Background

Big data is referred to a set of data that are too complex to handle and is bigger in size and this data can not be managed traditionally data processing method. This is very necessary to manage the data properly as sometimes higher complexity of data may lead to higher false discovery rate. There are several challenges that are faced with the use of big data includes data storage, search, sharing, transfer, visualization, querying, information privacy and data source (Hashem et al., 2015). The three concepts that lead to the idea of big data velocity, volume and variety. Later the concept of veracity and value also got associated with the concept of big data. Basically, the term big data tends to the use of predictive analytics, user behaviour analytics or certain other advanced data analytics method and are used to extract data value. The data sets used for big data gets increased rapidly as these gathers’ information from cheaper and numerous information sensing internet of things devices that includes wireless sensor networks, radio frequency identification, microphones, mobile devices (Al-Fuqaha et al., 2015). With the help of relational database management systems, software packages and desktop statistics helps in handling the data that are hard to visualize. The concept of big data is in use from 1990s and the credit goes to John Mashey as he made this term famous. The aim behind using big data analytics is to process the complex and varied range of data sets.

Big data analytics is used to enable analysing of big data, data related to scientists, predictive modellers and other analytics professionals are needed to analyse the growing volumes so that it can help in transaction data. This is a mix architecture that includes semi structured and unstructured data. For example- web server logs, text from customers via emails and survey responses. The three factors important for big data analytics are volume, velocity and variety and this are known as 3Vs of big data (LaValle et al., 2011). The Hadoop framework got launched in the year 2006 as an Apache and this is an open source project. The Hadoop has developed its features and has developed applications with the help of big data and were primarily engaged with getting the province of large internet (Maltby et al., (2017). The working of organization involves collection of data, processing the data and analysing these big data and this turns to be sometimes NoSQL database and also includes Hadoop. This involves use of tools such as:

YARN: this is the key feature of Hadoop for second generation and is a form of cluster management technology.

Map reduce: this provides a software framework that allows the customer to develop and process unstructured data in a parallel across of processors (Wang, Li, & Hu, 2015).

Spark: this is an open source and provides parallel processing framework enables the user to run large data analytics applications throughout the clustered systems.

HBase: this is a data value that is column oriented and stores data and run them on the top of Hadoop Distributed file system.

Implementation of big data:

Discussion

Big data has been implemented in different sections of our society some of the major implementations have been listed below along with the Big data analytic solutions which helps the business to move forward.

Implementation is retail industry:

Big data in the retail industry not only helps in understanding the ways by which the customers are looking in the product or are buying the product but are also associated with usage of the big data analytics in order to become more accurate (Wood, Goude, & Shaw, 2015). The retailers are firstly associated with the usage of the data so as to understand the behaviour of the buying customers which is followed by the synchronization of the behaviours with the products. After this they are responsible for making plans for the various marketing strategies so as to sell the products and earn a higher amount of profit.

Sensor analytics in big data analysis:

These is the process in which the statistical data are being created and analysed by wired or wireless sensors. The aim is to detect the anomalies. The sensor analytics includes examining the data in a correct way and also putting these data in right context so that it can be helpful for calculating the data. From the survey made it is noted that the need of data scientist who are comfortable in working with the sensor will increase with the increase in demand of Internet of Things and wireless sensor networks (Wamba et al., 2015). There is also a need of software that will be helpful for analysing the present data in such a way that it will be helpful for the users.

Big data analytics is used widely in the industry. The monitoring equipment’s has found great importance in healthcare sector. Healthcare sector have been associated with accessing a huge amount of data, however they have been plagues by various kind of failures while utilizing the data so as to curb the problems like rising cost of the healthcare and the inefficiencies in the various systems which are fast in stifling and are responsible for providing a better way healthcare all across the board (Mukhopadhyay, & Suryadevara, 2014). The major reason lying behind this is due to the unavailability of the big data along inadequate data or unusable data (Raghupathi, & Raghupathi, 2014). Besides this the healthcare databases which are responsible for holding health related information have been associated with making the process of linking the data very difficult. This data might help in showing the patterns which are very helpful in the medical field. These also includes monitoring the data that is supplied by the patients. This helps in quick accessing and developing reports in a short time.  

From today’s world it is evident that the usage of eth big data has greatly increased by the healthcare sector which includes the hospitals and many more. The usage of big data by the hospitals helps in providing the best clinical support along with helping in reducing the cost related to the management of the was needed for providing care along with managing the population of the patients having high risks (Russom, 2014). This also helps in managing and monitoring the activity of the equipment’s used within the hospital.

Application of using monitoring equipment in industry

Moreover, the vibration monitoring is done on the industrial equipment’s and this are collected in 2-1, 000Hz frequency range and is analysed, reported and are being altered in inches per second (IPS). This helps to determine the conditions of the machine used for industrial purposes. Thus, this is very much essential to monitor the conditions of the equipment’s used within the industry (Russom, 2014). After this with the help of predictive analysis the industry can extract the information from already existing data sets so that they can easily understand the pattern followed and can predict the outcomes. However, predictive analysis will no tell the future , it only tell the users about the trend followed and the possible outcome that the user can receive.

Today’s world has seen an increased demand of natural resources which includes the oil, minerals, natural gases and many more (Swan, 2013). This has initially been leading to the increase in the volume along with the complexity and velocity of data which is very difficult to handle. In a similar way the huge amount of data from the manufacturing industry are also untapped. So, the underutilization of this type of particular information is associated with preventing the production of improved quality of products, efficiency of energy, reliability and better margins of profit. Usage of the big data have greatly helped in soling this type of problems (Assunção et al., 2015). In the natural resource industry and manufacturing industry the usage of big data has been associated with allowing the predictive modelling so as provide support to the process of making decisions along with helping in the ingestion and integrating of large amount of data from the various geospatial data, temporal data and from other sources.

The usage of the big data in the insurance industry has been associated with helping the industry in having the insights of the customers so as to provide then with a transparent and simpler product (Wamba et al., 2015). Analysis and prediction of the customer behaviour by making use of the data that are derived from the various sources like the social media, GPS enabled devices and many more helps a lot in solving some of the major challenges faced by the insurance industry. Besides this the big data is also associated with helping in the retention of customers as well by the insurance companies (Lv et al., 2015). Whereas in cases of claim management the usage of the predictive analysis from the big data helps in offering a faster service as large amount of data are analysed specially in the stages related to underwriting.

And for satisfying the customers. Big data solutions are associated with helping in leveraging the data for the purpose of identifying along with exploring the new opportunities (Obermeyer, & Emanuel, 2016). This initially results in proving assistance to the process of strategic decision making along with helping in streamlined operations so as to improve the results of the bottom-line Some of the major advantages obtained by the various industries by making use of the big data have been listed below:

The big data technologies such as the Hadoop or the cloud-based analytics are very much helpful in providing advantages which are related to the cost and this is mainly in cases of storing the large amount of data along with helping in doing business in a more efficient way (Wortmann, & Flüchter, 2015).

Speed of big data applications like the Hadoop and the in-memory analytics are combined with the ability of analysing the new sources of data so as help the business in becoming capable of analysing the information immediately and also helps them in taking decisions at a faster rate depending upon the things that they have learnt.

The sensor and monitoring equipment’s have found great importance in several industry. The main challenge faced with the use of sensors in industry is that the device and sensor used maybe expandable and are not with a good quality and it may change with the change in location. This will impact the working of the sensors. This is very much necessary to understand the important of the sensor devices and maintain an accurate data footprint so that they can maintain a good coordination with multiple vendors. The challenges faced with monitoring equipment’s involves the need of experienced scientist. As analysing the data sets are necessary to predict the outcome, for this it is necessary to have expert. In order to overcome these challenges it is necessary to understand the partnership between vendors, service providers and customers. This will help to build system for capturing, integrating and distributing IIoT and sensor based industrial data.

The data used in big data analytics are generally collected from both internal and external sources, for example the data related to weather or demographic data of the costumers that are compiled by third party information service providers (Zakir, Seymour & Berg, 2015). Moreover, the streaming of analytics applications is becoming a common factor in terms of big data environment with help of stream processing engines that includes spark, fink and storm. The main issue faced with the use of data analytics are as these involves use of a mixture of several data, thus their rises a chance of management issue in area of data consistency, quality of data and governance. Moreover, it may lead to data silos due to the use of different type of platforms for storing data within the big data architecture. This is also necessary to understand the right mixture of technology that will benefit the big data analytics architecture. The pitfalls related to big data analytics includes lack of internal analytic skills within the employees (Zikopoulos & Eaton, 2013). Large amount of cost gets incorporated while appointing experienced scientist and data engineers so that the gaps can be filled. Earlier the big data system used to be developed within large organizations but nowadays cloud platform vendors such as Microsoft and amazon web services had made it easier to set up the system and manage the clusters of Hadoops within the cloud. The users are allowed to use the services as long as they want to and does not require any software license for this.

Conclusion:

The discussion conducted above helps in concluding to the fact that the usage of the big data helps a lot in overcoming the various problems that are faced by different kind of industries. Besides this the report has also been associated with discussing about the various big data solutions used by various kind of industries while monitoring, sensing and tracking the data. Some of the major advantages of the big data includes faster and accurate decision-making process along with delivering of new products at a faster rate and many more. Some of the major big data analytics tools has also been discussed in this report. The big data analytics is now shifting from the processing of the data to the optimization of the various insights from various data. The quicker availability of the big data is associated with allowing the decision makers to focus upon the budgeting and upon the performance monitoring along with the discovery and development of new and different kind of business opportunities.

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