Formative Data Driven Decisions Assignment Sample

Task 1: Introduction and project plan

Data driven decisions help in creating a proper plan to execute business operations and manage performance according to requirements. Through structured data driven operations, an organisation can arrange a synchronised format to control all possible combinations of data from external sources. The purpose of this study is to understand the sales performance of BIJ through a proper structured management plan within WFTT. This study provided information about the BIJ and its marketing campaign to increase sales performance in the context of several categories. This study also explained data quality issues and data analysis processes in the context of BIJ dataset.

Project plan

Project plan of WFTT can help in comprehending all required information that can help in creating opportunities for controlling project performance and progression rate. As followed by Amy Van Artsdalen (2017), through the assistance of the project plan, employees of WFTT can present all required information and data from different sources that can help in creating proper decisions regarding the sales performance. It can guide the authority and help employees to execute the project plan in a systematic manner to achieve their targets.

Data analytical framework

Data analytical framework helps in identifying the arrangement criteria of a particular dataset that can maintain the validation processes and incorporate strategic ideas to handle data analytical operations. The higher management of WFTT can incorporate a predictive analytics framework to understand the categories of a dataset and perform tasks accordingly to reach all targets and goals.

Figure 1: Predictive Data analytics

(Source: As inspired by Sagare et al., 2020)

Predictive data analytical framework helps in managing difficulty and value of all insights of a particular business through which employees can make proper assumptions to handle the situation. According to Sagare et al. (2020), using this predictive data analytics, employees of WFTT can rearrange the dataset of BIJ to improve their data management processes using structured data mining activities without any issues. Through this data analytical framework, difficulty and value added services can develop the sequential activities of BIJ dataset that can manage hindsight, insight and foresight to achieve goals.

Key performance indicators

Key performance indicators or KPIs give a proper assumption in understanding performance structure of a business that can control validation level of a particular dataset. It would assist an organisation to make proper decisions that can control the sequential operations without any issues. In this way, the quality and innovation of BIJ can be identified properly and can be presented by the employees of WFTT.

Task 2: Data quality issues and remedies

Explaining generic data problems

Data problems create complications for data analytics and data managerial activities that can reduce effectiveness and quality of data in a particular dataset. Due to data problems, structure in storing datasets and data sequences cannot be maintained according to requirements. As inspired by Rudin (2019), it would also negatively impact the process of managing all possible combinations due to which effective measurements cannot be handled effectively. For this reason, the line manager of WFTT is expected to take crucial steps in handling data quality issues that can improve quality in data structure.

List all data problems

Data redundancy, incorrect datasets and wrong information about sales can increase complexities and reduce quality processes in database management processes. Due to these data problems, employees of WFTT cannot incorporate structured processes and reduce operational changes. In this way, quality and structured processes cannot be controlled due to summative dataset of BIJ that can decrease the structure of sales activities and specific requirements of database management processes. As opined by Meredith et al. (2020), to counter these issues, the line manager can incorporate data cleaning techniques to solve these issues and improve data quality management according to specifications. Using data cleaning, accuracy and correct data formats can be organised successfully that can improve quality of data managerial processes without any challenges. It would bring structured changes and control validation levels due to which possible combination of datasets can be developed without any issues.

Task 3: Data analysis and commentary

Summarising all exploratory data

From the dataset, it has been found that all dataset can help in presenting specific information that can manage the quality and structure of a particular dataset. As proposed by Koesten et al. (2020), it would bring stability and control the procedure of understanding sales performance of BIJ that can manage all types of events without any issues. Through the assistance of a structured dataset of BIJ, employees can make proper assumptions and incorporate strategic changes including sales performance, sales rate and categories of items that can lead to manage improvements adequately. It would control validation processes and maintain the structured requirements that can help in creating structured sequences of categories related to their markets. This data is helpful in identifying quantities of markets with their sale values to develop their data managerial processes depending on requirements.

Figure 1: Summarising sales performance

Figure 2: Presenting all subtypes according to markets

Highlighting data key elements

Key elements of data assist in understanding all operational tasks that can control the validation processes through which structured performance can be maintained accurately. Key data elements are sale values, subtype and markets of BIJ dataset. Standard deviation of the sales value is 103188.7977 along with average value of £69,809. From the dataset, it has been identified that maximum value of the sales rate is nearly £1,011,205 and minimum value of the sales rate is nearly -£167,972. Through these key elements, employees can make different choices to handle these data elements without any issues. Range of these values is approximately £1179177 through which is has been identified that BIJ can easily gain profits through their service operations. Through these key data elements, employees and the line manager of WFTT can incorporate strategic changes that can avoid complexities.

Explaining data and its content

From the dataset, it has been found that employees can create a structured plan for arranging data elements depending on requirements. It would bring stability and control the validation ratio by controlling all requirements related to sales performance of BIJ. This information can guide the management of BIJ to modify their sales activities and control all valid assumption processes according to requirements. As opined by Abbasi and Jaafari (2018), it would maintain the structured sequence of this dataset and improve data sequence without any challenges. In this way, quality management processes and data structured management can bring all possible combinations that can avoid complexities in data managerial processes without any issues. It can lead to a proper data sequence for employees and higher authority of BIJ with the help of WFTT.

Reference List

Abbasi, A. and Jaafari, A., (2018). Evolution of project management as a scientific discipline. Data and Information Management, 2(2), pp.91-102.

Amy Van Artsdalen IGP, C.R.M., (2017). How to Develop a Vital Records Program Project Plan. Information Management, 51(6), pp.33-36.

Koesten, L., Simperl, E., Blount, T., Kacprzak, E. and Tennison, J., (2020). Everything you always wanted to know about a dataset: Studies in data summarisation. International Journal of Human-Computer Studies, 135, p.102367.

Meredith, J., McNicoll, I., Whitehead, N. and Ademoye, K., (2020). Defining the contextual problem list. In Digital Personalized Health and Medicine (pp. 567-571). IOS Press.

Rudin, C., (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), pp.206-215.

Sagare, S.C., Shirgave, S.K. and Kodavade, D.V., (2020). A System for Predictive Data Analytics Using Sequential Rule Mining. International Journal of Software Innovation (IJSI), 8(4), pp.96-112.

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