Literature Review and Proposal
In this big data world, organizations are increasingly collecting large amounts of data from several sources and storing them in their databases. The data can belong to the employees, their customers, their partners, the transactions, and any other data that is relevant to the company. These enterprises rely on the data analysts to model the client engagement, streamline their operations, inform the performance, improve production, inform business decisions, and counteract fraud (Lewis-Beck, 1995). The other individuals that carry out the function of data analysis are the business analysts or the data scientists. The data analysts in organizations comprise a crucial as well as a rapidly increasing user population of visualization tools and analysis (Juran, Godfrey, A&Blanton, M. 1999). Company data analysts carry out their responsibility within the context of the larger organization. They can work either as part of a business unit or an analysis team.
The data analysis involves the cleaning, inspection, transformation, and modeling of data aimed at discovering the information useful for decision-making and suggesting conclusions. It incorporates multiple strategies and facets encompassing diverse techniques. One of these techniques is the data mining that focuses on the modeling of data and knowledge discovery for the predictive purposes (Barko& Nemati, 2004). The other technique is the business intelligence that incorporates the data analysis that depends on the aggregation and focuses on the business information. The analysis can be further subdivided into descriptive, exploratory, and confirmatory data analysis. Te focus of exploratory data analysis is to discover new features in the data while the confirmatory data analysis focuses on confirming the existing hypothesis. The predictive data analytics involves the application of statistical models to achieve forecasting, prediction or classification from various textual sources (Johannesson& Söderström, 2008). Those are the varieties of data analytics of which the data analyst should be aware.
The data analysts are crucial resources in companies because of the increasingly huge amounts of data that need to be analyzed for the purpose of informing decision-making in organizations (Kandel, et al., 2012). They are the people that understand the existing organizational infrastructure, the available data and tools; and the social and administrative conventions within an organization. They do not only analyze the data, but they also form part of the advisory team within the organization that advises on the appropriate decisions that should be made to make sure that there is improved performance within the organization (Johannesson& Söderström, 2008). The data analyst breaks down the big data into separate components and examines them individually. That is because the data cannot make any meaning to the decision makers unless it is transformed into information. That can take place only when the data analyst obtains the raw data from various sources and converts it into information that can be then leveraged to make critical organizational decisions (Langley, 1999).
Organizations experience many issues that should be addressed for them to remain in business, and if the correct data analysis is not carried out, then the source of the problems may not be known. That means that those issues will continue to trouble the organization, and they will threaten its existing in the market. It is through data collection and analyses will the problems be identified and addressed properly to make sure that there is business continuity Johannesson, P. (2008). Thus, the data analysts are an important resource in any organization. The analyst has to make sure that he/she performs the analysis with a strategic business direction. We can also say that they are part of the performance determinants of organizations. They make sure that the organizations do not waste resources on investments that cannot bring the desired return on investment.
The data analysts also help the organizations to make the right decisions through addressing the right problems that are hindering their progress. When the analytics is aligned with a specific organizational challenge, it makes it easier to overcome many obstacles to its progress (Langley, 1999). The executives can state many problems and is the responsibility of the analysts is to make sure that they find out the source of those issues and how they can be effectively addressed as they also liaise with the system analysts and business analysts. The insights acquired from the data analysis process can help to illuminate the gaps that may be prevalent in the data infrastructure as well as the business processes. The time that could have been wasted in cleaning up the data can be diverted to specific data and processes thereby enabling the iterations of value (LaValle et al., 2011). The transformed enterprises are not only good at data capture but how well they can analyze that data to have informed decision-making for improvement (Ricahrd, 1984). The data analysts leverage various skills and techniques to ensure data-driven insights are unraveled and used at all the levels of an organization.
As a data analyst intern in Global Data Mart, I will use various data analysis techniques to make sure that I accomplish the projects assigned with the required level of expertise and results. My internship will be arranged in the form of iterations that will all be four in number with each containing the stage of planning, action, observation, and reflection. All those four iterations will revolve around data analysis so as to make sure that I gain sufficient skills and experience to become a competent data analyst. Below are the iterations.
Iteration 1: Organizational Orientation
My first iteration concerns the orientation into Global Data Mart and being explained to the basic functions of the organization including its major products, employees, branches, customer spectrum, and departments among other crucial information. A briefing on the rules and regulations by which the interns should abide to make sure that they do what is expected at any one time will also take place.
Iteration 2: Training
The second iteration will involve training, and it is whereas interns in the company I will meet the data analysis experts in the company who will then train me on all the techniques and tools for data analysis. They will also provide me with the information of the organization and the issues it is going through so that the knowledge will help me later when executing my duties and responsibilities as a data analyst in the company.
Iteration 3: Gathering the Requirements
In this third iteration, I will make sure that I gather all the requirements to make sure that everything is in place before I carry out the actual data analysis for the company. That will include the required tools, obtaining the relevant information from all the departments and any other information that will be relevant to my duty as a data analyst in the company.
Iteration 4: Performing Actual Data Analysis
In this last iteration, I will leverage the knowledge acquired from the training iteration and make sure that I accomplish the data analysis of the company. I will also work with the company’s decision makers to examine those issues and provide the direction to go in addressing them so as to ensure there is an organizational improvement at levels.
Barko, C. D., & Nemati, H. R. (2004). Organizational data mining: Leveraging enterprise data resources for optimal performance. Hershey, Pa. [u.a.: Idea Group Publ.
Johannesson, P. (2008). Information Systems Engineering: From Data Analysis to Process Networks: From Data Analysis to Process Networks. IGI Global.
Johannesson, P., & Söderström, E. (2008). Information systems engineering: From data analysis to process networks. Hershey: IGI Pub.
Juran, M., Godfrey, A. & Blanton, M. (1999). Juran’s Quality Handbook (5th Ed.). New York: McGraw Hill.
Kandel, S., Paepcke, A., Hellerstein, J. M., & Heer, J. (2012). Enterprise data analysis and visualization: An interview study. Visualization and Computer Graphics, IEEE Transactions on, 18(12), 2917-2926.
Langley, A. (1999). Strategies for theorizing from process data. Academy of Management review, 24(4), 691-710.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21.
Lewis-Beck, S. (1995). Data Analysis: an Introduction, Sage Publications Inc.
Ricahrd, V. (1984). Pragmatic data analysis. Oxford : Blackwell Scientific Publications.