Data analysis - Business - Analyics
Business analytics is often referred to as practices, technology and skill in continued iterative exploration and investigation of previous business performance to attain insight and conduct the planning of business. A specific example of business analytics application is data analysis. Data analysis entails the inspection, cleaning transformation and modeling of information in an aim to discover valuable information, proposing conclusion and support of decision making (Kennet & Ranaan 2011).
The application entails many approaches incorporating a variety of techniques in various fields. Unilever is one company that has revolutionized the use of data analysis; it uses both qualitative and quantitative methods which in turn, enhance productivity and gain in business. The company is chosen because it is a consumer company, and the nature of its operations is centered towards creation of products that are suitable for their clientele. Data analysis in unilever is vital essentially because it focuses primarily on influencing decision making through thorough data processing and interpretation.
[...] New York, Springer Science & Business Media. Kennett, R., & Raanan, Y. (2011). Operational risk management: A practical approach to intelligent data analysis John Wiley & Sons. Wolf, S. M. (2013). Unilever case studies. Grin Verlag. [...]
[...] In conclusion, it is evident that business analysis as a component of business is heavily reliant on data analysis as an application. Businesses, therefore, need to be particularly involved in this process to ensure they acquire customer satisfaction by allowing the process to inform the decisions made by the organization's management hence, maximization in overall company gains, as a result, of a content client base. References Berthold, M., & Hand, D. J. (2003). Intelligent data analysis: An introduction Springer Science & Business Media. Chambers, J. (2008). Software for data analysis: Programming with r. [...]
[...] Software in data analysis is used in enterprise data so as to identify patterns and establish links. Unilever employs the use of the palisades decision tools suite software which is essential in informing decisions concerning innovations (Wolf 2013). The software ensures that the project group has a full understanding of the scope of their decisions and has access to the tools and knowledge to come up with informed quality decisions. The use of this software prevents the overlooking of threats and opportunities and as such enhances Unilever's dexterity in the market. [...]
[...] The software also supports other business areas including supply chain, regulatory and an additional one off decisions. Decision makers are used to seeing the insight from business cases presented with histograms and advanced sensitivity. Data repositories are primarily developed to prevent problems that arise, as a result, of data proliferation and stop the need for separately installed data storage solutions. Data repositories were implemented, as a measure to reduce IT staff workload on maintenance witnessed in traditional data storage. The information repositories are hence automated through policies that have the ability to process information on the basis of time, events, data age and content. [...]
[...] It is inevitable that in the future, data analysis is going to be even more influential in the decision making process in Unilever as a business corporation. Data analysis provides an avenue for Unilever to tailor its products to client's tastes based on information acquired. It is imperative that with advancements in technology, the process is only going to improve in terms of its functionality and effectiveness in dissemination of data within the organization while remaining simple and precise to meet the needs of a diverse user base. [...]
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