Essay On Analytics

Map function is simple as it take out the year andthe temperature only because our focus is only

Before our clients can get to a point of having a dashboard of KPI’s and metrics, it’s imperative to understand the problems and the reasoning for diving into analytics. As a firm, why do you want to engage in analytics? Do you have the full support of the firm to include analytics in the decision making process? What are some of the issues that can be improved through the measures of data? What are you specifically trying to accomplish? What is the quality of the data being measured? These are all key questions that need to be answered before engaging in any sort of analytics and reporting. It’s important to remember that a firm should not do analytics, for analytical sake. There should be a reasoning and number of problems that could be solved through analytics. If not completed properly, the use of analytics could be a huge drain to cash flow and influence decisions that could have a negative impact on the firm.

The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of ``Big Data.’’ While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is currently a wide gap between its potential and its realization.

In your business, you have your own big data challenges. You have to turn heaps of data about various entities into actionable information. The reporting needs of institutions have evolved from simple single subject queries to data discovery and enterprise-wide analysis that tells a complete story across the institution. While the volume, variety and velocity of big data seem overwhelming, big data technology solutions hold great promise. The way I see it we can use this as one of the biggest asset for the company. We have the capacity to see patterns recounting in real time across complex systems. Huron is marshalling its resources to bring smarter computing to big data. With the Huron big data platform, we are enabling our clients to manage data in ways that were never thought possible before.

My company identifies and uses analytics to make decisions and we also share data with our customers so they can make decisions based off of the data we give them as well, and they can then make decisions. It builds a solid relationship with the customer and shows we want the customers to be profitable and willing to share what is needed for them to decide what direction they want to go and we then create tools, add ons, etc. for them to benefit the most with our service. It we cannot make our customers profitable with what we provide them, we cannot be profitable and analytics is definitely held at a high standard in my company.

After graphing our data it is clear that both masses are oscillating, however we notice that the period of oscillation for both masses does change after some time. It appears that the period of oscillation for mass 2 becomes shorter after some time. It is hard to make any conclusive conclusion of for the period of mass 1. However, it is clear that the two masses do appear to come closer to synchronization toward the end of the video. As seen in graph 1 and graph 2. Especially in graph 2 where the very last point at M1 and M2 appear to be close to synchronizing. Additionally, by closely watching

Creating a data analysis based on a sample population can become complicated if you are basing the frequency on exact numerical values that are continuous such as GPA, distance, weight and height. Creating a categorical analysis based on identical data, would display numerous bars within the charts and make the analysis nearly impossible. In a position where you have sampled a population that provides a wide range of variables to analyze, it would be more beneficial for the results to be analyzed in specified ranges to provide a more concise frequency. In this instance, is it is best to create a frequency distribution table where data can be put into numerical ranges instead of individual frequency’s. A frequency distribution table

In this highly competitive business environment, businesses are constantly seeking ways to gain traction and understand what is on the minds of current customers and potential customers in order to increase business efficiency. Many companies, such as American Express have turned to business intelligence (BI) and data analytics to maintain a competitive edge over the competition.

The rise of big data analytics has affected the 21st century American economy and businesses in many positive ways. One area where it is lagging, however, is the healthcare industry. For years, America has paid more for healthcare than any other country on Earth. This can be attributed to a number of reasons, but a large factor among these is the inefficiency of the current healthcare system and its failure to adapt to cost-saving analytics like other industries have. That is where big data analytics can step in and serve a great purpose. Big data is the process of taking mass amount of information across different, but interrelated areas in order to derive deeper meanings, insights, trends, and analysis through the usage of high-speed, high-capacity algorithms. This can be huge when one considers that as of 2014, there are 44 petabytes of information on patients in the electronic health records system. (Raghupathi) This can include medical history, imagery from patient scans, lab results, and a vast array of other information. Couple this information with the push to integrate individual’s social media posts, personal DNA sequencing, and vital data collected by smartphones and wearables, just to name a few, and it becomes evident that we as a species will be generating exuberant amounts of medical data. There are some people, however, who feel that having this information integrated into any kind of database poses a risk to the privacy of their most personal,

Using the right combination of Hadoop products and the other platforms can be sensational in terms of analytics because it has the capacity to analyze analysis of petabytes of Web log data in large Internet firms, and now is being applied to similar analytic applications involving call detail records in telecommunications, XML documents in supply chain industries (retail and manufacturing), unstructured claims documents in insurance, sessionized spatial data in logistics, and a wide variety of log data from machines and sensors. Hadoop-enabled analytics are sometimes deployed in silos, but the

Analytics, has existed since the dawn of time and are constantly used in the natural world. , Analytics is more than individual data points. Analytics is the process by which data points are collected and analyzed in order to illuminate specific areas awitin a give framework or structure. Is is the basis of decisions to migrate, has informed safety prtocols for activites from cooking to operating heavy machinery, and continues to inform the natural world on a variety of processes. However, pre- the information technology era, analytics was often hard to share vrom one group to another. Lack of formalized and interchangeable communication systems in conjunction with a lack of tools supporting wide sharing of data, often dictated that

The dataset used for the project is the German credit dataset that consists of customers’ financial and credit information and the resulting classification of customers as “good” or “bad” credit risks. This is a well-known publicly available dataset containing observations on 20 variables of 1000 past applicants of which 700 are classified as “good” credit risk and 300 are classified as “bad” credit risk.

The importance of analytics in big and small companies could not be overestimated. Every organization uses big data in order to manage their processes and improve its business. The term of "analytics" is commonly defined as "the extensive use of data, statistical and quantitative analysis, explanatory and predictive models and fact-based management to drive predictive models" (Davenport & Harris, 2007). Analytics appears to be a useful tool, which helps to gain necessary information and provide extensive support to all departments in the enterprise. Nowadays analytical innovations play a big role on the market as they help an organization to gain a competitive advantage and become a market leader. According to the researcher Svetlana Sicular, analytics helps the companies to organize big data and divide all the information into relevant and irrelevant. In such a way organization receive an opportunity to process this data and deliver necessary information to a customer faster and more efficient. Furthermore, new tools and applications help to build up a reliable channel of communication between clients and a support team (Wagner, 2016). That is why almost all the companies are constantly trying to improve their analytical methods in order to attract more customers and investors to their business. The examples of such organization as Netflix Inc., Harrah 's Entertainment and Amazon show that analytics could help an enterprise to develop in its field and gain a strong

Since the rapid growth of the cloud computing, data collection and information sharing are led to a higher level and are replacing the traditional computation. Several technologies using cloud in all areas are developing to adapt the revolution of information technology and one of them is cloud gaming.

There are many fundamental issue areas that need to be addressed in dealing with big data: data acquisition, data storage, data transfer, data management, and data processing. Each of these issues represents a large set of technical research problems and challenges in its own right.