What are some examples of data governance in your experience?

In my experience, data quality is pivotal for effective governance in data management. The accuracy, completeness, timeliness, and relevance of data significantly influence the reliability of analyses, reports, and user decision-making in a data warehouse. Encountering issues like missing values, duplicates, errors, outliers, and inconsistencies is common. To tackle these challenges, I've employed diverse tools and techniques such as data profiling, cleansing, validation, and regular auditing. Data quality scorecards offer a comprehensive visual assessment, while implementing rules, checks, and controls, including dimensions, indicators, thresholds, and dashboards, creates a robust framework for ongoing data quality management.

Data quality refers to the accuracy,completeness, reliability nd timeliness of data in dataset. In many fields, such as business,healthcare,research, high-quality data is critical for making informed decisions, conducting accurate analyses,nd achieving reliable results. Incorrect conclusions, flawed insights, and ineffective decision-making can all result from poor data quality. Combination of processes, technologies, and good practices is required to ensure DQ. Data profiling, cleansing, validation checks, regular audits, and the use of DQ tools are all common approaches. Establishing data governance policies and involving stakeholders in data quality management process r also critical for long-term data quality maintenance and improvement

One of the most effective ways to ensure data quality, is through data contracts. Especially in large companies, while ensuring that behaves the way is supposed to, accountability is set through all the data value chain. This goes a long way if it is set with pipeline observability and monitoring.

Data quality processes drive trust into the realm of raw data ingested, as well as applied business logic shared with the business users.

In my experience, Data governance is a vital practice that ensures the responsible management of an organization's data assets. It's about maintaining data quality, managing privacy, and ensuring compliance with legal standards. Key aspects include appointing data stewards for data integrity, implementing access controls to secure sensitive information, and establishing master data management to maintain a consistent and authoritative data source. If these standards are not followed or not taken seriously they can have major consequences. It's also about tracking data lineage for transparency and handling incidents effectively. At its core, data governance is about cultivating a data-aware culture and everyone adhering to them

In my view, securing the data depends on where the data is being processed or persisted (on-premise/Private Cloud/Public Cloud) to comply to standards. Some of the widely used and accepted techniques for vast majority country specific regulatory body are, 1. Tokenization 2. Masking 3. Encryption using strong algorithm at all layers In-trasit, at rest 4. Vaulting of Service accounts with rotation policy 5. OAuth based Authentication 6. SSO , MFA, User request Automation, User List Automation for audit 7. Toxic Access Management There are situations where regulators demands to setup incountry instance within their country based on Data (PII/Client Data)

Data Security has been my team's key focus while scaling AI projects in the manufacturing industry. Us ML Engineers, ensure that we work in cohesion with cross functional teams like Cloud, Security and Platform Engineers to architect a framework that secures sensitive data within VNETs. We use Azure Cloud and all resources created are segregated into subnets. This is more of an art and science where we estimate based on usage, how many subnets and IP ranges have to be issued. Once that is done, other factors come into play like data (and code) back up strategies, at-rest and in-transit encryption protocols, disaster management etc. Moreover, all stakeholders accessing the solution are assigned custom roles that dictate RBAC policies.

Data security also requires adherence to all regulatory compliance rules as well, including records retention (and destruction), availability/resiliency, data sovereignty rules, data audits and regulatory reporting, data access logging, and regional specific rules like GDPR

Data security ensures data integrity, confidentiality, and availability: 1. Healthcare Encryption: Patient records encrypted for privacy. 2. Banking Access Control: Role-based access limits data viewing. 3. Retail Data Masking: Customer card details masked for transaction security. 4. Government Audit Trails: Logs track document access and changes. 5. Cloud Service Backups: Regular backups for data recovery. 6. Corporate Network Security:Firewalls and intrusion detection protect data. 7. Educational DLP: Software monitors and protects student and staff data.

Data acceptability is also an aspect to be considered under governance. Once a data Lake is built the accessibility needs to be ensured to deliver value to business customers.

Data catalogues play a crucial role in enhancing data discovery, understanding, and utilization within a data warehouse. Tools like data dictionaries, glossaries, and lineage tools contribute significantly to maintaining a well-documented and organized data ecosystem, fostering better decision-making and compliance with data governance principles.

Data catalogs serve as comprehensive inventories, organizing and describing data assets across an organization. They offer a centralized platform for data discovery, understanding, and governance. Data Catalogs enhance data visibility, accessibility, and trustworthiness. They streamline data management processes, promote collaboration, and facilitate informed decision-making. Here are some ideas to start your journey: 1. Assess Needs 2. Define Metadata Standards 3. Select a Platform 4. Populate Catalog 5. Promote Adoption Building a data catalog lays the foundation for effective data governance and utilization, empowering organizations to harness data's full potential. #DataCatalog #DataGovernance #DataManagement

In my opinion, the mainly features of data catalog tools are: - Metadata Management - Search and Discovery - Data Lineage - Data Quality Information - Collaboration Features - Security and Access Control - Custom Taxonomies, labels and Classifications - Data Governance and Policy Management - Data Usage Tracking - User-Friendly Interface for business or non business users

A data catalog is something like an Index of the data warehouse. Similar to how we can look up indexes for a table, you can lookup data catalog for data warehouse. It maintains all the information about the structure and storage of data in the data warehouse. It ensures regularised and clean data in the data warehouse. The biggest advantage in such a scenario is you can add attributes to existing tables easily. It is very helpful while you are dealing with a large data warehouse. To put in brief it is like a warehouse Management System which maintains the information of the structure of your data warehouse

Data Catalogs are data marketplace for "Data Discovery" akin to what amazon is a marketplace for "Product discovery". Two impactful features which makes data catalogs worthwhile for data governance are as follows: Data Discovery akin to Product Discovery in Amazon: Just like you can discover a product in amazon using search, data catalog have search engine which can help you identify data you want to consume and who owns it, its column attributes and storage location. Data Trust akin to Product Trust: Just like in amazon you can like a product and prefer buying highly rated products, data catalog also provide users to rate the dataset thereby leading to improve in trust in data via data democratization.

Data Stewards play a key role. Their function is to act as a bridge between consumers and producers to resolve key data issues. Also, they are one of the key stakeholders in putting a process on what, how, and why the data issue appeared.

Honestly, Data Stewards empowers business users and analysts like personas to leverage data for insights. Typically, Stewards have domain knowledge, which could entail certain types of datasets, departments, business processes, and/or data products for both business and governance needs. This group's initiatives are to monitor, correct, and ensure all communication about the data is clearly stated around purpose, known biases/caveats, relevance, legal/contractual obligations, collection process, lineage, directional usage, etc.

There is no such thing as Data Stewardship. It is a dangerous over simplification of the roles and responsibilities needed to govern data from cradle to grave. It creates accountability and ownership conflicts, operating model confusion and miss allocated skills and activities. This is poor organisational design.

With my experience in the industry, I agree that data stewardship is multifaceted. The role involves bridging gaps between consumers & producers, ensuring clarity in data communication, and addressing key data issues. It is seen as crucial element in effective data governance by assigning ownership and accountability. However, the importance of a holistic approach to data governance beyond stewardship is acknowledged, considering factors like organizational design and skills alignment for comprehensive data management. Additionally, the emphasis on data literacy as a precursor to effective data stewardship aligns with the belief in the significance of understanding, interpreting, and responsibly utilizing data for informed decision-making.

Data stewards play a critical role in the success of a data governance program, as they are responsible for ensuring the quality, consistency, and usability of an organization’s data assets. They act as liaisons between business units and IT departments, bridging the gap between technical and non-technical stakeholders.

This is of the most pernicious inventions of the data consulting industrial complex. This is a business architecture problem to solve, which needs to get better at modelling business events, activity, interactions, and communication. There is no such thing as Data Culture, it is a symptom of a lack of business operational knowledge and understanding that has led to poor requirements definition. We need to fix the root problem within the business architecture discipline, this will, unfortunately, render a lot of data analytics activity as unnecessary.

In my professional experience, I have witnessed the direct influence of a data-driven culture on decision-making, leading to increased efficiency in product development, sales initiatives, inventory management, product transfers, among other aspects. When a company fosters a mindset that values the precision and reliability of data, it translates into more informed decisions and robust strategies. Additionally, I have been involved in initiatives aimed at strengthening the data culture, such as training programs and certification in data technologies. These actions not only contributed to raising awareness about the importance of data governance but also fostered more efficient collaboration and data-driven innovation within the team.

I agree that the term culture is an overloaded and over used term in organisations however it does have relevance because culture is about how you get people in your organisation to act the right way, that is the way that benefits the organisation including themselves your customers, suppliers, partners, etc. So when it comes to data how do you create a culture in which your personnel will treat data with due respect, as a valuable asset which helps to produce value and if not handled appropriately will cause friction and inefficiencies across the value streams.

Folks, we can’t now just look at the data as the “new oil”, rather we are at the dawn of a whole new revolution, the “data revolution”. The role data is playing in almost every aspect of our lives, at this forever accelerating speed and scale is becoming increasingly significant. To have a data centric approach at the very top of the organisation, including decision making, collaborating, strategising and responsibly reporting, the need of an excellent, well framed data culture has never been more significant. Carefully planned data orgs, data stewardship communications/ meet-ups, mandatory trainings followed by periodic communication and collaboration initiatives are the key to a robust data culture.

Transforming an organisation's culture to be data-driven involves implementing comprehensive data literacy programs, integrating data into decision-making, fostering open communication, and implementing recognition and feedback systems. The key challenges in this process are resistance to change and diverse data literacy levels, which persistent efforts and leadership commitment can overcome. This approach will lead to a culture where data is a respected asset that enhances strategic planning and decision-making.

Data governance framework, the sixth aspect, pertains to the structure, process, and methodology guiding data governance within a data warehouse. This framework aids users and stakeholders in planning, executing, and evaluating data governance activities. It provides a vision, strategy, roadmap, and governance model for data within the warehouse. In my experience, I've both followed and developed data governance frameworks for projects, drawing on methodologies like DAMA-DMBOK, DCAM, EDGO, or COBIT. Additionally, tools and techniques such as data governance charters, committees, maturity models, and scorecards have been instrumental in implementing and assessing data governance frameworks.

I wouldn't say that a DG Framework is specific to a data warehouse. In my opinion, a DG Framework is much more valuable when applied to the operational domain space of data. This would enable a much more homogeneous flow of governed data through basic inheritance. Further, the consumer endpoints would be my second prioritized application of a DG Framework, since this is where requirements are spawned. The data warehouse is the third major place of application, and if executed in the above order, would be a much simpler implementation. The overarching aspects of a comprehensive Framework is a true 360 view of data assets, data processes, and actors within the universe of discourse. A proper framework services this data mesh.

A well-defined data governance framework, such as DAMA-DMBOK, tailored to the organisation's context, can substantially enhance data management. The key lies in adapting the framework to fit the unique needs and culture of the organisation, coupled with strong leadership support and active stakeholder engagement. In implementation, essential elements should include a clear governance charter, a diverse committee, regular maturity assessments, and a performance scorecard. Overcoming challenges like departmental alignment and change management is crucial, highlighting the importance of communication, stakeholder engagement, and iterative framework refinement for effective data governance.

Moreover, I have experience using data quality tools like Informatica Data Quality and IBM InfoSphere Information Analyzer to assess, monitor, and improve data quality across different systems. These tools enable us to identify inconsistencies, inaccuracies, and redundancies in our data, which is essential for maintaining trust in our data-driven decision-making processes.”

Different systems and databases often have their own naming conventions, metadata definitions, and data types. This fragmentation leads to inconsistencies, making it hard to understand and integrate data across platforms. What I have found works well is the following but it's only the tip of the iceberg so to speak. Establish an organization-wide standard for metadata management. This includes unified naming conventions, data type definitions, and metadata schemas. Use APIs for integration, this allows different systems to communicate effectively and share data, all while maintaining your organisational standards. Its a good place to start.

While it isn’t industry standard to talk about data value measurement I believe including this under scope of governance communicates a culture that governance is required to get value from data and data driven cultures. Too often governance is looked at as a necessary evil or defensive play than an offensive play. In addition just in terms of data governance pillars we should consider data privacy and data observability also under this umbrella

Below are some examples: 1. Data Quality: Metrics: Data Accuracy Rate, Timeliness of Data Updates, Completeness of Data 2. Data Security/Privacy: Metrics: Number of Data Security Incidents, Compliance with Data Protection Regulations, Access Control Effectiveness 3. MDM: Metrics: Master Data Accuracy, Time to Resolve Data Conflicts, Adoption of MDM Processes 4. Data Catalog: Metrics: Number of Registered Data Assets, User Engagement with Data Catalog, Metadata Completeness 5. Data Lifecycle Management: Metrics: Data Retention Compliance, Storage Costs Optimization, Data Archiving Efficiency 6. Data Quality Improvement: Metrics: Improvement in Data Quality Scores, User Satisfaction with Data Accuracy, Cost of Poor Data Quality

Data Governance includes a crucial element—Data Protection, emphasizing the safeguarding of individuals' personal data and consent rights. Globally, numerous countries have enacted data protection acts. Compliance is vital for organizations, regardless of size, to adhere to relevant laws. The General Data Protection Regulation (GDPR) is an exemplary standard, setting rigorous data protection measures, empowering individuals with data control, and imposing strict obligations on data handlers. Other jurisdictions, such as California (CCPA), Canada (PIPEDA), and Australia, have their own data protection acts, underscoring the global importance of responsible data management.

Data governance in a nut shell (of my term ) is: 1. (Customer)MDM - capability to mastery the customer data as it becomes critical data asset in financial data domain. The MDM is enforcing the uniqueness, setting and save-keeping the golden record, etc. 2. MM (metadata management), this will Cataloging the technical meta-data and business meta-data and enable Providing view about backward/forward data traceability. This would provide a great beneficial to the data consumers by providing information on WHAT, WHERE and HOW the data context 3. RDM(Reference Data management), to catalogue the reference data, that qualifies the data context. Hence govern to get it standardize across enterprise is a must. 4. DQ(data quality) 5. Data Security.