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Company leaders looking to gain a competitive edge can prioritize data literacy for employees across departments and at all levels within their organization.

But from raw data to business outcomes, data literacy is ripe with challenges. The amount of data businesses generate is only as impressive as how much of it is not put to use. As companies leverage data and analytics tools and modernize their infrastructure, migrating to the cloud or hybrid cloud, data literacy becomes essential.

Knowing when data is fit for business and how to store data, meet compliance and governance demands, and clean data for business intelligence dashboards or AI and machine learning predictive technologies is at the core of data literacy.

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Why is data literacy important for your business?

Data literacy enables everyone in the organization to ask the right questions, gather the right data and connect the right data points to derive meaningful and actionable business insights. It even ensures employees understand how to manage and use data in ways that are ethical and compliant.

As such, data literacy skills and culture have shifted from being strictly technology-industry related to essential for all business operations. All departments and roles within an organization need to be data literate; this includes leaders, managers, C-suite executives and even board members.

Leaders and executives need to understand the basic principles of data to make better decisions, approve new software or projects, and understand presentations and communications. Leaders are also responsible for building a strong data literacy culture throughout the entire organization. They must become data literacy ambassadors and lead by example.

SEE: Discover strategies for raising data literacy among your customers and employees.

According to a 2022 Qlik data literacy survey of 6,000 employees, which included 1,200 executives, 85% of business leaders believe data literacy will be critical for business success in the future. In addition, the survey highlighted that the majority of business leaders expect their teams to make decisions based on data.

And while remarkable technological strides have been made in machine learning, artificial intelligence and big data, there is a lack of data-savvy professionals who have the skills to use data effectively. With appropriate data literacy training, organizations will have the in-house knowledge to optimize these emerging technologies for a variety of industrial and consumer use cases.

Benefits of data literacy

Data literacy can help individuals and organizations improve customer service, reduce costs and increase profits, manage risk more effectively, make better use of resources, create a more data-driven culture and build stronger data governance postures. These improvements come about as a result of several key benefits of data literacy:

  • Improved decision-making: Data literacy helps individuals and organizations make better decisions by providing them with the ability to understand and analyze data.
  • Increased productivity: Data literacy increases visualization and analytics, which in turn affects performance. This allows companies to solve problems more rapidly and efficiently, as well as potentially increasing sales and production or decreasing costs and risks.
  • Increased reputation and innovation: Through data literacy, companies can build brand reputation benefiting their workers and customers, and by fostering innovation, they can be first in line for new opportunities and technologies.
  • Enhanced communication: Understanding data enables employees to better communicate complex data analysis clearly and concisely, thus improving collaboration.

Data literacy examples and use cases

Data ecosystems

Data literacy is useful in establishing and maintaining a reliable data ecosystem, which can include physical infrastructure, such as cloud storage, or service space and non-physical components, such as software and data sources.

Data governance

Organizations use data governance to manage their data assets so that they are complete, accurate and secure. Data governance is not the sole responsibility of any particular team; the entire workforce must have the appropriate data literacy levels to contribute to its success.

SEE: Learn more about data governance frameworks.

Many organizations have a data policy that all employees must understand and adhere to. This includes how to access sensitive data, how to ensure data remains secure and other data processes.

Data wrangling

Data wrangling is the process of converting raw data into a more structured and usable format. Data wrangling helps reduce errors in the data. An organization might have individuals or automated software for data wrangling, but every employee that works with any form of data also plays a role in keeping data in an acceptable format.

Data visualization

Creating a visual representation of data, such as a chart or graph, allows data professionals to more effectively communicate insights derived from data. Visualization can include infographics, tables, videos, charts and maps. Both the creators of these visualizations and the stakeholders to whom they are presented need at least baseline levels of data literacy to understand the implications of the data in front of them.

What are the important data literacy skills?

Data types and structures

Understanding data as it evolves through different stages and data structures is a basic data skill. This involves not only recognizing numeric data from text data or categorical data, but also identifying raw data from data fit for business or data that is poorly formatted, incorrect or outdated.

SEE: Learn more about data management.

How data is structured and stored and knowing where it came from are vital to building comprehensive data inventories and data flow charts, which will serve as the foundation for most data operations.

Data cleansing

A business intelligence dashboard that executives and leaders use to make decisions can be a liability if the data used to build it is incorrect. This means selecting raw data that is useful and making sure it is properly formatted and is not incorrect in any way.

Data generation

Data generation involves all the processes and endpoints used by a business to collect or create new data. Usually, the first stages of data generation are ripe with raw data. However, when collecting or generating new data, compliance, security and reliability of the data as well as a proper inventory are vital.

Data analysis

Data analytics involves recognizing which data is useful for a business goal and selecting that data as a “feature.” There are many automated technologies today that excel in data analytics, including automatic feature engineering.

SEE: Explore these top data science tools.

However, humans are still vital for data and analytics to work well. Not only must the right data be identified, but it must be correlated with other data; patterns need to be found; and comparisons, conclusions and projections need to be made.

Data stories

Using data to tell data stories is a vital data literacy tool. No one wants to see endless Excel sheets during a presentation or read through them in an email. A person may be the most talented analytic expert on a team, but if they cannot use data to tell a story or communicate, connect and engage with others, the value of the data is lost.

Instead of using tables, data stories use high-impact graphs, visualizations, videos, animations, maps and other elements that make data more engaging and easier to understand.

Incorporating data literacy in an organization

Because data literacy affects every aspect and worker within an organization, companies often struggle when creating strategic plans to incorporate data literacy effectively into their culture. Where should leaders begin?

In addition to making data accessible and easy to use for everyone and encouraging a culture of innovation, companies need to:

  • Identify people, processes and technology: Developing a clear strategy that outlines people, processes and technology is the foundation of a strong data literacy culture.
  • Open communication channels: Communication channels between all workers, executives and board members are an excellent way to get everyone involved in data literacy efforts; encourage innovation and feedback; and identify problems, challenges, risks and opportunities.
  • Incorporate top to bottom and bottom to top: Data literacy needs to be built from top to bottom and from bottom to top to identify leaders in all departments and areas of the company that can act as data stewards to help drive data literacy at all levels.
  • Assign data leaders: Leaders can model data literacy by using data in their own work, sharing data-driven insights with their teams and asking questions about data.
  • Know the company’s data literacy status: To improve data literacy, companies should make an evaluation of its data literacy level to help identify the strengths and weaknesses as well as the specific needs of different teams and individuals.
  • Set goals: Leadership should set measurable goals that are aligned with the business’s strategies for the state of data literacy.
  • Develop a data literacy training plan: Companies should offer data training, skilling, seminars and simulations designed around assessments and goals to level up data literacy skills throughout the organization.
  • Reward learning: Rewards should be incentives and not competitions that build pressure and recognize those who make progress toward data literacy.

Data literacy is an organization-wide effort

For organizations to be truly data-driven, it should not just be the tech experts who become data literate; everyone in the workplace must develop data literacy skills to keep the business competitive and compliant.

Business intelligence experts and data scientists can coach their peers on becoming data literate. However, it has to be an organization-level commitment that covers all employees with data literacy training courses and other resources for support.

Businesses may not immediately see the value of providing data literacy education to all of their employees, but the long-term benefits are clear: Data-literate individuals are able to expertly question and analyze data logic, applying their data-driven knowledge to each business problem they’re asked to solve.

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