Your team is drowning in data. How can you keep your head above water?

Data hygiene is always essential.Know to filter what is useful and what is redundant.Not all data serve the purpose.Team should be very clear on the objective and scope of the work. Regular monitoring and sanitization of data is to be done,probably by sharing of responsibilities within tbe team.When this part is consistent it becomes easier. Also a minimum knowledge on excels,pivots,vlookup functions etc. is always helpful.If organization has a digital dashboard then ok,else need to learn macros in excel for creating your own.Dashboards bring perspective and ease of understanding.Stick to the discipline of visiting and updating the dashboard routinely.

There are two perspective to data problems first and important one is addressing the business problem. If you know the business problem to be solved focus on the data points contributing to understanding the problem domain or its solutions. If the business is not sure what problem they are solving and want you to come up with something, always target the most reliable data points and start with basic dat points and what they reveil untill you reach a point where business gets excited!

If your team is struggling to keep up with the data management, a Business intelligence software such as Power BI can help in several ways, including: Data analysis: Business intelligence software can help you analyze large amounts of data quickly and efficiently, allowing you to identify trends and patterns that might be difficult to spot otherwise. Data visualization: Business intelligence software can help you create visualizations of your data, such as charts and graphs, that make it easier to understand and communicate your findings. Data sharing: Business intelligence software can help you share your data with others in your organization, making it easier to collaborate and work together on projects.

Data governance is a foundational. Many organizations do not invest in it as the return of investment is seem as not direct. I would design and implement a data governance including the business areas. It is important to avoid to have a data team as data monkeys. The foundational is even more important now in the AI Tranformation era.

Remember what you are trying to solve or do with the data instead of thinking "just in data". Is very common to see teams more worried about collecting a lot of data than thinking what the way to solve and then identify the data that is needed. So, keep mind clear and remember that data can come from many (sometimes not that traditional source) and think first about the problem and then about the solution one wants to find in the data.

Implementing a data governance strategy offers vital benefits that are essential for any organization prioritizing data management: 1. Enhanced Data Quality and Consistency: Establishing clear policies improves the accuracy and reliability of data across departments, enabling informed decision-making and ensuring regulatory compliance. 2. Improved Regulatory Compliance: A robust data governance framework helps organizations adhere to data privacy and protection laws, mitigating risks of financial penalties and reputational damage. 3. Increased Operational Efficiency: By streamlining data management practices and clarifying roles, a data governance strategy reduces redundancies and promotes easier data integration, improving productivity.

One of the biggest data governance benefits is establishing controls around execution of data strategy. Regulatory compliance is another outcome and benefit of data governance.

Data governance improves decision-making by ensuring data is accurate and reliable, and it upholds data standards by minimizing legal risks and protecting privacy.

Implementing data governance brings numerous advantages, especially when dealing with vast amounts of data. It ensures data accuracy, enhances security, and improves compliance with regulations. Good governance helps in making data more accessible and valuable for decision-making, reducing the risks of data mismanagement, and increasing overall efficiency. Recognizing these benefits is key to motivating your team to embrace data governance practices.

The appropriate approach depends on the nature of the data and the strategic goals. So, first the objectives should be established, knowing where to go and helping to focus on the relevant information. Often the strategy of dividing and filtering can help focus on specific aspects of the business, to define data domains that groups data into specific interest areas that matter to drawing adequate conclusions. Data Governance tactics should be implemented to ensure quality, security and compliance of data, setting up processes and policies and improving collaboration and communication.

Identify source of the truth (Golden source) for the data you need for your purpose. Define key data elements across different data domain. Build stewardship and DQ monitoring on KDEs to check health of your data on periodic basis.

There is no one-size-fits-all data governance framework which you could just follow and all your data problems would vanish. It will have to be tweaked and updated often to suite your organizational structure and needs. The essence, though, is that it should cover the topics like governance strategy, vision, stewardship, lineage, mdm, metadata, glossary, access, privacy & security, process and operating model, tools, people, storage, sharing, retention and disposal.

Key tenets for data governance 1. People and Organization Data roles and responsibilities to ensure data definition. Data sponsors to drive data management scope Data classification and regulation adherence. 2. Data Catalogue data catalogue will be one stop shop for browsing data. 3. Value Realization quantifies links between transformation and core processes. 4. Data Quality Management. Properly managed data quality and cleansing is pivotal in becoming data driven.

Keep it simple. The framework itself (e.g. DCAM, DAMA, CDMC etc.) is less important than actually beginning to build the capabilities detailed. Focus on the basics - cataloguing, lineage, quality and ownership. Ownership is super important. Remember to sell the vision and strategy... so the stakeholders can see where you're heading and why.

1. Identificar e classificar; 2. Otimizar armazenamento e componentes da arquitetura; 3. Investir (tempo, dinheiro e/ou esfor?os) no processo de ETL; 4. Construir modelos eficazes de análise; Times de negócios atuando em conjunto com os times de Dados ajudam a construir indicadores relevantes. Um produto "final" de sucesso também ajuda a definir os melhores componentes e práticas de organiza??o.

Start with an enterprise catalog that is robust, enterprise grade, and scalable to understand what you have. Then add governance, business process, quality, master/reference, tagging, access mgmt.

Caution is needed here... educate your stakeholders on the role of tooling in DG. Otherwise they will assume that DG is a technology challenge, and that buying & implementing tooling will be the answer.

Data governance tools are the ones that mainly look after the entire life cycle of data within an organization. There are plenty of tools in the market out of which some are Collibra, Informatica, Alation etc. While choosing a data governance tool for your organization, it is important that you retrospect your need and match it with the output from the tool. You should also consider the working knowledge of your teammates with the tool or the ease of interface so that they could be trained easily. It is always good to go with a tool that offer a wide range of offerings such as data catalog, data quality, data privacy, data migration and data analytics so that it could be your one stop solution for clean data.

Data governance tools are essential for automating and optimizing governance tasks. They fall into five categories: Data Catalog Tools: Create and maintain a centralized metadata repository. Data Quality Tools: Ensure data accuracy, completeness, and consistency. Data Security and Privacy Tools: Protect data through encryption and access controls. Data Compliance Tools: Facilitate adherence to regulatory standards and policies. Data Access and Integration Tools: Enable efficient data access, integration, transformation, and delivery. These tools collectively support a robust data governance framework, enhancing data management efficiency, security, and compliance, and ensuring data is a strategic asset for decision-making.

Data catalog tool could be good start how to get data under control a bit better. At least from technical perspective. You will consolidata information to one source of true for metadata. But this will take a looong time to get to full coverage. So start simply with critical use cases and describe them end-to-end. And settle the basic rules regarding metadata: RACI & WoW. Parallelly work on what is now a bit euphemistically called “data literacy”. In practice it means talking to your stakeholders and teaching them what DG is about. Focus esp. on the benefits of using tools for DC and DQ. Secure budget, engage relevant people and start consolidate metadata & design, approve, get leadership commitment and ENFORCE rules.

Establishing a centralized data governance function is crucial for setting universal standards, frameworks, and programs that align with overarching business strategies, ensuring non-invasive governance that supports rather than hinders business operations. By adopting a federated model for data stewardship, organizations can maintain the flexibility of department-specific needs while ensuring coherence and compliance across the board, effectively balancing autonomy with centralized oversight.

Effective data governance is not about one-size-fits-all rules. Some best practices may at first sight look counter intuitive - like embracing centralization, preferring agility over hard-coded process, emphasizing collaboration over control, measuring impact not compliance, etc. Implementing these counter-intuitive practices alongside traditional approaches can create a more flexible, collaborative, and ultimately, more successful data ecosystem within your organization.

Score and rank your data elements to determine impact to the business at the element level. For each element, how many reports is it used to drive business intelligence, is it used as a performance measure, what processes does it drive, if you did not have this data would you still be able to drive those functions? These are the nuts and bolts questions you need to answer with your data to determine if it is useful or noise. Data cataloging tools can help with this if well utilized. Each time a new report or process is built, all the elements in it should reflect the usage in the data catalog. From that, you can quantify usage and impact to the operation. It can be a heavy front-end effort but the benefits once set up can be incredible.

Ao implementar práticas recomendadas de governan?a de dados, você fortalece a descoberta de dados em sua organiza??o. Ao alinhar estratégias, você garante que a governan?a de dados esteja integrada aos objetivos e metas do negócio. A participa??o ativa das partes interessadas e usuários é crucial para entender as necessidades e garantir a ades?o aos processos de governan?a de dados. Com defini??es claras de fun??es, responsabilidades, políticas e padr?es, todos na organiza??o compreendem seu papel na gest?o de dados.

To navigate the challenges of overwhelming data, prioritize key information, establish robust governance, invest in data management tools, and enhance team data literacy. Implement quality controls, foster clear communication, embrace scalable solutions, automate repetitive tasks, and encourage cross-team collaboration. Stay informed on technology trends to ensure ongoing adaptability. These strategies collectively enable effective data management, transforming data into a valuable asset for informed decision-making.

This is all wrong as a question! If your team is drowning in data then you don't know what the business strategy is and how you drive value. That's where your team should be focused. Work from that angle and you will be successful in building the right data strategy for business transformation.

- Build a Data Governance framework and work from there to build standards and policies for Catalog management, Data lineage management and Data quality management - Identify a Data Management tool from the market that would fit your needs - roles and responsibilities need to be identified and trained on the tool - First start cataloging data within the tool - classification of data based on criticality and sensitivity must be done - lineage and data quality checks need to be built - continuous metrics of adoption and maturity need to be created and reported out to the roles accountable and responsible

Most companies have a pool of data and spend a lot on building warehouses, data lakes and what not to store data but are not aware of the strategic importance of it. First and utmost companies need to understand their data and have data catalogues, data classification, data dictionary for available data. Next step after identifying is to prioritise data, to do this , they have business stewards and data stewards work hand in hand creating business and data glossary. Other key components are data quality , data security and proper data ownership. Currently there are many data governance tools and frameworks to create data policies and standards which in-turn helps data as well business team to take innovative and strategic decisions.

The best order is: 1 Data discovery/cataloging 2 Data governance benefits & best practices 3 Data governance framework 4 Data governance tools Define a Data Management Policy is really important to be sure to align all business users in the company. DG is a Business topic not an IT one. Tools are numerous and must be considered at the end of a successful DG journey.

The key for a data team to survive and thrive is to build strong partnerships with users. Learn about their problems, needs and aspirations. Understand which data and insights are most critical to them and focus on that. Prioritize what matters relentlessly. Help them understand the options and optimal ways to get what they need. Celebrate success together.