The quality of an organization’s data often gets overlooked. Once this happens, all the problems that might arise are not always apparent. Business skills that improve data management must be rooted in the fundamental practices of the organization.
What is Data Quality?
The quality of data that impacts the business cycle can be felt on a fundamental level. Optimal data will lead to good business decisions. This can be traced back to the moment that data becomes the foundation for organized information. Applying knowledge (i.e. wisdom) flows directly out of the intelligence obtained from a company’s storehouse of information. Data cycles are as important as any factor of a company’s business model.
The Importance of High Quality Data and its Assessment
Industry reports suggest that data management was responsible for over $3 trillion in 2016. The benefits of well-managed data are enormous. Conversely, poor data quality can have cascading effects throughout an organization. It can compromise operations at all levels. Furthermore, because bad data enters the business cycle on a fundamental level, the errors it causes can be difficult to track down.
The ability to assess data at all levels is critical to performance. This involves accuracy, completeness, reliability, timeliness, and of course, relevant reporting. Quality from the very beginning is essential for high-level business strategies. When overlooked, it can lead to all sorts of operational errors, especially variable and intangible factors.
High-level Managerial Approaches
It is important to recognize that the quality of data is as much of an issue for management as it is for IT departments. IT professionals can help organize, secure and keep data uncorrupted. However, managers must still ensure that data is practical for operations. There are several fundamental approaches to data quality management. These includes:
- applying data appropriately
- integrating information
- understanding data and its sources
- mastering chosen data
- remaining focused on business initiatives
- establishing a culture of responsibility across departments
- avoiding complacency
Establishing the necessary framework and questions that accompany high-quality data is challenging. This is because, the degree to which a data application will impact a specific department or operational area can vary substantially. It is important to get implementations right because overall corporate agendas depend upon the entire organization functioning well together.
How to Improve Quality through Specific Management Strategies
Organizations also need to mitigate human errors, departmental communication gaps, and inefficient data strategies. These areas have been shown by research as the most disconcerting for top-level management. Customer insights are also considered important to the managerial process. Monitoring consumer practices can significantly improve analytics for audits and risk management.
Specific skills for improving data quality management include:
- assess data across top-level management and departments
- define roles, terms, and aspects of governance
- keep accurate and relevant reporting (including metadata)
- determine the root cause of issues
- maintain an awareness that includes indicators (KPIs) that lead to complete, unique, and consistent data
- keep data entry and solutions cost effective
Each of these skills supports the integration and mastery of data matching, profiling, product information, digital assets, reporting, and overall governance of an organization.
Data management will invariably be a complex endeavor. Nevertheless, it is still a predictable variable of success. Solutions begin at an organization’s core values and operations. Managers must be well versed at interrelated skills while being adept at communicating across departments.