How do you select new data tools for your architecture?

To help decide which tool features are most important, it should meet the following requirements for new or recently launched governance programs: -Ensure it has basic data finding capabilities, like data discovery, KDI relationship management. -Look for management capabilities, having DQ management, provide data domain and master data lifecycles. Performs data movement, data lineage views, process reporting and captures change history. Maintains drill-down of lineage information and has visual representations. -.Storage of archives that provide metadata support for document classification and document lifecycle management. -Contains governance operation capabilities like data roles and responsibilities, SLAs and enable change approvals.

When selecting new data tools for your architecture: - Assess your current data infrastructure. - Identify pain points and limitations. - Define clear objectives and goals. - Evaluate scalability requirements. - Analyze total cost of ownership. - Ensure compatibility with existing systems and future needs.

After assessing the state of your data, assess the state of your meta-data documentation, analysis, and design capabilities. Do you have or plan to build the capability to deal with meta-data as object models, or do you see them as flat lists of columns in tables. In the later case, modeling capabilities of data catalog product are a realistic and well supported approach to get your metadata under control. Otherwise, you need to consider your data modeling strategy as part of your data management strategy, and avoid duplication of activities. There is only one conceptual business view, that will manifest as a glossary, ideally backed up with a conceptual, semantic model, putting the glossary terms in a formal relation.

Here's some things to look for: 1. ??? Features: Assess functionalities. 2. ?? Integration: Check compatibility. 3. ??? Security: Align with governance. 4. ??? Customization: Gauge adaptability. 5. ?? User Interface: Evaluate ease of use. 6. ?? Flexibility: Confirm data adaptability. 7. ?? Performance: Check speed and reliability. 8. ??? Cost: Fit within budget. 9. ??? Timeline: Assess deployment time. 10. ?? User Needs: Meet stakeholder needs. 11. ?? Business Objectives: Confirm alignment. 12. ?? Documentation: Ensure support. 13. ?? Compliance: Check regulations. 14. ?? Updates: Examine future plans. 15. ?? Vendor Reputation: Investigate reviews. 16. ?? Accessibility: Confirm multi-platform access. If you find this helpful upvote! ??

One important aspect to be thought out is how much customization is possible for individual needs in terms of volume, tracking, observability, and features(adding/removing). What is the scale of upgrades every year in terms of new releases?

I am curious as to where, within this, there is mention with regards to how the people and process defined these technological steps. There isn’t mention of it at all yet it is quintessential in order for the technology to be defined and follow the steps suggested. Is it simply the context of the conversation that omits such a relevant topic?

Future-Proofing: I would consider the tool's roadmap and updates and how well they integrate to ensure long-term viability. As a part of the check it's important to : -Check the tool's roadmap, updates, and adaptability to tech trends. -Assess integration capabilities, compatibility, scalability, vendor support, and community. -Consider customization, compliance, and vendor stability for tools that can evolve with your changing business needs.

Identify Specific Needs: Start by clearly defining your organization's specific data requirements and objectives. Whether it's data analytics, data warehousing, data integration, or data governance, having a precise understanding of your needs will guide your tool selection process. Evaluate Scalability and Compatibility: Ensure that the new tools align with your current architecture and are scalable to accommodate future growth. Compatibility is crucial to avoid potential bottlenecks or data silos. Assess Vendor Reliability and Support: Research the reputation and track record of the tool's vendor or provider. Look for reviews, customer testimonials, and case studies to gauge their reliability and customer satisfaction.