Business Intelligence (BI)
Discuss about the Business Intelligence in Contemporary Organizations.
In today’s competitive business environment, it is essential for the organizations to collect and store the information related to the various fields in order to make strategic decisions. In this, business intelligence plays an important role in collecting and analyzing business related information significantly to develop decisions. In this way, this paper discusses the business intelligence and the effectiveness of business intelligence in providing competitive advantages to the firm. Along with this, this report also discusses the use of data mining and analytics impact on the decision making in an organization.
In current dynamic and complex business, firms seek ways to manage information effectively, which raises popularity of BI. BI applications become one of the major technical priorities of firms recently. In the words of Hribar RajteriÄ (2010), “Business intelligence (BI) is a broad category of technologies, applications, and processes for gathering, storing, accessing, and analyzing data to help its users make better decisions.” It defines BI as technological applications, which facilitates better management of data and decision making in business. On the other hand, Gurjar & Rathore (2013) state BI as frameworks, applications and technologies, which allow organizations to transform raw data into valuable knowledge for facilitating informed decisions at the strategic and operational levels. BI enables businesses to make insightful use of information for improving and optimizing performance.
BI includes range of technological applications such as data warehouse, data analytics, data integration, master data management and others, which helps a firm to manage information systematically and effectively. Currently, businesses accept information from variety of sources such as loyalty card, ATM card, social media, mobile phones and other similar tools, which develop huge amount of information (Elena, 2011). BI includes a bunch of methods and technological applications to store, integrate, analyze and disburse the information to the different departments of an organization for allowing them to make meaningful and sensible strategies to contribute in performance improvement (Rust et al., 2011).
The demand of big data analytics and software is likely to grow significantly in future. In 2020, around 40% of an organization investment is expected to focus on implementing tool and applications of BI (Forbes, 2016). These technologies allow businesses to take an overview of historical, present and predictive state of the operations and to manage them accordingly. BI includes execution of different technologies for facilitating data management and analysis to provide valuable knowledge to the businesses regarding their operational areas on real-time basis (Elena, 2011).
BI for Competitive Advantage
BI includes several decision support technologies that provide businesses a capacity to leverage data asset, which they obtain from the range of sources. Without leveraging data, it is difficult for firms to get success and to sustain themselves in the highly competitive business environment. An effective use of BI requires skilled personals to make useful and relevant interpretation from the analyzed data (Chaudhuri et al., 2011). It is an important factor that causes difference in the application of BI technogies for performance improvement.
Knowledge becomes a rare and valuable resource or asset in the contemporary organizations due to its importance in creating competitive advantage. Firms use several tools to interact with the customers in contemporary organization such as feedbacks, website, debit card transactions, social networking sites, e-mail and others, which develops a very large amount of data. Through BI, firm can store, organize, manage and compare data effectively for understanding needs and preferences of consumers effectively and to make informed improvement in the offerings. It may help a firm to attract customers and to increase sales, which provides as source of competency (Phan & Vogel, 2010). Cost advantage and differentiation are two major strategies of achieving competitive advantage in this complex business environment. For example: Tesco, UK’s largest retailer uses data analytics tools to determine relevant discounts and vouchers for the diverse customer segments (Sabherwal & Becerra-Fernandez, 2011). Through this, Tesco makes its offerings unique in the market that helps to improve customer satisfaction and to improve profits.
Additionally, firm becomes highly customer-centric and BI can be used to tailor products and services in accordance to the needs and preferences. Customer can be profiled more effectively that would help a firm to provide personalized customer service experience. It can be effective to enhance relationship with the customers and to improve their satisfaction, which may create competitive advantage in turn (Sturdy, 2012).
Similarly, BI can be used by businesses to reduce cost, which improves performance and creates competitive advantage in turn. Data analytical tools and techniques can visualize the operation and to identify areas of improvement that may help to reduce cost (Williams & Williams, 2010). For example: United Parcel Service, Inc. (UPS), world’s largest package provides has used to BI to analyze big data for developing route-optimization. It has helped this company to optimize operations. The BI tools makes possible for company to visualize vehicle performance and driver’s efficiency in different routes. With this knowledge, it minimizes driving time, which saves money, time and fuel. In 2013, 1.5 million gallons of fuel was saved by UPS that caused low carbon emission and development of operational competency (Dix, 2014).
Data Mining and Analytics’ Impact on Business Decision-Making
In retail businesses, tools of data mining and analytics are used by firms in the significant manner to make decisions. Tesco, Wal-Mart, Sainsbury, Asda and many global retailers offer customer loyalty cards to the customers. Data mining and analytics can impact the decisions of business. Loyalty cards primarily uses to give additional incentives to the customers and to increase purchase level. In Tesco, loyalty card tracks demographic aspects, purchase item and their frequency, customer spending and other information related to the shopping behavior of consumers (Tufféry, 2011). With the help of data mining and analytics, Tesco can use loyalty card information to unlock value by learning demogrpoahics based preferences of customers. For example: Tesco can determine relevancy of age with the purchase pattern that could be effective to make more informed decision regarding store display and offers (Blanchard, 2010). It can allow it to give relevant offerings to the customers and to increase sales.
Similarly, loyalty card in Tesco can be used to determine purchase pattern of customers in a store, which may help to predict the list of best items to stock in a particular month. It can allow retail firm to make informed decisions related to inventory management. By predicting sales of each item through data mining and analytical, it can be effective for firm to ensure availability of right stock at the right time for each customer segment. This will improve the effectiveness of inventory decisions of a firm (Esper et al., 2010). In addition to this, loyalty card also provides information to Tesco regarding their purchase. Through data analytics and mining, retail firm can determine sell of the complementary products. On this basis, firm can take informed decisions to customize assortments and to place products in the stores. Through this, firm can improve decisions of displaying products in stores and providing offers (Walsh, 2011). In this way, loyalty card can be used by Tesco more effectively to improve business decisions with the application of data mining and analytics.
Conclusion
It can be concluded that BI is a competitive weapon for contemporary organizations as it helps them to analyze information on real-time basis that improves decisions and adds value in the offerings.
References
Blanchard, D. (2010). Supply chain management best practices. USA: John Wiley & Sons.
Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88-98.
Dix, J. (2014). When you have an organization the size of UPS, every little bit of efficiency you can squeeze out of daily operations translates into a big deal. Retrieved from: https://www.networkworld.com/article/2850874/big-data-business-intelligence/how-ups-uses-analytics-to-drive-down-costs-and-no-it-doesn-t-call-it-big-data.html
Elena, C. (2011). Business intelligence. Journal of Knowledge Management, Economics and Information Technology, 1(2), 1-12.
Esper, T. L., Ellinger, A. E., Stank, T. P., Flint, D. J., & Moon, M. (2010). Demand and supply integration: a conceptual framework of value creation through knowledge management. Journal of the Academy of marketing Science, 38(1), 5-18.
Forbes (2016). Roundup Of Analytics, Big Data & BI Forecasts And Market Estimates, 2016. Retrieved from: https://www.forbes.com/sites/louiscolumbus/2016/08/20/roundup-of-analytics-big-data-bi-forecasts-and-market-estimates-2016/#623f38cf49c5
Gurjar, Y. S., & Rathore, V. S. (2013). Cloud business intelligence–is what business need today. International Journal of Recent Technology and Engineering, 1(6), 81-86.
Hribar RajteriÄÂ, I. (2010). Overview of business intelligence maturity models.Management: Journal of Contemporary Management Issues, 15(1), 47-67.
Phan, D. D., & Vogel, D. R. (2010). A model of customer relationship management and business intelligence systems for catalogue and online retailers. Information & management, 47(2), 69-77.
Rust, R. T., Moorman, C., & Bhalla, G. (2010). Rethinking marketing.Harvard business review, 88(1/2), 94-101.
Sabherwal, R., & Becerra-Fernandez, I. (2011). Business intelligence: Practices, technologies, and management. USA: John Wiley & Sons.
Sturdy, G. (2012). Customer Relationship Management using Business Intelligence. UK: Cambridge Scholars Publishing.
Tufféry, S. (2011). Data mining and statistics for decision making. USA: John Wiley & Sons.
Walsh, P. F. (2011). Intelligence and intelligence analysis. UK: Routledge.
Williams, S., & Williams, N. (2010). The profit impact of business intelligence. Morgan Kaufmann.
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