Achieving data fluency

1. Achieving data fluency

Hi! In this video, we will go through the framework for achieving data fluency.

2. Why do we need a framework

The purpose of this framework is to provide a structured approach for individuals and organizations to achieve data fluency. Data fluency is not just a single skill or tool; it's a holistic concept that involves both individual capabilities and a supportive organizational culture and ecosystem. By following this framework, individuals can develop the necessary skills to work with data effectively, while organizations can create an environment where data-driven decision-making becomes the norm. Let’s dive in!

3. Data fluency for individuals

To start, let’s look at data fluency at the individual level. Data fluency emphasizes the development of data skills among all employees. This includes business users such as business executives, sales representatives, marketing and operations managers as well as data experts such as data analysts and data scientists. These people require data to make informed decisions. For example, the marketing manager needs to know if the latest campaign hit the target sales projections to decide if they should proceed with a similar campaign next month. Similarly, a data analyst needs to diagnose the recent increase in customer churn. But what are the skill components of individual data fluency that will enable these people to make data-driven decisions? Let’s have a look.

4. Link business problems with data

First, it encompasses the skills and mindset required to link business problems with data. This includes setting measurable goals, identifying problems using key metrics, and translating those problems into analytical questions.

5. Work with data

Second, we have the skills to work with data in a meaningful and impactful way. This includes the ability to perform data cleaning, data exploration techniques such as data visualization as well as skills to describe data with basic statistical concepts and measures to uncover trends and relationships in data.

6. Communicate with data

Besides working with data, individuals require skills to communicate with data. This includes data storytelling, the ability to narrate a compelling story around data, and present data-driven insights. It also includes data interpretation which is the ability to translate complex data analysis results into actionable insights and decisions.

7. The role of data experts

The advanced data skills of the data experts such as data analysts, scientists, and engineers play an essential role in the context of enabling data fluency. These experts possess specialized knowledge and technical expertise, enabling them to handle more complex data tasks, advanced analytics, and data infrastructure management. Their presence accelerates data-driven projects, provides mentorship, and ensures the organization's data efforts are aligned with industry best practices and standards.

8. Organizational environment for data fluency

As you know already, achieving data fluency is not only about individual data skills. Individuals trained in working with data will grow frustrated in an organization without the tools and expectations that align with their skills. It is important for the organization to create an environment in which data fluency can flourish. That is, data fluency should also be enabled at an organizational level.

9. The data fluent culture

Organizational data fluency encompasses the data culture, representing a work environment where data is embraced, valued, and integrated into decision-making processes.

10. The data ecosystem

Second, we have the data ecosystem. This includes the infrastructure that provides data access and ensures data quality, security, and compliance. It also includes the tools used to collect, store, analyze, and visualize data effectively. Lastly, processes encompass the workflows, protocols, methodologies, and organizational structures designed to facilitate data-related activities, collaboration, and communication within the organization.

11. The complete data fluency framework

Here it is! Our framework is now complete, covering both individual and organizational components. Please take a moment to pause the video to take a closer look. The presence of these elements bridges the gap between people and data to ultimately achieve data fluency.

12. Let's practice!

Now, let's test your understanding of the framework with some practical examples.

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