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Vittoria.io shares 4 Key Steps to Improving Data Quality
Data quality is crucial for companies aiming to leverage their data and assets. Vittoria.io outlined four key steps to ensure high data quality.
Vittoria.io has identified 4 fundamental steps to ensure data quality : audit and definition of uses, definition of governance rules, selection and implementation of appropriate solutions, and continuous improvement. Here’s a detailed overview of these key steps.
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Data evaluation and definition of uses
The first step is a thorough assessment of existing data. This includes identifying data sources, analysing their current quality, and understanding potential problems such as duplicates, errors, or missing data. At the same time, it’s crucial to define the future uses of data: what information is critical to the business ? How will it be used? What are the specific needs of different business areas ? This phase establishes a solid foundation for the next steps, and ensures that data quality efforts are aligned with business objectives and needs.
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Establishing rules for governance and formulation
Once the audit has been carried out, clear rules for data governance and formulation need to be put in place. This means defining standards for data management, including collection (who can create this data, what is the master tool/source…), processing (in which process is the data used, who can modify it, when…), and data storage. Governance rules must also include security and confidentiality policies to protect sensitive data. At the same time, precise formulation rules, such as data field formats, naming conventions and validation methods, must be established to guarantee data consistency and reliability.
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Choice and implementation of solutions
The third step is to select and deploy the tools and technology solutions required for data quality. These may include data cleansing software, master data management (MDM) platforms, and data quality management (DQM) solutions. The choice of solutions should be guided by the specific needs identified during the audit and by defined governance rules. Efficient implementation also involves a test phase, for example by setting up a PoV (Proof of Value), to ensure that the selected solutions meet expectations and are correctly integrated into the existing IS architecture.
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Continuous improvement of data quality
Data quality is not an objective to be achieved once and for all, but an ongoing process. It is therefore essential to put in place regular monitoring and control mechanisms to proactively detect and correct anomalies. Periodic audits, performance analyses and user feedback should be used to adjust and improve existing processes. In addition, it is important to promote a culture of data quality within the company, by raising awareness and regularly training employees in good data management practices.
Data quality is a structured process that requires a methodical and continuous approach. By following these four key steps, you’ll have a good basis for ensuring that your data is reliable, consistent and ready to be exploited to generate value.
Source: Vittoria.io
