Evolution Of Using Data Analytics

1. Define data analytics in general and provide a brief overview of the evolution of utilizing data analytics in business.

One of the main functions of any business is to be able to use data to leverage a strategic competitive advantage. The use of relational databases is a necessity for contemporary organizations; however, data warehousing has become a strategic priority due to the enormous amounts of data that must be analyzed along with the varying sources from which data comes. Company gathers data by using Web analytics and operational systems, we must design a solution overview that incorporates data warehousing. The executive team needs to be clear about what data warehousing can provide the company.

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

The ability to compete on analytics is made possible by certain qualities some companies possess which allows them to collect and use immense amounts of data in a way that differentiates the success and practices of those companies amongst any other businesses. Davenport and Harris (2007) define analytics as “extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions” (p. 7). Therefore, to be able to compete on analytics, a firm must not only use the data to extrapolate and execute strategies and models in order to drive business, but also to use that data better and smarter than their competitors. This requires forward thinking and continual developments of current analyses and practices. With regard to Davenport and Harris’s criteria and concepts on the ability to compete on analytics, Old Navy LLC’s practices will be analyzed to find whether the company is able to compete, is a competitor, and how it competes, if at all.

Through informational interviews with seven industry experts and a thorough literature review, the team explored the concept of “big data” and generated key insights which will guide the Federation’s approach as the organization develops its members’ data analytics capacities. Additionally, the team identified a clear business case for implementing data analytics at CDCUs using strategies appropriate for the level of resources within each individual organization. The team also developed a set of survey questions for the client to use when gauging the level of interest and capacity within any individual CDCU.

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

Stage 4, Analytical Companies – Analytics have been applied at an enterprise-wide level and are being used to drive decision-making, performance, and innovation, but results may not yet have been realized

The analytics team could then start to analyze the data using data mining and business intelligence techniques. All three types of business analytics: descriptive, predictive, prescriptive analytics techniques should be utilized. The goal of the analysis would be to look at indicators and correlations that lead to incidents occurring and try to determine ways to help prevent these occurrences in the future. Identify proactive ways to change behaviors and actions will be important.

Data is the magic word that we constantly hear amongst every employee at this institution. Analytics is using data to make decisions such as funding, staffing, and various resources. Analytics helps us to answer the question of “why we do what we do”. The problems leading up to the

Fact and data based decision making is a crucial element for success in today’s ultra-competitive business world. Many companies employ a multitude of analysts and statisticians to gather, organize and evaluate the mounds of data collected through various information systems. Late into the twentieth century companies finally halted the ancient ritual of storing data in basement file cabinets and began to rely on information technology systems to store enormous amounts of data on computer servers. Large amounts of data was being collected and stored but businesses lacked an effective way to utilize the data. During the same time period, numerous emerging technologies in computer science promised to enable the practical use of large amounts of data. SAS, originally referred to as Statistical Analysis Systems, is a company whose purpose is to create a way for users of its technology to access and utilize large amounts of data for analytical decision-making. SAS has made this possible through the creation of variety products and services that can manage a business’s data and customize its statistical analysis to improve business performance.

Today, the world’s trend in operating business focuses on data availability to enact the best suitable decision to improve, develop, and increase business revenues. Moreover, the availability of data helps to monitor and control the quality of provided products or services. However, the availability of data without proper analytics operations would have no meaning (1). Data analytics provide an important aid to an organization to figure out their position in the market in comparison with their competitors. Also, data analytics helps to identify what is the organization’s competitive ability in the market, what they should bet on, and what they should strive for. With that being said, many of today’s most successful organizations utilize

With “big data” and analytics being a hot topic as of late, drawing headlines like “The Sexiest Job of the 21st Century: Data Analyst” (Morris, 2013), it’s clear that this isn’t just a fad passing through. Data analytics has been around for a while now, but it is finally getting the recognition in deserves by many businesses. More businesses are seeing the value in using their data to make better business decisions, and one method of doing that is through using predictive analytics. According to Teresa Meek, “Predictive analytics can improve retailers’ pricing, inventory control, customer service, and ultimately, their bottom line” (2015). This paper is going to focus on looking at the different types of predictive analytics in relation to the retail industry. After highlighting the techniques and different software available to do predictive analytics, different retail businesses will be examined on how they are currently using predictive analytics as well.

Out of all the profiles or positions out there in information technology I aspire to work as a data analyst which in some cases is also referred to as a data scientist. This is because I am good with numbers and statistics and fascinated by data. With the combination of my courses taken in my Master’s degree and my past industry experience, I am looking forward to work in the field of data science and data analytics. I would like to use my technical skills to draw out business information and help the organization understand the impact of the trends and numbers on the business and thus, help them improve their existing processes. Analysis of data is a process of inspecting, cleaning, transforming and modelling data with the intent of discovering useful information, predicting

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.

The company deals in fast moving consumer products and requires an analytics system that helps it serve the needs of its stakeholders more effectively. It has three major rivals in the industry and seeks to streamline its supply chain and inbound inventory processes to improve its cash flows. This implies that the ability of its employees to gather and act on different sources of data will allow them a chance to perform their responsibilities in a timely fashion. Thus, by becoming more conversant with various trends in the market, the organization will be in a position to predict future performance in its key business units. Moreover, modern firms