The term Big data is describe as the large amount of data for both structured and non structured that inundates a business on a day-to-day basis .It is not necessary that the data is important or not .It’s what organizations do with this data that matters. Big Data can help to analyzed that lead to better decisions and strategic business moves.

It is a term that is used to refer the study and applications of Big data sets that are so big and complex that traditional data processing application software are inadequate to deal with that data , Big data challenges with capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and  other data source.

History and Current Considerations:

The term BIG DATA is relatively new, the act of gathering and storing large amounts of information for eventual analysis is not latest.The BIG DATA characteristics are is as follows.


Many variety of data collect sources  by the Organizations such that business transactions, social media and information from sensor or machine to machine communication data. In the past time it is to difficult that  storing the data  would’ve been a problem  but new technologies (such as Hadoop) have eased the burden to storing the data.


The rate at which the data is getting generated ,Data streams in at an unparalleled speed and must be dealt with the timely manner.


Different type of data that is

Structured data for example MySql.

Semi-Structured data for example json

Unstructured data for example text,audio,video

In SAS ( “Statistical Analysis System”), we consider two additional dimensions when it occurs to big data:


As the velocities and varieties of data is increasing , the flow of data is higher inconsistent with the periodic peaks.Daily data from the seasonal AMD other peak data can be difficult to manage.It is more so with  unstructured data.


The data comes from multiple sources that is makes it  difficult to link, match, cleanse and transform data across systems. It is more necessary to connect data such as correlate relationship,hierarchies and multiple data linkages.



  • Recommendation engines.
  • Analyzing call detail record(CDR).
  • Fraud detection.
  • Market basket analysis.
  • Sentimental analysis.



The importance of big data application is not that how much data you have but problem is that how can you manage such big data ,for example the reputed companies  ,that can complete their last 10  years have big data such as all employees details such as salary ,age ,designation etc.

When you combine the detail of  big data with high-powered analytics then you can accomplish business related tasks such as follows:


  • Determining main reason of failures, issues and defects in near real time.
  • Producing coupons when the point of sale based on the customer’s buying habits.
  • Calculating entire risk portfolios in minutes which makes it time saving.
  • Finding fraudulent behavior before it affects your organization .


Information Technology

Since 2015, big data has come to promise with the Business Operations such as a tool to help employees work more efficiently and streamline and  the collection and distribution of Information Technology (IT). The use of the big data to resolve information technology and to collect the data issues with the enterprise is called IT Operations Analytics (ITOA).The principles of big data applying to the concept of the deep computing and machine intelligence.The business of ITOA were also starting to play the main role in system management to that brought several data silos together.


Internet of Things (IoT)

Big data and the IoT,they both work together. To find the data from IoT devices provides a mapping of device inter connectivity. These mappings used by the media industry, companies and governments to more accurately target their audience and increase media efficiency for their improvement of business. IoT is based on the technology that is to collect sensory data,and it is used by the medical manufacturing  and transportation.

In the words of  Kevin Ashton defines the IoT that if we have computer with the knowledge of everything,we can save time ,money and loss.


Health insurance providers are gathering the data on social “determinants of health” that is , food and TV consumption, marital status, clothing size and purchasing habits, for which they make predictions on health expenditure, in order to spot the issues of health for their clients. It is use to know the market price detail to know the average price, which would give you the best idea.


when we come to know that how the media use the big data,it is first to know that the mechanism by the media process.Media and Advertising uses the big data as many activity done by these .The industry can be moving away with the old approach by using media environments that is newspapers, magazines, or television shows and instead taps into users with technologies that reach targeted people at optimal times in optimal position. The main aim is to serve ,message and content that is in the line with the user’s mindset.For example publishing environment increasingly such as advertisement and article.


The number of universities including University of Tennessee and UC Berkeley, have created masters program to meet the demand. Personal boot camps have also developed programs to meet the demand, including free programs such as  The Data Incubator or paid programs like General Assembly. In the field of marketing, the main problems stressed by Wedel and Kannan is that marketing has many sub domains that is advertising, promotions, product development, branding which is use in different types of data. As one size fits all analytical solutions are not desirable then business schools should prepare marketing managers to have huge knowledge on the different techniques used in these sub domains to get large picture and work effectively within analysts.


Bank are faced to finding new and best ways to manage big data which is produce by the large amounts of information streaming in from countless sources.It is quit important to understand customers and increase their satisfaction,that is important to reduce the risk and fraud for maintaining regulatory compliance.

Large amounts of information streaming in from countless sources, banks are faced with finding new ways to manage big data. While it’s necessary to understand customers and boost their desire, it’s very important to reduce the risk and fraud while maintaining regulatory compliance.


The relationship of costumer is critical in the retail industry,and it is one of the best way to manage big data.Retailers want to know one of the best way to market to customer,the way to handle the transections and to bring back lapsed nusiness.


The use and to adopt the big data in the governmental processes which allows in the cost ,creativity and innovation , but does not come without its flaws .Data analysis requires number of part of government that is local and central to work both in collaboration and create new innovation processes to find the desire result.

Civil Registration and Vital Statistics is the name that collects all the death and birth certificate status. It is the main source of big data for the governments.

International development

It is to find out that the usage of information and communication technologies for the development is that big data technology can play a main role to contribute and also represent the unique challenges for international development.It is also useful for the cost cutting opportunities to improve decision making in critical development areas that is health care, employment, economic productivity, crime, security, and natural disaster and resource management.Apart from this ,to generate data that is new opportunities to give the unheard a voice.In longstanding for the development of regions such as inadequate technological infrastructure and economic and human resource scarcity exacerbate existing concerns within the big data which is the privacy and imperfect mythology.


On the basis of TCs2013 Global Trend Study find out that the improvements in supply of planning and the quality of the product provided that the benefit of big data for manufacturing.It provides infrastructure for transparency in the manufacturing industry,that is the tendency to unravel chances such that inconsistent component performance and availability.In future manufacturing as an applicable approach toward near-zero downtime and transparency requires large amount of data and new prediction tools for a systematic process of data into useful results.The concept of framework that predictive productivity starts with the data acquisition where different type of sensory data is present to acquire such as acoustics, vibration, pressure, current, voltage and controller data etc. Large amount of data for the sensors in addition to historical data construct the big data in manufacturing.This would create the big data which acts as the input for the predictive tools and preventive strategy that is Prognostics and Health Management PHM.

Advantages of Big Data

Following are the advantages of Big Data:

  • Big data analysis the driven innovation solutions. Big data analysis to helps the knowing and contacting customers. It helps to business optimize
  • Big data is to helps in improving science and research.
  • Big data improves healthcare and public health as the availability of record of patients.
  • Big data helps in financial tradings, sports, polling, security law enforcement etc as the availability of records of last year.
  • Big data help to access vast information via surveys and deliver answer of any query .
  • Big data helps for Every second additions are made.
  • In big data ,One platform carry unlimited information.


Disadvantages of Big Data

Following are the disadvantages of Big Data:

  • Traditional storage can very costly to store big data.
  • Most of the big data is unstructured.
  • Big can share your privacy.
  • Big data can be used as a fraud purpose.
  • Big data may increase classes within a group.
  • Big data needs to be analyzed for longer duration to leverage its benefits.
  • Big data analysis results are misleading sometimes by giving the wrong information.



Big data architecture depends on the  mechanisms for ingesting, protecting, processing, and transforming data into database structures. Analytical tools and analyst queries run by the environment to mine intelligence from data at which outputs to a various different vehicles.

The architecture of big data has multiple layers that is the four layer . Let us try to understand the Big Four logical layers that exist in any big data architecture.

1.Big data sources layer:

Data sources that is ,for the big data architecture are all over the map. Data can come through by company servers and sensors and third party data providers. The big data environment  that can sending data into the batch or real-time. A few examples of data source include enterprise applications like ERP , MS Office docs, data warehouses and relational database management systems (RDBMS), databases, mobile devices, sensors, social media, and many more devices.

2.Data massaging and storage layer:

This layer gain data from the any of the sources.It is on choice that convert unstructured data to its format.The big data is stored on the basis of structured data that is RDBMS,and unstructured data can be in file system like Hadoop Distributed File System (HDFS), or a NoSQL database.

3.Analysis layer:

This layer indicate to the stored data for the extract business intelligence.Many analytical tools operate for the big data environment.Structured data  can supports mature technologies such as sampling, while unstructured data requires more advanced  specialized analytics tools.

4.Consumption layer:

Consumption layer analysis the result and the presents them for the output.A large number of outputs comes from viewer,applications and business processes.


The presentation of Big Data, less cost commodity hardware, and advanced information management and analytic software to  produced a unique moment for the history of data analysis.The changes of these that means that we all have ability to require the analyse astonishing data which sets quickly and price effective for the first time in history.These abilities are neither theoretical nor trivial.It may represent the genuine leap forward and the clear opportunity to feel enormous gains in terms  the of efficiency, creativity,revenue etc.

The Age of Big Data is now starting, and these are confirming revolutionary times if both of them that is business and technology professionals continue to work together and deliver on the promise.

we actually do not really care about the truth of data unless they can deliver on that promise that comes with the data.

I am going to try and bring the things to come back to a practical level and i want to say with this quotes that is “Information is powerful it’s what we use it for that will define us.”

Then please let us know that AIS Mobile Apps is a software development company which has expertise resources in the field of Big data and they have successfully created a very good bonding and relationship with large enterprise as well as small enterprise businesses.




Leave a Reply

Your email address will not be published. Required fields are marked *