profit. It is extremely important that businessmen keep eyes fixed on the activity of the market and see how other fellows are making positive use of the data.
Study and analyze the ins and outs of venture area.
The major reason behind companies failing because of big data is their inability to remain focused on the correct data. Too much data and on top of that, irrelevant data is the recipe to fail a venture you are trying your luck at. In order to succeed, a company is required to properly investigate and study the patterns contemporary market actors are making use of their data through. A company should not just study the models of successful businesses but also those that failed miserably. This is the way they can get proper insight and understand the pitfalls they need to avoid in their endeavors.
It has been observed that marketers mistake in managing and identifying the right kind of data and use irrelevant heavy data in the business. This exhausts the customer when trying to find the required information from the pool of random unnecessary data consuming space. Corporations need to educate employees regarding the management of data to avoid any failure.

Migration.

Heads of different corporations collectively called migration the reason for their usage of low quality data in business. It was also found that more than most of the data migration costs above the estimated amount and take longer time. So, it should not be believed that migration is an easy process, it requires only the technically skilled to complete the task.
Companies should take help of specialists from all departments inclusive of the real data users. It should be made sure that they are familiar with the processes of migration and data management. They know about people with access to the data and how those people are making use of it. The specialists should ensure that data migration takes place step by step, which means that all requirements for the migration are met during and after the process. It has been observed that companies that try to migrate in haste usually cause themselves bigger troubles and financial losses. It takes them more time fixing these issues than allocating proper attention to the step by step process of migration.
A company before migrating data should consider some elements like redefining data and checking the quality of it. It should make a map of all strategies and techniques, also take into account the scope and budget of the data movement.
How to Avoid Internal Collision.
Giant corporations have large data distributed in different places of the organization. Each form of data serves a unique purpose and is regulated by different sets of people. There is hardly any communication among the users of that data as each form of data is relevant to its concerned department.
This can be extremely helpful for many companies as there is no internal collision of data used by different departments. Devolution data is a great step in the management of big data. This step minimizes the chances of a bottleneck situation within the company’s various departments. Segregating big data into sections is the most important function of the management of data. It is a delicate business and requires extremely balanced approach to separate data into right categories.
If data is not stored in the right way, it can affect the decision making of a company, therefore, its considered an important move in the data management and eventually for company’s business results. It is recommended that data is stored in the form of dashboard, reports, services etc. it should be filtered as per the data roles into categories varying from least to most important or however the organization deems fit. However, the main idea behind this division is splitting big data into exclusive smaller sections so, the data consumers can make best use of the available data.
Incorrect Data.
Employees that work with data know that mismanagement of data can leave irreparable effects on the decision making of a company. This can also shake customers’ trust in the company.
Invasive Data Mining
Invasive data use against a customer can infuriate him. A company should follow these ethical rules as principle policies to not use any customer’s information or make predictions regarding their situations based on their purchases. The basic information of a customer’s purchase should be kept secret from people, as violating this ethical code is tantamount to invasion of your customer’s privacy.
Some great online startup business ventures that transformed the way data was perceived.
Data analytics is a big growing industry with many potential investors and companies aiming at developing their business through it. There are many new but already renowned businesses that are leading the way for instance Uber, Foursquare, Spotify and Feedzai.
These companies have been using big data to their advantage and making best use of it.
Final Word :
Big data, market intelligence is one of the fastest growing techniques in the world. This helps the trader assess the market and understand the needs of customers.

Thursday, November 16, 2017

Basic information of bigdata

What is Big Data?

Data provides information. Accumulation of information is equivalent to accumulation of power and achieving more control over the related events and results. Enormous volume of data with diverse nature is generated In the modern world  that storing them and analysing them to get the required output had become a big challenge. The data could be anything from a real time transaction, climatic conditions, clicks on computers, mobile logs, posts or tweets from social media and much more. If the data so collected becomes impossible for a single machine store and process then such data could be named as Big Data.

Data which are very large in size is called Big Data. Normally we work on data of size MB(WordDoc ,Excel) or maximum GB(Movies, Codes) but data in Peta bytes i.e. 10^15 byte size is called Big Data. It is stated that almost 90% of today's data has been generated in the past 3 years.

Saturday, November 11, 2017

IoT Eco System and IoT Gateway security



Cybercriminals have an array of potential attack vectors to choose from when targeting IoT implementations. Here’s how to work towards comprehensive security in Internet of Things applications.
The Internet of Things may have a significant economic potential, but it also gives malicious actors an ever-expanding toolbox for cyber attacks. Gartner estimates that 5.5 million “things” get connected each day. It’s no wonder that hackers are beginning to target IoT devices with weak security for botnets and other attacks: they are often low-hanging fruit.
As both physical and digital threats increase, the need to find technologies to reduce such risks is also rising. This article will discuss the vulnerable points in an IoT application and the key strategies to resolve them, including details on maintaining supply chain integrity. It will also cover the fundamental elements needed to create a robust security paradigm.

Potential attacks for IoT applications

A handful of IoT-related attacks seem to receive the most attention in the popular press. There is, of course, the Mirai botnet that brought down a chunk of the internet last year. There’s BrickerBot, which renders insecure IoT devices unusable. On the industrial side, Stuxnet is famous for causing physical damage to nuclear centrifuges in Iran. And then there is BlackEnergy — a malware variant that shut down a portion of Ukraine’s power grid.
Attacks with a physical component: IoT attacks at the physical layer of the OSI Model require unauthorized access to physical sensing, actuation and control systems. Consider how electronic car theft works as an example. Since cars are essentially computers on wheels, hackers have a variety of options at their disposal. They can clone the radio signals from a key fob to open a locked vehicle. A hacker with physical access to a vehicle’s Controller Area Network (CAN) bus underneath the steering wheel can cause all sorts of mischief: They can unlock the car’s immobilizer that stops a thief from driving away and reprogram a new key for the vehicle. Access to the CAN bus could also enable them to hack the speedometer, door locks and other components.
The similar threat applies to industrial control systems, which have a decades-long history. Many industrial machines make use of supervisory control and data acquisition (SCADA), a technology that was created decades ago without much thought about security. As a result, an attacker with physical access to a SCADA system can cause significant damage to industrial facilities and critical infrastructure.
Similar threats could apply to medical devices. An attacker could gain access to an implantable device such as a cardioverter defibrillator or an external medical device such as an insulin pump to install malware.
Pure software attacks: This category includes malware variants such as viruses and trojans and worms. Also in this category is fuzzing, in which random data is thrown at software to see how it reacts. Distributed Denial of Service (DDoS) attacks can be software-based as well, although they can also occur at lower levels of the OSI Model. One potential example of an IoT-related DDoS risk would be safety-critical information such as warnings of a broken gas line that can go unnoticed through a DDoS attack of IoT sensor networks.
Network attacks: One of the biggest vulnerabilities of IoT devices is their wireless connectivity, which can make them remotely exploitable. Here, there are a variety of possible attacks that are possible on the devices, or “nodes,” connected to the network.
In an enterprise Internet of Things context, those nodes typically communicate with the gateway that is the core of that implementation. The node connects all of the IoT devices to the cloud.
Let’s assume that we have an industrial IoT application with interconnected gateways linked to each other in a mesh network. If a hacker jams the functionality of a gateway with denial of service requests, they can bring down the whole IoT project. Thus, a single attacker can stop the IT and OT elements of a system from interacting, as we discussed in the article “IoT gateway architecture: Clustering ensures reliability.” 
Cryptanalysis attack: In this type of exploit, a hacker tries to recover an encrypted message without access to an encryption key. Examples include brute-force attacks when a hacker tries every possible password combination to gain access to a system. The known-plaintext attack, with roots stretching back to WWII, is another example, in which a hacker has access to unencrypted text as well as its....Continue reading
Article By : Mohiit Bhardwaj

Monday, November 6, 2017

Industrial Robotics Market Analysis



The industrial robotics market is expected to grow from USD 38.11 Billion in 2016 to USD 71.72 Billion by 2023, at a CAGR of 9.60% during the forecast period. The main objective of the report is to forecast the industrial robotics market size in terms of value and volume for traditional industrial robots and collaborative robots. Further, it includes the detailed information regarding the drivers of the industrial robotics market, such as increase in investments for automation in industries and growing demand from small and medium-scale enterprises in developing countries. It also includes detailed information about restraints, opportunities, and challenges for the industrial robotics market. The study of the value chain of the industrial robotics market is also one of the objectives of the report, which includes information about suppliers and integrators in the value chain of the industrial robotics market.

Years considered for this report:

Base Year: 2016 
Estimated Year: 2017Projected Year: 2023Forecast Period: 2017–2023

Major players in the industrial robotics market ecosystem are identified across regions, and their offerings, distribution channels, and regional presence are understood through in-depth discussions. Also, average revenue generated by these companies, segmented by region, is used to arrive at the overall industrial robotics market size. This overall market size is used in the top-down procedure to estimate the sizes of other individual markets through percentage splits from secondary sources directories, databases (such as Hoovers, Bloomberg Businessweek, Factiva, and OneSource), and primary research. The entire procedure includes the study of annual and financial reports of the top market players and extensive interviews with industry experts such as CEOs, VPs, directors, and marketing executives for key insights.


To know about the assumptions considered for the study, download the pdf brochure


The industrial robotics market ecosystem includes traditional industrial robot and collaborative robot manufacturers such as ABB Ltd. (Switzerland), KUKA AG (Germany), Mitsubishi Electric Corp. (Japan), FANUC Corporation (Japan), Kawasaki Heavy Industries Ltd. (Japan), Yaskawa Electric Corporation (Japan), Seiko Epson Corporation (Japan), Stäubli International AG (Switzerland), NACHI-FUJIKOSHI CORP. (Japan), DENSO CORPORATION (Japan), Comau SpA (Italy), DAIHEN Corporation (Japan), Omron Adept Technologies, Inc. (US), Universal Robots A/S (Denmark), and CMA ROBOTICS SPA (Italy), among others. The ecosystem also includes system integrators such as Dürr AG (Germany) and Artech Automation AS (Norway).

Key Target Audience:

  • Original equipment manufacturers (OEMs)
  • OEM technology solution providers
  • Research institutes
  • Market research and consulting firms
  • Forums, alliances, and associations
  • Technology investors
  • Governments and financial institutions
  • Analysts and strategic business planners
  • End users who want to know more about the technology and the latest technological developments in the industry

The study answers several questions for the stakeholders, primarily which market segments to focus on in the next 2–5 years (depends on the range of forecast period) for prioritizing efforts and investments.


Report Scope:
In this report, the industrial robotics market has been segmented into the following categories:

  • Market, by Type:

    • Traditional Industrial Robots
    • Articulated Robots
    • SCARA Robots
    • Parallel Robots
    • Cartesian Robots
    • Others
    • Collaborative Robots

  • Market, by Industry:

    • Automotive
    • Electrical and Electronics
    • Plastics, Rubber, and Chemicals
    • Metals and Machinery
    • Food and Beverages
    • Precision Engineering and Optics
    • Pharmaceuticals and Cosmetics
    • Others

  • Market, by Geography:

    • North America
      • US
      • Canada
      • Mexico
    • Europe
      • UK
      • Germany
      • France
      • Italy
      • Spain
      • Rest of Europe
    • APAC
      • China
      • Japan
      • Republic of Korea
      • Taiwan
      • Thailand
      • India
      • Rest of APAC
    • RoW
      • Middle East and Africa
      • South America

  • Competitive Landscape
  • Company Profiles: Detailed analysis of the major companies in the industrial robotics market



Available Customizations:

With the given market data, MarketsandMarkets offers customizations according to the company’s specific needs. The following customization options are available for the report:

Product Analysis
  • Product matrix that gives a detailed comparison of product portfolio of each company.
Company Information
  • Detailed analysis and profiling of additional market players (up to 5).

Public Safety Using Internet Of Things For Smart Cities




Create Safer, more efficient cities by transfoming insfrastructures, building and services with iot solution.

Building managers throughout the world are more frequently looking to incorporate IoT devices and solutions into their infrastructures in order to reduce costs and improve the quality of their buildings.

Leveraging the power of information technology, advances in communication and the Internet of Things, smart cities will be at the vanguard of intelligent and environmentally sustainable living in which all the facets like transportation, healthcare, security, water supply, waste management, etc. are interconnected to make the city a better place to live. Essentially, the main idea of a smart city is to make the entire functioning and governance of a city people centric so that people can directly participate in the key decisions affecting the city and get their grievances resolved by the power of ICT as soon as possible.

The main components of a smart city consist of building, transportation, energy, healthcare, education, security, water network system and governance. There is a huge investment potential in these sectors in order to enable them to be smart and people centric. Billions of dollars are going to be spent in these and other sectors in order to make any city smart. 

Big data is everywhere, and our analytic solutions and sensors are making already smart cities even smarter. The smart sensor bank is an array of standard sensors mounted on light poles that detect location (GPS), air quality, proximity to detect traffic/pedestrian movement, light level monitoring, moisture, temperature and more. 

By Application-

Smart Building
Smart Transportation
Smart Energy
Smart Healthcare
Smart Education
Smart Security
Smart Water Network System
Smart Governance


By Component-

Hardware
Software
Services

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