Como você relata o desempenho da qualidade dos dados?

Start by identifying and defining the specific metrics that will be used to measure data quality performance. These metrics should cover various aspects of data quality such as accuracy, completeness, consistency, timeliness, and validity. Tailor these metrics to the specific needs and goals of your organization, ensuring they are relevant and actionable. Clear definitions help stakeholders understand what each metric measures and why it's important, providing a solid foundation for your data quality reporting process.

In my experience, the dedicated data governance department should start with a broader spectrum of data quality metrics. Iteratively, the proposed list of metrics has to be reviewed and narrowed down by all relevant business units. As a result, a small sub-set of metrics (ideally three to five) agreed upon and acknowledged by all the parties has to be established. The finalized data quality metrics have to be frequently and openly communicated by top management within the organization thus ensuring transparency and mutual commitment. Good examples of data quality metrics are completeness, consistency, accuracy, timeliness, and others.

In our client projects, we tailor data quality metrics to align closely with their specific industry standards and operational needs. For a retail client, we focused heavily on the accuracy and completeness of their inventory data, essential for effective supply chain management. This customization allows for more targeted improvements and better resource allocation.

We integrate advanced data quality tools that automatically track and analyze data against established metrics. For a financial services client, we implemented real-time monitoring to detect and resolve issues like duplicates and inaccuracies in transaction data promptly. This proactive approach helps maintain high data integrity and supports compliance with stringent regulatory standards.

Once your metrics are defined, the next step is to collect data quality data across your datasets. This involves implementing data quality assessment tools or processes to systematically evaluate data against your established metrics. Analyze this data to identify trends, patterns, and areas of concern. This analysis will form the basis of your data quality performance report, highlighting where data quality meets the set standards and where improvements are needed

Once aligned on the data quality metrics within the whole organization, ensure that the intended data points can be continuously measured. Either use well-established tools or write routines yourself. Independent of the tooling, collect historical data points to identify improvements or declines.

Transform your data quality analysis into visual reports and summaries that clearly convey the findings. Use charts, graphs, and dashboards to represent data quality performance across different metrics and datasets. Summarize key insights in a way that is accessible and understandable to all stakeholders, regardless of their technical expertise. Effective visualization helps to communicate the state of data quality in a compelling manner, making it easier for stakeholders to grasp the implications and take action.

The data quality should be continuously traced and most importantly visualized in a dashboard. Given the mutual commitment to data quality metrics across the organization, the dashboard should be used as a public source for data quality review, a goal setting for improvement as well as a source for celebration of achievements. Promote data quality importance by publicly visualizing and emphasizing data quality heroes.

To make data quality insights actionable, we use advanced visualization techniques that highlight areas needing attention. In a project for a healthcare provider, we employed heat maps to identify inconsistencies in patient data across different systems, helping to prioritize data cleansing efforts. This method proves highly effective in engaging stakeholders and expediting decision-making.

Effectively visualizing and summarizing data quality results has been instrumental in my work to enhance data-driven decision-making. Utilizing various graphical representations like bar charts, pie charts, and heat maps, I've been able to clearly depict the state of data quality over time and across different data dimensions. This approach not only aids in identifying trends and anomalies but also facilitates easier comparison and communication of complex data quality issues to diverse audiences. Tailoring these visual summaries to the needs of different stakeholders ensures that the insights are actionable, driving improvements where they are most needed and fostering a better understanding of the data's impact on our operations.

Next to being accessible and transparent across the organization, data quality progress should be regularly reviewed with stakeholders, for example quarterly. Within the review, the targets for the next period for data quality improvement have to be specified.

Communicate appropriately for your target group. Don't overwhelm your C-levels with technical key figures. Create transparency and/or key figures on improvements in business processes that have been achieved through your data quality improvement measures. This approach will secure your future budgets.

Effective communication tailored to the needs of different stakeholders is crucial. For instance, we provide executive dashboards that summarize data quality performance and its impact on business outcomes for C-level executives, while detailed technical reports are prepared for IT teams to address specific data issues. This ensures that all parties are informed and can take appropriate actions.

Business needs are dynamic and can change based on various factors such as projects, market trends, and regulatory requirements. Consequently, the data quality needs and performance metrics must be regularly communicated to stakeholders to ensure alignment with evolving business objectives. This involves establishing a systematic process for reviewing data quality standards and performance on a periodic basis. By fostering open communication channels and conducting regular reviews, organisations can adapt their data quality initiatives to meet changing business needs effectively.

Next to agreeing on the data quality metrics itself, the level of the intended quality needs to be defined. Should we be striving for 100% completeness? Is it reasonable? What level could be sufficient and why? Also here the mutual commitment of all business stakeholders is the key to success.

Drawing on lessons from past projects, we emphasize the importance of continuous improvement in data quality processes. For example, after a project with an e-commerce client, we introduced iterative review cycles that gradually enhanced data quality, leading to a 30% increase in customer satisfaction due to more accurate product information and availability data. This iterative approach fosters a culture of quality and continuous enhancement.

A melhor forma de relatar o desempenho da qualidade é por meio da demonstração dos percentuais e evidências sobre a qualidade dos dados. Para isso ter um processo de data profiling estabelecido é fundamental. Um relatório com informações sobre as regras aplicadas, com os percentuais de aderência à qualidade estabelecida(dados limpos) e dados fora da qualidade (dados sujos), bem como a evidências encontradas nos dados não conformes, emitidos ao fim de cada operação de data profiling, é suficiente para demonstrar de forma simples e prática o estado atual da qualidade dos dados da empresa.