- What is Data Literacy?
- A Framework for Data Literacy
- How to Begin Your Data Literacy Journey
- Data Literacy Use Cases by Industry
- Data Literacy Courses
The 2021 NewVantage Partners Big Data and AI Executive Survey found that despite 99% of organizations making active investments in data science and AI, only 29.2% have experienced any transformational outcome, and 24% of organizations claim they are data-driven. Interestingly, the number of organizations that claim they are data-driven has been in steady decline—decreasing from 37% in 2017, to 31% in 2019, and to 24% today.
This is because organizations are realizing that organization-wide data-literacy and culture are essential to the adoption of data-driven decision making at scale. In the 2021 survey, 92% of organizations claimed data culture and skills were the biggest impediments to becoming data-driven.
To build organizational data literacy, leaders must embark on transformational programs that provide their workforce with the skills, access, and tools to work with data at scale and transform their data culture.
“We live in a different era now,” said DataCamp CEO Jonathan Cornelissen. “Every industry is digitized and generating enormous volumes of data, but many organizations don’t have the right skills to put that data to work and outpace competitors. Just as companies had to develop computer literacy and software literacy in the past, now we must become data literate. Data literacy is driving the transformations of every industry and enables every department to deliver better results. It starts with building role-appropriate data skills at every level of your organization.”
What is Data Literacy?
DataCamp defines data literacy as the ability to read, write, communicate, and reason with data to make better data-driven decisions. From an organizational perspective, it is a spectrum of data skills ranging from data-driven decision making to advanced technical skills in data science, data engineering, and machine learning, resulting in everyone in the organization generating value from data at scale.
The Human Impact of Data Literacy report, a study authorized by Accenture and Qlik, uncovered that only 21% of the global workforce is comfortable working with data.
Addressing the data skill gap is in the best interest of organizations of all sizes. Not only does a lack of data literacy impact employees’ productivity, but it also blocks organizations from reaping the benefits of a data-informed business. The study also revealed that data literate organizations have three to five times greater market capitalization than organizations with low data literacy.
A Framework for Data Literacy
Becoming a data literate organization is a journey that requires scaling many levels. At DataCamp, we use the Infrastructure, People, Tools, Organization, and Processes framework (IPTOP), to help organizations understand what they need to scale on their path to data literacy.
Data Infrastructure: Ensuring access to data for all
In a data literate organization, data is collected, discoverable, reliable, understood, compliant, and actionable by everyone in the organization. Governed access to high-quality, trustworthy data ensures that an organization can make decentralized data-driven decisions at scale.
People: A role-based approach to data literacy
People are arguably the most important element of the framework alongside infrastructure. Scaling people entails forging a data culture where everyone understands the value of data and can speak, write, communicate, and reason with data. This includes rolling out personalized data literacy upskilling programs that cover the entire range of skill levels and accommodate the entire spectrum of data literacy skills.
Tools: The tooling spectrum underlying data literacy
Tools refer to the wide variety of tools data practitioners across all skill levels use. Data literate organizations are intentional about providing inclusive tooling that accommodates different types of data personas and use cases, from drag-and-drop business intelligence tools like Tableau and Power BI, to open-source programming languages like R, Python, and SQL. Another component is the development of ready-to-use templates and frameworks across all these tools that reduce time to insight and lower the barrier to entry when working with data.
Organization: Organizing data talent for success
Organization refers to how to effectively organize data talent to promote the access of data insights at scale. There are a variety of organizational models. On one end of the spectrum, organizations can opt for a centralized model which positions the data team as a support function (e.g. teams file requests, and the data team prioritizes based on business needs). On the other side of the spectrum is a decentralized model where data experts are embedded in business functions. There are pros and cons to both models, and many hybrid models that sit in between.
Processes: Driving data literacy with collaboration
Finally, processes are about establishing processes within and between teams to drive data literacy at scale. For example, Microsoft has adopted The Team Data Science Process, which provides transparency to stakeholders as they can: easily view the project and understand the requirements; distinguish who is responsible for what stage in the process; and utilize templates for data analysis. This extends to the processes of business stakeholders working with the data team and ensuring that they’re meant to scale.
How to Begin Your Data Literacy Journey
A 2019 study by McKinsey & Company revealed that organizations consistently making decisions based on data were 1.5 times more likely to report revenue growth of at least 10% over the prior three years. Numbers like these are the reason why many organizations are asking the same question: How can we become data literate?
The adoption of data literacy can be broken down into three stages.
Assess for data literacy
A data literacy assessment is an opportunity to benchmark the current data skills across the organization. The assessment should answer questions such as:
- How many people are currently leveraging data to make decisions?
- How many people can interpret simple statistics (i.e. correlations, mean, etc.)?
- How many people are capable of proposing new initiatives backed by relevant data?
- What is the current competency level of data scientists in areas such as machine learning, programming, data manipulation, and more?
More importantly, assessments need to be adapted to the different relationships people have with data, including the types of data skills they need to succeed in their role and the level of competency their role requires. A thorough evaluation of the assessment results should provide direction on how the organization can target specific learning content, as well as personalized learning recommendations for various types of learning personas.
Enable data access for all
For teams to become data-driven, access to data is vital. However, it’s important to ensure that data is high-quality, actionable, understood, and compliant. As such, there needs to be proper data governance set in place, with data stewards ensuring that organizational data quality standards are being communicated and enforced throughout the organization.
Governed data access must be combined with access to modern data tooling so that data insights may be derived by employees of all skill levels. Data scientists, who are typically quite technical, require access to programming languages such as Python, R, or SQL in order to manipulate, analyze, create predictive models, and share their insights.
Whereas less technical employees, such as business analysts, require more accessible tools like Power BI and Tableau that allow them to make data-driven decisions that impact day-to-day operations.
Roll out a data literacy upskilling program
People are at the cornerstone of data maturity across stages. When an organization seeks to drive data literacy, the initial challenge is making data a default consideration in most decisions and actions taken. A major blocker organizations face is insufficient data literacy which restricts the ability of non-experts to conduct independent work when using data.
A best practice when rolling out organization-wide training is to create personalized learning paths for various roles. For example, Airbnb created Data University, a program aimed at upskilling the entire organization on data skills, with content curated for specific data teams in their Data U Intensive program. Cohorts were able to achieve a 30% sustained increase in SQL usage.
In a data literate organization, learning paths cover a range of topics from general data literacy, to data science, machine learning, data engineering, and more. Ultimately, organizations can adopt a common data language as well as reinforce data science as a habit and methodology used to approach business problems.
Data Literacy Use Cases by Industry
In a world increasingly powered by data, the importance of organizational data literacy is significantly enhanced. An organization’s success is heavily dependent on its employees’ ability to operate with data—thus, professional performance is closely associated with a solid grasp of data to aid organizations in making better decisions.
HealthIT recorded that more than 85% of health providers in the US now use Electronic Health Records (EHRs), a number which has more than doubled since 2008. However, clinical staff are not typically taught about analytics in college or medical school. Hence, 35% of younger healthcare professionals are overwhelmed by patient data or are unclear on how to use patient data and analytics to inform care.
With industry-wide data literacy, healthcare providers can decrease costs, increase the effectiveness of patient care, and improve patient outcomes.
Here are some examples of how data literacy can benefit the healthcare industry:
- Healthcare providers can provide preventive measures to patients by interpreting descriptive analytics derived from patient data.
- Medical insurance providers can automate claims and eliminate fraud with machine learning, decreasing overall costs on the healthcare system.
- R&D specialists in pharmaceuticals can supplement their subject matter expertise with programming skills and machine learning, leading to faster drug deployments and improved health outcomes for some populations.
The expenditure of the public sector represented 49.3% of GDP in 2012 for the European Union. Hence, a more efficient public sector could mean fewer resources such as taxes would be required to provide the same level of service to the public.
As the public sector around the world becomes increasingly aware of the value to be gained from data and data literacy, here are some use-cases to realize the value:
- Government agencies can leverage data to provide faster crisis response. A key example is using dashboards and maps for tracking wildfires and prioritizing actions.
- Government agencies can prioritize interventions based on insights. A key example comes from the city of New York, leveraging data to identify which landlords are most likely committing illegal tenant harassment.
- Government agencies can empower the public to make better decisions. For example, the Business Atlas in New York City helps small businesses make data-driven decisions on where and what to sell.
Despite being one of the most data-rich industries, the Human Impact of Data Literacy report revealed that only 38% of financial services employees are fully confident in their ability to read and work with data. Not only does this take its toll on the organization's ability to reap the benefits of data, but it also affects the employee’s overall engagement at work.
The following examples serve as use cases of where financial services organizations can realize the benefits of their existing data:
- Financial analysts can leverage business intelligence tools to reduce time to insight and monitor financial crime indicators with dashboards.
- Banks and insurance organizations can optimize their distribution and go to market motions with customer churn and customer lifetime value prediction.
- Investment banks can apply quantitative risk management techniques to better manage risk and optimize investment portfolios.
- Machine learning can be used to automate manual processes such as credit risk modeling.
Customer expectations of retailers have significantly increased. Now, retailers are expected to provide a personalized customer experience, a seamless experience between online channels and physical stores, and an easy way to make a purchase. Failure to meet such expectations results in losing customers. The ability to meet such expectations lies in an organization's ability to equip employees—from the shop floor to the warehouse—with at least a basic level of data literacy. This is further backed by the Data Literacy Index, which shows that organizations with strong data literacy exhibit up to 5% higher enterprise value.
Retail merchants can realize this increased enterprise value with use cases such as:
- Applying market analysis techniques to forecast demand and help to reduce cost.
- Analyze customers’ search and purchase history to recommend additional items that the customer may be interested in.
- Analyze customer shopping habits to provide a personalized shopping experience.
- Analyze and interpret customer journeys to answer questions such as: Where do customers look for product information? At what point are we losing customers? What are the most effective ways to reach customers and compel them to purchase?
Data Literacy Courses
As a first step on the path to data literacy, DataCamp’s Data Literacy Fundamentals skill track will help your organization gain the essential skills your employees need to speak the language of data. No prior knowledge or coding skills are required. Through hands-on exercises, teams will learn how to understand data and expand their knowledge of key data topics, including data science, machine learning, data visualization, and even data engineering and cloud computing. They’ll also learn about the many roles, technologies, and frameworks in data science.
The Data Literacy Fundamentals skill track includes five courses:
- Data Science for Everyone: Gain insights into data science without code as you dive deeper into what data science is and why it is so popular.
- Machine Learning for Everyone: Learn what’s behind the machine learning hype with many hands-on exercises to get you past the jargon.
- Data Visualization for Everyone: Data visualization is one of the most impactful ways to communicate with data. Learn the best practices of data visualization and discover the most user-friendly data visualization tools.
- Data Engineering for Everyone: Finding gold in data is impossible without the mine. It is the responsibility of data engineers to lay the foundations that make data science possible. Discover how data engineers facilitate the flow of data through an organization.
- Cloud Computing for Everyone: Cloud computing has become a part of many a company’s tech stack, but what is the cloud? Why has cloud computing become so popular? Get the answers to these questions and so much more in this beginner-friendly course.
Data literacy is essential for organizations that wish to remain competitive, and there is no time like the present to get your organization on the path to full data literacy. In addition to DataCamp’s diverse catalog of data literacy resources, there are also several online resources you may find helpful on your upskilling journey. Some recommended reads include:
- Data-Driven Decision Making For Business
- A Data and Analytics Leader's Guide to Data Literacy
- Why Data Literacy is Important for Your Team
- Boost your team’s data literacy
- The Data Literacy Index Qlik
- Why is Data Literacy Important for Any Business?
- Building a Scalable Data Strategy with IPTOP: Infrastructure, People, Tools, Organization, and Processes
- Roadmap for Data Literacy and Data-Driven Business Transformation: A Gartner Trend Insight Report
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