Analytic Summary

The data analytic process is one in which a large amount of information is collected using software specifically geared towards collecting, identifying and storing information for use by the company. The information is gleaned from different forums, with social media being the most rich and useful. The information is then quickly sorted and organized for use by the collecting agency (Turban, Volonino, Wood, & Sipior, 2002, p. 6). The use of data analytics really took flight in 2010 when different companies offered software that enabled a company to implement their own data analytics. This led to better marketing campaigns, improved customer relations and it gave companies using the software a bigger advantage over their competitors (Savitz, 2012).

In today’s companies, the analytics software plays the important role and guides the future activities to a great extent.

Data analytics is the science of examining raw data with the purpose of drawing conclusions about certain information that is drawn from the data. By gathering data, it must be captured and reviewed then it can be turned into information. There are different types of analytics such as descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics will describe, what happened during the process. Diagnostic analytics describes, why did it happen. Predictive analytics describes, what will happen. Prescriptive analytics describes, how can the process happen with a different approach. By applying these different types of analytics, it will answer several questions during the auditing process. Involving analytics to a process it requires

In Competing on Analytics by Thomas Davenport and Jeanne Harris, the pillars of analytic completion are stated as: “(1) analytics supported a strategic, distinctive capability; (2) the approach to and management of analytics was enterprise-wide; (3) senior management was committed to the use of analytics; and (4) the company made a significant strategic bet on analytics-based competition” (Davenport & Harris, 2007, pp. 511-512) . This section will describe Aramark’s position within these pillars.

Stage 3, Analytical Aspirations – A conscientious effort has been made to integrate analytics into multiple departments and their application is in support of the company’s distinctive capabilities

In the New Science of Winning book, (Davenport & Harris, 2007, p.7) analytics is defined as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.” [1]. To be successful in today’s competition, my current employer, DLL Financial Solutions Partner (DLL), is competing on analytics and fully aligned its core strategies to be supported by extensive statistical and computer based decisions. DLL is a global financial services company with operation in 36 countries, and its main focus is in the commercial equipment finance sector. In the following paragraph, I will explain DLL’s position in the industry and its ability to successfully compete on analytics with regards to its core business functions.

The upward trajectory of analytics in the recent years is proof of the overwhelming support for the notion that the application of science to the selection, management and alignment of people brings about tremendous return. (Bersin) (Kapoor)

Given how turbulent global economies are and the industries that compete within them, it is understandable to see analytics and Big Data continually increase in popularity throughout enterprises globally. The greater the level of turbulence in economic conditions, the more reliance on technologies, processes and systems that are adept at mitigating risk. The continual investment in analytics is setting a solid foundation for completely redefining how businesses manage the decision making process. It is also changing forever how businesses manage customer expectations relative to experiences, and how these factors are all combined to manage the new product development and introduction (NPDI) process.

Two related articles (Baller & Alan 2009, Galit & Otto 2010) demonstrate that “Business Analytics (BA) refers to the skills, technologies, practices

Web analytics can help businesses or organizations with a web presence stay competitive, leading to profitability. The use of analytics can be a powerful tool in a company’s strategic approach that can be used to differentiate it from its competitors and serve as a form of competitive advantage for the organization in competing in this twenty first century. In this essay, various definitions of web analytics and other key terms used in this field will be examined.

It’s quite common that every task that we perform, we check whether it is being executed in the expected way. Ultimately, if it’s going in a right path well and good but the actual question arises when it’s the other way. This is the point where analytics come into picture. The need for analytics is very much known to us because it can be one of the most powerful driving factors to tune the performance of an organization’s processes, strategies and uplifting revenue.

big data is a dynamic that seemed to appear from almost nowhere. But in reality, Big Data is not new – and it is moving into mainstream and getting a lot more attention. the growth of Big Data is being enabled by inexpensive storage, a proliferation of sensor and data capture technology, increasing connections to information via the cloud and virtualised storage infrastructure, as well as innovative software and analysis tools. It is no surprise then that business analytics as a technology area is rising on the radars of CiOs and line-of-business (lOB) executives. to validate this, as part of a recent survey of 5,722 end users in the uS market, business analytics ranked in the top five It initiatives of organisations. the key drivers for business analytics adoption remained conservative or defensive. the focus on cost control, customer retention and optimising operations is likely a reflection of the continued economic uncertainty. however,

The field of business analytics has improved significantly because users are generating more data and the process of analysing this data has been enhanced too. Nowadays a data analyst can analyse large amounts of fast moving data from different sources and gain insights that were never possible to

Based on Davenport and Harris (2007)’s pillars of analytical competition, there are four pillars: distinctive capability, enterprise-wide analytics, senior management commitment, and large-scale ambition.

It is said that analytics works best when it’s a natural part of people’s workflow. In 2017, analytics will be embedded into applications people use every day, be it Salesforce or internal portals. This seamless integration will drive visibility and action on these analytics, often by people who’ve never explored data, like store clerks, call-center workers, and truck drivers.