Data science and analysis is a broad field that includes programming, statistics, mathematics, algorithmic learning, programming, programming, and other related skills. This skill set is not necessary for data literacy employees. They’d be data scientists if they had these skills!
Data literacy is still an emerging concept and must be understood by learning and development program developers. Data Literacy Training encompasses a broad range of skills that require varying levels and technical and analytic expertise.
To create value out data and understand analytics outputs, employees should try to master at least some of the 7 skills listed below.
- Data concepts and applications
Employees should have a basic understanding of how data is used and applied. They need to be able to understand the major issues and challenges related to data. They should be able to comprehend the importance of data as well as concepts such as data ethics and security.
- Access and collection of data
The next step in using data to answer questions and inform decisions is the ability of employees to find and access information from multiple sources. Employees must be knowledgeable about the sources available and what information it contains.
They should also be able to evaluate the usefulness and trustworthiness of data sources. To be able to assess the trustworthiness and usefulness of data sources, you must plan and address the issues of data access.
- Data management and data synthesis
Another essential building block is the ability of employees to organize, examine and make sense of different types of data in the context of their business/role. Employees should be familiar with concepts and techniques to pool different data and determine their impact.
- Data relevancy
The misunderstanding of which data is appropriate for their job is a major barrier to employee data literacy. Employees who don’t know what data to use feel overwhelmed when their efforts don’t make sense. Employees need to be able to identify which data is relevant to their specific problem and prioritize the data they should use. They need to understand the relationships among data sources.
- Data-driven inquiry
Employees should eventually learn how to identify and formulate hypotheses or questions about business problems. This is basic data analysis. They should be able to collect and analyze data and/or find someone to help them.
This is the place where internal connections can be made to the data scientist’s team. Data scientists can help data-savvy employees translate their information inquiry into a plan of action.
- Data tools
Continuously, data exploration and analytical platforms are evolving. Employees must be knowledgeable about common tools and techniques for data analysis and should be able to select and apply them.
- Data communication
Employees must feel at ease communicating with data to and from various stakeholders. They should be able to explain to others why data is valuable and which stakeholders it should be used for. They should be trained in data visualization techniques and be able to analyze graphical representations of data to determine their relevance and errors.
A Roadmap for Establishing Data Literacy
Expert predicts in 2020 that 80% of organizations have already started to implement internal data literacy initiatives to improve their workforce.
Human Resources have worked hard over the last few decades to source, recruit, and hire expensive, difficult-to-retain data specialists. HR now has to help create an organizational foundation for data literacy. This will ensure that data science resources are successful. This is not an easy task.
Data leaders and HR learning and management managers need to find ways to effectively teach the rest of their organization data science so it can have a true strategic impact.
This is why we have created a high-level roadmap to help you roll out a data literacy program across your entire enterprise.