You're starting a new data analytics project. What tools should you use to ensure proper data governance?

One time at work, we initiated a new data analytics project and quickly realized the importance of a data catalog. A data catalog is a vital tool that assists in documenting, organizing, and discovering data sources. It offers crucial metadata like data definitions, schemas, lineage, and usage patterns. This tool helped us in efficiently searching for and accessing our data. It was instrumental in understanding the nature of our data, its origins, how it was being utilized, and who was responsible for it. Furthermore, it played a key role in enforcing our data policies and standards, such as naming conventions, data quality rules, and access control measures, ensuring proper data governance.

When starting a new analytics project, ensuring proper data governance is crucial for maintaining data quality, security, compliance, and effective data management. Selecting the right tools is a key part of this process. The first type of tools you should consider Data Quality Tools: The purpose is to ensure the accuracy, consistency, and reliability of data. The key features to be interested on are Data validation, cleaning, enrichment, deduplication. For example: Talend, Informatica, IBM InfoSphere.

The second type of tools you should consider is Data Cataloging, with the purpose to create an organized inventory of data assets, making data easy to find and understand. Featuring Metadata management, data discovery, classification.For example Alation, Collibra, Apache Atlas. The third type of tools you should consider is Master Data Management (MDM), aim at providing a unified, consistent view of key data entities commonly used across the organization. With the key feature: Data harmonization, hierarchy management, centralizing data. A clear example is SAP Master Data Governance, Oracle Master Data Management.

Data catalogs are important part of a data governance if you are going to scale your data. One of the frequent mistakes is to neglect this activity and never apply cataloging. However in long term this activiy increases the productivity and the analytics bias on large-scale products.

Embarking on a new data analytics project requires careful tool selection for robust data governance. Opt for tools like Apache Atlas for metadata management, Collibra for comprehensive data governance, and Apache Ranger for access control. Leverage Alation to enhance collaboration and IBM InfoSphere Information Analyzer for data profiling. These tools collectively ensure data quality, lineage, and compliance, fostering a robust governance framework for your analytics journey.

The fourth type of tools you should consider is Data Security and Compliance Tools, with sole purpose to protect data from unauthorized access and ensure compliance with data protection regulations (like GDPR, HIPAA).Utilising key Features such as Access control, encryption, audit trails, compliance reporting.Example in this category are Varonis, IBM Guardium, Symantec Data Loss Prevention. The fifth type of tools you should consider is Data Integration, to consolidate data from different sources for unified analysis. For ETL (Extract, Transform, Load) processes, real-time integration, API management proposes. For example Microsoft SQL Server Integration Services (SSIS), Talend, MuleSoft.

One thing I've found helpful in data analytics is the use of data quality tools. These tools are crucial for measuring, monitoring, and enhancing the accuracy, completeness, validity, and consistency of data. They enable us to identify and address various data issues like errors, duplicates, outliers, and missing values. In my experience, ensuring data quality is essential for meeting business requirements and expectations. Moreover, it significantly improves data analysis and decision-making processes by providing reliable and high-quality data, which is the foundation for any effective data analytics project.

The robustness of a model most of the cases depends on the quality of the data rather than a model itself. Some models are so efficent however they suffer if in data outliers are present. Make sure to treat outliers good before writing a model.

The sixth type of tools to consider is Data Lifecycle Management to manage the flow of data throughout its lifecycle, from creation to deletion. With features such as Data retention policies, archival, disposal. For examples: Informatica Data Archive, IBM Optima. Another type of tools to consider is Data Lineage, mainly to track the origin, movement, characteristics, and quality of data in the analytics process. Such as Visualization of data paths, impact analysis, version history. A clear example: Manta, Collibra Lineage, Informatica Enterprise Data Catalog.

One time at work, while dealing with a complex data analytics project, I realized the importance of considering elements that don't always fit into conventional data analysis categories. For instance, the impact of organizational culture on data interpretation, or how team dynamics can influence the outcome of an analysis. These factors, often overlooked, can be crucial in understanding the full context of data analytics projects. It's important to look beyond the numbers and algorithms and consider the human and organizational aspects that might affect the data and its interpretation.

Privacy is a sensible topic of today. Particularly in Europe. Remember OpenAI was banned from the Italian market by Garant due to its privacy issues? So make sure that your data storage and security are compliant with GDPR, Otherwise, you might have huge fines.

Remember, the specific tools you choose will depend on the scale of your project, the technologies you're using, and your organization's specific needs and preferences. Additionally, a successful data governance strategy also involves establishing clear roles and responsibilities, fostering a data-driven culture, and regularly reviewing and updating your governance policies.

In addition, another type of tools you should consider is Data Policy Management Tools. to establish and enforce policies for data access and usage. Mainly for Policy creation, enforcement mechanisms, monitoring. For examples: Apache Ranger, Axiomatics. The last set of tools to consider is the Business Intelligence (BI) and Reporting Tool. For analysing data and generating insights in a governed environment. It mainly for Ad-hoc reporting, dashboards, data visualization. For examples: Tableau, Power BI, Qlik.

Use collaboration tools to facilitate communication and collaboration among team members working on data governance. Platforms like Slack, Microsoft Teams, or Confluence can enhance collaboration.

Data integration is the backbone of effective data-driven decision-making. It empowers organizations to harness the full potential of their data by merging diverse sources seamlessly. With transformation, cleansing, and enrichment, it ensures data accuracy and consistency. This unified view not only enhances analytics but also fosters collaboration and data-driven insights, ultimately driving business success.

In a recent data project, we focused on making information clear and easy to understand through data visualization. We used Power BI to create visual representations of our data. These tools helped us present complex information in a simple and accessible way, making it easier for everyone to grasp key insights. The visualizations enhanced communication within the team and with stakeholders, allowing us to make informed decisions based on a clear understanding of the data.

Data visualisation with good filters/ data cuts conveys the intent of the project in an appealing manner. Providing appropriate charts and visuals become a key in project demonstrations

For comprehensive data governance in a new data analytics project within the Microsoft stack, utilize Microsoft Purview. It offers robust metadata management, data cataloging, lineage tracking, and compliance capabilities essential for ensuring data governance throughout the project lifecycle.

The suite of Oracle tools, such as Oracle Data Integration, played a pivotal role in my career. These tools not only bring together diverse data sources but also ensure their uniformity, cleanliness, and enrichment. This process sets the stage for cohesive collaboration and accurate insights that empower effective decision-making. In alignment with the essence of data integration highlighted in the article, tools like Oracle ODI not only unite disparate data but also facilitate its transformation, making it consistent across various formats and sources

Finally, the right mix of tools depends on the specific needs and objectives of your analytics project. It's important to assess the data governance requirements of your project, including data quality, integration, security, and compliance needs, and then select tools that best meet these requirements. Additionally, it's crucial to ensure that these tools are interoperable and fit well within your existing technology ecosystem.

Data Governance is defined by Google as : "setting internal standards—data policies—that apply to how data is gathered, stored, processed, and disposed of. t governs who can access what kinds of data and what kinds of data are under governance."

Data governance is really about process, but when looking at tools, consider: ??Apache Atlas: An open-source framework for governance and metadata. It offers cataloging, lineage, and compliance. ??Collibra: A comprehensive governance platform with centralized data asset management, stewardship workflows, and compliance features. ??IBM InfoSphere: Part of a suite, it offers robust data governance with data discovery, lineage, and quality monitoring. ??Alation: A catalog platform focusing on data accessibility and understanding, featuring cataloging, lineage, and collaborative governance. ??Informatica Axon: Facilitates data policy enforcement, quality rule definition, and compliance, establishing a solid governance framework.