To be data literate you need to have the ability to derive meaning from data and communicate that meaning to others. Data literacy competencies include the knowledge and skills to read, analyze, interpret, visualize and communicate data as well as to understand the use of data in decision-making.

Data literacy also means having the knowledge and skills to be a good data steward, including the ability to assess the quality of data, protect and secure data, and take responsibility for its ethical use. The ethics part of this should be non-negotiable.

A quick search on data literacy skills within Ibbaka Talent provides a listing which has been growing over time.

When we expand this search in two directions. Moving up a level we see the different domains and disciplines that require data literacy. Some of these are …

• Content Marketing

• Design Thinking

• Resource Exploration

• Resource Operations

• Operations Management

• Pricing (design, analysis, optimization)

• Scenario Planning

• Talent Management

Drilling down a level, one gets to the technical and tool skills that are used …

Tools

• Conjoint.ly

• Python

• R

• Keras

• Sawtooth

• Tensorflow

• …

Technical skills

• Bayesian statistics

• Causal Modeling

• Conjoint Analysis

• Frequentist statistics

• Probability

• Machine Learning

• …

There are also Design skills to consider

• Data Visualization

• Infographics Design

• User Experience

• …

At Ibbaka, we categorize skills by business, domain, technical, design, foundational, language, social and tools. So for me, I’d like to introduce my thoughts on some skills required based on this categorization.

Business - Skills categorized under business are those that are required to conduct business. As many companies are beginning to recognize the value of data, a key business data literacy skill is data monetization. Data monetization takes into account the ability to leverage data to capture quantifiable economic benefit. Being able to identify ways to extract value from data is a growing need. Data itself is rapidly becoming an indispensable value creation ingredient. To foster a culture of data literacy, we also need to have business skills related to strategic planning as we need to define our data-driven ambitions.

Domain - Domain skills are those associated with broad areas of human knowledge and expertise. A domain specifies an area of specialization. A domain area that is growing is IoT. As more devices get connected to the Internet of Things, the volume of available data increases exponentially. Being articulate in IoT skills such as: architecture, device management, data storage, management, and databases, as well as data analytics will become more valuable. 

Technical - Technical skills are those used for STEM (science, technology, engineering, and mathematics). When it comes to data literacy, we often default to technical skills. Data Analysis is a key skill often associated with data literacy and revolves around the ability to ask and answer a range of questions by making sense of data. This will include selecting and using the appropriate statistical approaches, tools and techniques. Data analysis involves interpreting, evaluating and comparing results with the aim of generating insight that can be actioned. Another technical skill related to data literacy is Data Modelling. Data modelling requires the ability to apply advanced statistical and analytic techniques and tools (e.g. regression, machine learning, data mining) to perform data exploration and build accurate, valid and efficient modelling solutions that can be used to find relationships between data and make predictions about data.

Design - Design skills are those used in creative work. Often these skills are fundamental to creating new solutions and fostering innovative thinking. Data visualization skills allow us to create meaningful ways to represent and communicate meaning in our data. The aim is to enable data to be consumed at an intuitive level. A table, chart or graph can drive more effective understanding and better represent data outcomes or insights. Data visualization plays a role in being able to tell as story with your data.

Foundational - Skills used to build other skills are categorized as foundational. Research skills are a foundational skill of data literacy. Research involves searching, locating, extracting, organizing, evaluating, presenting and applying information. Research skills will also depend on critical thinking skills. When considering data gathering, we first need to understand the question we are trying to answer. What forms of data will support our needs? We may choose to plan, develop, and execute surveys and interviews. We will need to find commonalities and differences in the answers. Can we find patterns? What insights can we learn from others? 

Social - Social skills are those skills used to work with others. Two social data literacy skills I would like to highlight include storytelling and data ethics. Storytelling is an essential skill especially in today’s information saturated world. Messages will not resonates unless packaged in a great story. Stories create “sticky” memories. We use stories to get our point across, sway opinions or close deals. To have credibility, the data behind our stories matter more. From a data literacy standpoint, we want to have the skills to describe key points with statistically based information. We also need to be able to identify the context in which our audience can relate to, so that we can build common understanding. Looking at social skills associated with data literacy, skills around data ethics are important. Data ethics is a body of knowledge that will enable us to have good governance around data. Data ethics shape the data we acquire, use, interpret and share data in an ethical manner (within our moral principles). We will need to consider our legal and privacy obligations. These decisions will shape our data culture.

Tools - Skills within the tools category are specific tools used in doing work. Data tools include software, tools and the processes used to gather, organize, analyze, visualize and manage data. At Ibbaka, our Ibbaka Talent Platform is a tool that gathers, organizes, analyzes, visualizes and manages data around skills and competencies. Our Ibbaka Value Pricing Dashboard is a tool that gathers, organizes, analyzes, visualizes and manages data around value that is being delivered. 

Analytics and Business Intelligence platforms are among the key tools for data literacy. I share with you Gartner’s Magic quadrant on this evolving space.

I would like to hear other people’s skills suggestions for data literacy. Ibbaka is conducting a number of interviews to gather input on data literacy initiatives within corporate environments. Please reach out to us if you are willing to dive deeper into this topic to share your views. Allow me to start the conversation about data literacy skills in this post. You can reach me at karen@ibbaka.com.

Other posts on data literacy

• What skills are required for data literacy? (This Post)

• Data Literacy and Skills: Asking the Right Questions

• What does data literacy do for you?