A Data Literacy program trains employees to embrace and leverage data across organization but takes time, effort and the support of C-level management and HR is fundamental.
In order to become data driven we need to provide data and technology (supply) and also develop a scientist mindset in employees across the organization (demand side)
The main hurdle to become data driven is not technology, it is people, processes and chttps://towardsdatascience.com/platform-products-for-machine-learning-3d3749443d2ulture.
Extracting insights and value from data to solve business problems and identify oopportunities is not a responsability only for the data science team. Data literate users can embrace and leverage data across the organization.
Success in data-driven culture and data science initiatives comes down to people. People = HR. The data literacy program is the bridge between data driven culture and business functions.
Data literacy goals:
- Employees are able to critical think and analyse using data
- Make data-based decisions instead of intuition ones
- Communicate ideas relying on data to improve products or operations
- Understand data visualizations
The skills needed by employees are critical and analytical thinking using data. In particular:
- Data Concepts and applications: high level issues and challenges associated with data and data ethics and security
- Data access and collection: Ability to access and find information in multiple data soruces. Employees should be able to find sources and understand how data has been collected in those assessing the trustworthiness of that data.
- Data management: Ability to organize and make sense of multiple types of data
- Data relevancy: Be able to prioritize the use of data
- Data driven inquiry: Be able to identify business problems and pose the correct questions which might be answered using basic data analysis.
- Data tools: Common data analysis tools and techniques
- Data Communication: Employes should be able to read and produce communications with data. Basics of data visualization
Create data vision and roadmap Identify areas of business where data is critical. Management and HR communicate why data maters. This provides context to employees on why they need to upskill. C level support is a necessary condition for achieving a data driven company.
Data literacy vision Vision must include desirable skills, abilities and level of data savviness for different business units. Questions:
- Do we need EVERYONE to have a data language understanding?
- Certain roles in operations or strategic planning require a comparative analysis level of understanding?
- Which roles has a higher priority in data literacy upskilling?
- How data-driven we should be at different levels?
We need to build a framework to achieve data literacy goals, measure progress and move forward:
- Decide skills required by whom
- How to measure skills development
- Which decisions need to be data-driven
Design a plan to achieve data literacy goals Roll out to the entire organization or starting with a pilot? Identify data evangelists, quickly upskill them and share results and iterate. HR decide what formats are best and budgets and resources needed. Decisions on access to data, automation tools and what level of data literacy they should have before providing access to tools and databases.
Assess workforce data skills Workforces are highly diverse, important not to treat everyone the same. Basic foundations needed by everyone, HR should determine how to assess baseline skills. These assessments should start during hiring processs for new employees. Skill mapping should also asses employee level of interest.
Assessment should be efficient and consistent
Create individual and team data literacy learning development plans HR identifies and tracks areas of improvement in data literacy education Once HR and managers have identified gaps in data skills, HR construct learning programs Include a program where to find data related to their role, governance rules, data ethics, tools used…
Create data literacy delivery modules Delivery should be execute with different levels: from beginner, introductory to self-service data science. Introductory courses should make sure everyone on the same page of data concepts, ethics and potential uses.
Aditional learning modules specific by business unit (People Analytics for HR, design with data).
- in-serveice with data science teams
- Project-based learning.
Provide ooportunities to practice and reward data literacy skills Opportunities to use and experiment with data. use of data visualizations in every day work. Be able to feel comfortable reaching out the data science team Promote culture of curiosity and critical thinking
Track, measure, rinse and repeat We need to periodically assess improvements in data literacy and adjust the program to support new needs
Business leaders must allow employees to invest time to become data literatre