The Three Levels Of Analytics, Descriptive, Predictive, And Prescriptive

1. Given a business situation in which managers require information from a database, determine, analyze and classify that information so that reports can be designed to meet the requirements.

“There have always been three types of analytics: descriptive, which reports on the past; predictive, which uses models based on past data to predict the future; and prescriptive, which uses models to specify optimal behaviors and actions” [4]. This white paper addresses predictive analytics, which is the analysis of past and present data in order to predict future risks and opportunities. Although reactive decision-making has been successful in the past, it

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).

To ensure that your money isn’t wasted, you need to spend even more. The way to spend it is by following Avinash Kaushik’s “10/90” rule. This directive suggests that for every $10 you spend on your analytics tool and implementation, you should spend $90 on intelligent digital analysts who can convert your data into actionable insights. Companies are eager to collect data, but that data is worthless until someone translates it into insights that drive business results (Gibson A., 2013).

In the world of technology that we live in today has forced companies in almost every industry to use whatever tools that are available to help them be competitive in their business industry. There are a few ways to do this, one of those ways is the use of Web analytics, which is the collection of raw data from users browsing habits and then taking the raw data and assemble the data into clear comprehensive results. This type of analysis is very useful for companies, as it helps them learn what users are doing and their habits and the best way to target these users.

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

While it can be difficult to find that there are processes, procedures, and business decisions in place that may not be working as intended, for leaders, analytics are unavoidable as the insights gained add value and drive leaders to make better evidence-based people decisions (Hirsch, Sachs & Toryfter, 2015). Analytics are used in various business practices including the validation of pre-employment assessments. Validation is one of the most essential aspects of any legitimate pre-employment assessment program.

Each type of analytics as seen on the diagram above, could share a common sub group which could in turn have additional classifications. understanding and reviewing the different types of analytics systems and choosing those that best suite an organization is very helpful in determining the analytic plan for the future of the business. Succeeding in this, will definitely give a boost to the overall value of a business platform.

Having data is not valuable but using data is. Analytic insights are changing the way corporates strategize and also redefining customer expectations. Analytics is the new differentiator between success and failure in the cut throat e-commerce and internet services based industry. The huge proportions of data generated from the increasing number of smart phones, the social networks and the ever more penetrating internet are automating customer centric marketing and other services. The idea is to predict what a customer may want to buy even before the customer realizes what they need. The techniques to achieve these results are broadly classified as Predictive Analytics.

Over my Bush School career, I have learned about several analytical strategies. However, no analytical method is perfect. Thus, no one strategy will perfectly accomplish all of the goals one sets out to accomplish. Matter of fact, even if a combination of strategies is used, there will still be gaps in the analysis. As an Administrative Assistant, I encountered the limitations of quantitative analysis. Furthermore, as an Audit/Investigator Intern, I encountered the issue of time limitations. Thus, I have learned it is important to identify the most important goal, identify which analytical strategy best helps you accomplish this goal, determine the limitations of that strategy, and determine whether that strategy is still defensible.

Healthcare analytics is the extensive use of data, fact, statistical and quantitative analysis to facilitate decision making within healthcare systems (Davenport, 2013). Analytics relies on historical data to predict future trends, evaluate clinical decisions, and to measure performance of processes and outcomes. The analytical tools include data, especially big data, statistical analyses and quantitative approaches to decision making with regard to patients and medical care. These tools form the basis of evidence-based practice. Healthcare analytics not only aids in achieving better healthcare, but also promotes cost-effectiveness. It can be useful in determining the level of resource use and directing limited resources to the

It is through these three types of analytics, that business analytics can be distinguished from just being the plain process of analytics. These three types of analytics help make this process useful in the business environment. It requires “a clear relevancy to business, a resulting insight that will be implementable, and performance and value measurement to ensure a successful result.” Business analytics includes a combination of all three types to generate new information used for business organization decision-making. Descriptive analytics basically gives you a general idea of the data you are looking at. Predictive analytics takes the next step and predicts what is likely to happen next. Finally, prescriptive analytics figures out what is the best course of action. All three of these types of analytics can be very useful when used in combination with one another. Business analytics can be instrumental to a business’ success when implemented properly. (Schniederjans et al., 2014).

Business analytics, in a nutshell, is usage of the type of data that can help one analyze a particular business situation and decide how to improve it. Instruments used for such an assessment include statistics, and both quantitative and qualitative analysis, as well as predictive and explanatory modeling.

Analytical tools enable greater transparency, and can find and analyze past and present trends, as well as the hidden nature of data. However, past and present insight and trend information are not enough to be competitive in business. Business organizations need to know more about the future, and in