Data Quality Management
- Master data management
- Meta data management
- Data Preparation
- Data Advisory
- Data Integration
Is data quality management the key to analytical decision making? The world we live in has huge and unmanageable information flows for some businesses. The strategic decisions of leaders are increasingly difficult to take, but they must be taken. To be effective, these decisions must be based on a comprehensive analysis of quality master data. Data Quality Management is therefore at the heart of each company's Master Data Management strategy.
Make decisions based on quality data
The world of data is changing rapidly and the world of master data management will have major implications for company results. How does your company anticipate information flow management? Are your business decisions made after analysis of actual data ? Are all data streams enabled and are you using each of them? To anticipate, understand and make the right decisions: the quality of the collection, processing and therefore the quality of your data governance strategy is and will be one of the most important aspects to take into account in the coming years. Data flows are huge and your business must process them and ensure their quality and accessibility / processing.
Data Quality Management
Despite the different priorities of each member of the company, the goal is a common one. This objective is to contribute to the success of the company for which we work by managing the data in the most optimal way. There is one thing we all have in common: the quality of the data. Data quality is the most difficult challenge to meet. Before starting to master the data itself, it's important to understand what is involved. According to another Gartner study, data quality is examined by several different points, including:
- Existence (does the organisation have data to start with?)
- Validity (are the values acceptable?)
- Consistency (when the same data item is stored in different places, do they have the same value?)
- Integrity (the degree of accuracy of the relationships between data elements and datasets)
- Accuracy (if the data accurately describes the properties of the object to be modelled).
- Relevance (whether or not the data are appropriate or not in support of the objective)
Harvard Business Review has claimed that poor data management cost US companies $ 3.1 billion in 2016. This figure is astonishing even for people who are aware of the importance of master data quality. Data quality is a strategic issue that can only be addressed if IT supports the initiatives taken by the company. Acting as a team with the same goal is the key. Companies must also strive to take data quality seriously. Putting master data quality on the agenda of board meetings (or senior managers’ meetings) should not seem strange to companies. This actually means that management teams can take decisions based on reliable data. The key to success could be data governance, not just basic data.