What is the impact of not having a data governance framework?

Putting data quality standards in place helps streamline operations by minimizing the need for manual data entry, correction and reconciliation. When data is accurate and consistent, employees can trust the information at hand, reducing the time spent on verifying and correcting errors. This efficiency translates into time saved across various departments, allowing employees to focus on more value-added tasks.

Inconsistent data entry across departments utilizing the same CRM (customer relationship management) system can be a major data integrity issue. Data governance frameworks that establish clear data entry guidelines can ensure consistency, as long as there is robust communication about the policy, along with appropriate training and education for end-users.

A ausência de uma estrutura de governança de dados pode resultar em sérios impactos negativos. A falta de padronização e controle pode levar a dados imprecisos e inconsistentes, comprometendo a tomada de decisões. A violação de normas de privacidade e segurança torna-se mais provável, expondo a organização a riscos legais e danos à reputação. Sem governança, a colaboração eficiente e a responsabilidade pela qualidade dos dados diminuem, prejudicando a eficácia operacional e a confiança nas informações.

Issues like errors, inconsistencies, duplicates, missing or outdated information need to be diligently monitored, corrected or - better - prevented. As Rathna rightly pointed out, "Putting data quality standards in place helps streamline operations by minimizing the need for manual data entry, correction, and reconciliation." I fully agree, so implementing a robust data governance framework is vital to ensure reliability.

Data Governance serves as a framework outlining the management and accessibility of data. Without established Data Governance practices, the risk of data breaches and loss increases significantly. In the absence of Data Governance, navigating through vast amounts of data becomes a daunting task. Losing control over data not only poses financial risks but also undermines stakeholder confidence in the business.

Without a proper data governance framework in place, in large scale projects involving PII data (Personally Identifiable information), it becomes quite difficult to manage restrictions. It is crucial to spend time analyzing and understanding the sensitivity of the data to be managed, and in turn, to find and label sensitive data that can be an easy target in times of frequent data breaches. An added layer of data security needs to be implemented in cases where projects handle sensitive data that might be financial or personal.

I will use again the sailing metaphor (as I did to answer a previous question) to describe the data strategy challenges. A data governance framework is like the map (and the compass) of the data lifecycle, without that, it would be like a random journey across the high seas. Datasets would become like forgotten wrecks, obsolete and ignored: underutilization or duplicated would be very strong risks because users would not be able to discover and retrieve new information. A governance framework is the chart, ensuring not a byte is wasted on the "digital expedition".

Additionally, the latency of data sourced from different teams in an organization varies quite significantly. So at the end of the day to get an overall picture at the one timeframe it becomes quite challenging. Implementing a data ownership model enhances trust in the data lineage and hence increase the accuracy of the KPIs.

Data Digitization.. Though Data Governance and Management is a very wide subject wrt Telecom, BFSI, Manufacturing, Power and IoT sectors. In practical (not academic) Data Management is a part of strategy & planning which is the toughest task to design optimum Data Flow, Processes, logical structure + all the above said aspects. Secondly to understand the difference between Data Management & Governance. Most of the people wrongly understood the data storage, ingestion,DQ, curation, security, analysis & visualisation as Data Management, whereas, these are the part of the operation or Governance. Wrongly designed Data Management will impact heavily on the Governance in the long run and the organisation will keep bleeding.