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Q1.Elucidate a relationship between strategy and data quality.

Correlation Between Strategy and Data Quality
Organisations are turning to be more dependent on data; nearly every modern organisation depends upon data and creates huge quantities of data. So, to meet the requirements of the organisation, a comprehensive data management program is necessary. But data is distinctive from other resources and requires diverse management techniques.

Organisational framework required to tackle the above concerns often does not exist. Hence, it is necessary to develop a comprehensive data quality strategy that can address many of these issues. For example, as with many organisations, formal data quality strategy was not documented for the New Zealand Ministry of Health. A planned data quality framework was later introduced which informed the overall development of a data quality strategy for the Ministry of Health. All results of the development of the strategy were documented for each collection of data. And it enabled internal members to access all the information about data collection. The documents are:
• Guidelines for the usage of data and original purpose for the collection
• Roles and responsibilities
• Results of assessment of collection using data quality framework
• Action plan for quality improvement
• Current and previous data quality initiatives
The data was available both on paper and Web.
As defined by Joseph Juran3, “Data are of high quality, if they are fit for their intended uses in operations, decision-making and planning. Data are fit for use if they are free of defects and possess desired features”. Hence, a data quality strategy in an organisation is a must to define the level of quality required to make the data useful. The data quality strategy also needs to look forward to the future potential uses of the data.

The strategy for data quality is acquired from basic checking calculations to the analysis of data collection and system controls. The Audit Commission has identified five key standards that strengthen this process. They are:

• Governance and leadership
• Policies and procedures
• Systems and processes
• People and skills
• Data use and reporting

Data quality standards outline a corporate approach to quality of data and set a strategy throughout the organisation. It also defines a framework of management activities to ensure that the quality of data collected is appropriate and is used to manage and report business activities.
These standards are intended to promote better data quality, rather than as a firm set of requirements. Alternative approaches to accomplish these aims may also be appropriated to secure reliable, which supports decision making.
In this section, you will learn the five data quality standards that are well defined and identified by the Audit Commission.
The five key standards are explained in the following sub-section:

Data governance1 is a discipline that controls quality, consistency, usability, security and availability of the organisation’s data. Through data governance, organisations are implementing positive control over the processes and methods used by knowledge-workers to handle data.
Data governance ensures that important data resources are properly managed in all the departments of the organisation. It also ensures that data can be trusted and people can be held responsible for low data quality. In an organisation, people are put in-charge to fix and prevent data issues so that they become more efficient. It also describes the company’s evolutionary processes, changing its way of thinking and setting up the processes to handle information. It reduces risk and costs, improves business alignment and increases data management.
Leadership plays an important role in management. It is an integral part of an effect strategy. Leaders establish unity of purpose and direction of the organisation. They should create and maintain the internal environment in which people become fully involved in achieving the organisation’s objectives.

Key components
The following key components outline arrangements for accountability and responsibility of data quality:
• A member of the managerial team is responsible for the quality of data, and this responsibility is not delegated.
• Organisational objectives for the data quality are clearly and formally defined. These objectives are directly linked to business objectives and the organisation’s activities that are adopted by the managerial team.
• Strategic approach for the data quality is associated with a delivery plan. The plan consists of clearly identified actions and timelines to support improvement.
• Assurance for the quality of data is clearly communicated to the employees, which describes their responsibility for the data quality.
• Accountability for the data quality is properly defined and it forms a part of the performance appraisal system.
• Risk management activities of the data quality are regularly assessed for risks that are directly related to reliability and accuracy of information produced and used.
• Data is subjected to robust examination by the management team to report data quality issues.
Data is periodically reviewed and reported to support key performance measures and indicators of the quality of data. Whenever necessary, the managerial team takes action to address the results of previous internal and external reviews of data quality.


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