What are the most important data governance resources for software developers?

The content doesn't match the title. Software developers are typically not involved with Data Governance unless they need to produce a Data Artifact. The Data Artefact should be part of the SDLC to ensure that Data Governance is embedded into the organization. Developers would benefit from the following Data Management (as defined by the DMBOK) resources: 1. Business Glossary 2. Data Models 3. Data Quality 4. Data Storage To list a few interdependencies.

Start with data governance standards such as ISO/IEC 27001, HIPAA, or GDPR. Read important books, enroll in Coursera or LinkedIn Learning classes, participate in forums, look through tool documentation, read blogs like Data Governance Institute, utilize internal training, and refer to HHS or ICO resources. Make connections with mentors to gain useful insights.

Books like "Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program" by John Ladley and "Building a Data-Driven Organization" by Carl Anderson provide comprehensive insights. Platforms such as Coursera, Udemy, and LinkedIn Learning offer tailored courses covering security, privacy, and compliance. Understanding industry standards like GDPR, CCPA, and frameworks like DAMA-DMBOK is crucial. Engaging in forums like Stack Overflow and Reddit, exploring tools like Collibra or Alation, attending webinars, reviewing case studies, joining internal training, and following expert blogs offer diverse perspectives for mastering data governance in software development.

1. DAMA DMBoK 2. EDM DCAM 3. dgpo.org Most of the leading consulting companies and software vendors have their own frameworks and maturity assessment approaches.

For software developers, mastering data governance is essential. I'd recommend exploring data governance frameworks like DAMA-DMBOK for structured management approaches. Utilizing tools like Collibra and Informatica enhances collaborative governance efforts. Reading books on data governance offers in-depth knowledge while enrolling in courses builds practical skills. Participating in data governance communities like LinkedIn groups fosters learning through peer interactions. Accessing guides and best practices sharpens understanding and implementation strategies. Pro tip: Use a comprehensive tool like Talend for data integration and governance, which helps manage, transform, and ensure data quality effectively.

By adhering to clear guidelines and implementing data governance practices throughout the development lifecycle, software developers can ensure that the data they produce is: - Meaningful: Data should be accurate, relevant, and contextual to its intended use. - Integrable: Data should be structured consistently and interoperable with other systems to facilitate seamless data integration and analysis. - Ready-to-use: Data should be packaged and presented in a readily usable format for downstream applications and users. By facilitating the adherence to these principles, software developers can contribute to the creation of valuable and actionable data assets that support organizational objectives.

Establishing a robust data governance framework in the banking sector involves mainly focusing on Data Privacy and Regulatory Compliance”, Data Quality and Accuracy, and Data Security and Cybersecurity pillars. Banks operate in regulated environments and setting up robust data governance practices to ensure they adhere to privacy regulations and regulatory frameworks as well as managing and protecting sensitive customer information while enabling necessary data-sharing for business operations can be challenging. Maintaining data quality and accuracy is crucial, especially for risk management, and regulatory reporting. Inconsistent or inaccurate data leads to operational inefficiencies, errors in decision-making, and compliance issues.

A data governance framework is vital for organizations, offering guidelines to align data goals with business objectives. It helps identify stakeholders, establish data quality measures, and ensures security. Frameworks like DAMA-DMBOK, DCAM, and CMMI-DMM provide a common vocabulary, assess maturity, and evaluate data quality, aiding in defining a data vision and sustaining excellence through improvement plans.

Establishing a robust data governance framework in the banks involves mainly focusing on Data Privacy and regulatory Compliance, Data Quality and Accuracy, and Data Security and Cybersecurity pillars. Banks operate in regulated environments and setting up robust data governance practices to ensure they adhere to privacy regulations and regulatory reporting requirements as well as managing and protecting sensitive customer information while enabling necessary data-sharing for business operations can be challenging. Maintaining data quality and accuracy is crucial, especially for risk management, and regulatory reporting. Inconsistent or inaccurate data leads to operational inefficiencies, errors in decision-making, and compliance issues.

The data governance tools discussed in this article are primarily intended to assist data stewards and analysts, rather than software developers. These tools do not delve into the intricate details of transformation that are concealed in scripts, nor do they verify the accuracy and completeness of scripts created by developers. As companies adopt data mesh or data fabric methodologies and the industry shifts towards a shift-left approach, there is a need for tools that can truly aid software developers in the most technical aspects. Tools like Orion EIIG play a crucial role in this regard.

In my opinion, software developers would really benefit from well defined and described: 1. technical metadata (i.e. the technical specifications of a field/column in a table) 2. business metadata (i.e. what is the definition of customer name) - this provides needed context

One thing I found definitely helpful is the existence of a truly one stop Data Catalog. Not a myriad of different tools (one for lineage, one for tech metadata, one of data security, etc.). A single place to fully understand the data they will be manipulating, the relationship between technical metadata and business glossary, the lineage, the impact assessment of changes to data sets, sampling of data, etc. Only this way in a simple and fast manner they can truly understand the data and act according to the data governance practices in place in the organization

Even if you load all metadata into a 'DG tool', it is not enough to create its value. You need constant guidance from a team of change agents. In our experience, role coaching works best, you start by teaching specific intra-role groups and then move to inter-role groups.

As a software developer I would look either for open-source tools like datahubproject.io, amundsen.io, open-metadata.org or tools with rich APIs like Collibra, Atlan or Alation. There's a lot of opportunities to contribute and develop custom features and connectors.

Dataplex by Google and datapurview by Microsoft can help in GCP and Azure landscape. There are many other 3rd part tools available too. Consideration of these tools is based upon organization's requirements.

Effective data governance tools are a way to analyze and manage metadata and track the data flow and lineage of your data assets .

Data governance tools, like Collibra, Alation, and Informatica, are essential for automating tasks such as data discovery, cataloging, lineage, quality, security, and compliance. With features such as data catalogs and lineage tools, these tools provide a unified and collaborative approach to efficiently manage my organization's data assets. Collibra offers tools for data cataloging, lineage, quality, privacy, and intelligence. Alation includes features for cataloging, lineage, quality, stewardship, and data literacy. Informatica covers cataloging, lineage, quality, security, and engineering. Leveraging these tools ensures that I can analyze and ensure the quality and compliance of my data effectively.

Before selecting a data governance tool, financial institutions should conduct a thorough assessment of their specific requirements, considering factors such as regulatory compliance, data complexity, scalability, and user adoption. Additionally, evaluating the integration capabilities of the chosen tool with existing systems and workflows is crucial for seamless implementation and operation. IBM InfoSphere provides Data Cataloging, Data Lineage, and Data Quality and comes with a data Governance Dashboard. As an added benefit, its integration with IBM's broader data management and analytics solutions which many banks use becomes easier. It also supports regulatory compliance and risk management.

I feel with the help of these tools, a robust data governance framework can act as a shield for any organisation. Tool like such as Collibra, Informatica Axon, Alation, erwin Data Intelligence, Apache Atlas, IBM InfoSphere Information Governance Catalog, and Microsoft Azure Purview. These tools assist in metadata management, data quality monitoring, compliance tracking, and overall governance across diverse data sources and platforms.

Some recommended books on data governance include: 1. "Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program" by John Ladley 2. "The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling" by Ralph Kimball and Margy Ross 3. "Building a Data Warehouse: With Examples in SQL Server" by Vincent Rainardi 4. "Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program" by Steven Sarsfield 5. "Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information" by Danette McGilvray These books provide insights, strategies, and practical guidance on establishing and maintaining effective data governance practices.

Delving into data governance books like "Data Governance" by John Ladley, "Non-Invasive Data Governance" by Robert Seiner, and "The Data Governance Cookbook" by Scott Taylor offers me profound insights into concepts, methods, and practical tips. These recommended reads cover diverse aspects, including strategy, organization, processes, and tools, providing a step-by-step guide for implementing effective data governance programs. The books share experiences from various industries, introducing a non-invasive approach to formalize data accountability without disrupting existing processes. Using a cookbook metaphor, they present data governance as a recipe for success, complete with ingredients, instructions, and valuable tips.

"Data Governance for Banks: A Data Governance Quick Start Guide" by Sunil Soares: It offers a practical guide specifically tailored for banks looking to initiate or enhance their data governance programs, covering key topics such as regulatory compliance, data quality, metadata management, and the role of data governance in supporting business objectives in the banking sector.

Some online courses to recommend (Note: I've authored some): 1. Data Governance in Practice (O'Reilly) 2. Learning Data Governance (LinkedIn) 3. CIMP in Data Governance (eLearningCurve) 4. Practical Data Governance: Implementation (LightsOnData) -> this one also has downloadable templates 5. Data Governance (Udemy)

Some key areas and types of courses that banks may find beneficial for data governance, especially as it pertains to challenges and regulatory requirements: - Courses that specifically cover regulatory frameworks relevant to the banking sector, such as BCBS 239, GDPR, Dodd-Frank, and others. There is also Certified Information Management Professional (CIMP) - Financial Data Management Track, offered by the International Association for Information and Data Quality (IAIDQ), the CIMP certification includes a track specifically focused on Financial Data Management.

Exploring data governance courses like Coursera's "Data Governance and Data Quality," LinkedIn Learning's "Data Governance Foundations," and Udemy's "Data Governance Masterclass" is helping me acquire and update my skills. Covering principles, drivers, challenges, and success factors, these courses offer practical insights, examples, and even prepare me for the CDMP exam by DAMA International in the case of the Coursera course. With quizzes, exercises, and case studies, these resources provide a comprehensive learning experience to advance my career in data governance.

Let's also not forget about DAMA. Though it covers more than just Data Governance, they often have subgroups focused on data governance.

Besides joining DAMA, DGPO, DGS, and GDSCoP here are some great LinkedIn groups you can join for discussions with other industry experts (like yourself). 1. "Data Quality and Data Governance Leadership Forum (Discussion Only)" 2. "Data Governance Professional Organization" 3. "Data Quality and Metadata Management"

Being part of data governance communities like DGPO, DGS, and DGS CoP is an enriching experience. These platforms connect me with professionals, offering valuable resources and opportunities for development. DGPO provides webinars and forums, DGS offers blogs, podcasts, and certifications, while DGS CoP facilitates discussions and events. Engaging in these communities not only broadens my knowledge but also allows me to contribute to and stay updated on the latest trends in data governance.

I would say that their most important resource is their own Data Governance team: The people who own and steward the data and know how it is used by the business, what is needed to become better in creating value with the data, which data is critical and what quality criteria are essential, .....

Data Governance requires a blend of technical and functional/business knowledge. While business knowledge will help in understanding the business needs, technical know -how will help in achieving those goals. While all other resources like books, courses, tools, communities etc. are necessary, but the primary knowledge that a data governance team should imbibe is the Business requirement. One has to understand that the main task of a DG tool is to aid in achieving the business strategy. This can be reached through constant communication and feedback with business user.

In my experience, more than technical knowledge, you have to be attentive and open to detect the business case that fits perfectly with your strategy as one of the most important resources. Because when you have something to which you can apply all those DG concepts and resources, you will be prepared to consider the challenge in your organization. You will learn more by doing...