How do you choose data innovation tools?

1 Data governance strategy

Your data governance strategy outlines how you manage, monitor, and control your data assets, as well as how you align your data activities with your business objectives and values. Therefore, when selecting a data innovation tool, it is important to consider how the tool fits into your data governance framework and roles, how it supports your data quality, security, and privacy principles and practices, how it enables data traceability, auditability, and accountability, and how it facilitates data communication and collaboration across your organization. A data governance strategy can help ensure that the chosen tool is compliant, consistent, and trustworthy while also supporting your data policies and standards.

2 Data quality and security requirements

Data quality and security are essential for data innovation, as they affect the validity, credibility, and value of your data insights and outcomes. When selecting a data innovation tool, you should consider how it ensures data accuracy, completeness, timeliness, and relevance. Additionally, you should examine how the tool detects and resolves data issues, such as duplicates, missing values, or outliers. Furthermore, it’s important to understand how the tool protects data confidentiality, integrity, and availability. Lastly, you should make sure the tool complies with data regulations like GDPR or CCPA.

3 Data culture and skills

Your data culture and skills reflect how your organization values, uses, and learns from data, as well as the proficiency of your staff in data-related tasks and technologies. These elements are essential for data innovation, as they influence the adoption, engagement, and performance of your data innovation tools. When evaluating a tool, you should consider how it aligns with your data vision, mission, and values; if it matches the data literacy and competency levels of your staff; if it provides user-friendly and intuitive interfaces and features; and if it offers training, support, and feedback to your staff.

4 Data innovation goals and metrics

Before selecting a data innovation tool, it’s important to clarify how it supports your data innovation objectives and strategies, enables data exploration, experimentation, and discovery, facilitates data visualization, storytelling, and decision making, and tracks and reports your data innovation outcomes and benefits. Your data innovation goals and metrics define what you want to achieve with data innovation, as well as how you measure and evaluate your progress and results. These goals and metrics can help you align your data innovation tools with your business priorities and expectations, as well as demonstrate the value and impact of your data innovation initiatives. Choosing the right tool is not a one-size-fits-all process; different tools may suit different organizations depending on their data governance, quality, security, culture, skills, goals, and metrics. Thus, by asking yourself these questions and criteria you can narrow down your options and find the best data innovation tools for your needs.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?