What do you do if your data assets are in disarray?

If data assets are disorganized, start by assessing the current state of data access, identifying gaps, and understanding the reality of data usage. Set clear objectives for data governance aligned with business goals. Define policies and standards for data classification, security, and compliance. Implement a governance framework encompassing data stewardship, metadata management, and data quality controls. Proactively monitor the governance process, enforce policies, and address issues promptly. Regularly enrich metadata to improve data discoverability and understanding. If on AWS; use S3, Glue, and AWS IAM to streamline data governance processes - ensure security, compliance, and data integrity.

Conduct a comprehensive assessment to understand the scope and extent of the data disarray. Identify the types of data assets, their sources, formats, quality issues, and usability challenges.

1) Start by identifying the most critical data elements for your organization and prioritize these elements based on their impact on business processes, decision-making, and compliance. 2) Establish clear guidelines/policies for data creation, storage, access, and disposal. Define roles and responsibilities for data stewardship. 3) Collaborate with business units to understand their data needs. Engage stakeholders in defining data standards and best practices.

First, I assess the current situation of data assets to identify issues. Next, I prioritize corrections based on their criticality. I establish clear organizational policies to ensure data order. I update documentation related to assets to reflect the changes made. Finally, I create continuous monitoring processes to ensure that order is maintained and new challenges are addressed promptly.

?Podrían dar ejemplos de normas y políticas en la gobernanza de datos? El alineamiento específico es muy importante en todo el proceso.

Implementing documentation policies during Data Asset development significantly enhances metadata quality and depth. By integrating controls within an operational framework, you ensure that development tasks are executed consistently and in accordance with established standards.

Often, programs neglect the most critical succesfactors during the implementation of data governance frameworks: change management and active sponsorship from senior management! The implementation is not just about technical solutions or lists of data (where the focus mostly lies), but about changing and structuring behavior of data owners, data stewards and other stakeholders. Strong messages from leadership are of vital importance, as well as them holding people accountable for the data they are responsible or accountable for. At the same time, a solid change management approach is the driving force of the necessary development in the organization’s data maturity.

Start with the business pain points and scale it up from there. One of the biggest mistakes is to try to boil the ocean in one go. No way: the risk is to spread the resources too thinly and lose momentum. Better focus on critical issues and their data, regroup them in a given asset, consolidate quality, protect the living & cyber daylight out of it and then tell everyone that a major milestone has been achieved. Then watch the stakeholders line up to be the next item you pick up :)

1) Set Key Performance Indicators (KPIs) and define specific metrics that align with your data governance goals. Such as cost reduction, effort required to comply, time to submit and accuracy of data. 2) Monitor and Track the KPIs Regularly by establishing a routine for reviewing.

Enhance your data platforms with observability to address a range of critical questions and quantify benefits: - Service: How are ETL/ELT processes performing to ensure timely data updates? - Data Quality: How does the data being ingested measure up in terms of uniqueness, completeness, validity, consistency, and accuracy? - Data Models: How do performance, scoring, and drift impact your data models? - Business: How do KPIs around sustainability, costs, monetization, and data usage align with your business objectives and terminology?

For this point it's important to manage attributes in your Data Assets. The Data Asset metadata keep the Data Asset status and details but it can evolve over the time. Add some new attributes helps the company to track initiatives, regulatory requirements or any other business need.

One of the most important Establish Feedback Loops: Encourage feedback from stakeholders. Gather insights from data users, data stewards, and business units. Adapt your data governance strategies based on this feedback.

Additionally, when dealing with disorganised data assets and aiming for sustainability, consider: - Establishing a data lineage to document the journey of the data helps in tracing data quality issues, ensuring compliance, and maintaining transparency in data handling. - Deploying a data catalog creates a centralized repository of metadata and assets, aiding in discovering data sources and usage. - Promote Data Literacy ; according to your data governance framework - This may includes how to conduct impact assessments before implementing any changes (system upgrades, data migrations) to understand potential risks and dependencies. - Regular and/or automated audit/controls to adapt the processes and provide recommendations