Data Analytics And An Organization

The goals have been set and data analytics best practices need to be monitored. The experienced gained in this phase will shape the next course of action based on external and internal issues. As the data is formulated, it will identify the strengths and the weaknesses, threats and opportunities for improvements. Because the internal and external issues will continue

3. Determine the fundamental obstacles or challenges that business management in general must overcome in order to implement data analytics. Next, suggest a strategy that business management could use to overcome the obstacles or challenges in question. Provide a rationale for your response.

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).

Data analytics is defined as the process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making.

In broad terms, data analytics is the process by which any company collects information about its targeted consumer-base, makes sense of the data collected, and based on the resulting assessment of that data, enables business management to determine if change is necessary to better reach the customer. While that description may seem simple enough, the means by which that data is collected has radically changed in the last twenty years and in which a new era in data analytics capabilities as emerged. Not long

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

Today, data is a growing asset that various businesses are having difficulty converting into a powerful strategic tool. Companies need help turning this data into valuable insight, which can diminish risk and enhance returns on investments. Companies are struggling to make sense and obtain value from their big data. Superior and reliable

In the era of 2007-10 where most of the economies of the world slowed down and companies restrained themselves from making new investments there were others that were gearing up to reverse this trend. Viewing this as an opportunity lot of new companies entered into the consulting and business advisory domains. This phenomenon lead to an inception of once such company named “Analytics Quotient”. Analytics is once such field, which is not only making a mark upon the business of the companies but also changing the business trends by focusing on data utilization across functions and optimizing the available information that the company has.

Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data. Descriptive statistics do not, however, allow us to make conclusions beyond the data we have analysed or reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data.

At the end of 20th century, Fortune magazine published an article named “Why CEOs Fail” (Charan & Colvin 1999). It stated that “The problem is that our age’s fascination with strategy and vision feeds the mistaken belief that developing exactly the right strategy will enable a company to rocket past competitors. In reality, that’s less than half the battle”. The author argues that increasing the capability to execute on an appropriate strategy can be regarded as the missing half of the battle. Actually, periods after this article came out, at the beginning of 21st century, the technology of information sharing and data collection have developed day by day.

Descriptive Analytics. This type of business analytics is to answer the question “what is happening” and “why did it happen?”. The process of descriptive analytics is looking at past performance and understanding this performance. Data mining, data aggregation, segmentation, profiling are some tools that can be used to perform the descriptive analytics. The output of this analytics, for example, is the age range of bank customers, the income distribution of the customers, number of children in the household, and customers’ preferences.

Business thrive when they have the most accurate, up-to-date, and relevant information at their disposal. This information can be used for a plethora of pertinent markers in small and large businesses, relating to accounting, investments, consumer activity, and much more. Big data is a term used to describe the extremely large amounts of data that floods a business every day. For decades, big data has been a growing field, facing controversy on many levels, but as of late, it has been a major innovator in the challenge of making businesses more sustainable. Big data is often scrutinized for its over-generalization and inability to display meaningful results at times. When applied correctly, data analysis can bring earth-altering information to the table.

Analytics is defined, according to online-behavior.com, as the “process of obtaining an optimal or realistic decision based on existing data.” [1]. Davenport and Harris (2007), defined analytics, as the “extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.” [2]. An article titled Outsmart the Competition by Jackie Zack in Teradata Online magazine states that “analytics can help an organization optimize their business processes to make them effective as possible.” [3]. It is a proven fact that proper use of analytics can lead an organizations to success, providing them with that distinctive advantage over their competitors. In this essay, details will be provided on how analytics has helped my organization to compete in their business segment.

Within descriptive analytics, there are many useful tools that may be used. For example, dashboards are useful analytical information that is represented in the form of small like info-graphs. Another example would be sparklines which

Data analytics and analysis are often used in conjunction with one another, and can be applied in variety of situations, enterprises, and domains. Data analytics and analysis often fall under the umbrella of data science, which is the discipline associated with structured and unstructured data [1]. However, there are altering views of what each term represents, as well as how they are interrelated. One source describes analytics as a subset of analysis, with analysis being the larger entity [2]. They describe analysis as the sum of human activities driven to gain insight into the given dataset, with or without exceptional data processing techniques applied [2]. The exceptional data processing techniques falls under the analytics portion of analysis, which encompass many advanced statistical tools and machine learning algorithms.