CHAPTER 6: RESEARCH CONCLUSIONS AND RECOMMENDATIONS
6.2 Research objectives addressed
6.2.2 Practical objectives
The sections that follow discuss the key findings concerning the practical objectives in order to improve data quality and management in one South African SME for data analytics as the main objective of this study.
Practical objective 1: Design an artefact that one SA SME could use to capture, store and manage data for analytics.
The researcher followed the design science research paradigm in developing an innovative artefact to be used by the SME. The design science research model by Vaishnavi et al.
(2004/2019:14) as discussed in section 3.8.3 was used in this study. The process of developing the artefact was discussed in Chapter 4.
The IT administrator (P1) and data capturer (P2) were involved in the development and testing of the artefact where various test cases were used to evaluate the functionality of the artefact. The evaluation criteria by Sonnenberg and Vom Brocke (2012:5) in section 3.9 was used to evaluate the artefact.
To obtain feedback from participants about the artefact, semi-structured interviews, observation and questionnaires were applied in this study as data collection methods. The researcher presented the results received from participants in section 5.5. The results from the data analysed using ATLAS.ti presented in section 5.5 indicated that P1 and P2 are happy with the functionality of the artefact and that the artefact adds value to the organisation, because they are able to efficiently capture, store, retrieve and manage data. Additionally, results presented in section 5.5 from the questionnaire analysed using SPSS indicated that they find the artefact pleasant to use and that they are satisfied with the overall functionality of the artefact.
Practical objective 2: to propose guidelines and technologies for improvement of data quality in one SA SME.
Subsequently, the literature explored in Chapter 3, the DSR methodology applied in constructing the artefact (Chapter 4) and the results gathered from the participants in section 5.5, were used to compile guidelines and technologies that could be suitable for improving data quality and management.
Table 6-1: Proposed guidelines for improvement of data quality in organisations.
Guidelines for improvement of data quality
Senior management in one SA SME must educate or train employees about the importance of data quality in the organisation especially its impact in decision making processes.
Senior managers in one SA SME must develop a data-driven culture and mind set required to manage and preserve data quality.
Senior managers in one SA SME must develop a data-driven culture and mind set required to manage and preserve data quality.
It is senior management’s responsibility to create enterprise-wide data quality awareness programs and data quality training.
It is important for executive managers in one SA SME to acknowledge the existing data quality issues within the organisation as early as possible and develop a plan to mitigate before they spread to other systems.
One SA SME employees must know that it is every employee’s responsibility to protect the integrity of the data.
One SA SME must ensure that employees are comfortable with using the data capturing solution and understand how it works.
One SA SME need to ensure that their data capturing systems or interfaces are equipped with adequate validations and checks to prevent erroneous data from entering the system.
Data capturing systems must use selection options such as checkboxes, dropdown lists and radio buttons as much as possible rather than free text capturing fields.
One SA SME must understand what “fitness for use” means to customers and to management responsible for making key business decisions.
Guidelines for improvement of data quality
The database or system administrator is required to perform data and system management duties.
The database administrator must perform data profiling as regularly as possible.
The database or system administrator must develop constraints to protect the integrity of data.
For effective data quality management, one SA SME must adopt a data stewardship approach.
One SA SME must have standard methods of handling data.
One SA SME must develop a formal data governance program.
Fragmented data systems, data silos and disparate departmental data stores must be avoided.
One SA SME must determine data quality dimensions that the enterprise data must conform to and that are important to be fit for operational and analytical use.
Before data are used for reporting and analytics, they must be transformed and cleansed to ensure that they are of quality.
Organisations must have a single trusted source for integrating high quality organisational data for reporting, querying and analytics.
Based on the feedback received, Microsoft technologies such as Microsoft SQL Server 2019 and SQL Server Data Tools 2019 proved to have capabilities of improving data quality and management in one SA SME. Microsoft visual studio 2019 enabled the researcher to develop a high quality artefact for capturing and retrieving data. Organisations can develop SQL relational databases to store their transactional data and develop data warehouses to integrate and store data from various sources for reporting, querying and analytics. These tools can be adopted by any organisations looking to develop a data management solution in-house.
Data profiling tools can be used by SMEs to analyse and assess the state and quality of the data.
For SMEs looking to leverage big data, they can use cloud-based open-source big data tools to manage and improve data quality. Cloud-based open-source tools can be accessed easily by any organisation looking to exploit big data.
Apache Hadoop is an open-source technology that can be implemented to assist with storing and analysing unstructured data.
ETL tools can be used to integrate, cleanse and transform data to improve data quality. Software vendors such as IBM, SAS, Oracle, Talend, Trillium software, etc. have cloud-based data management tools that can be adopted by SMEs to improve data quality and management.
Furthermore, organisations can follow the five phases of the Six Sigma DMAIC approach to improve data quality.