Big Data in Farming

The role of big data could be beneficial in the agriculture sector in terms of both smart farming and related supply chain management (Wolfert et ah, 2017). Big data technology with the IoT could produce a huge amount of data related to farming and agriculture. The data generated and collected with the smart sensors could be of very large volumes and can be useful in the prediction and decision making process that might be fruitful for any type of farming (Figure 9.6).

The big data technology has come up as a game changer for any business that would definitely improve the business process of any organization. Monitoring of crops could be done using big data technology; farmers can protect the crops from diseases, and proper pest control can be executed based on the history of the crop by feeding both previous and current data into a predicting system. The right period of the crop can also be checked to protect the crop from extreme weather conditions, which was not possible earlier without the big data technology. The farmers now have the choice to sell the crops to a good buyer from a list of buyers maintained and not limited to or dependent on a single buyer who buys the crops on his own conditions. The list of buyers could also show which buyer is going to buy which crop and prices could also be mentioned. The risk assessment and analysis using big data could be beneficial in farming, which could help a farmer take the appropriate actions using the real-time data so that there would be minimal damage to the crop.

Farmers can check the previous production with respect to demand and supply of the crop. This information can be used by the farmer for the production of the crop, which would improve the farming operations.

FIGURE 9.6 Big data in farming.

Big Data in Cloud Computing

Merging big data with cloud computing is a powerful combination and it can transform any organization (Chan, 2018). Big data, as the name says, is a huge volume of data; for storing this huge volume of data, there has to be some storage that can accommodate any quantity of data. The traditional storage system does not have enough capacity to store large volume of data and neither can they able to process this large volume of data, especially when the data is coming at fast pace. So there has to be a better method and place where the data is processed and analyzed. That’s when a data storage method comes to the rescue; it’s called cloud storage where any amount of data can be stored (Figure 9.7).

Big data can have the volu me of petabytes and exabytes; it may soon reach to zettabytes of data, and since a cloud has the scalable environment that can store any volume of data, it is possible to deploy any kind of application that may boost up the analysis of any business by making the big data easy to access and streamline. There are various companies that provide cloud storage, and a good company ensures that the work of any business or organization always goes perfect without any problems. Earlier, many companies used to invest huge amount in setting a big data center for storing the data, but with the cloud computing the responsibility of data, storage is taken by the cloud providers, and the user company just needs to pay the service charges. A cloud storage is a dynamic storage that means the cloud storage could expand or reduce depending upon the volume of incoming data. Awodele (2016) discussed some of the issues and challenges associated with big data in cloud computing. One of the challenge big data and cloud storage come up with is the rate at which big data is being generated every second. The resources associated for the processing of this big data not able to match with the rate of flow of data because of limited bandwidth of network connections.

The issues that can come across with big data are security issues related with the data in cloud storage, which comes up at network level where the issues are related

FIGURE 9.7 Big data in cloud computing.

to the network security. There are challenges at the user authentication level that includes the encryptions or decryption techniques and the authentications methods used for accessing the cloud storage. Protection of data is also a challenge in the cloud storage.

Big data analytics is another thing that is an important process in analyzing big data. Many websites use big data analysis in order to understand the user’s requirements and thus helps to improve the quality and customer satisfaction.

There are some other benefits that big data analytics provides that can help a business grow and find better insight of the data.

Cost Reduction: Using the big data analytics, a cost-effective storage system can be maintained for huge data sets.

Faster and Better Decision Making: Using big data analytics, a company or an organization can gather quick and reliable information that can be very useful in making quick decisions for the betterment of the company.

Big Data Challenges

There are quite a few challenges when dealing with big data. The challenges could be engineering challenges, and they could be semantic challenges. The engineering challenges could be related to storage or queries, whereas the semantic challenges could be dealing with the huge amount of unstructured data. There are a few more challenges mentioned by (Jones, 2012) regarding the management of big data.

• • The processing of big data is challenging when dealing with a high volume of data with low-power processing tools.
• • It could become difficult to analyze the data in real time.
• • Sometimes it is difficult to design a scalable storage system where efficient data mining techniques could be applied.

Furthermore, there are some other challenges with big data (Jianqing Fan, 2014).

• • Big data could be a complex data, when the data is received from multiple sources.
• • Measurement errors, missing values, and outliners in the big data arise due to some noisy data.
• • Dependent data could be big challenges where in some values are dependent on other values.

Mardis (2016) has discussed a few challenges related to big data. Big data for any company can either be generated by a company, or it can be collected from a third party. If the big data is being generated within the company, the company knows the processing and computational cycles of the data; but if the data is being collected from outside the company, then it would be a challenge for the company as the company needs to know the volume of the data being collected and also the computational cycle required in order to compute this data. Furthermore, the type of the data and the algorithm required also needs to be checked. The accessing and sharing of big data are also a big challenge. The data collected from different sources might be in different formats and from different platforms that might be challenging for accessing the data. The sharing of the data might involve some legal tasks or documents that need prior approval from the signing authority.

There could be some other challenges that come across while uploading the data. The rate of uploading the data should match with the rate at which the data is received.

There are some challenges associated with the security and privacy of the big data. A company needs to work upon the security and privacy of the big data either generated or collected. The security issues come up when the data is being accessed not only by the authorized users but when some unauthorized users also access the data of any company. In the same manner, the privacy of the data is also a concern from the malicious users when the data is of electronic medical record then the security and the privacy of the data is of major concern. The healthcare data can be misused by any person like it could be misused by an insurance company. Another issue of leaking of medical data and the modification or some tampering of medical record could also be done.

The storage and management of the big data is another challenge that comes across while dealing with the big data for any company.