What are the most common data literacy challenges and how can you overcome them?

1 Lack of data culture

One of the biggest barriers to data literacy is the lack of a data culture in the organization. A data culture is a set of values, behaviors, and practices that promote the utilization of data for decision making, problem solving, and learning. Without a data culture, data literacy becomes a standalone ability that is not integrated into the everyday objectives, strategies, and operations of the business. To overcome this challenge, it is essential to foster a data culture by communicating the advantages and value of data literacy to all stakeholders, providing data literacy training and resources to help employees enhance their data skills and confidence, creating data champions and mentors who can advocate for data literacy and instruct others in using data effectively, and encouraging data curiosity and experimentation while rewarding data-driven outcomes and insights.

2 Data quality and accessibility

A common challenge to data literacy is the lack of data quality and accessibility. Data quality is related to the accuracy, completeness, consistency, and reliability of the data, while data accessibility refers to how easy and fast it is to access and retrieve the data. Poor quality and accessibility can reduce the trustworthiness and usefulness of the data, making it difficult for data literate professionals to perform their tasks. To address this issue, you need to improve data quality and accessibility by establishing governance and standards for collecting, storing, and managing data; implementing checks and audits to identify and resolve errors, gaps, or inconsistencies; providing catalogues and metadata; and leveraging platforms and tools that enable easy access, integration, and analysis.

3 Data complexity and diversity

A third common challenge to data literacy is the increasing complexity and diversity of the data. Data complexity refers to the volume, variety, velocity, and veracity of the data while data diversity describes the different types, sources, formats, and dimensions of the data. As the amount and variety of data grows exponentially, it becomes more difficult for data literate professionals to process, interpret, and communicate the data effectively. To combat this difficulty, a clear and relevant data strategy should be developed that aligns with business objectives and priorities and defines the scope, purpose, and requirements of the data. Additionally, appropriate data analysis methods should be applied for the type and nature of the data such as descriptive, diagnostic, predictive or prescriptive analytics. Furthermore, using data visualization tools such as charts, graphs, dashboards or stories can simplify and enhance the presentation and interpretation of the data. Finally, collaborating with experts and stakeholders who can provide different perspectives on the data can also be beneficial.

4 Data ethics and privacy

A fourth common challenge to data literacy is the ethical and privacy implications of the data. Data ethics and privacy refer to the principles and standards that guide the responsible and ethical use of data. As data literate professionals collect, analyze, and share data, they need to adhere to data ethics and privacy by understanding the laws and regulations that apply to their industry, region, and context. Moreover, informed consent and permission must be obtained from data owners before collecting, using, or disclosing their data. Additionally, personal and sensitive data should be minimized and anonymized, with encryption and other security measures employed in order to protect the data from unauthorized access or misuse. Lastly, transparency and accountability should be maintained for the data used and insights generated, respecting the rights of the data owners and subjects.

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?