How Does Your Company Create Competitive Advantage With Analytics : An Analysis

Data driven decision-making is leading to school success for many apparent reasons. Data driven decision-making is the direct correlation between teachers, curriculum coaches, principals, district educational supervisors, superintendent, and board members. According to Boudett and Moody (2005), the first important step in a successful data driven environment is the gathering of the group that will bare the responsibility for the procedural and executional procedural stages of data analysis.

The four pillars of analytics competition are (1) Support of a strategic, distinctive capability, (2) an enterprise-level approach to and management of analytics, (3) Senior management commitment, and (4) Large-scale ambition (Davenport & Harris, 2007).

This report finds that data-driven decision making involves the collection, combining and crunching of data received from multiple sources throughout the organization. The technique can contribute to the improvement of Acme’s decision making process as a whole. Acme gathers

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

In the life of human being, the 10/90 rule means that 10% of life is shaped by what happen to us while rest of 90% of life is decided by our responses/actions. This implies that one cannot avoid over 10% of what is happening to him. This is inevitable. Our reactions to what happened could determine 90% of the outcome (Covey, 2010). This can be compared and applied to business situations to some 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

Our market consists of domestic and international higher education institutions that have a desire to strategically grow their program and course offerings through online development. Upon inception we specialized in online program management, but over the years we have grown to become a full service partner that also offers fee for service opportunities to those institutions that might not fit our defined target market. I would consider us 95% a service organization that has begun to experiment and expand into learning management toolsets. A typical client receives: marketing, recruitment, and learning management services.

Soon, predictive data analytics will provide a refined synthesis of collected data which will produce in-depth outcomes that can be applied in all sorts of situations. In the selection of courses, the data collected from social media, school’s electronic swipe cards, departmental data among other sources could enable the matching of students with the appropriate professors and subjects or programs. One such program is being applied by the Regents Online Campus partnership which begun the pilot program in 2012 by using a predictive data analytics tool from “Desire2Learn”.

Thinking back to the day the application to SNHU for this Data Analytics Program was submitted, it was months of weighing options before the final decision to take the plunge. There were a few reasons to why this program was chosen. Career growth was definitely the first reason for this motivation. The realization of a need to find a professional ‘me’ was the decision maker. Seeking something interesting yet challenging was the goal. Realizing that calling out anomalies and outliers, seeing trends and patterns is a personal strength, hence, the analytics path was chosen. Working with data and reviewing information has always been something of interest, moreover, and the interest was even deeper

Current business and technology conditions that complicate effective application of business analytics to business intelligence and knowledge management data, and the prospects for improvement

Currently the Office of Institutional Effectiveness with the help of the Office of Instruction and Student Services, has launched a campaign to increase the understanding and use of data in the decision making process. To become a culture that relies on data as an integral part of the decision making as well as measuring the effectiveness of our policies and practices, we must first understand the various types of data and how they can be used by everyone.

In Analytics at Work: Smarter Decision, Better Results, Thomas H. Davenport, Jeanne G. Harris and

When analyzing the data the focus was on identifying key analysis skills, student performance and the amount of instruction given. The survey data was compiled and organized into those three categories for further analysis.

The term “Big Data” has been around for quite some time and has been catching everyone’s attention with remarkable speed. A plethora of questions do pop up in our mind sometimes. What is big data, is this something absolutely new, how can it be leveraged to create value for an organization and so on. For many years, companies have used various transactional records stored in relational databases to make competitive business decisions. But how long can we sustain or depend on these traditional methods of doing analysis and coming to a conclusion. There is an ocean of non-traditional, less structured data such as weblogs, social media, email, sensors, and photographs that can be mined not only for useful information but also to make strategic decisions.

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.