Data Analyst: Job Analysis and Training Needs

NB: We do not resell your papers. Upon ordering, we do an original paper exclusively for you.

NB: All your data is kept safe from the public.

Table of Contents

Introduction
Job description – duties and responsibilities
Tasks
Knowledge and skills
Training needs in the content areas
Training methods
References

Introduction

Data analysis involves collecting and analyzing data to derive useful insights for decision-making in organizations. A data analyst is a well-qualified and trained expert who conducts analyses by using different mathematical concepts and algorithms to indicate how a given sample of data can facilitate decision-making in organizations. Therefore, a data analyst must derive useful insights from data, which would otherwise be useless to an organization. In most cases, a data analyst’s job involves risk evaluations, sales, and profit projections, competition, and fraud analysis among others to determine how these factors affect the operations of a company. Today, most businesses have embarked on data analysis to determine important trends in their operations for expansion strategies and improvement of business practices. Consequently, data analysis has become an important and highly demanding field but well rewarding.

Job description – duties and responsibilities

A data analyst relies on the technical knowledge of data analysis to manipulate data sets by using advanced computer software. The analyst identifies data patterns through technical processes, which are supported by advanced algorithms.

Defining data for analysis
Cleaning data for analysis
Determining rules for analyzing a given set of data
Conducting queries in databases
Extracting useful information
Analyzing the extracted data to derive useful patterns
Report writing
Presenting findings in a manner that people without technical expertise in data analysis can understand easily
Recommending what executives should do with the insights
Store data and results

Tasks

There are five fundamental processes in the tasks of a data analyst (Adèr & Mellenbergh, 2008).

Data cleaning

This stage involves the elimination of erroneous data by detecting outliers.

Initial analysis

In this stage, the data analyst evaluates the quality of data for analysis and defines hypotheses for the study.

Main data analysis

The analyst extracts useful information from the data.

Final analysis and report writing

This stage involves testing and screening results for valuable insights.

Presentation of the findings

A data analyst must present findings to executives and other members of an organization who will utilize the results for decision-making in business processes. The mode of presentation must be easy to understand and void of any analysis jargon.

All these stages require certain sets of skills, which involve specific knowledge in data analysis software, algorithms, hypothesis formulation, and testing and presentation skills.

A data analyst works with specific sets of data for a given industry. Therefore, an analyst must be familiar with data in an organization. For instance, data analysis skills are useful in corporate entities, not-for-profit organizations, the public sector, and the political arena.

Since many employees, politicians, agents, and other business experts do not possess technical expertise in data analysis, they require the services of data analysts.

Knowledge and skills

A clear sense of intellectual curiosity

A data analyst must strive to challenge conventional wisdom by being inquisitive. An analyst must focus on the ‘whys’ and ‘hows’ in any given situation. He or she must demonstrate strong business acumen and work out specific alternatives to overcome challenges.

Mathematical expertise

Any analyst requires a sound knowledge of mathematical concepts and algorithms. In addition, a data analyst must understand statistical software and other mathematical tools.

Critical thinking

A data analyst must focus on analysis, use numbers, derive trends, manipulate data, and generate outcomes for new insights, decision-making, and developing the big picture for an organization. This process involves an analysis of several data sets with perfected attention to detail.

Detail-oriented

A data analyst may work with large volumes of data. However, the analyst must concentrate on specific details.

Interpretation and presentation skills

An analyst must be able to interpret results, write reports, and present them to the audience. The results should solve data problems or provide insights into such issues. When others are not able to understand findings, then such findings are not useful and indicate nothing to an organization. Therefore, a data analyst must possess the skills to interpret findings within a specific business context for business strategy development.

Ability to communicate effectively and clearly with different people.

Training needs in the content areas

One can easily acquire data analysis skills through training.

Training in emerging skills and knowledge

An analyst needs to review statistical methods to understand concepts and different models for specific emerging problems in data analysis.

Continuous training on advanced statistical packages

New forms of data analysis emerge frequently and an analyst requires training on such software.

Training in predictive modeling techniques, large data, and big data analysis

Training on emerging usages of data.

Training methods

Effective approaches to on-the-job training could equip a data analyst with the required skills, attitude, knowledge, and competencies (Klink and Streumer, 2002). These skills would allow a data analyst to perform the job effectively. Data analysis is a practical-oriented process, which a trainee can only understand well when performed with the necessary tools and data.

References

Adèr, H.J. & Mellenbergh, G.J. (2008). Advising on Research Methods: A consultant’s companion. Huizen, the Netherlands: Johannes van Kessel Publishing.

Klink, M. R., and Streumer, J. (2002). Effectiveness of on-the-job training. Journal of European Industrial Training, 26(2-4), 196-199.

Click Here To Order Now!