What data governance tools and technologies should you learn to begin your career?

To embark on a progressive and successful career in data governance, it's essential to adopt a holistic approach while mastering key tools and technologies. Begin by building foundational knowledge of data management platforms (ex: Collibra, Informatica). Next, delve into data quality profiling & optimization techniques leveraging tools like Talend, Trillium etc. to ensure data accuracy. Additionally, gain proficiency in data cataloging solutions, (ex: Alation), for efficient data discovery. Familiarity with metadata management tools ex: IBM InfoSphere, is crucial. Lastly, acquiring skills in data governance frameworks like DAMA-DMBOK is essential to understand the underlying principles that drive successful data governance practices.

In my opinion someone need strong foundation in data management and then dive deeper into data governance. 1/ BigData Eco-system: Apache Hadoop, Spark, Kafka, Atlas, Ranger, EMR, HDInsights, Dataproc & DataBricks 2/ SQL and Relational Database, NoSQL Databases (MongoDB, Cassandra, Amazon DynamoDB) 3/ Data Warehousing (Snowflake, Amazon Redshift, GCP BigQuery, Azure Synapse) 4/ ETL Tools (Apache Airflow, NiFi, AWS Glue, DataFlow) 5/ Data Governance Platforms (Collibra, Informatica, AWS Lake Formation) 6/ Data Quality Tools (Trifacta, AWS Glue DataBrew) 7/ ML Platforms (TensorFlow, PyTorch, Amazon SageMaker) 8/ Data Security and Encryption: KMS, Varonis, Symantec etc Stay updated on industry trends, standards, and regulations.

Conquering the data governance realm requires a diverse set of expertise. Some essential tools and frameworks to equip yourself include data discovery and cataloging, data quality management, and data security and privacy. You can pick some of the key vendors in each category. Following are some of the critical frameworks. - DAMA DMBOK2: Industry standard for data governance best practices. - Data Management Common Body of Knowledge (DM-CBOK): Focuses on data management skills and knowledge. - ISO/IEC 38500: International standard for information governance. Additionally, stay updated on emerging technologies like AI. You also need strong analytical and communication skills for effective data governance implementation.

Beginning a career in data governance requires familiarity with data governance frameworks. These frameworks provide a structured approach to managing, using, and sharing data, outlining roles and responsibilities of various stakeholders. Key frameworks to learn include DAMA-DMBOK, COBIT, ISO 8000, and DCAM. Understanding these frameworks equips you with the knowledge of best practices, goals, and scope essential for effective data governance in various contexts. Mastery of these frameworks is a foundational step for anyone aspiring to excel in the data governance field

As per my experience with data governance projects , I believe you do not need any specific framework if you know about your data sources or data providers the data governance capabilities and data consumers. That's it .

I would like to confirm it's not very straightforward and you can't spend so much money without proper evaluation and requirements hence you must assess your core governance requirements and then take conscious decision to go for a governance platform. So far I believe if you have Data Cataloging and Data Quality tool in your governance platform that is more than enough.

A key component for anyone starting in data governance is mastering data governance platforms. These platforms, like Collibra, Alation, Informatica, and IBM, automate and streamline various governance tasks including data cataloging, quality management, lineage, security, and privacy. They facilitate collaboration, monitoring, and reporting, providing access to trusted data sources and documentation. Learning to effectively use these platforms can greatly enhance the efficiency and scalability of data governance processes, making them indispensable tools in the modern data governance toolkit. My personal choice Informatica

Each platform has certain strengths and weaknesses. You have to evaluate them based on your specific needs and priorities. In my personal experience Alation has very good Data Catalog, Ataccama good Data Quality, Atlan for modern data sources and more.

From my experience, the fundamentals of Data Engineering and DQ are more important than the tools. For a beginner, it is more important to learn about the possible DQ issues & DQ dimensions . For smaller datasets, Excel can be a powerful tool. But for real-time projects with huge data, there are many choices, such as Informatica, IBM, SAP, Collibra, Talend, and Trillium .

One important aspect about Data Quality is missing in this section. No Data Quality tool can automatically correct your DQ issues/errors hence you must have business or task force to reconcile the errors in sources when you identify errors using DQ tools. Hence the flow would be like : Data Source -> DQ Tool -> Capture Issue -> Analyze Issues -> Correct in source then again perform DQ check. You can use any tool but make sure it has capability to perform both OOTB and Custom DQ rules.

In my experience most effective tools for data quality are informatica and Ataccama. But irrespective of the tool you should have people and process to correct the DQ issues in source systems.

Tools like Informatica, Talend, and IBM InfoSphere QualityStage are crucial for ensuring data quality. They help in cleansing, deduplicating, and validating data. For instance, a bank might use Informatica to ensure the accuracy and consistency of its customer data.

For those beginning a career in data governance, proficiency in data quality tools is essential. Tools like Talend, Trifacta, Dataiku, and SAS are instrumental in assessing and enhancing data quality. They enable tasks such as data profiling, cleansing, standardization, enrichment, matching, and deduplication. By mastering these tools, you can effectively identify and resolve quality issues, ensuring the data meets accuracy, completeness, and timeliness standards. This skill is crucial for ensuring data integrity and meeting the needs of data consumers and stakeholders. My personal choice Informatica

The 3 very attractive tools missing in this section : Collibra , talend and Alition. All metadata tools are more or like same but you need to see they should have both business and technical metadata collection capabilities. Ultimately you would like to create this as self service platform.

Understanding metadata management tools is crucial for those starting in data governance. Tools like Apache Atlas, Dataedo, AWS Glue, and Google Data Catalog assist in managing metadata - the data about data. These tools enable the creation and maintenance of a data catalog, making data assets easily searchable and browsable. They're also key for tracking and visualizing data lineage, and clarifying the history and journey of data. Mastery of these tools enhances data understanding, discovery, and trust within an organization, making them vital for effective data governance. My Personal choice Informatica

Understanding tools like Collibra, Alation, and Informatica's Enterprise Data Catalog is important for effective metadata management, which is crucial for understanding data context, lineage, and usage. For example, a healthcare provider might use Collibra to manage the metadata of patient records for easier compliance with health regulations.

Data security and privacy tools are essential for anyone in data governance. Tools like Microsoft Azure Purview, IBM Guardium, Privitar, and Protegrity provide robust protection against unauthorized access and breaches. They facilitate key security measures like encryption, masking, and access management, and ensure compliance with regulations like GDPR and HIPAA. Proficiency in these tools is critical for safeguarding data against threats and demonstrating accountability and transparency in data management. Mastering these tools is crucial for maintaining the integrity and trustworthiness of data in any organization. My personal choice Privitar that is now embedded in Informatica

In terms of tooling, it’s always better to get closer to the data to gain some insights. Visualization will help make it pretty but prior analysis will help determine what and how to visualize. For most structured data in databases, SQL can be a powerful skill to learn. Most cloud platforms now even provide SQL wrappers on top of files e.g. Athena. This is useful to gauge the data and it’s much easier to do complex filters, joins and existence checks in SQL as compared to visualization tools like Power BI or tableau. A combination of SQL and visualisation/scripting tools mentioned would be the best way to go about it.

Most of the data governance tools provides the capability of custom dashboards where you can see the stats and insights for your meta-data access and governance activities across the organisations.

Some Data Visualization tools include: Power BI, Tableau, and Looker powered by Google. These are great tools for data storytelling.

Tools like Tableau, Power BI, and Qlik are crucial for reporting and visualizing data governance metrics. A firm could use Power BI to visualize data quality metrics across various campaigns.

I want to supplement this comprehensive list with the inclusion of Master Data Management. MDM is not merely a technology and tool-based approach but an enabler of data governance at the execution layer. Although policies, rules, and standards can be documented and set, MDM provides a means to ensure that these are applied to the data and that data quality can be continuously measured and monitored. Trusted data is generated while everyone adapts to the change, believing the intended actions are effective.

Let's face it. Data and analytics governance is more fractured than any of us would care to admit. Tools, actors—it’s like we are all responsible for ensuring the house is up to code, but we are using different measurement systems, code books, etc. Specialize, yes. But also find time to learn about the domains adjacent to your own to be a connector and understand the power of governance on the data value chain as a whole.

All the tools, frameworks and guidelines are important, but the most important thing is to know the value of data itself and what solution it can provide. Secondly, Change Management is important, not only at business or organisational level, but at individual level. Unlearn and Relearn to enlarge the boundaries of your wisdom so that all the tools and framework serve your need to solve a problem or devise a solution.

Familiarity with data integration tools like Microsoft SSIS, Oracle Data Integrator, or Apache Kafka is beneficial for managing the flow of data across systems. An e-commerce company might use Apache Kafka for real-time data integration to personalize customer experiences.