Developing an analytics team

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Introduction

With the continuous surging of the data tsunami, it has become less expensive and easier to understand analytics tools. In the contemporary society, businesses are advised to consider their large data strategy and future plans to avoid being left behind (Rodger & Blenko, 2006). Coming up with an effective analytics system helps to manage and store data within a firm. Therefore, in the competitive business environment, one of the most effective strategies is having employees who are competent data analysts. The workers of a firm are key resources to the organization since they affect the overall performance. Having a big data team comprises an effective business analyst, machine-learning expert, and data engineer (Rodger & Blenko, 2006). Currently, the manufacturing systems at my firm are being upgraded in order to use new suppliers of raw materials. For the past months, the firm has been experiencing cases of delays in the delivery of raw materials by the suppliers. It is important for me to manage how to test such systems using internet of things methods in order to be able to change process settings. To do so successfully, I must establish an effective analytics team that will test the systems.

Organizational Approaches to Data

Based on the issue mentioned above, it is evident that the company needs an effective and reliable analytics team that will help to test the upgraded manufacturing systems as expected. Lack of testing the systems will directly contribute to an ineffective supply chain for the raw materials required for production (Anderson, 2015). Therefore, selecting an effective analytics team will help in the formation of an efficient supply and manufacturing system. The current nature of the organization is such that it has been offering quality products to its customers. However, the company’s current suppliers have registered high cases of delays and a sudden surge of prices when supplying the raw materials. This has developed the need to upgrade the manufacturing systems. The employees are also focused towards fulfilling organizational goals. One of the company’s analytical abilities is the skill to identify arising problem (Anderson, 2015). With this skill, the company’s analysts have managed to spot some of the key issues that tend to affect an organization’s performance. Another current analytical capability at the organization is critical thinking, which has enabled the management to evaluate information and make a decision based on findings; hence solving the problem for the company.

Additionally, there are various gaps found in the firm’s existing resources as well as capabilities that can assist in addressing the organizational problem (Hall, 2013). First, one of the main gaps facing our organization is lack of adequate creativity amongst some of the employees. With this challenge, most workers and analysts are unable to spot trends in the data that others would not trace. Another gap is that the firm lacks workers with effective data analysis skills. For this challenge, there is need to build an analytics team. To address the organizational problem, the management needs a team with creativity skills for them to implement effective solutions to big problems. Second, it is imperative for workers at the firm to have data analysis skills, which will enable them to examine large volumes of data and identify trends in the data. By developing an analytical team, our organization will manage to address the need of having an effective manufacturing system. Moreover, having an analytical team will also ensure that workers possess creativity, critical thinking, and data analysis skills (Davenport, 2014). Employees are the main resources of the firm; hence, their competencies boost its overall performance. Through effective analytics strategies, the organization will be in a position to address its needs as required.

Structuring an Analytics Team

To address the above organizational needs, it is imperative to build and maintain a productive analytics team. Foremost, it is crucial to build an effective team by first establishing a healthy relationship with each of the workers (Hall, 2013). Learning more about the team members helps to understand their skills set, their likes and dislikes, and the factors, which motivate them. In addition, building an effective team will involve fostering teamwork in order to ensure employees work together towards solving the organizational needs. Additionally, to structure an analytical team, it is a key requirement to ensure that the members possess certain skills, for example, communication, creativity, and research skills. With these capabilities, the team members will be in a position to determine whether the new manufacturing system will be flexible and constructive enough for the production process. In order to recruit various team members, the firm will have to implement certain strategies. Firstly, despite having adequate technical and data knowledge, it is important to recruit team members who have great communication and presentation skills (Davenport, 2014). This strategy is key since it will ensure that the members are not only able to come up with game-changing insights, but can also explain them to business executives and other interested parties. Another strategy will be to ensure that the team members selected are machine-learning experts. Such individuals are statistically-minded and well-experienced in the forming of data models and programming.

After recruiting potential candidates as the analytics members, the organization will provide them with coaching and feedback. According to O’Neil (2014), coaching and training are key strategies that enhance the competencies and capabilities of workers. In this case, the coaching process will focus on enhancing the main analytical skills, which include communication, data analysis, creative, and critical thinking skills. Team working skills are also essential in ensuring that workers cooperate when analyzing existing data. Providing feedback to these team members is also a critical strategy for building an analytics team (O’Neil, 2014). Positive feedback encourages employees to maintain the level of motivation, whereas negative feedback challenges them to do better. Becoming a qualified analyst requires dedication and commitment since the members must maintain certain skills.

Benefits for Building Analytical Capacity

Lastly, building and maintaining a productive analytical capacity will benefit the firm and boost its overall performance (Hill, 2011). The strategies mentioned above will play a critical role in the formation of an analytics team that is able to address the organization’s needs. To communicate with the nontechnical professionals, the analytics team members will have to work on their communication skills. Having effective communication skills will enable the members to break down complex data into non-technical form, which is easier to comprehend. Therefore, this analytical capacity will boost the relationship between the firm and the non-technical professionals. Second, having an adequate analytical capacity will also help the firm to improve on its manufacturing system; hence boosting the relationship it has with the customers due to lack of delays (Stewart, 2015). With an effective team of analytics, the organization will also manage to identify risk or issues that could negatively affect the performance of an organization. Another key requirement is for the company to defend the resources used for forming the analytics team (Hill, 2011). To do so, the management will establish frequent training programs for the analytics skills in order to ensure that the company’s employees are competent and reliable analysts. Precisely, the training process will ensure that workers possess the key analytical skills that were confirmed during the recruitment process.

Conclusion and Recommendations

In summary, having an effective analytics team is a key strategy and competence in enhancing a firm’s performance. In the competitive environment, an organization is expected to have a plan for identifying the possible challenges that might arise within. The internal operations of an organization play a critical role in determining its position within the competitive environment. Based on the above analysis, it is evident that the key need of the organization is to analyse the efficiency of the new manufacturing system. The formation of an analytics team will help in the process of problem identification and solving. To recruit qualified team members, it is important to select individuals with analytics skills such as communication, data analysis, research, problem solving, creativity, and critical thinking skills. These competencies play a crucial role in the formation of a reliable analytics team. The above research also explains that the organization should also come up with a strategy for building an effective team, which will help in meeting the needs of the business plan. Overall, the successful formation of a functional analytics team will boost the company’s performance by enabling it to come up with the best solutions for the arising needs.

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  1. Anderson, C. (2015). Creating a data-driven organization. Beijing: O’Reilly Media.
  2. Bartlett, R. (2013). A practitioner’s guide to business analytics: Using data analysis tools to improve your organization’s decision making and strategy. New York: McGraw-Hill Professional.
  3. Davenport, T. H. (2014). Competing on Analytics. Harvard business review, 84(1), 98.
  4. Hall, K. (2013). ‘Making the Matrix Work’, Training Journal, 4(3), pp. 45-48.
  5. Hill, L., A. (2011). Being the Boss: the 3 imperatives for becoming a great leader. Boston: Harvard University Press.
  6. O’Neil, C. (2014). Weapons of Math Destruction. Crown: New York.
  7. Rodger, P., & Blenko, M. (2006). Who Has the D?: How Clear Decision Roles Enhance Organizational Performance. Harvard Business Review, 9(1), pp. 3-11.
  8. Stewart, G. L. (2015). A meta-analytic review of relationships between team design features and team performance. Journal of management, 32(1), 29-55.
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