What do you do if you're an entrepreneur looking to select data governance tools and technologies?

It is critically important that the selection of a data governance tool or technology is performed following a thorough analysis of requirements and needs. If those requirements drive a buy decision, then use those requirements to lead to the proper decision. Remember that effective data governance relies on crafting a holistic solution that activates people, process, and technology to drive cultural change. Too many purchase a technology solution hoping that it will solve the problems of the world and become disappointed when it falls short. Follow your requirements and needs, drive people and process, and then move on technology when it is needed.

Data governance platform is not just a piece of software that gets embedded into the technology stack. The platform in reality must offer better user interface, interacting and collaborating multiple process, automating repeated tasks, handling effective communication and best in use when it comes to curate and maintain data catalogs. The data quality cannot be siloed tool offering but deeply integrated with the catalog, glossary, datasets, lineage, policies and business processes. One of the feature most vendors and partners do not focus is the aggregation and visualization of data governance KPIs.

Start by evaluating specific business requirements & nature of the data you handle. Identify key features required, such as DQ management, compliance tracking, or security features to support your business goals. Consider scalability to ensure the tools can grow with your business. Research & compare different solutions. Opt for tools that integrate easily with your existing systems to avoid unnecessary complexity. Use the free trials of tools to test them in your environment to ensure they meet your expectations before decision. This practical approach will help you invest in a solution that not only fits your current needs but also supports future growth and compliance.

1) First understand Your business needs. Check your specific business objectives. Consider factors like scalability, compliance, collaboration, and ease of use. 2) Research and compare data governance tools, Consider factors such as cost, scalability, vendor reputation, and customer reviews. 3) Prioritize tools that meet regulatory standards (e.g., GDPR, HIPAA). Choose tools that can grow with your business and consider long-term scalability and flexibility.

"An entrepreneur" is an incredibly broad term that covers a wide range of types of people, from garage hackers to franchisees to substantially funded startups. All of these will have different needs and different amounts and types of data to govern. The need for data governance tools increases with increased needs for centralization, along with central policy-setting including security and management. In this sense, being an entrepreneur is no different from any other business -- the data and the needs indicate how much and what type of data governance you need, not whether the organization is a partnership, corporation, or such.

I would look to Solo Development Frameworks studies and thesis, as an entrepreneur consider niche software could be expensive for a startup approach. There are plenty of solutions in the open source space that can help building a proper governance, plus with the right guidelines later could be used to be integrated/migrated to bigger solutions at the right time

To effectively research data governance tools: 1. Align with Business Goals: Choose tools aligned with needs and scalable. Technical Capabilities and Usability: 2. Balance features with user-friendly interfaces. Consider tool complexity with "Ferrari vs. Corolla." 3. Cost Considerations: Evaluate pricing models: upfront costs, scalability. Compare value versus cost: Is the tool affordable? 4. In-depth Analysis: a. Explore Tool Features: Focus on automation, integration, metadata. b. Review Industry Reports: Gartner, Forrester for insights. c. Assess User Feedback: Read reviews and case studies. Select tools matching business needs, balance features with usability, consider costs, and leverage industry research.

Key considerations that I suggest entrepreneurs to include: 1) Evaluating data cataloging tools like Collibra and Alation for metadata management, data quality platforms such as Informatica for ensuring data accuracy, and data security solutions like Immuta for robust data protection. 2) Exploring data lineage & Impact Analysis tools like IBM InfoSphere can help track data flow, while considering integrated platforms like Collibra can provide a comprehensive approach to data governance. 3) Consider Data Security and Access Control Solutions 4) Evaluate Metadata Management Platforms 5) Prioritizing tools that support data stewardship, data analytics, and data governance frameworks will empower them to make informed decisions

Make sure it fits into your strategy and approach. Frameworks and best practices created by vendors may not be optimal for your use cases and organizational approach. Ensure that the tool is flexible enough to evolve approaches and is not set on a legacy framework. It must have good usability and ease of use to ensure adaption without mandating it.

Here's a framework to help you evaluate costs effectively: 𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭: 35% ↳ License or Subscription Fees ↳ Implementation Costs ↳ Hardware and Infrastructure 𝐎𝐧𝐠𝐨𝐢𝐧𝐠 𝐄𝐱𝐩𝐞𝐧𝐬𝐞𝐬: 15% ↳ Maintenance and Support ↳ Training and Education ↳ Additional Features or Modules 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐆𝐫𝐨𝐰𝐭𝐡: 10% ↳ Scaling Costs ↳ Volume-Based Pricing 𝐂𝐨𝐬𝐭-𝐁𝐞𝐧𝐞𝐟𝐢𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: 10% ↳ ROI Calculation ↳ Total Cost of Ownership (TCO) 𝐕𝐞𝐧𝐝𝐨𝐫 𝐏𝐫𝐢𝐜𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬: 15% ↳ Perpetual License vs. Subscription ↳ Pay-as-You-Go vs. Fixed Pricing 𝐇𝐢𝐝𝐝𝐞𝐧 𝐂𝐨𝐬𝐭𝐬 𝐚𝐧𝐝 𝐑𝐢𝐬𝐤𝐬: 15% ↳ Integration Complexity ↳ Data Migration ↳ Vendor Lock-in

To evaluate costs effectively, consider Total Cost of Ownership (TCO) by assessing implementation, training, and hardware upgrade expenses. Factor in potential ROI from improved data quality and compliance. Review pricing models as subscription, per-user licensing, and usage-based tiers. Analyze long-term value versus upfront costs, factoring in TCO and ROI to make informed decisions about data governance investments. Evaluate cloud costs (IaaS, PaaS, SaaS) including subscription fees and scalability. Assess solution integrator costs for design, implementation, and ongoing operational expenses for maintenance and support. Understanding these cost elements is crucial for budgeting and determining the value of data governance investments.

One good example of this is to have a custom dashboard design feature. For use cases like data quality monitoring, users would want ease in usage which come by being able to eyeball on a high level the overall metric score and the health of key systems. To be able to allow for custom changes is key for any DG tool to fulfill such user expectations.

Customer Support 1) Explore tool demos and trials: Request demos or trial versions to test usability and functionality. Involve relevant stakeholders in the evaluation process. 2) Assess the vendor’s support services and community forums. A strong support network ensures timely assistance when needed. 3) Some tools cater to specific industries (e.g., healthcare, finance, retail). Choose tools aligned with your sector’s unique requirements.

One of major problems of open free software is exactly who supports the product, what perspectives for bug correction and new implementations. I already used four or five of them , and when detect abnormal behavior of software, in all cases have a extreme difficulty in stablish a workaround to resolve them. In many times is better pay for use and have all support instead implement and freeze process because problems with software. If you need to use because company have no budget for buy license of Data Governance Software, ok , make it clear, problems that could be occur in case of this option be made.

Implements Data Governance envolves stablish processes and creates organization tactical and operational groups for discuss and align data in company , make Proof of Concepts of softwares to make metadata control, classification, qualification and create and maintain business catalogs , stablish data lineage , treat data security and how disponible data is for company, and assure that persons that use data are in compliance with local data treatment laws. These are the very first steps in data governance implementation.

A set of factors needs to be analyzed to hire a tool, such as cost analysis, support analysis, and implementation compliance, among other essential topics. Still, I think the tool is implemented to support the data strategy. There is no point in having the best tool on the market if the company does not have a well-defined, implemented, and communicated data strategy.

Ne pas oublier surtout que ce n’est qu’un outil et qu’il doit être « convivial » … « L’outil simple, pauvre, transparent est un humble serviteur ; l’outil élaboré, complexe, secret est un maître arrogant ».