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Developing Global Management Competencies


The team of analysts were chosen by the company, RLAM (Royal London Asset Management) to carry out an investigation of the SAS Enterprise systems could be used to effect the front ends operations of the company’s GUI (Graphical user interface). The project charter that the team were to create would pitch the idea of integrated business intelligence for the company and would provide an understanding of several advantageous scenarios that could be derived through a use of the same. Business and customer intelligence are key properties of the innovation that can lead to a more effective level of performance during subsequent business operations.

To report several skills and competencies that were dispensed to carry out the project, the learner has compiled this critical document. This document will be essential for future scope when similar projects occur. As an analyst and a member of a project management team, it is essential to report the various situations and the skills to circumvent any problematic work scenarios. Skills administration is an essential part of project management as it is derived from the fact that analysts are able to assess the present and future operations of firms and provide valuable feedback and recommendations for the same.

Appraisal of the project management and planning skills

The company, Royal London Asset Management is a U.K. based investment management company who handle a portfolio of almost £100 billion. The company handle several high level clients and are required to maintain a very effective level of performance to ensure customer satisfaction levels are high, while business operations are handled in the most market conducive manner.

Thus, as a part of an analyst team, the learner must analyse the skills portfolio that led to efficient work. RLAM intended that they derived further economic benefits from the operations of the analyst team as they expected recommendations in the form of more streamlined business operations and greater customer acquisitions and retention (Degeorge et al., 2013).

This project relied on planning and strategy as the market externalities involved with any discrepancies would have serious repercussions on the future operations of RLAM (Coombs, 2014). A project management team must ensure that they are able to prevent a loss of time and resources for companies such as RLAM. Yet, due diligence must be conducted in the most in depth manner so that any solutions that are devised for the company are effective.

The company had provided three key areas where they wanted the SAS solutions to impact their graphical user interface. These were customer acquisition, data security and management and improved business innovation.

Business innovation is led by high production levels at lower operational costs. This was the business mantra that the analyst team worked with when they adopted business competencies into their working plan. A core competency refers to the main strengths or a strategic advantage that is derived from a business (Kandel et al., 2012). This is a situation that is brought about because planning is done by pooling resources and technical capabilities. This essentially ensures that the business is competitive in the market and provided acquisition of newer markets and customers. This is a method that would be very beneficial for implementation of digital solutions into the business of Royal London Asset Management.

Unfortunately replicating continuous success becomes problematic as this entails that the team get along in terms of all the decisions that are to be taken in relation to several business solutions.

Data analytics and intelligent business solutions are essentially those business occurrences, which analysts will try to facilitate through the use of market research and investigation. One of the most important skills that the learner used was the use of communication. Project management can only be made efficient with an effective communication model. Team members had to ensure that their presentation skills were on point. This would facilitate a more transparent environment, which would be conducive (for the company). Effective communication would ensure that there are different insights into how the project can become a success. If communication does not exist, planning and project management skills will suffer (Lohr, 2012).

Personal organization is also a very important aspect of project management. Personal organization is a skill which ensured learner was able to reflect their personal philosophy onto project implementation. This became apparent in the planning stage of the project, when each team member realised notes taking and documentation would provide a critical understanding of the different processes that occurred during project implementation and solution adoption. If team members were not able to display those skills, which suited these effective requirements, then other team members would have to point out the same and ensure that the team was able to move forward in a pro-active manner.

Scheduling entails the divestment of methods, which would lead to goals being achieved within the decided timeframe. The team dealt with data analytics, this meant that different stages would include, testing and tweaking and market implementation. Monthly allocations meant the team were working in line with deadlines, which meant that scheduling helped them allocate tasks as per business requirements and priorities. When the team were not implementing scheduling into their project management and planning, objectives were unnecessarily drawn leading to time and financial losses.

Risk assessment and controlling are a part of risk management. This is one of the most important skills used in the process of project management and planning. The team had to ensure that they assessed live risks that could occur when the project was being carried out (Miller, 2014). Analysts must possess sufficient market knowledge as this will help them carry out projects with a sustained long term success in mind. Risk management helped the team manage risks for the ongoing success of the project. Of course, the team could not have anticipated all the risks that could have occurred over the lifecycle of the project, which led to several unanticipated frictions.

Critical thinking and quality management are skills that were used to assess the project as they led to mitigating any logistical issues, while also ensuring that each problem was dealt with the most innovative solutions, which would facilitate market solutions, dispensed in such a way that business motivation is driven by profits and market capitalisation (Bar Isaac et al., 2011).

Appraisal of the skills needed by a Data Analyst

A data analyst must ensure that they realise that the most important they posses is that of teamwork. Vital experience of working in teams ensures that analytical work is approached in ways that are conducive to an effective work environment (Chen and Zhang, 2014). Having to work in teams means that a data analyst must possess interpersonal skills, which are important to understand issues from the point of view of their teammates and those of their customers. This will provide the data analyst a chance to leverage skills to take advantage of greater opportunities.

The data that is used by the data analyst can be used so as to practice and organise data in processes which will meet the lifecycle needs of any organisation. The data analyst can use data management and ensure that methods are implemented in processes such as accounting statistics, logistical planning and other logistical disciplines.

Scripting and statistical languages are important computer skills that a data analyst must possess as key skills. This will ensure that their work is made easier as they are able it dispense this skill in a more effective manner.

A creative and critical way of thought is an intra personal skill that is very important for a data analyst. A data analyst may experience several problems in the course of their work. This means that they must understand there are several approaches that must be used by them to circumvent any issues or prepare for any future shocks. Unfortunately, this may mean that the data analyst may tend to over think certain problems and ignore the obvious and most effective option.

An eye for detail is important for the portfolio of skills that a data analyst must possess. These analysts, when hired for projects such as the RLAM assignment (Aken et al., 2010). This is because analysing issues and reporting large swathes of data in customer specific ways. Once a data analyst has been hired, it behoves them to explain data, which could be used to carry out essential project management and for future market operations. Unfortunately, this may again lead to a situation of over thinking, as they may not immediately the most effective option immediately (Therasa and Vijayabanu, 2015).

Written and verbal communications skills are one of the most important skills that a data analyst must possess; as it will allow them leverage this as a strength for future opportunities. Written and verbal communication will facilitate the data analyst to take advantage of their analytical skills as this creates a drive within them to drive team and company centric solutions. Written and verbal communications skills are an essential part of any job. This becomes even more important in a data context as few people can effectively analyse trends and insights which data is able to provide, thus, the data analyst must provide the ease of access of data to their co workers and managers and project leads at RLAM as they can use the same for project continuity (Ahmed et al., 2012).

Flexibility and adaptability are also essentials that a data analyst must possess, essentially when they are tasked with duties such as consultants. This is because data analysts essentially make use of all of the different factors that facilitate the means to an end. This means that when they are tasked with certain, they must look for the effective solutions. In case the work that comes with their way is not as per the training that they have received, they must be flexible enough to look for better options. These better options must facilitate better results for the company that have hired them. In terms of the work that was done by the learner and their team, the task revolved around adopting digital solutions for the company RLAM (Schoenherr and Spieer Pero, 2015). As individuals who were paid for the facilitation of better business processes for RLAM, it was important that the company received various options for future success. The analysts could only provide such options if they were flexible enough to examine the various options that existed in terms of market conditions and variables.

There are several variables, which relate to the operations of a data analyst in the current market situations. There are several strengths, weaknesses and opportunities, which are part of their job portfolio. These are essential for the explanation of how they work. Below is a SWOT analysis for the role of a data analyst and explain how the learner and the team are positioned in the market.


The use of an analytical mind means that they are proficient at market analysis and are best placed for any kind of predictions of market variables.

Proficient handling of computer systems ensure that the data analyst is able to divest their skills in other computer and information technology fields.


A data analyst may be affected by over thinking situations, which may lead to them ignoring the most effective options.

A data analyst may have to use several industry related jargon which may confuse other team members when consultancy projects are being carried out.


Several consultancy projects may be offered to data scientists as their skills make them valuable assets to information analysis.


Data analysts’ jobs are under threat because of the rise in artificial intelligence, which can carry out work in a more time and cost effective manner (Rotman, 2013).

Figure1: SWOT Analysis of Data Analyst

(Source: Created by the learner)


This assignment has intrinsically looked at the different skills and abilities involved with the job of a data analyst, especially that of a consultant who aims to adopt business intelligence and customer intelligence models into the existing operations of a business. It has been discovered that this is a line of work that needs an extremely systematic way of thinking which would enable analysis with goal of facilitation of effective processes.

It is recommended that the company, Royal London Asset Management, adopt SAS enterprises as per the recommendation of the analyst team as the solutions that have been discovered have been analysed and market scenarios have been repeatedly played over to understand the impact that this will have on the business. Generally it has been noticed that such operations would have a positive impact on the business and market operations of the company.

Reference List

Ahmed, F., Capretz, L.F. and Campbell, P., 2012. Evaluating the demand for soft skills in software development. IT Professional, 14(1), pp.44-49.

Aken, A., Litecky, C., Ahmad, A. and Nelson, J., 2010. Mining for computing jobs. IEEE software, 27(1), pp.78-85.

Bar-Isaac, H. and Shapiro, J., 2011. Credit ratings accuracy and analyst incentives. The American Economic Review, 101(3), pp.120-124.

Chen, C.P. and Zhang, C.Y., 2014. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, pp.314-347.

Coombs, W.T., 2014. Ongoing crisis communication: Planning, managing, and responding. Sage Publications.

Degeorge, F., Ding, Y., Jeanjean, T. and Stolowy, H., 2013. Analyst coverage, earnings management and financial development: An international study. Journal of Accounting and Public Policy, 32(1), pp.1-25.

Kandel, S., Paepcke, A., Hellerstein, J.M. and Heer, J., 2012. Enterprise data analysis and visualization: An interview study. IEEE Transactions on Visualization and Computer Graphics, 18(12), pp.2917-2926.

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

Miller, S., 2014. Collaborative approaches needed to close the big data skills gap. Browser Download This Paper.

Rotman, D., 2013. How technology is destroying jobs. Technology Review, 16(4), pp.28-35.

Schoenherr, T. and Speier‐Pero, C., 2015. Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), pp.120-132.

Therasa, C. and Vijayabanu, C., 2015. The impact of big five personality traits and positive psychological strengths towards job satisfaction: a review. Periodica Polytechnica. Social and Management Sciences, 23(2), p.142.


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