Data Analyst: Roles, Skills, And Eligibility Criteria

Rising Challenges in Data Analysis

Question:

Write an essay on “Performance Management and Recruitment”.
 

In the present days, one of the critical challenges associated with the data analysis lies in the multiple domains linked to various industries and the tools utilized by the data analyst field employees. Thus the attempt to master each and every “data analysis tool” in the work place might be fruitful in future. In a data analyst, the strong understanding of the roles, skills, duties, and techniques are incorporated. A data analyst can manage the assessment of the data through the use of various tools and systems (Skupski et al. 2015).

One of the strongest attributes in a data analyst should be self-motivation which allows them to produce their effort and performs under tremendous pressure and also against hostile deadlines. Even under pressurized circumstances they should try to remain cool and subtle to meet the need for the working condition. Being an analytical thinker, the data analyst is gifted with the quality to recognize, scrutinize, streamline and modify complex job process (Lohr, 2012).

According to Lohr (2012), data analysis is an art of collection and evaluation of data in order provide the organization the opportunity to use the data in future for completing their marketing, political or business practices, and insurance. The data analyst is a trained professional who is efficient in performing mathematical calculations in order to determine how such samples can be applied to produce profitable business revenues. A data analyst is also technically equipped and sound in computerized works. Moreover, an expert analyst assesses the risk, culling numerical information to identify the fact whether an organization is accidentally causing damage to itself or not. Since most of the companies are born with the objective of expanding their business and improving their strategies and business practices, the role of a data analyst is vital in any field and is also profitable.

As stated by Abbott (2014), a business metric called “Key Performance Indicator (KPI)” is used in order to evaluate the factors that are essential for the accomplishment of an organization. The KPI differ from industry to industry. The KPI depends on the objective of the project. The industrial goal might be quick, precise, whole data analysis. Then one needs to have a KPI for how lengthy it takes to comprehensive the scheme, one more for how numerous mistakes are established afterward, and one more for how various times requirements for explanation or more examination is required.

The key performance metrics of a data analyst are as follows:

1. While analyzing the data one should not forget that to keep in mind to generate a high return and the net profit margin.
2. The discrepancies in the actual revenue and the revenue projected should be compared.
3. Data analyzing based on customers satisfaction is a long-term priority and a vital indicator.
4. “Measuring your employee satisfaction through surveys and other metrics is vital to your departmental and organizational health.”
5. Collaboration with the cross functional team to enhance the data management process.
6. Archive learning knowledge from all data analysis work to complete all kinds of applicable projects is also an indicator and parameter.
7. Understanding of the market strategy to formulate data compilation and management is also an important measure (Abbott, 2014).

The candidate has to perform alongside the “Group Insight Director,”“Industry Insight Manager” and “Consumer Insight Manager” to commission the compilation and be occupied in the scrutiny of market data.  He or she has to supervise the whole research process, ranging from procedural decisions, questionnaire preparation, and data examination to data evaluation, report making and presenting to the Board (Narendra, 2016).

The following are the eligibility criteria for the post of a data analyst:

• Graduate from Bachelor’s degree program in any technical field (Science/Commerce/Arts) such as Mathematics, Statistics and Computer Science.
• Expert in SQL skills.
• Expert in data analysis tools such as Python, Pearl, SAS, SPSS, Hadoop, etc.
• Familiarity with LTV and other models.
• Knowledge on CRM, positioning, segmentation, etc.
• Data analysis and manipulation skills.
• Expertise in data collection techniques with excellent knowledge of the system tools for measuring the qualitative and quantitative data.
• Ability to prepare reports, power point presentation.
• Translate the need of an executive for business metrics compiling it into data layer.
• Immense knowledge in Microsoft office.

According to Narendra (2016), the duties of a data analyst include:

• Preparation of market research plans in order to collect statistical data.
• Management of the routine data collection activities.
• Data analysis and processing.
• Delivery and analysis of robust.
• Designing of technical and administrative reports.
• Auditing research and develop innovative approaches (Kämpgen et al. 2014). 

Self-Motivation: A Strong Attribute of a Data Analyst

As stated by Zoumpatianos et al. (2013), our organization operates in the trade to client domain and needs a brilliant “data analyst” to lead commission and administration the data generation for the group.  This is a vital employment for the team and a chance to work with an innovative, forward thinking and thrilling company.

We are looking for a passionate “Data Analyst” offering a salary £35,000, and the company is based in Melbourne X1. There will be several days of training to sharp the edges of knowledge of the candidate. The team with the vacancy needs a numerous amount of budgeting, report preparation and data compilation. The candidate has to possess good administrative, technical and system skills. Moreover, the candidate should be proficient in the English language (Kandogan et al., 2014).

The interview would consist of three rounds. The first round would be a walk-in interview which comprises of written multiple choice questions for one hour on 21^st of May 2016. The written examination will start from 9.00 am and end by 10 am. All the candidates are requested to reach the office sharp at 8.30 am for verification of the documents. If the candidates clear the written interview, then they will be called for the second round of interview on 25^th of May 2016 (Lake & Drake, 2014).

The second round of interview will comprise on a group discussion. The topic of the group discussion will be based on the field survey. The individuals would be divided into groups and then allowed to speak. The two best teams will be eligible for the next round of interview.

The third round of interview will be conducted on the same day which will comprise of a verbal interview based on general introduction, followed by operational questions based on the skills of the position interviewed for. The last type of questions will be role specific to judge the confidence of the candidates. Results will be furnished on 27^th of the month. If the fulfillments are met, apply on our website www.a.b.com for the required position (Mathur et al. 2013). 

During the interview, a feedback form will be supplied to all the interviewers in order to judge the candidates based on certain specified criteria’s. This form will consist of parameters on which the candidates will be judged and reviewed. The parameters will also include specificities during the training period (Hartnett, 2014).

Particulars	Poor	Average	Good	Excellent
Written total
Communication
Verbal skills
Technical knowledge
Operative knowledge
Training efficiency
Overall

As stated by Broome & Gillen (2014), several adjustments made in the recruitment process throughout the hiring procedure might bring about perfect impacts about the candidates’ perceptions.

• Responsiveness towards the candidates should be the highest priority. Promises made should be maintained.
• Developing an FAQ page on the company’s website would bring about transparency to in the brand name.
• A career blog development where he seniors would contribute some contents might be helpful for the applicants.
• Preparation before interviewing the candidates can be fruitful. The resume should be properly read, and knowledge about the candidate is very important.
• The interviewers should not get personal with the rejection process.
• A final impression should be created on the interviewees even if they are not selected.
• The proactive checking of the list of the previous candidates might result in the review of a better candidate for the position.
• The setting of candidate expectations in relation to the success factors of the job is a key recommended.
• Planning of each step of the hiring process is important in order to bring about transparency in the hiring procedure (Hughes et al., 2015).

Their experiences and professional knowledge make them adaptable, flexible, and capable to administer multiple needs at the same time (Hughes et al., 2015). 

References:

Abbott, D. (2014). Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst. John Wiley & Sons.

Broome, K., & Gillen, A. (2014). Implications of occupational therapy job advertisement trends for occupational therapy education. The British Journal of Occupational Therapy, 77(11), 574-581.

Hartnett, E. (2014). NASIG’s Core Competencies for Electronic Resources Librarians Revisited: An Analysis of Job Advertisement Trends, 2000–2012.The Journal of Academic Librarianship, 40(3), 247-258.

Hughes, T. B., Varma, V. R., Pettigrew, C., & Albert, M. S. (2015). African Americans and Clinical Research: Evidence Concerning Barriers and Facilitators to Participation and Recruitment Recommendations. The Gerontologist, gnv118.

Kämpgen, B., Weller, T., O’Riain, S., Weber, C., & Harth, A. (2014). Accepting the xbrl challenge with linked data for financial data integration. InThe Semantic Web: Trends and Challenges (pp. 595-610). Springer International Publishing.

Kandogan, E., Balakrishnan, A., Haber, E. M., & Pierce, J. S. (2014). From data to insight: work practices of analysts in the enterprise. Computer Graphics and Applications, IEEE, 34(5), 42-50.

Lake, P., & Drake, R. (2014). Staff. In Information Systems Management in the Big Data Era (pp. 103-123). Springer International Publishing.

Lohr, S. (2012). The age of big data. New York Times, 11.

Mathur, A., Arora, T., Liu, L., Crouse-Zeineddini, J., & Mukku, V. (2013). Qualification of a homogeneous cell-based neonatal Fc receptor (FcRn) binding assay and its application to studies on Fc functionality of IgG-based therapeutics. Journal of immunological methods, 390(1), 81-91.

Narendra, A. P. (2016). Big Data, Data Analyst, and Improving the Competence of Librarian. Record and Library Journal, 1(2).

Skupski, D., Pevear, J., Owen, J., Mann, J., Talucci, M., Rumney, P., & Wing, D. (2015). 87: Cultural differences affecting recruitment in the NICHD fetal growth studies. American Journal of Obstetrics & Gynecology, 212(1), S62.

Zoumpatianos, K., Palpanas, T., Mylopoulos, J., Maté, A., & Trujillo, J. (2013, November). Monitoring and diagnosing indicators for business analytics. In CASCON (pp. 177-191).