Why Data Literacy Is Important for Your Team

Welcome to the golden age of data. Today, companies have the privilege of collecting and storing ever-increasing amounts of data in all shapes and sizes. That’s great news, but how good are companies at extracting value from data? Not excellent, according to a Forrester report that cites that 60%to 73% of all data within an enterprise remains unused for analytics in 2019.

Why is that so? One of the reasons companies are stopping short of becoming fully data-driven, according to Accenture, is the lack of data skills that stifles workplace productivity. Surprisingly, in a survey of 9,000 companies, Accenture found that three-quarters of the employees feel overwhelmed or unhappy when working with data. Some employees report feeling as such because they are not ‘keeping up’ with the rapid changes in technological transformations. In fact, 59% of employees report feeling burnt out when using business intelligence and data analytics tools. Such unhappiness can be averted if employees are equipped with the skills required to deal with data, or, more precisely, data literacy.

Data literacy can be defined as the ability to read, write, analyze, and communicate with data. Much like how one does not need to be Shakespeare to be considered literate, one does not need to become a data scientist to be data literate. Data literacy is not about learning every single programming language or the most advanced data science skills. Instead, it is about understanding and making decisions with data.

Achieving data literacy is an essential step in a company’s roadmap to become data fluent. More concretely, it helps employees make data-driven decisions, interact with data critically, form effective data governance, and make ethical data decisions.

Data literacy is the keystone to making data-driven decisions

McKinsey found that employees are more likely to buy into a change when they understand it. Employees who understand both data science and its business application can spearhead the end-to-end development of use cases, and in turn influence others to make data-driven decisions.

For instance, the Houston Astros used analytics to build its competitive advantage in a zero-sum industry, paving its way to victory in the Major League Baseline in 2017. Their secret? Executives who champion data literacy and make data-driven decisions. They employed data literate coaches (who can program in SQL) who could translate data insights to a language understood by players. Beyond that, the Astros built a strong data culture that permeates every facet of the team’s game strategy running the gamut from the process of player selection before the game to that of player position during the game. Such data-driven decisions would not have been possible without a data literate team.

Data literacy is a prerequisite for employees to interact with data insights meaningfully and critically.

Data-literate employees are capable of creating and analyzing data visualizations that are integrated into the company's decision-making process. An example of such a company is PepsiCo, which uses Tableau and Hadoop to visualize large volumes of data that drive million-dollar sales decisions.

Moreover, being data literate is also a prerequisite to one’s ability to critically evaluate the validity of the data used to create visualizations.

These employees who interrogate the reliability, correctness, and consistency of the data sources can potentially catch costly mistakes, thereby improving the company’s confidence and effectiveness in making data-driven decisions.

Data literacy forms the foundation of effective data governance

Data governance is the set of policies, processes and organizational structure that define how the organization’s data is managed. It ensures that data is readily available, relevant to create value for the company, of high quality, and compliant with existing regulations. To build strong data governance, leadership must have basic data literacy to first understand the data context and needs of the organization before setting data policies.

McKinsey suggested that the foundation for effective data governance involves a data organizational structure with three components:

1. the data management office (DMO) in charge of defining policies and standards
2. domain leaders who set and execute domain-specific strategies
3. the data council that connects the domain leaders and DMO

Conceivably, the leaders in each of these components should understand data processes sufficiently to set up unambiguous and fair data governance roadmaps. For instance, data-literate domain leaders who understand the value of the data can effectively work with the DMO to design and deploy an appropriate enterprise data lake for a domain.

Data literacy helps companies make ethical AI decisions

Companies that deploy AI systems for real-world use cases face the risk of AI systems, such as AI accidents, breaches of data privacy, and AI bias. One way to mitigate such a risk is to engage technical and non-technical stakeholders to critically examine the processes and outputs of the data science system. Business stakeholders who are data literate play a crucial role in evaluating the risks of AI systems and ensuring that existing AI systems are fair and ethical.

For instance, as Facebook has come under intense scrutiny in recent years over its recommendation systems, the Facebook Responsible AI Initiative was founded. It consists of a multidisciplinary team of not just data scientists, but also philosophers and social scientists. This team evaluates existing systems and determines ethical standards openly. These qualitative standards are then translated and implemented by data scientists as quantitative metrics. Such debates on AI risks are productive only when all the stakeholders are well-informed and have a minimum level of data literacy to weigh the cost and benefits of an AI system. Similarly, organizations looking to operationalize and govern AI need a multidisciplinary team of business and data experts that have a common data language.

Building Organizational Data Literacy

Clearly, inculcating a strong data literacy in all employees brings about a myriad of benefits. Data literacy programs take many forms, including existing skills initiatives, e-learning courses, or specialized classroom training. Unsurprisingly, designing in-house data literacy programmes is a non-trivial and long-drawn process that requires meticulous planning. This is whyDataCamp for Business provides an interactive learning platform for companies that need to upskill and reskill their people on data skills. With topics ranging from data literacy, and data science to data engineering and machine learning, over 1,600 companies trust DataCamp for Business to upskill their talent.