Does Big Data Bring Big Rewards?
Does Big Data Bring Big Rewards?
Original Query
Does Big Data Bring Big Rewards? - A case study analysis examining various types of big data collected by organizations, business intelligence technologies implemented, business benefits obtained, and management considerations before adopting big data solutions.
Types of Big Data Collected by Organizations
There are many different kinds of big data outlined in the case. Research by Wu, Zhu, Wu & Ding (2014) describes many complex, diverse data types. Examples of big data, data types comprise of structured, unstructured data, and semi-structured data. Big Data can also include tabular data (relational databases), text, hypertext, image, audio and video data. Within the case study, the different kinds of data collected include many data types. The case study helps understand that there are many types of big data within each of the data types.
The Laudon & Laudon (2015) "Does Big Data Bring Rewards?" case study describes many kinds of data. We will describe the kind of data and type of data for each organization described in the case. Green Mountain Coffee used unstructured voice calls data and structured data from a customer interactions database. The unstructured voice data converted using software to transform the speech into text for analysis. AutoZone took advantage of various structured databases to uncover information about cars driven around the retail store locations. Products specials then targeted to cars discovered from the databases to be most popular in that retail area.
Health data sensors sold by Nike, Sony, and Jawbone, enable consumers to increase their wellbeing looking at summary data of themselves and comparing to others results. The data created would be from structured data collected from the sensors worn as a wristband, synced with a phone, uploaded to a cloud for the company to analyze further.
Skyscanner assists customers in consolidating flight, hotel, and other travel data from hundreds of websites, travel agents, and timetables to increase the ease booking the most reasonable priced travel plans. The data Skyscanner data collecting structured unstructured and semi-structured data and formatted and organized and presented to the user to speed and simplify organizing traveling.
A Google team used structured web search data and algorithms to determine the number of people that had the flu. Wu et al. (2013) discuss in their research that Google uses an efficiently structured data storage method called Bigtable to store web data. Bigtable widely know to be suitable for web pages' storage and management.
The last kind of data discussed was at Sears Holdings and their subsidiaries Sears and Kmart. They used many structured databases, where product purchase data, marketplace data, credit card holders and purchases. They also gathered web search, location-based, data warehouse, personal shopping data, and online and mobile interaction data.
Business Intelligence Technologies
Many different business intelligence technologies are covered in Laudon & Laudon (2015) "Does Big Data Bring Rewards?" case study. Business intelligence as described by Laudon & Laudon (2015) is terminology used by computer vendors and computer consultants to outline the structure for warehousing, integrating, reporting, and analyzing data that is derived from the business's information technology systems. Three business intelligence technologies are described. They are Carlabrio Analytics, NuoDB, and Hadoop.
Calabrio Analytics used by Green Mountain Coffee can use its Business Intelligence to provide analysis by converting voice calls to text. Calabrio (n.d.) discuss that it took the data and produced analytics that can deliver insight into business topics such as the companies brand, compliance issues, amount of marketing happening during calls, if cross selling or sales occur and how well team member do at retention during calls. This business intelligence enabled different business units in the organization understand how the business is interacting with the customer, and for example, might help marketing or sales to understand current customer to help sell and market to similar prospects in the future.
AutoZone used the database software NuoDB, which runs in the cloud environment, and makes it easy scale as needed so the product can analyze any amount of data without bringing down the system. NuoDB was important to AutoZone when it needed to load up various third party data about cars driven around the retail store locations. Once loaded the product could scale and provide analytics results quickly. Nuodb (n.d.) describes the product as a database that can be dynamically tuned to customer requirements and can easily horizontally scale its services based on the needs the client has.
Sears Holdings used Apache Hadoop and big data technology to increase the speed it could return answers against very large data sets. Laudon & Laudon (2015) points with the new processing technologies implemented all the data could be a process instead of only ten percent at a time. With Hadoop parallel processing cluster they processed 2 billion rows in 1 minute longer than it took to process 100 million rows in the old analytics process system. Laudon & Laudon (2015) point out that Hadoop is an open source software that assists massively parallel processing of immense amounts of data across cheap computers. Hadoop takes a large data query and break it down into sub-queries, distributes them across a cluster of computer processing nodes, and then merges the result into a smaller data set that is easier to analyze.
Business Benefits and Competitive Advantages
Companies and services described in the case need to maintain and analyze big data to help uncover, understand and harvest answer to business questions using big data. Michael Porter's competitive forces model is identified by Laudon & Laudon (2015) as the most popular model for understanding competitive advantage. Porter's model includes the strategic position the company, its strategies, the firm's view, company's competitors, and the business environment. Laudon & Laudon (2015) go on to discuss that the model focuses on the firm's four competitive forces. They are new market entrants, substitute products, customers, and suppliers. Staying competitive the business obtains the benefit of staying at the top of their vertical market. By using data to find answers they can beat out a competitor as described in the porters model.
Pranjic (2011) points out that practicing methods and using business intelligence tools; a company collects information from a massive amount of data it has collected. From that data, the company discovers unknown details that contribute to the business. Using analytics discover links between events which affect business and which seem unconnected to each other before the analysis started. Problems in the firm can be detected easier through knowledge acquired via business intelligence. Pranjic (2011) discuss that Business Intelligence is one of the most significant inputs for making right business decisions and is why companies and service need to maintain and analyze data. The reason companies need to maintain and analyze big data is to boost efficiency within the business. The company information systems need to provide business team members with correct, suitable, and appropriate information. They must have in place information policies and data governance to ensure data quality. The business needs to take advantage of referential integrity within a database management system forces data quality checks for business.
Companies and services described the case study required to maintain and analyze big data to obtain a competitive edge. The companies benefited and helped in different ways. Green Mountain Coffee used voice to text analytics to improve the marketing material, website content, online advertising, and increase representatives' success in the contact center. AutoZone, an auto parts retailer, targeted deals to customers by use a variety of data gleaned from databases using an elastic database called NuoDB, to understand the types of cars driven by people living in the area. Sears used big data to personalize marketing campaigns, coupons and offers to individual customers. Skyscanner used big data information from hundreds of airlines, travel agents, and timetables to speed the booking of the trip.
Three Key Decisions Improved by Big Data
Green Mountain Coffee improved business decisions by using speech analytics tools to convert voice to speech to utilize and understand customer that called the contact center. The company was able to improve the marketing material, website content, online advertising and increase representatives success in the contact center by analyzing voice contact center big data.
Sears Holdings replaced old data processing techniques with Hadoop massively parallel processing software and inexpensive of computer hardware improve the speed at which they could derive answers from extremely large data sets. This technology help decreased time to analyze marketing campaigns for loyalty club members from six weeks to one week and improved some online and mobile commerce analyses targeting became much more precise sometimes targeting down to the individual customer.
Consumer are buying devices that collect big data via sensors in the home or even wearing devices that collect the big data. Personal health sensors sold by Nike, Sony, Jawbone, have emerged to improve health by collecting data from the sensors and helping consumers analyze routines, diets and how long they sleep as compared to fellow users.
Should All Organizations Adopt Big Data Analytics?
Organizations should collect and analyze big data about the business or in some cases purchase data to assist in answering business questions. The reason why a company should be collecting and analyzing data using business intelligence tools is they will realize new ways to gain the competitive advantage for the industry the company is competing. Laudon & Laudon (2015) discuss how data mining provides insights into corporate data by finding hidden patterns and relationships in massive data sets and inferring rules from them to predict the future.
Questions that should be addressed before a company decides to work with big data is to get rid of data redundancy and inconsistency in file system data or convert file systems data to a database management system or enterprise systems that help to share of data across business units. Data redundancy occurs when different business units have the same information stored in various files or places and business units manage same data differently. Each business unit may think they own and maintain the data as the single unit of the truth but causes redundancy and misinformation. Each business unit needs to realize the data is shared and enterprise systems should be in place to manage business data across all business units of the organization.
Before management works with big data and business intelligence, they would need to adopt a database management or centralized system, so all teams are updated and pulling data from a common source. Have a database to store common data will allow reports and analysis done easily compared with data captured in the desperate file system and stored by each Business Unit. Business units building their data silos can cause management issues and make the company dysfunctional.
References
Calabrio Analytics Product Overview
Description: Calabrio's speech analytics platform that converts voice calls to text and provides insights into customer interactions, brand perception, compliance, marketing effectiveness, and sales performance metrics for contact centers.
Laudon & Laudon - Management Information Systems: Managing the Digital Firm, 14th Edition
Description: Comprehensive textbook covering management information systems, business intelligence technologies, big data analytics, and digital transformation strategies for modern enterprises. Published by Pearson Education.
NuoDB Database Platform
Description: Cloud-native distributed SQL database designed for elastic scalability and high availability, enabling organizations to dynamically tune performance based on workload requirements and horizontally scale operations.
Pranjic - Influence of Business and Competitive Intelligence on Making Right Business Decisions
Description: Academic research examining how business intelligence tools and competitive intelligence practices enable organizations to extract insights from large datasets, identify business patterns, and improve strategic decision-making processes. Published in Ekonomska Misao i Praksa, 2011.
Wu, Zhu, Wu & Ding - Data Mining with Big Data
Description: IEEE research paper analyzing big data characteristics, data mining techniques, and processing frameworks including structured, unstructured, and semi-structured data types. Published in IEEE Transactions on Knowledge and Data Engineering, 2014.
Wu et al. - A Multilevel Index Model to Expedite Web Service Discovery
Description: Technical research on efficient data storage and retrieval systems including Google's Bigtable architecture for managing large-scale web data and improving service composition in distributed computing environments. Published in IEEE Transactions on Services Computing, 2016.
Apache Hadoop Official Documentation
Description: Open-source software framework for distributed storage and processing of massive datasets using commodity hardware clusters, enabling parallel processing and scalable big data analytics across organizations.