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Abstract Big data have become increasingly popular among businesses. In the report, the big data technology has been explored from different perspectives. Following the discussion, the impacts of big data on businesses have been explored. Even though big data has various benefits to businesses, it also have some serious issues to consider. 1
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Table of Contents Abstract.......................................................................................................................................................1 Introduction.................................................................................................................................................3 What is Big Data?........................................................................................................................................3 3V’s of Big Data......................................................................................................................................3 Big Data Analytics..................................................................................................................................4 Conventional BI tools Vs. Big Data Analytics........................................................................................4 Data Warehouses and ETL......................................................................................................................5 Big Data Ecosystem................................................................................................................................6 Business Impacts of Big Data......................................................................................................................7 Positive Impacts......................................................................................................................................7 Negative Impacts.....................................................................................................................................8 Conclusion...................................................................................................................................................8 References...................................................................................................................................................9 2
Introduction The aim of this paper is to discuss big data and its business impacts. A high volume of data flows through the Internet. Different organizations capture and analyze the data to find out meaningful information. This huge amount of data comes from various sources like social media, businesses, and sensor networks, IoT or the Internet of Things and so on. The streaming data sets are highly important for enterprises and organizations. It helps them to understand their market, customers, products, and the level of competition in the market. As the digital economy has made the business world more competitive so all businesses want more information for decision making and to win competitive advantages(John Walker, 2014). In the following sections of the report, there is a detailed discussion of the big data technologies and analytics and how it impacts businesses in decision making. What is Big Data? In computing parlance, big data refers to the voluminous data sets from heterogeneous data sources. Such data can be structured, unstructured, or semi-structured. Businesses use bog data daily for their business processes. Even though the volume of big data is an important characteristic, there are other characteristics that define it. Businesses are more interested in the value of information that can be taken out from the processing and analysis of such high volumes of data in real-time(Wamba, Akter, Edwards, Chopin, & Gnanzou, 2015). Traditional database management systems are no longer sufficient to help a business in data processing and analysis for decision making. Such database management systems are suitable for storing and processing structured data. Big data comprises mostly unstructured and semi- structured data. Other than volume, the complexity of big data stems from the characteristics such as high velocity, and variety. When a high volume of data is generated in real-time by heterogeneous sources and in different formats, then it becomes a difficult task to process and analyzes the data to find out reliable and meaningful information from that data. Hence, businesses face challenges in every step of big data processing and analysis. Some of the challenges are related to capturing, storing, analyzing, searching, sharing, transferring, querying, visualizing, updating, and retrieving information and to ensure the reliability, protection, and privacy of data(Wamba et al., 2015). 3V’s of Big Data The three main characteristics of big data are termed as 3V's of big data. The characteristics are Volume, Velocity, and Variety. There are other attributes too. For example, value, and veracity. The volume of big data refers to the terabytes of data being generated and stored. The volume of big data is very important as it plays an important role in determining the value of the data. Structured data are usually the data bound by some predefined data structures(Wamba et al., 2015). For example, data collected from relational databases. Semi-structured data may have some structure in it but usually, don't have any data model associated with it. And unstructured data don't have any predefined data model or data structure. Big data can have different types of nature. For example, there may be structured, unstructured or semi-structured data. At the same time, there can be data of different formats like text, audio, video, XML etc. The velocity of big 3
data refers to the speed at which data gets generated and processed. Very often, big data is available in real-time or the value of big data can be obtained only if the data is processed in real-time. Variability is another characteristic of big data. The inconsistency found in big data collected from different sources is known as the variability of big data. Variability affects the efficiency of the process of handling and managing big data. Data quality is important. Big data captured from different sources do not have the same data quality. The reliability and accuracy of results of various big data analytics depend on the quality of big data(John Walker, 2014). Big Data Analytics Businesses use various bog data analytics tools to examine large and varied datasets. These tools help to uncover various business insights, patterns, correlations etc. Businesses use that outcome to understand their customers, preferences of the customers, market trends, and forecasts and so on. Big data analytics give the businesses the power to make informed business decisions (Loebbecke & Picot, 2015). Big data analytics use specialized systems and software. Businesses can leverage the power of big data to generate more revenue, exploring new possibilities, running more effective marketing campaigns, delivering better customer services, improving operational efficiency and by winning competitive advantages over their rivals(George, Haas, & Pentland, 2014). Statistician, predictive modelers, data scientists, and other big data professionals work on the analysis of big data. The growing volume of unstructured data cannot be processed efficiently by traditional business intelligence or BI tools. Big data analytics tools are capable of working on semi-structured, unstructured, and structured data. Some examples of semi-structured and unstructured data are logs from various web servers, clickstream data, content from social media, responses collected from the various online survey, mobile call and message records, data streams from IoT sensors and so on(Loebbecke & Picot, 2015). Conventional BI tools Vs. Big Data Analytics Conventional business intelligence tools allow businesses to run various queries on their data. It helps businesses to improve their business operations and performance of the supply chain management. On the other hand, big data analytics tools and techniques offer more advanced data analysis processes using complex analytics tools. For example, predictive models based big data analytics tools can help to understand forecasts, statistical methods help to understand the scenarios of various what-if analysis(George et al., 2014). The growth of big data is enormous. It is being generated and collected continuously from various information sensing IoT systems and devices. Some examples are aerial remote sensing, mobile devices, software logs, RFID or Radio Frequency Identification-based systems and so on (Schmarzo, 2013). Conventional BI tools are often integrated with relational database management systems and various statistical analysis tools that can run on stored data. On the other hand, big data usually requires parallel processing capabilities to work on massive data sets stored across tens to thousands of servers connected over a network or the Internet. To realize the value of big data, users need to have high performing big data analytics tools. 4
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The conventional BI based approach is more structured and repeatable in nature. Business users use the tools for determining what kind of questions must be asked. On the other hand, big data analytics are used to deliver a platform to the business and information technology professionals so that the users can find useful insights from data(Schmarzo, 2013). BI tools are usually limited to the structured data. On the other hand, big data analytics tools can handle structured, semi- structured, and unstructured data. Some of the BI-related technologies like in-memory databases, column-based databases, and data warehouses are capable of working with big data analytics also. However, when it comes to the velocity of big data, the frequency of generating data or the frequency of delivery of the results, data warehouses may not perform well with high-velocity data streams. The reason is, data warehouses work mostly with batch-oriented data models and data flows. On the contrary, big data needs solutions that can handle real-time or quasi-real-time data streams and more agile processes to work on the data streams. BI and big data are not a replacement for one another. Rather a business can use both efficiently to improve their business processes and decision-making activities. Data Warehouses and ETL Big data requires data warehouses that are fault tolerant, and can handle time-sensitive data streams and can work well with various big data analytics. The primary goal is to allow businesses in making decisions quick(Schmarzo, 2013). Usually, traditional data warehouses, use temporary locations to collect data from different sources. Then it loads the collected data into the data warehouse. The temporary areas are called staging areas. This solution is not suitable for big data as it adds a delay in processing data and not suitable for real-time data streams. So, the staging areas are replaced using some suitable big data file system. It allows new data sources having unstructured and semi-structured data to be loaded on the data warehouse faster. Metrics are created for loading such data on the data warehouse. Unstructured data are more important to find out new ideas and business insights. It also provides detailed explanations to various business queries. ETL stands for Extract- Transform- Load. ETL processes are used by businesses to extract data from heterogeneous sources, transforming the data so that it becomes suitable for further analysis and then loading the same to desired business intelligence or big data analytics platforms. Data may be stored in various data marts during the extraction phase. The MapReduce platform works well for the transformation phase in a big data analysis process. It provides a parallel and scalable solution for transforming data. The outcome of the transformation process provides data in some consumable format so that the big data analytics tool can consume and process the data faster. Rather than using ETL scripts, MapReduce scripts are used for ETL processes for big data processing. In traditional data warehouses and BI tools based applications, the whole execution part takes place on different servers. It requires to move data from one server to another and takes longer time. On the other hand, MapReduce scripts allow data warehouses to do the execution part where the data resides. It cuts down the execution time and saves bandwidth by reducing the unnecessary data flows between servers. 5
Big data technologies like Hadoop allows enforcing different methods to make the queries more meaningful and find more insights from data sets. MapReduce follows the batch-processing method and direct querying method on the installed big data file system. It also offers in-memory stream processing to support real-time query processing for big data. The primary objective of a data warehouse is to provide consolidated information to businesses so that they can use the information for the business processes, create reports and so on. On the other hand, the primary objective of an ETL tool is to transform data from one format to another as required by the data warehouse and other analytics tools. An intermediate location is used for the ETL transformation process before loading the data into a data warehouse(Kwon, Lee, & Shin, 2014). The three primary functions of an ETL process are, Extracting data from data sources. Converting the format of the extracted data sets into a suitable format according to the targeted database or data warehouse. The rules of the transformation are set by the users. Writing down the data into the target database or the data warehouse. With the emergence of big data technologies, ETL processes also have improved. It can support and integrate traditional data warehouses with other operational and transactional systems being used by a business, cloud platforms, Hadoop platforms, various MDM hubs and BI platforms. Hence, ETL is now capable of extracting, transforming, and loading data for big data solutions (Kwon et al., 2014). Various software tools are used during various intermediate phases to clean, profiling and to audit data. ETL tools can be extended further to add various data mapping, data quality checking, data cleaning tools. Advanced ETL supports massive parallel data reading and writing activities also. Big Data Ecosystem Big data ecosystem consists of various techniques to analyze big data, big data visualization, and other big data-related technologies. For example, A/B testing, natural language processing, and machine learning based applications are majorly used for analysis of big data. Whereas, business intelligence tools, cloud computing platforms, and other database technologies are used to implement a big data solution. Visualization is an important part of any big data-based solution. It helps to deliver results to users using visual content like graphs, charts and so on(Kaisler, Armour, Espinosa, & Money, 2013). Tensor-based computations are suitable for big data processing. A tensor is a representation of multidimensional big data. An example of tensor-based processing is multi-linear subspace learning. Other technologies like MPP or massively parallel processing databases, data mining, search-based applications, distributed databases etc. are used for various big data processing tasks. 6
Cloud computing based solutions and advanced storage solutions also help to improve the parallel processing tasks. For example, big data analytics use shared storage architectures like NAS or Network Attached Storage, SAN or Storage Area Network etc.(Kaisler et al., 2013). Business Impacts of Big Data Businesses are using various analytics and technologies to gain the advantages of big data. However, there are both positive and negative impacts of big data on businesses. Some of those impacts have been presented below. Positive Impacts Big data can bring various positive impacts to a business. Some of those are, Big data helps business in decision making. It makes the whole decision-making process more data-driven and agile. The business insights and information found from various big data analytics implemented for a business can help in fine-tuning the business strategies of a business. Big data supports other cutting-edge technologies like cloud computing, machine learning, natural language processing etc. Businesses can develop more intuitive and user-friendly customer relationship management and feedback systems(Sharma, Mithas, & Kankanhalli, 2014). Predictive analysis and other big data based analytic tools can help to identify risks related to market, competition, and products or services early and can provide more information so that a business can take a well-informed strategic move to mitigate the risks. Big data technologies can also help to improve the operational efficiency of business processes. The ETL processes and data warehouses allow to move data at various intermediate storage areas and to process data there. It reduces the processing time and improves the business operations. The integration of data warehouse and other big data technologies also allows focussing on important data faster(Erevelles, Fukawa, & Swayne, 2016). Big data tools like Hadoop, other open source solutions, and various cloud-based big data analytics also help in saving the operational cost of a business. These tools are capable of working efficiently for larger data sets(Sharma et al., 2014). In-memory processing and in-memory analytics tools help to identify new data sources quickly and can add that in data processing activities. Thus, the businesses can always make decisions based on the current data available. Businesses can explore the results found from various customer-centric big data analytics tools. The results from the applications of these tools can help to create and deliver customer-focused products and services. Thus, big data can help businesses to develop new products that can meet the preferences and demands of the customers(Erevelles et al., 2016). Analysis of big data helps to understand various conditions of targeted markets. Businesses can use the information to go ahead of the competitors. 7
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Sentiment analysis of the customers can be done using suitable big data analytics tools. Online reputation of a business can be maintained using various big data tools and analysis processes. Further, they can improve and monitor their online presence (Erevelles et al., 2016). Negative Impacts There are some negative impacts of big data on a business. Some of the impacts are, Big data consists of GPS information, social media activities, data collected by IoT devices and sensors and so on. Most of these data are user-centric and may contain private information. Businesses collect these digital traces to deliver more personalized services and products. However, there are security and privacy issues(Kaisler et al., 2013). Most of the users do not share the data intentionally for a business. For example, the Facebook posts of a user may not be intended for a business. But the business may find the information and consider the person to be a prospective customer. Then they can send customized offers to the user that the user is not willing to receive(Kshetri, 2014). The storage, extraction, and processing of sensitive personal information of individual increases the risks of unauthorized access to data, attacks from the cyber world, and so on(Kaisler et al., 2013). While extracting data from heterogeneous sources, it may collect low-quality data having reliability issues, or personally identifiable information or intellectual properties. In that cases, the results produced from the analysis of these data may be unusable or not reliable enough to make decisions. It may lead to legal consequences also(Kaisler et al., 2013). Conclusion In this report, big data technologies and the impacts of big data on businesses have been covered briefly. Big data has become common for businesses. Businesses from different industries are implementing various solutions based on big data and analytics to win competitive advantages, to accelerate business processes, to make well-informed business decisions and so on. Big data also helps businesses to decide new strategies and to win the competition in the market. The readers can understand the context of big data on businesses and about the technologies. 8
References Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing.Journal of Business Research, 69(2), 897-904. George, G., Haas, M. R., & Pentland, A. (2014). Big data and management.Academy of Management Journal, 57(2), 321-326. John Walker, S. (2014). Big data: A revolution that will transform how we live, work, and think. In: Taylor & Francis. Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013).Big data: Issues and challenges moving forward.Paper presented at the System Sciences (HICSS), 2013 46th Hawaii international conference on. Kshetri, N. (2014). Big data׳s impact on privacy, security and consumer welfare. Telecommunications Policy, 38(11), 1134-1145. Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics.International Journal of Information Management, 34(3), 387-394. Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda.The Journal of Strategic Information Systems, 24(3), 149-157. Schmarzo, B. (2013).Big Data: Understanding how data power big business: John Wiley & Sons. Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organizations. European Journal of Information Systems, 23(4), 433-441. Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. 9