The New Frontier - Data Analytics - Case Study Example

The New Frontier: Data Analytics The New Frontier: Data Analytics Data analytics entails qualitative and quantitative methods and ultimate processes that are utilized to enhance business productivity by way of extracting data (Minelli, Chambers & Dhiraj, 2012). The data extracted is utilized for analysis and identification of behavioral data and patterns necessary for organizational requirements. Global organization use data analytics to streamline business operations, customers, and market economics. Brief Overview of the Evolution of Utilizing Data Analytics in Business Data analytics started as a component of decision-making (Davenport, 2013). Business analytics was used in the mid-1950s to produce and capture a large pool of business information. The information was then analyzed using computing technologies. Companies that invested in large-scale information systems sustained the commerce of business analytics. According to Davenport, (2013) analytics was used for enterprise data warehouse. The information would be captured, queried, and reported through intelligence software developed for businesses. The commerce industry then developed the need for new competencies to assist in managing data. The data sets were analyzed in the warehouses in small volumes and with a static velocity. However, analysis of data set consumed time, and it took time for a business to respond to the findings. The reports produced dwelled in the past instead of providing information that could be used for predictions. The potential of data analytics in giving businesses competitive advantage came in 2000s when internet and advanced technologies were introduced in full throttle. The need to build fresh capabilities and woo more customers created the need for companies to rely on high powerful analytic tools. Big data was born, and database classes, and machine learning methods took effect and defined the commerce industry to date. Main Advantages and Disadvantages of Utilizing Data Analytics at Wal-Mart Wal-Mart is one of the biggest retailers in the world and a company that has realized the potential of data analytics. Wal-Mart is interested in an online activity, social mentions, and in-store purchases. The company is need of profound mentions from its customers around the world. However, using data analytics presents the company with potential advantages and disadvantages. Advantages Wal-Mart is able to understand the buying behavior of its customers. The company utilizes the department of human resource to make expertise analysis about its annual projects (Jain & Malehorn, 2005). Additionally, Wal-Mart can conduct worthwhile research using online and offline data to comprehend the mind of its clientele. The company employs unique survey tactics to know the interests of its associates. Disadvantages The company may invest in advanced technology in implementing data analytics measures but without expanding the growth area of its customers. Data analytics is restricted to how Wal-Mart customers respond to its retail business other than how the company performs in the retail market. The company will need cutting edge technology for data analytics to keep up with the speed of retail business. Analytics drive organizations and every diverse group that relates to the company must be factored in every process (Jain & Malehorn, 2005). Fundamental Challenges The business management, in general, must overcome fundamental challenges in order to implement data analytics in organizations. The obstacles include; Companies have misconceptions about the data analytics procedures and the potential it has in the business. The need for analyzes operations, market economics, and the general operations through the commercial lenses is essential. Business management must engage the HR departments to devote adequate manpower (Brynjolfsson & McAfee, 2012). The manpower will be charged with collecting and conducting relevant analysis on the data gathered. Companies are dealing with too much information to an extent of affecting the leverage of the firm. The information must be selected wisely. Business management has too many data sources to monitor and keep track. The data will require significant power who will deal with information coming from traditional internet sources and social media outlets (Brynjolfsson & McAfee, 2012). social media outlets have become the inevitable data analytics tools because customers are utilizing the outlets to express and share their interests with firms. There is a limited time that is devoted to data collection and analysis (Minelli, Chambers & Dhiraj, 2012). Business management is torn between unveiling new strategies to combat rivals and come up with new products and getting data that is of great value to the organization. The web data is highly dynamic, and it would be impossible to keep track of all trends at the same time. Strategy for Overcoming the Obstacles Companies should come up with customized databases to overcome the obstacles of implementing data analytics (Brynjolfsson & McAfee, 2012). A firm can seek the partnership of the digital technology solutions agency to make an analytics database. The database is developed to meet all the challenges that company face when implementing data analytics platform. Data can be collected from all sources including company website, email marketing outlets, social media platforms and advertising campaigns that use digital tools. A customized database is user-friendly that consolidates all data coming from different data sources. The effectiveness of each outlet is ascertained from a single platform. Not only will companies monitor customer behavioral trends but aggregate theirs needs better than using scattered systems. The Way Data Analytics Changed Wal-Mart The customer responsiveness and satisfaction has changed for Wal-Mart due implementation of effective data analytics procedures. Wal-Mart has spent e-commerce technologies to change the experience that customers get when they are shopping at its outlets in virtually every part of the globe (Walmartlabs.com, 2013). The implementation of WalmartLabs has brought it teams and technology that enable the company to make faster decisions. Prices are approved faster and changed according to the trends in the retail market. Data analytics tools sich as recommenders posted customer feedback, and product descriptions have improved the shopping experience at Wal-Mart (Walmartlabs.com, 2013). The retail company has created a market where the consumers can enjoy an epic shopping experience in Wal-Mart than any other company. The retail market for Wal-Mart now enjoys more customer response in physical outlets and online. Consumers select products that meet their specifications from the retail shops in Wal-Mart’s chain. Personalized promotions have become possible online (Brynjolfsson & McAfee, 2012). The promotions are done on social media to increase the participation of Wal-Mart customers. The process has become speedier by day to provide clients with products that are of greater value than rival companies. The promotions are tailored according to the tastes and preferences of customers. Wal-Mart Data Analytics Trend in the Next 10 Years Wal-Mart will change the trend of using data analytics in the next ten years to boost its market share in the retail industry. Jain & Malehorn (2005) argues that the company has mastered the basics of implementing data analytics. Therefore, Wal-Mart has the ideal tools for changing the way it will deal with trends. The company will capitalize on investing in customer and sales data in the digital systems. The company will not cease to harvest data for its customers and sales trends in order to keep in touch with its market. The data will be integrated with the data warehouse that the company has devised and help to come up with a relevant product mix. The company will use market basket analysis as a tool for data analytics. According to Jain & Malehorn (2005) no other retailer has the advantage of using market basket analysis to study shopping patterns than Wal-Mart. The company will manage to analysis groceries, sporting goods, household items and other diverse collections that are in the product lineup. A company that handles at least 64 million transactions per week will get information from its clientele than any other retail could do (Walmartlabs.com, 2013). The company will depend on the NCR database technology to collect information because of self-maintenance, reliability, and scalability abilities. Additional Data for Data Analytics One additional type of data that can be used for data analytics is qualitative data on differences within consumer groups. A company is able to respond more to personal needs of customers than when information for clientele is captured in general. The information and insights that Wal-Mart can derive from the U.S market can be broken down to individual inclinations to foster relevant production process. The process improves customer services and experience. References Brynjolfsson, E., & McAfee, A. (2012). Big Data: The Management Revolution. Harvard Business Review. Retrieved from https://hbr.org/2012/10/big-data-the-management-revolution/ar Davenport, T. (2013). Analytics 3.0. Harvard Business Review. Retrieved from https://hbr.org/2013/12/analytics-30 Jain, C., & Malehorn, J. (2005). Practical guide to business forecasting. Flushing, N.Y.: Graceway Pub. Co. Minelli, M., Chambers, M., & Dhiraj, A. (2012). Big data, big analytics. Raj, P., & Deka, G. (2014). Handbook of research on cloud infrastructures for big data analytics. Walmartlabs.com,. (2013). @WalmartLabs » bigdata. Retrieved from http://www.walmartlabs.com/category/bigdata/ Read More