What are the most common metrics to evaluate data governance program success?

This is a loaded question. Please list for me ten companies off the top of your head you are willing to bet a year's salary had a "successful" data governance program completed. I am betting you can't - so are common metrics really the answer? First decide why you need a DataGov program in the first place - audit issues, compliance, dirty data, too many definitions, etc? If the answer is all of the above - you need to pick somewhere to start. Next pick three metrics that fit that answer. Baseline and monitor. If they move the right direction and persist for 90 days - you can move on to more questions and more DG KPI.

Agreed Data Governance programs are ambitious and need to be anchored in very clear and specific business objectives. The most common of which is Data Quality in source systems. This is with the hope to improve it for both operational reasons (improve customer service) and the BI environment for analytics. Without some “executive” sanctioned intervention (aka Data Governance Program) DQ remains a problem with no one to hold accountable for. Quality at source is what everyone salutes - but achieving it is not trivial. You get what you measure - and “Data Governance ” programs supposedly help customer facing BU’s balance DQ measure with growth measures. Ironically you don’t need heavy lifting Data Governance to do this.

While all factors are critical for a good data governance practice, I would rank data usability metrics as the top. Ultimately, the practice of data governance is about democratizing data and making quality data available to all business and functional user. If data usability is low but quality is high, then one might consider wasted effort. Same goes with security and availability. Data usability is a composite across many aspects of data use, quality and curation and is based on the final productivity of the data consumer.

Data quality should be measured along the complete supply chain of data. Good data supply chains improve the quality of data that passes through them, on the contrary, a badly designed supply chain would reduce the quality of data. A lot of data quality issues get identified during the data engineering process, Good data governance should ensure that such issues are highlighted to appropriate individuals in a timely manner. Data governance programs should encourage creation of data quality dashboards, while the data engineering pipelines are being constructed. Once these pipelines are built, and concrete is poured, it would be too late to highlight data quality issues.

1.The first and foremost thing for right data quality metrics are to identify the critical data elements on which DQ should be performed. 2.We do a lot of metrics, but ,finally realize those were not the right field or attributes we were supposed to put our time on, so , identifying the right key business elements is very important. 3.Are these data quality metrics tying to the business process or the ERP data creation process?or do we know how they are getting created at the point at the first door of entry into our IT system, and how this data is transported between our Data Landscape’s ? 4.It can be effectively done, only if we have a defined process how our data is being created and used

The most successful data security metrics blend quantitative and qualitative measures. Therefore, a comprehensive approach also involves measuring employee training impact and fostering a security-conscious culture. Experts advocate for metrics that provide a holistic view, facilitating proactive threat mitigation and continuous improvement, thereby fortifying the companies' data security posture in an ever-evolving threat landscape. Robust metrics assess vulnerability management effectiveness, incident response times, and user awareness. Monitoring unauthorized access attempts and encryption efficacy ensures data confidentiality. Regular audits and compliance adherence metrics guarantee legal requirements are met.

In the context of Business Intelligence, data security metrics are crucial for maintaining the integrity and confidentiality of business data. As an IT Specialist, I've seen how metrics like data breach and loss rates are vital indicators of an organization's cybersecurity health. They enable IT managers and CTOs to pinpoint vulnerabilities and implement strategic countermeasures. Furthermore, compliance and encryption rates are not just about adhering to regulations; they're about building customer trust by ensuring their data is handled responsibly. In my experience, these metrics are fundamental to a robust BI strategy, as they directly impact decision-making and risk management processes.

Data security metrics plays an important role in Data governance. Protecting data from unauthorized access, from breach, ensuring compliance with privacy laws and regulations. It is used to manage user access and authentication. It is very crucial to protect sensitive and critical data. Organization can use encryption to protect data and also can implement robust access control to restrict user access to access certain data.

It is important to make sure that the business Key Performance Indicators(KPIs) published for the executives have DQ scores associated with the data sources which got fed into the metrics. This will automatically uplevel visibility to DQ issues and help drive DQ stewardship in the source systems.

While the question would be more pertinent for organizations which have such a Data Governance Program in place, of utmost importance would be meeting the Data Compliance and Regulatory requirements depending on the geography of operations , followed by adherence to organizational Data Governance Policy by its employees and relevant interested parties, Data Security, Data Availability aspects of Data Backup , Retention and Restoration, Data Quality and Integrity would all play an important part for aspects of monitoring and measurement metrics of a reasonably good Data Governance Program.

A disponibilidade dos dados é uma das principais métricas para determinar se um projeto de BI será efetivamente utilizado pelas áreas de negócios. O time de dados pode ter construído os dashboards utilizando as boas práticas de modelagem para criar dimens?es e fatos; porém, se esses dados n?o estiverem disponíveis - se, no momento em que o usuário acessar esses painéis, os dados n?o estiverem atualizados - ele provavelmente voltará a usar suas planilhas e concluirá que este BI n?o funciona ou n?o atende às necessidades do negócio.

Data governance metrics measures how well an organization manages and controls the Data assets. Data availability metrics plays an important role in easy accessibility and readability of the data. It includes Data uptime, Data downtime, Data throughput, Data latency and Data availability rate.

Aqui vejo que é importante enfatizar que além de medir esses aspectos, a interpreta??o e a resposta a essas métricas s?o fundamentais. Por exemplo, identificar padr?es ou tendências nas taxas de tempo de inatividade e latência pode levar a melhorias na infraestrutura de dados ou otimiza??es nos processos de ETL.

A garantia de que os dados estejam disponíveis para serem utilizados nas análises do negócio quando necessário define a importancia das métricas de disponibilidade de dados. Com análises sobre as taxas de atividades e inatividades temos como garantir a disponibilidade, assim como prevenir determinadas falhas como por quantidade de uso em determinado momento, prever uma escalabilidade e corrigir possíveis problemas que tornem os dados indisponíveis por algum motivo.

Comme partout, la question sur un indicateur de disponibilité des données doit être reliés aux actions qui suivront. Les indicateurs de disponibilité techniques des plateformes et solutions doivent permettre aux équipes techniques de palier à tout problème en RUN et induire des améliorations continues. La mesure de la satisfaction des utilisateurs quant à leurs besoins (accessibilité, taux de rafra?chissement, etc) sera plut?t mesurée par sondages ou enquêtes et induira des évolutions à moyen terme, comme une évolution d'architecture pour passer d'un mode batch à un quasi temps réel.

Agree, usability consider availing data in the right format for end user. For example an executive will be interested to see dashboards and scorecards. Mid level management will ask for reports and ad hoc information while joniourd will be interested in operational reports.

Metrics like data error rate and data satisfaction rate are critical for ensuring that BI systems are not only accurate but also user-centric. As an IT Specialist, I've seen firsthand how these metrics, when monitored and acted upon, can lead to significant improvements in data governance. For instance, a low data adoption rate might indicate that the data isn't aligned with user needs or expectations, prompting a review of the BI system's usability. Similarly, tracking the data ROI rate can justify further investments in data quality initiatives, as it directly correlates to the value derived from the data. These metrics are not just numbers; they are actionable insights that can drive a company's data strategy forward. - powered by #evyAI

Termos a informa??o da usabilidade de dados é importante para o seguimento do projeto e para sabermos de todos os dados que foram disponibilizados quais dados realmente est?o sendo utilizados, com qual frequência os analistam consultam essa informa??o, se olham somente uma vez por dia será uma carga D-1, e n?o algo em real-time como vários analistas gostam de pedir ou falam que precisam dessa informa??o.

A usabilidade de dados é importante para o seguimento do projeto, acompanhamento de resultados e para sabermos de todos os dados que foram disponibilizados quais dados realmente est?o sendo utilizados, com qual frequência os analistam consultam essa informa??o. A frequente visualiza??o traz o benefício ao negociado. Uma vez que tenha tecnologia correta, seus dados ser?o disponibilizados online e a??es de preven??o poderiam ser realizadas e/ou como varejo, a??o para melhorar suas vendas.

A métrica de usabilidade dos dados tem importancia na oferta do servi?o que estamos disponibilizando, nada adianta gastarmos tempo de desenvolvimento, espa?o em disco/nuvem, custo operacional, se os dados n?o est?o sendo utilizados, é importante saber de que forma o dado está sendo ou n?o utilizado para tomar decis?es como possível mudan?as na arquitetura, a fim de prevenir dados disponíveis sem usabilidade.

Data readiness for AI use is a growing area for DG. Are data assets tagged, are associated data policies in place and linked to data assets for the use of new gen AI tools. Readiness or maturity measures are important and tie directly to how much the output can be trusted.

Data governance spans much wider than data quality, security, availability and usability. Data governance also needs to address the data integration process, metadata management, data lineage tracking, access controls and audit trails. Governance policies should define standards for data integration, such as data mapping, transformation rules and data merging techniques. Metadata management could include data dictionary, data lineage, and data ownership information. Auditing and monitoring could include monitoring user activity logs, tracking data lineage, and conducting regular audits of the integrated data.

Il est essentiel de mettre en ?uvre une cartographie des données de l'entreprise, sur laquelle pourra s'appuyer le programme de gouvernance des données. C'est le socle. Mais cette tache est immense et peut effrayer l'entreprise. Il convient d'engager cette cartographie en adoptant une maille macro qui permettra d'atteindre une couverture globale avec une chance raisonnable de succès. Il sera ensuite possible d'ajuster, de compléter en fonction des lectures transversales et des besoins exprimés.

I would argue that Data integration metrics also play an integral role in the success of programs. This should allow for the ability to assess the seamless flow and effective combination of data across different systems and platforms within an organisation. This evaluation is crucial because in today’s interconnected digital environment, the ability to integrate and use data from various sources is vital for informed decision-making and operational efficiency.

Dans mon expérience, la problématique n°1 de la qualité des données est que le consommateur est souvent différent du producteur de la donnée (silos), qui lui-même n'a aucun intérêt à modifier son processus de production de la donnée, car l'analyse a besoin d'informations que le niveau opérationnel ignore. C'est pourquoi, une gouvernance des données doit promouvoir la synergie et avoir un sponsoring transversal qui motive l'intelligence collective, et récompense celui qui fait des efforts pour les systèmes avals. Ceci implique de mesurer cet effort collectif avec des indicateurs comme le montant investi par un département pour améliorer la qualité des données produites qui serviront d'autres domaines ou départements.