Analytics As A Service Of Delivering The Insights Based On Your Own Data

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).

And yes, this information is valuable. But these analytics tools alone can’t offer the insights that drive successful business decisions. This level of analysis has to come from a certified “analytics ninja,” a person or team of people that can dig through the haystack to find the insights that move the needle. Even if you spend hundreds of thousands of dollars on the best analytics products out there, if you don’t have some certified analytics ninjas on your team, you’re just blindly wasting your cash. Essentially, your Web analytics tool is only as powerful as the person using it.

Analytics in fact has reached a far new level in today’s competitive and constantly changing environment and is only bound to grow further in the industry. Although it emerged as an arm of consulting but looking at the volume, variety and velocity with which data is growing on the internet and even otherwise, analytics of going to leave consulting far behind, But that is not to say that consulting is losing out because consulting jobs still are in demand today and companies in the future are going to feel the need to outsource their projects to consulting companies or individuals because analytics can’t be considered a substitute of consulting. Thus we see that, although consulting remains important today but analytics is something that has moved beyond it and carved a niche for itself. The analytics industry is only going to grow in the future that too with immense pace and can be said to have already emerged out of the shadows of

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

Data is very important and critical to any organisation, so is in the telecommunication industries. Information that can be easily used must be available so that the decisions makers within an industry can make informed decisions based on visible facts projected by the data available. If this is not the case, decision-making processes can become extremely challenging and, in some cases, impossible. The flow of information within an organization is critical to the success of that organization (Davenport & Prusak, 1998). Organizations struggle in collecting data, retrieving information and making decisions based on the extracted information. A decision making process consists of making judgments based on the qualitative data.

In chapter 12, the book offered 7 strategies to improve decision making efforts. These strategies were broken in to two separate groups, the first three are broad sweeping changes, and the last four are techniques to improve decisions we are making. Managers are measured on the decisions that they are making. A good manager will know what it takes to make sound, and reasonable decisions. Decisions define who we are, and they can have profound effects on others, so we must choose wisely when it comes to the final result.

Today, new organizations are being formed continuously and, as a result, competition is fierce. It is key that organizations should use be able to make well-informed decision in order to be successful in business. However, most of these organizations are not able to make decisions effectively due to business data are not kept appropriately. Furthermore, if organizations do not provide strategic information for making decisions, the organizations will not gain competitive advantages, nor predict future outcomes. Thus, revenues and profits of organizations will be affected. Therefore, by considering this issue, it might be possible to offer solutions for organizations to develop effective decision making strategies.

The aim of this report is to look at information based decision making to help identify and select sources of information, analyse and present information to support decision making and communicate the results of information analysis and decisions. I will look at the key models and

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.

The book is all about the things that make one to be a smart consumer of cutting-edge analytics, facilitating to frame the judgement, questioning about the information and the procedure, operating to comprehend the consequences, and using them to progress results for his or her business. Even though it sounds direct, I directly acknowledged it as a deceptively single-minded set of purposes of the book. The writers planned the first half of the book about an analytics outline that entails of six stages: problem acknowledgment, evaluation of previous results, displaying, data assortment and data examination, and outcomes demonstration and action. This planned method to discerning about analytics is one of the most important notions that Davenport

Thinking critically and making decisions are important parts of today’s business environment. It is important to understand how the decision making process works and the steps involved. The nine steps of the decision making process are: identifying the problem, defining criteria, setting goals and objectives, evaluating the effect of the problem, identifying the causes of the problem, framing alternatives, evaluating impacts of the alternatives, making the decision, implementing the decision, and measuring the impacts. (Decision, 2007.) By using various methods and tools to assist in making important business decisions an individual can ensure the decisions they make will be as successful as possible. In this paper it

There have to be many important decisions made in organizations these decisions could determine whether an organization is on a successful path or whether the organization is bound to fail.

A study published in the winter 1997 volume of Business Strategy Review suggests the major factor in a decisions success is the decision process itself. The study, by Paul Nutt, suggests that poor decision making

Business analytics is different to Business intelligence which also uses statistical methods. Business analytics uses statistical methods and data to arrive at fresh understanding and illumination of a situation, whereas business intelligence uses statical data to serve as querying, reporting or alerts. Business analytics, therefore, is, one may say, the detective side of business using the statistics to develop and progress the business or direct it in new directions rather than revamping it with existent and supportive data. Business intelligence, in other words, uses the data to inform stakeholders and others what is occurring with the business, what the action is, and what steps are being taken.

Based on an environmental scanning made on emerging technological trends for advanced analytics, the immediate finding was that a new tool, in the open source world, is created each month. Analytics is a very fast pace world. The reason behind this is that, in an open source world, the data science community is very active in providing help resolving issues other data analysts are having. Because of this strong data science community, we are seeing a trend in big IT companies such as IBM that is now providing their platform with multiple open source tools being integrated to it. This system is called hybrid solutions.