Data is all around us and in everything we do. It’s generated every time we use a computer, smartphone, or other device. While data has become critical in all aspects of human life, the workplace is certainly no exception.

Businesses collect data from a variety of sources, including customer transactions, social media activity, surveys, online tracking, and more. And that data is powerful! Organizations can use it to improve products and services, make better decisions, gain insight into customer behaviors, and identify industry trends.

In the past, businesses primarily collected data through manual methods, like observations, surveys, and questionnaires. With technologies like big data, artificial intelligence, and the Internet of Things (IoT) on the rise, however, we’ve seen unprecedented rates of both data creation and data collection.

In fact, in 2020, it was reported that the global datasphere had reached 64 zettabytes or the equivalent of 64 trillion gigabytes. At that time, 90% of the world’s data had been created in the last two years alone. And even more: it’s estimated that by 2025, global data creation will grow to over 180 zettabytes.

As the volume of available data continues to grow exponentially, the question becomes: are businesses and organizations using that data to the best of their ability? And the answer, at least for now, is no. Research from Qlik and Accenture suggests that, while organizations collect a lot of data, 60-73% of that data is never analyzed. So, how do we change that?

Data is all around us and in everything we do. It’s generated every time we use a computer, smartphone, or other device. While data has become critical in all aspects of human life, the workplace is certainly no exception.

Businesses collect data from a variety of sources, including customer transactions, social media activity, surveys, online tracking, and more. And that data is powerful! Organizations can use it to improve products and services, make better decisions, gain insight into customer behaviors, and identify industry trends.

In the past, businesses primarily collected data through manual methods, like observations, surveys, and questionnaires. With technologies like big data, artificial intelligence, and the Internet of Things (IoT) on the rise, however, we’ve seen unprecedented rates of both data creation and data collection.

In fact, in 2020, it was reported that the global datasphere had reached 64 zettabytes or the equivalent of 64 trillion gigabytes. At that time, 90% of the world’s data had been created in the last two years alone. And even more: it’s estimated that by 2025, global data creation will grow to over 180 zettabytes.

As the volume of available data continues to grow exponentially, the question becomes: are businesses and organizations using that data to the best of their ability? And the answer, at least for now, is no. Research from Qlik and Accenture suggests that, while organizations collect a lot of data, 60-73% of that data is never analyzed. So, how do we change that?

Data is all around us and in everything we do. It’s generated every time we use a computer, smartphone, or other device. While data has become critical in all aspects of human life, the workplace is certainly no exception.

Businesses collect data from a variety of sources, including customer transactions, social media activity, surveys, online tracking, and more. And that data is powerful! Organizations can use it to improve products and services, make better decisions, gain insight into customer behaviors, and identify industry trends.

In the past, businesses primarily collected data through manual methods, like observations, surveys, and questionnaires. With technologies like big data, artificial intelligence, and the Internet of Things (IoT) on the rise, however, we’ve seen unprecedented rates of both data creation and data collection.

In fact, in 2020, it was reported that the global datasphere had reached 64 zettabytes or the equivalent of 64 trillion gigabytes. At that time, 90% of the world’s data had been created in the last two years alone. And even more: it’s estimated that by 2025, global data creation will grow to over 180 zettabytes.

As the volume of available data continues to grow exponentially, the question becomes: are businesses and organizations using that data to the best of their ability? And the answer, at least for now, is no. Research from Qlik and Accenture suggests that, while organizations collect a lot of data, 60-73% of that data is never analyzed. So, how do we change that?

What’s changed?

The Past

Until quite recently, data was only accessible to a select few within an organization. In many companies, teams made up of data scientists, data analysts, and data engineers were entirely responsible for the data within their organization. Data teams were responsible for everything from collecting and cleaning the data, to analyzing the data, to visualizing and modeling the data, to providing insights and recommendations, and more. In the more traditional models of the past, data teams controlled the data; they were the gatekeepers. Users had to request the data from those teams, wait for their request to be processed, and then act on the insights, which proved to be arduous and inefficient.

In other instances, data was segregated by the department in which it was used, making it inaccessible to other business units. In either scenario, the data was not widely available throughout the organization; it was siloed.

According to Deloitte, data silos can be defined as “collections of data, which are isolated to groups of people across the business.” Whether due to company culture, company infrastructure, lack of technology adoption, or limited software integration, data silos are ineffective and unsustainable, given the substantial growth of data.

When data is the responsibility of a small group of individuals alone, it can cause significant negative impacts, such as limited cross-departmental collaboration, wasted resources, inaccuracies in the data, incorrect analysis, increased cost, and reduced productivity. On the other hand, when data is more widely accessible to a larger group of people, it can lead to better decision-making, increased productivity, and improved collaboration. As the amount of available data continues to increase, it becomes even more important to encourage greater data and information sharing within an organization.

Until quite recently, data was only accessible to a select few within an organization. In many companies, teams made up of data scientists, data analysts, and data engineers were entirely responsible for the data within their organization. Data teams were responsible for everything from collecting and cleaning the data, to analyzing the data, to visualizing and modeling the data, to providing insights and recommendations, and more. In the more traditional models of the past, data teams controlled the data; they were the gatekeepers. Users had to request the data from those teams, wait for their request to be processed, and then act on the insights, which proved to be arduous and inefficient.

In other instances, data was segregated by the department in which it was used, making it inaccessible to other business units. In either scenario, the data was not widely available throughout the organization; it was siloed.

According to Deloitte, data silos can be defined as “collections of data, which are isolated to groups of people across the business.” Whether due to company culture, company infrastructure, lack of technology adoption, or limited software integration, data silos are ineffective and unsustainable, given the substantial growth of data.

When data is the responsibility of a small group of individuals alone, it can cause significant negative impacts, such as limited cross-departmental collaboration, wasted resources, inaccuracies in the data, incorrect analysis, increased cost, and reduced productivity. On the other hand, when data is more widely accessible to a larger group of people, it can lead to better decision-making, increased productivity, and improved collaboration. As the amount of available data continues to increase, it becomes even more important to encourage greater data and information sharing within an organization.

Until quite recently, data was only accessible to a select few within an organization. In many companies, teams made up of data scientists, data analysts, and data engineers were entirely responsible for the data within their organization. Data teams were responsible for everything from collecting and cleaning the data, to analyzing the data, to visualizing and modeling the data, to providing insights and recommendations, and more. In the more traditional models of the past, data teams controlled the data; they were the gatekeepers. Users had to request the data from those teams, wait for their request to be processed, and then act on the insights, which proved to be arduous and inefficient.

In other instances, data was segregated by the department in which it was used, making it inaccessible to other business units. In either scenario, the data was not widely available throughout the organization; it was siloed.

According to Deloitte, data silos can be defined as “collections of data, which are isolated to groups of people across the business.” Whether due to company culture, company infrastructure, lack of technology adoption, or limited software integration, data silos are ineffective and unsustainable, given the substantial growth of data.

When data is the responsibility of a small group of individuals alone, it can cause significant negative impacts, such as limited cross-departmental collaboration, wasted resources, inaccuracies in the data, incorrect analysis, increased cost, and reduced productivity. On the other hand, when data is more widely accessible to a larger group of people, it can lead to better decision-making, increased productivity, and improved collaboration. As the amount of available data continues to increase, it becomes even more important to encourage greater data and information sharing within an organization.

The Present

In recent years, there has been a notable shift toward data-driven decision-making. Companies are recognizing the importance of utilizing data more fully and relying less on gut feelings and instincts. With this shift in strategy comes a change in organizational infrastructure and culture, with a focus on collaboration and sharing of data.

Concepts like data culture and data democratization are becoming increasingly more commonplace. Creating a strong data culture within an organization means creating an environment in which employees value, practice, and encourage the use of data to improve decision-making. This leads to an overall competitive advantage: improved insights, more agile decision-making, data transparency, empowered employees, and collaboration and alignment within the company.

In recent years, there has been a notable shift toward data-driven decision-making. Companies are recognizing the importance of utilizing data more fully and relying less on gut feelings and instincts. With this shift in strategy comes a change in organizational infrastructure and culture, with a focus on collaboration and sharing of data.

Concepts like data culture and data democratization are becoming increasingly more commonplace. Creating a strong data culture within an organization means creating an environment in which employees value, practice, and encourage the use of data to improve decision-making. This leads to an overall competitive advantage: improved insights, more agile decision-making, data transparency, empowered employees, and collaboration and alignment within the company.

In recent years, there has been a notable shift toward data-driven decision-making. Companies are recognizing the importance of utilizing data more fully and relying less on gut feelings and instincts. With this shift in strategy comes a change in organizational infrastructure and culture, with a focus on collaboration and sharing of data.

Concepts like data culture and data democratization are becoming increasingly more commonplace. Creating a strong data culture within an organization means creating an environment in which employees value, practice, and encourage the use of data to improve decision-making. This leads to an overall competitive advantage: improved insights, more agile decision-making, data transparency, empowered employees, and collaboration and alignment within the company.

But creating a strong data culture isn’t the entire solution. Remember those data silos? Data democratization turns that concept on its head. Democratizing data is the idea that a wider range of people should have access to the data within an organization. This is specifically in opposition to the more traditional approach mentioned above: data is no longer the responsibility of a small group of people, but rather it becomes the responsibility of all users, regardless of their background or technical abilities. The Human Impact of Data Literacy, a global research study done by Qlik and Accenture, found that 65% of employees surveyed say they read and interpret data as part of their roles, but only 21% are confident in their data skills. This mismatch in skill level vs. job responsibilities is cause for concern.

According to Amplitude, there are three key principles an organization needs to embrace in order for data democratization to happen:

But creating a strong data culture isn’t the entire solution. Remember those data silos? Data democratization turns that concept on its head. Democratizing data is the idea that a wider range of people should have access to the data within an organization. This is specifically in opposition to the more traditional approach mentioned above: data is no longer the responsibility of a small group of people, but rather it becomes the responsibility of all users, regardless of their background or technical abilities. The Human Impact of Data Literacy, a global research study done by Qlik and Accenture, found that 65% of employees surveyed say they read and interpret data as part of their roles, but only 21% are confident in their data skills. This mismatch in skill level vs. job responsibilities is cause for concern.

According to Amplitude, there are three key principles an organization needs to embrace in order for data democratization to happen:

But creating a strong data culture isn’t the entire solution. Remember those data silos? Data democratization turns that concept on its head. Democratizing data is the idea that a wider range of people should have access to the data within an organization. This is specifically in opposition to the more traditional approach mentioned above: data is no longer the responsibility of a small group of people, but rather it becomes the responsibility of all users, regardless of their background or technical abilities. The Human Impact of Data Literacy, a global research study done by Qlik and Accenture, found that 65% of employees surveyed say they read and interpret data as part of their roles, but only 21% are confident in their data skills. This mismatch in skill level vs. job responsibilities is cause for concern.

According to Amplitude, there are three key principles an organization needs to embrace in order for data democratization to happen:

Most importantly, employees need to be able to feel confident in their data skills in order for true data democratization to take place. For organizations to build a strong data culture and truly become more data-driven, there must be a company-wide focus on foundational data skills.

Most importantly, employees need to be able to feel confident in their data skills in order for true data democratization to take place. For organizations to build a strong data culture and truly become more data-driven, there must be a company-wide focus on foundational data skills.

Data literacy as a core competency

What is data literacy?

As access to data continues to expand and the use of data to make decisions continues to grow, data literacy has become an essential core competency within any organization.

There are many definitions of data literacy but, at its most basic, data literacy is the ability to read, interpret, analyze, communicate, and make decisions with data. It’s important to note that data literacy skills exist on a spectrum (not all of us need to become data experts!) but all departments and roles can benefit from developing these skills.

Gartner makes the case that there are five levels of proficiency in data literacy and that a person’s level of proficiency should depend on their specific role. The levels of proficiency range from conversational, a person who has basic understanding of data but cannot necessarily explain it; to literate, a person who can speak, write, and engage in data; to competent, a person who can design, develop, and apply data analysis; to fluent, a person who is fluent in three core elements of data literacy (reading, communicating with, and working with data); to the highest level of proficiency: multilingual, a person who is fluent in all three elements across multiple business domains and industries.

In other words, people on a conversational level might focus on reading and understanding data, but not communicating the results of an analysis. As people continue to increase their data literacy level, they might begin to analyze and reason with data. Those at the highest level of proficiency are the ones who are able to read, understand, and analyze data, but most importantly, they are the people who can communicate the analysis and insights to others. Not everyone can, or should, be expected to be “multilingual” or even “fluent” in their data literacy skills. In terms of proficiency, what matters most is if the person has the data skills necessary to do their job.

Levels of proficiency aren’t the only consideration when thinking about data literacy though. Because different roles require different levels of understanding and knowledge, it’s also important to think about how each person might use data. KPMG has defined four data roles within a business, each with its own data skills and capabilities. A data believer is a person who has limited knowledge of analytics but needs to understand and engage with data to make business decisions. A data user is a person who incorporates data analysis into their day-to-day activities. Their focus tends to be on identifying and understanding what each dataset contains, as well as what insights the data can provide. A data scientist is a person who has deep knowledge of analytics and statistics. They are able to design, develop, and apply data and analytics programs. Finally, the data leader is a person who has a strong understanding of data, can interpret results and analyses, and can communicate insights to business users with more limited analytics knowledge.

Data literacy is vital because it allows everyone in an organization to understand and use data effectively, leading to better decision-making, increased productivity, improved employee retention, increased innovation, and revenue growth. In fact, the Data Literacy Index states, “In a world increasingly powered by data, data literacy is as important as literacy itself. Organizations that want to stay competitive in a data-rich world must prepare themselves to take advantage of every opportunity for it to inform their business practices and decisions.” This means that becoming a more data-literate organization is no longer a choice, it’s a necessity.

As access to data continues to expand and the use of data to make decisions continues to grow, data literacy has become an essential core competency within any organization.

There are many definitions of data literacy but, at its most basic, data literacy is the ability to read, interpret, analyze, communicate, and make decisions with data. It’s important to note that data literacy skills exist on a spectrum (not all of us need to become data experts!) but all departments and roles can benefit from developing these skills.

Gartner makes the case that there are five levels of proficiency in data literacy and that a person’s level of proficiency should depend on their specific role. The levels of proficiency range from conversational, a person who has basic understanding of data but cannot necessarily explain it; to literate, a person who can speak, write, and engage in data; to competent, a person who can design, develop, and apply data analysis; to fluent, a person who is fluent in three core elements of data literacy (reading, communicating with, and working with data); to the highest level of proficiency: multilingual, a person who is fluent in all three elements across multiple business domains and industries.

In other words, people on a conversational level might focus on reading and understanding data, but not communicating the results of an analysis. As people continue to increase their data literacy level, they might begin to analyze and reason with data. Those at the highest level of proficiency are the ones who are able to read, understand, and analyze data, but most importantly, they are the people who can communicate the analysis and insights to others. Not everyone can, or should, be expected to be “multilingual” or even “fluent” in their data literacy skills. In terms of proficiency, what matters most is if the person has the data skills necessary to do their job.

Levels of proficiency aren’t the only consideration when thinking about data literacy though. Because different roles require different levels of understanding and knowledge, it’s also important to think about how each person might use data. KPMG has defined four data roles within a business, each with its own data skills and capabilities. A data believer is a person who has limited knowledge of analytics but needs to understand and engage with data to make business decisions. A data user is a person who incorporates data analysis into their day-to-day activities. Their focus tends to be on identifying and understanding what each dataset contains, as well as what insights the data can provide. A data scientist is a person who has deep knowledge of analytics and statistics. They are able to design, develop, and apply data and analytics programs. Finally, the data leader is a person who has a strong understanding of data, can interpret results and analyses, and can communicate insights to business users with more limited analytics knowledge.

Data literacy is vital because it allows everyone in an organization to understand and use data effectively, leading to better decision-making, increased productivity, improved employee retention, increased innovation, and revenue growth. In fact, the Data Literacy Index states, “In a world increasingly powered by data, data literacy is as important as literacy itself. Organizations that want to stay competitive in a data-rich world must prepare themselves to take advantage of every opportunity for it to inform their business practices and decisions.” This means that becoming a more data-literate organization is no longer a choice, it’s a necessity.

As access to data continues to expand and the use of data to make decisions continues to grow, data literacy has become an essential core competency within any organization.

There are many definitions of data literacy but, at its most basic, data literacy is the ability to read, interpret, analyze, communicate, and make decisions with data. It’s important to note that data literacy skills exist on a spectrum (not all of us need to become data experts!) but all departments and roles can benefit from developing these skills.

Gartner makes the case that there are five levels of proficiency in data literacy and that a person’s level of proficiency should depend on their specific role. The levels of proficiency range from conversational, a person who has basic understanding of data but cannot necessarily explain it; to literate, a person who can speak, write, and engage in data; to competent, a person who can design, develop, and apply data analysis; to fluent, a person who is fluent in three core elements of data literacy (reading, communicating with, and working with data); to the highest level of proficiency: multilingual, a person who is fluent in all three elements across multiple business domains and industries.

In other words, people on a conversational level might focus on reading and understanding data, but not communicating the results of an analysis. As people continue to increase their data literacy level, they might begin to analyze and reason with data. Those at the highest level of proficiency are the ones who are able to read, understand, and analyze data, but most importantly, they are the people who can communicate the analysis and insights to others. Not everyone can, or should, be expected to be “multilingual” or even “fluent” in their data literacy skills. In terms of proficiency, what matters most is if the person has the data skills necessary to do their job.

Levels of proficiency aren’t the only consideration when thinking about data literacy though. Because different roles require different levels of understanding and knowledge, it’s also important to think about how each person might use data. KPMG has defined four data roles within a business, each with its own data skills and capabilities. A data believer is a person who has limited knowledge of analytics but needs to understand and engage with data to make business decisions. A data user is a person who incorporates data analysis into their day-to-day activities. Their focus tends to be on identifying and understanding what each dataset contains, as well as what insights the data can provide. A data scientist is a person who has deep knowledge of analytics and statistics. They are able to design, develop, and apply data and analytics programs. Finally, the data leader is a person who has a strong understanding of data, can interpret results and analyses, and can communicate insights to business users with more limited analytics knowledge.

Data literacy is vital because it allows everyone in an organization to understand and use data effectively, leading to better decision-making, increased productivity, improved employee retention, increased innovation, and revenue growth. In fact, the Data Literacy Index states, “In a world increasingly powered by data, data literacy is as important as literacy itself. Organizations that want to stay competitive in a data-rich world must prepare themselves to take advantage of every opportunity for it to inform their business practices and decisions.” This means that becoming a more data-literate organization is no longer a choice, it’s a necessity.

Why is data literacy needed?

Data literacy skills benefit both the organization and the employee. For organizations, improved data literacy skills positively impact productivity, innovation, customer experiences, profits, and more. Employees experience increased job satisfaction, decreased anxiety when handling data, improved productivity, and higher retention rates.

The Data Literacy Index is a study that looked into the gap between how organizations perceive the importance of data and the levels of data literacy within those organizations. The study defined “corporate” data literacy as, “the ability of an organization to read, analyze, utilize for decisions, argue with and communicate data throughout an organization.” The Data Literacy Index found that, based on the average organization size in their study ($10.7 billion enterprise value), enterprises with higher corporate data literacy scores can have $320-$534 million in higher enterprise value. In addition, improved corporate data literacy also showed a positive impact on other measures of corporate performance, such as gross margin, return on assets, return on equity, and return on sales.

Furthermore, a Forrester survey, Building Data Literacy: The Key To Better Decisions, Greater Productivity, And Data-Driven Organizations, asked decision-makers to compare the value of data-literate employees against those without data skills; the results show marked benefits of data literacy across entire organizations, including better decision making by employees (58%), faster decision making by employees (54%), increased productivity (50%), better ability to innovate (50%), increased confidence (46%), a higher retention rate (45%), and a better ability to provide strong customer experiences (39%).

Data literacy skills benefit both the organization and the employee. For organizations, improved data literacy skills positively impact productivity, innovation, customer experiences, profits, and more. Employees experience increased job satisfaction, decreased anxiety when handling data, improved productivity, and higher retention rates.

The Data Literacy Index is a study that looked into the gap between how organizations perceive the importance of data and the levels of data literacy within those organizations. The study defined “corporate” data literacy as, “the ability of an organization to read, analyze, utilize for decisions, argue with and communicate data throughout an organization.” The Data Literacy Index found that, based on the average organization size in their study ($10.7 billion enterprise value), enterprises with higher corporate data literacy scores can have $320-$534 million in higher enterprise value. In addition, improved corporate data literacy also showed a positive impact on other measures of corporate performance, such as gross margin, return on assets, return on equity, and return on sales.

Furthermore, a Forrester survey, Building Data Literacy: The Key To Better Decisions, Greater Productivity, And Data-Driven Organizations, asked decision-makers to compare the value of data-literate employees against those without data skills; the results show marked benefits of data literacy across entire organizations, including better decision making by employees (58%), faster decision making by employees (54%), increased productivity (50%), better ability to innovate (50%), increased confidence (46%), a higher retention rate (45%), and a better ability to provide strong customer experiences (39%).

Data literacy skills benefit both the organization and the employee. For organizations, improved data literacy skills positively impact productivity, innovation, customer experiences, profits, and more. Employees experience increased job satisfaction, decreased anxiety when handling data, improved productivity, and higher retention rates.

The Data Literacy Index is a study that looked into the gap between how organizations perceive the importance of data and the levels of data literacy within those organizations. The study defined “corporate” data literacy as, “the ability of an organization to read, analyze, utilize for decisions, argue with and communicate data throughout an organization.” The Data Literacy Index found that, based on the average organization size in their study ($10.7 billion enterprise value), enterprises with higher corporate data literacy scores can have $320-$534 million in higher enterprise value. In addition, improved corporate data literacy also showed a positive impact on other measures of corporate performance, such as gross margin, return on assets, return on equity, and return on sales.

Furthermore, a Forrester survey, Building Data Literacy: The Key To Better Decisions, Greater Productivity, And Data-Driven Organizations, asked decision-makers to compare the value of data-literate employees against those without data skills; the results show marked benefits of data literacy across entire organizations, including better decision making by employees (58%), faster decision making by employees (54%), increased productivity (50%), better ability to innovate (50%), increased confidence (46%), a higher retention rate (45%), and a better ability to provide strong customer experiences (39%).

Conversely, when employers don’t enable their employees to become more confident in using data insights, it can negatively impact the employees’ comfortability and productivity in their role. The Human Impact of Data Literacy report found that “...when accounting for data-induced procrastination and sick leave due to stress resulting from information, data and technology issues, companies lose an average of more than five working days (43 hours) per employee each year.” In the United States alone, the yearly productivity cost to the economy was over $100 billion.

It’s clear to see that there are enormous benefits of investing in data literacy for organizations but that isn’t the entire picture. Upskilling in data literacy also positively impacts employees.

Employees who have high data satisfaction show improved performance, increased satisfaction, and higher worker retention. Employees in the Forrester survey also agreed with decision-makers, saying that they made better decisions (83%) and faster decisions (82%) when using data. In addition to improved decision-making, employees also felt more competent, more motivated, and more productive than their low data satisfaction peers.

Improved work performance wasn’t the only benefit of data literacy programs. Data training also was shown to improve employee retention. In the same survey, 80% of employees reported that they were more likely to stay at a company that trains them in the data skills they need. They are significantly more likely to report satisfaction within their organization and with their team and department when compared to low data satisfaction employees.

It’s obvious that improved data literacy and confidence in data skills have benefits for both employers and employees. It’s also clear that when an organization’s workforce is not adequately trained in data literacy, there are sizable risks, including slowed decision-making, inaccurate decision-making, decreased innovation and productivity, failure to perform against competitors, and more. Many leaders are just beginning to recognize the enormous advantages of data literacy training and the potential downfalls of not investing in data literacy training, but this recognition is in the early stages of translating into opportunities for upskilling and data literacy programs.

Conversely, when employers don’t enable their employees to become more confident in using data insights, it can negatively impact the employees’ comfortability and productivity in their role. The Human Impact of Data Literacy report found that “...when accounting for data-induced procrastination and sick leave due to stress resulting from information, data and technology issues, companies lose an average of more than five working days (43 hours) per employee each year.” In the United States alone, the yearly productivity cost to the economy was over $100 billion.

It’s clear to see that there are enormous benefits of investing in data literacy for organizations but that isn’t the entire picture. Upskilling in data literacy also positively impacts employees.

Employees who have high data satisfaction show improved performance, increased satisfaction, and higher worker retention. Employees in the Forrester survey also agreed with decision-makers, saying that they made better decisions (83%) and faster decisions (82%) when using data. In addition to improved decision-making, employees also felt more competent, more motivated, and more productive than their low data satisfaction peers.

Improved work performance wasn’t the only benefit of data literacy programs. Data training also was shown to improve employee retention. In the same survey, 80% of employees reported that they were more likely to stay at a company that trains them in the data skills they need. They are significantly more likely to report satisfaction within their organization and with their team and department when compared to low data satisfaction employees.

It’s obvious that improved data literacy and confidence in data skills have benefits for both employers and employees. It’s also clear that when an organization’s workforce is not adequately trained in data literacy, there are sizable risks, including slowed decision-making, inaccurate decision-making, decreased innovation and productivity, failure to perform against competitors, and more. Many leaders are just beginning to recognize the enormous advantages of data literacy training and the potential downfalls of not investing in data literacy training, but this recognition is in the early stages of translating into opportunities for upskilling and data literacy programs.

Conversely, when employers don’t enable their employees to become more confident in using data insights, it can negatively impact the employees’ comfortability and productivity in their role. The Human Impact of Data Literacy report found that “...when accounting for data-induced procrastination and sick leave due to stress resulting from information, data and technology issues, companies lose an average of more than five working days (43 hours) per employee each year.” In the United States alone, the yearly productivity cost to the economy was over $100 billion.

It’s clear to see that there are enormous benefits of investing in data literacy for organizations but that isn’t the entire picture. Upskilling in data literacy also positively impacts employees.

Employees who have high data satisfaction show improved performance, increased satisfaction, and higher worker retention. Employees in the Forrester survey also agreed with decision-makers, saying that they made better decisions (83%) and faster decisions (82%) when using data. In addition to improved decision-making, employees also felt more competent, more motivated, and more productive than their low data satisfaction peers.

Improved work performance wasn’t the only benefit of data literacy programs. Data training also was shown to improve employee retention. In the same survey, 80% of employees reported that they were more likely to stay at a company that trains them in the data skills they need. They are significantly more likely to report satisfaction within their organization and with their team and department when compared to low data satisfaction employees.

It’s obvious that improved data literacy and confidence in data skills have benefits for both employers and employees. It’s also clear that when an organization’s workforce is not adequately trained in data literacy, there are sizable risks, including slowed decision-making, inaccurate decision-making, decreased innovation and productivity, failure to perform against competitors, and more. Many leaders are just beginning to recognize the enormous advantages of data literacy training and the potential downfalls of not investing in data literacy training, but this recognition is in the early stages of translating into opportunities for upskilling and data literacy programs.

Who needs data literacy?

It’s clear to see that data literacy skills have become increasingly crucial as the role of data within companies continues to evolve. Kevin Hanegan, Chief Learning Officer at Qlik, states, “Most firms are sitting on a gold mine of talent. They need to be able to build teams that can realize the true value of data, by investing at the right level, in the right areas, and on the right tools. Do this, and they will be able to unleash their workforce’s potential to make data-driven decisions a reality, rather than a novelty.” In order for organizations to fully tap into the potential of data, everyone within the organization must have some level of data literacy.

It’s clear to see that data literacy skills have become increasingly crucial as the role of data within companies continues to evolve. Kevin Hanegan, Chief Learning Officer at Qlik, states, “Most firms are sitting on a gold mine of talent. They need to be able to build teams that can realize the true value of data, by investing at the right level, in the right areas, and on the right tools. Do this, and they will be able to unleash their workforce’s potential to make data-driven decisions a reality, rather than a novelty.” In order for organizations to fully tap into the potential of data, everyone within the organization must have some level of data literacy.

Where are we?

Despite the availability of data expanding, there is a noticeable gap between the proven benefits of data and the actual use of data within organizations and enterprises, both from the executive level and from employees. For example, despite 52% of C-level executives feeling confident in their data skills, 45% of them continue to make decisions based on gut feeling, rather than data insights. Moreover, 42% of those executives don’t always trust that the data being used to inform their decisions is up-to-date and accurate. At the same time, 89% of leaders expect that their employees will be able to explain how data has informed their decisions.

While leaders estimate that 55% of their workforce is data literate, the reality is that that estimate is not a reflection of how their employees gauge their own data literacy skills.

According to Forrester’s survey, 87% of decision-makers and 51% of employees believe that basic data skills are the most important skills for employees to succeed in their day-to-day work; however, the availability of data literacy training doesn’t reflect the apparent importance that both decision-makers and employees perceive.

Despite the availability of data expanding, there is a noticeable gap between the proven benefits of data and the actual use of data within organizations and enterprises, both from the executive level and from employees. For example, despite 52% of C-level executives feeling confident in their data skills, 45% of them continue to make decisions based on gut feeling, rather than data insights. Moreover, 42% of those executives don’t always trust that the data being used to inform their decisions is up-to-date and accurate. At the same time, 89% of leaders expect that their employees will be able to explain how data has informed their decisions.

While leaders estimate that 55% of their workforce is data literate, the reality is that that estimate is not a reflection of how their employees gauge their own data literacy skills.

According to Forrester’s survey, 87% of decision-makers and 51% of employees believe that basic data skills are the most important skills for employees to succeed in their day-to-day work; however, the availability of data literacy training doesn’t reflect the apparent importance that both decision-makers and employees perceive.

Despite the availability of data expanding, there is a noticeable gap between the proven benefits of data and the actual use of data within organizations and enterprises, both from the executive level and from employees. For example, despite 52% of C-level executives feeling confident in their data skills, 45% of them continue to make decisions based on gut feeling, rather than data insights. Moreover, 42% of those executives don’t always trust that the data being used to inform their decisions is up-to-date and accurate. At the same time, 89% of leaders expect that their employees will be able to explain how data has informed their decisions.

While leaders estimate that 55% of their workforce is data literate, the reality is that that estimate is not a reflection of how their employees gauge their own data literacy skills.

According to Forrester’s survey, 87% of decision-makers and 51% of employees believe that basic data skills are the most important skills for employees to succeed in their day-to-day work; however, the availability of data literacy training doesn’t reflect the apparent importance that both decision-makers and employees perceive.

Do you believe that basic data skills are the most important skill for success at your organization?

Based on survey data from Forrester.

The Human Impact of Data Literacy found that the majority of workers report that they already interact with data as part of their roles. Seventy-five percent of workers stated that they read data, while 65% stated that they both read and interpret data. Furthermore, 63% of employees communicate data internally and make data-driven decisions. Despite the high level of expectation in regards to interacting with data, less than a quarter of those surveyed felt fully confident in their data literacy skills. However, further demonstrating the importance of data literacy skills, 95% of those who did report feeling confident in their data skills also reported that they regularly review and use data to inform their decisions.

Ultimately, the lack of confidence in, or experience with, data literacy skills has a large impact on performance and satisfaction and is limiting workplace productivity. In fact, 74% of employees reported feeling unhappy or overwhelmed when working with data and 36% of overwhelmed employees stated that they spend at least one hour per week procrastinating specifically over data-related tasks.

Organizations are not necessarily providing data literacy training commensurate with how important they deem those data skills. Twenty-seven percent of employees say they have had formal data literacy training and only 21% say they feel that their employer is appropriately preparing them for a more data-centric workplace. Almost half of employees feel anxious that their employer isn’t taking responsibility for providing training for the skills needed to succeed in a more data-driven workplace. Importantly, leaders are prioritizing data training for people working in data-related roles but those in non-data-related roles report that data literacy is already needed in order to fulfill their current job responsibilities.

The Human Impact of Data Literacy found that the majority of workers report that they already interact with data as part of their roles. Seventy-five percent of workers stated that they read data, while 65% stated that they both read and interpret data. Furthermore, 63% of employees communicate data internally and make data-driven decisions. Despite the high level of expectation in regards to interacting with data, less than a quarter of those surveyed felt fully confident in their data literacy skills. However, further demonstrating the importance of data literacy skills, 95% of those who did report feeling confident in their data skills also reported that they regularly review and use data to inform their decisions.

Ultimately, the lack of confidence in, or experience with, data literacy skills has a large impact on performance and satisfaction and is limiting workplace productivity. In fact, 74% of employees reported feeling unhappy or overwhelmed when working with data and 36% of overwhelmed employees stated that they spend at least one hour per week procrastinating specifically over data-related tasks.

Organizations are not necessarily providing data literacy training commensurate with how important they deem those data skills. Twenty-seven percent of employees say they have had formal data literacy training and only 21% say they feel that their employer is appropriately preparing them for a more data-centric workplace. Almost half of employees feel anxious that their employer isn’t taking responsibility for providing training for the skills needed to succeed in a more data-driven workplace. Importantly, leaders are prioritizing data training for people working in data-related roles but those in non-data-related roles report that data literacy is already needed in order to fulfill their current job responsibilities.

The Human Impact of Data Literacy found that the majority of workers report that they already interact with data as part of their roles. Seventy-five percent of workers stated that they read data, while 65% stated that they both read and interpret data. Furthermore, 63% of employees communicate data internally and make data-driven decisions. Despite the high level of expectation in regards to interacting with data, less than a quarter of those surveyed felt fully confident in their data literacy skills. However, further demonstrating the importance of data literacy skills, 95% of those who did report feeling confident in their data skills also reported that they regularly review and use data to inform their decisions.

Ultimately, the lack of confidence in, or experience with, data literacy skills has a large impact on performance and satisfaction and is limiting workplace productivity. In fact, 74% of employees reported feeling unhappy or overwhelmed when working with data and 36% of overwhelmed employees stated that they spend at least one hour per week procrastinating specifically over data-related tasks.

Organizations are not necessarily providing data literacy training commensurate with how important they deem those data skills. Twenty-seven percent of employees say they have had formal data literacy training and only 21% say they feel that their employer is appropriately preparing them for a more data-centric workplace. Almost half of employees feel anxious that their employer isn’t taking responsibility for providing training for the skills needed to succeed in a more data-driven workplace. Importantly, leaders are prioritizing data training for people working in data-related roles but those in non-data-related roles report that data literacy is already needed in order to fulfill their current job responsibilities.

Where do we need to be?

The role of data will only continue to grow as technology becomes more sophisticated. Forrester’s research predicts that 70% of employees will be expected to work heavily with data by 2025 and data needs will continue to expand across all functions within an organization. In fact, employees across every department, including traditionally non-data teams (HR, product, operations, etc.) stated that data skills were the most important skill for success in their roles.

And it’s not just employees who see the importance of improving these skills! Leaders and decision-makers are also recognizing the necessity of data literacy. The shift required to cultivate a strong data culture needs to start at the top. Executives need to embrace data-driven actions and decisions. Addressing the immediate need for data literacy is vital: a vast majority of executives (85%) believe that data literacy will be essential in the future.

Creating a data-literate workforce cannot happen overnight. In organizations where data has long been siloed or accessible by only a small group of people, a cultural change needs to occur. It has been repeatedly shown that employees often do not have the skills, knowledge, experience, or confidence to use data in their day-to-day roles. If companies expect their employees to upskill in regard to data literacy, they also need to create a culture that believes in and relies on data. Louise Brownhill, Chief HR Officer and Chief Learning Officer at PwC UK stated, “We treat this not as an upskilling program, but as a change program. It’s important to create a mindset shift in all of our people from the very top to the very bottom, to help people to get confident with data and digitalization.”

The question is no longer: should we invest in data literacy training? It has now become: how do we invest in data literacy training?

The role of data will only continue to grow as technology becomes more sophisticated. Forrester’s research predicts that 70% of employees will be expected to work heavily with data by 2025 and data needs will continue to expand across all functions within an organization. In fact, employees across every department, including traditionally non-data teams (HR, product, operations, etc.) stated that data skills were the most important skill for success in their roles.

And it’s not just employees who see the importance of improving these skills! Leaders and decision-makers are also recognizing the necessity of data literacy. The shift required to cultivate a strong data culture needs to start at the top. Executives need to embrace data-driven actions and decisions. Addressing the immediate need for data literacy is vital: a vast majority of executives (85%) believe that data literacy will be essential in the future.

Creating a data-literate workforce cannot happen overnight. In organizations where data has long been siloed or accessible by only a small group of people, a cultural change needs to occur. It has been repeatedly shown that employees often do not have the skills, knowledge, experience, or confidence to use data in their day-to-day roles. If companies expect their employees to upskill in regard to data literacy, they also need to create a culture that believes in and relies on data. Louise Brownhill, Chief HR Officer and Chief Learning Officer at PwC UK stated, “We treat this not as an upskilling program, but as a change program. It’s important to create a mindset shift in all of our people from the very top to the very bottom, to help people to get confident with data and digitalization.”

The question is no longer: should we invest in data literacy training? It has now become: how do we invest in data literacy training?

The role of data will only continue to grow as technology becomes more sophisticated. Forrester’s research predicts that 70% of employees will be expected to work heavily with data by 2025 and data needs will continue to expand across all functions within an organization. In fact, employees across every department, including traditionally non-data teams (HR, product, operations, etc.) stated that data skills were the most important skill for success in their roles.

And it’s not just employees who see the importance of improving these skills! Leaders and decision-makers are also recognizing the necessity of data literacy. The shift required to cultivate a strong data culture needs to start at the top. Executives need to embrace data-driven actions and decisions. Addressing the immediate need for data literacy is vital: a vast majority of executives (85%) believe that data literacy will be essential in the future.

Creating a data-literate workforce cannot happen overnight. In organizations where data has long been siloed or accessible by only a small group of people, a cultural change needs to occur. It has been repeatedly shown that employees often do not have the skills, knowledge, experience, or confidence to use data in their day-to-day roles. If companies expect their employees to upskill in regard to data literacy, they also need to create a culture that believes in and relies on data. Louise Brownhill, Chief HR Officer and Chief Learning Officer at PwC UK stated, “We treat this not as an upskilling program, but as a change program. It’s important to create a mindset shift in all of our people from the very top to the very bottom, to help people to get confident with data and digitalization.”

The question is no longer: should we invest in data literacy training? It has now become: how do we invest in data literacy training?

How do you build it into an organization?

Now is the time to invest in data literacy training in order to keep up with fast-moving technology, increasing amounts of data, and the workforce’s growing need to learn data skills; but, one of the biggest challenges when thinking about data literacy training is: how do we begin? There are many roadmaps available for organizations to build data literacy skills but in each of those resources, there are a few overarching goals:

Encourage and begin building a strong data culture.
Understand the workforce’s current skills and abilities with data.
Develop goals and objectives.
Invest in data literacy training.

Now is the time to invest in data literacy training in order to keep up with fast-moving technology, increasing amounts of data, and the workforce’s growing need to learn data skills; but, one of the biggest challenges when thinking about data literacy training is: how do we begin? There are many roadmaps available for organizations to build data literacy skills but in each of those resources, there are a few overarching goals:

Encourage and begin building a strong data culture.
Understand the workforce’s current skills and abilities with data.
Develop goals and objectives.
Invest in data literacy training.

Now is the time to invest in data literacy training in order to keep up with fast-moving technology, increasing amounts of data, and the workforce’s growing need to learn data skills; but, one of the biggest challenges when thinking about data literacy training is: how do we begin? There are many roadmaps available for organizations to build data literacy skills but in each of those resources, there are a few overarching goals:

Encourage and begin building a strong data culture.
Understand the workforce’s current skills and abilities with data.
Develop goals and objectives.
Invest in data literacy training.

So how do you do it?

It’s been said time and time again but it’s worth repeating. Data culture comes from the top. A strong data culture is necessary in order to create buy-in from leaders and employees but that means leaders need to create a culture in which data is democratized, trusted, and relied upon. Chantilly Jaggernaugh, Founder and CEO of Millenials and Data (#MAD), said, “...People with the curiosity, confidence, and capabilities to work with data insights not only deliver greater value to the business, they also feel more empowered to trust their decisions and are more trusted by their managers, in turn. This positive data culture is achievable with the right human investment and should be what all companies seek to foster.” Building a strong data culture is an ongoing process but it involves making sure your employees understand the relationship between data and business objectives, being able to trust available data, valuing and using data in decision-making, and involving employees from all departments in the use of data.

It’s been said time and time again but it’s worth repeating. Data culture comes from the top. A strong data culture is necessary in order to create buy-in from leaders and employees but that means leaders need to create a culture in which data is democratized, trusted, and relied upon. Chantilly Jaggernaugh, Founder and CEO of Millenials and Data (#MAD), said, “...People with the curiosity, confidence, and capabilities to work with data insights not only deliver greater value to the business, they also feel more empowered to trust their decisions and are more trusted by their managers, in turn. This positive data culture is achievable with the right human investment and should be what all companies seek to foster.” Building a strong data culture is an ongoing process but it involves making sure your employees understand the relationship between data and business objectives, being able to trust available data, valuing and using data in decision-making, and involving employees from all departments in the use of data.

Based on research, there is often a gap between where an organization’s leaders think their employees’ data skills are and where those data skills are in reality. It’s important to have a good understanding of what data skills your employees truly possess and what data skills they need to gain. This can be done through surveys, self-assessments, and skills tests. It’s also necessary to recognize how your employees work with data and make sure employees understand how that work supports the organization’s overall goals.

Based on research, there is often a gap between where an organization’s leaders think their employees’ data skills are and where those data skills are in reality. It’s important to have a good understanding of what data skills your employees truly possess and what data skills they need to gain. This can be done through surveys, self-assessments, and skills tests. It’s also necessary to recognize how your employees work with data and make sure employees understand how that work supports the organization’s overall goals.

Without clear goals, it can be challenging to put together a comprehensive data literacy training plan. Goals need to include things like:

Defining how different employees with different job functions will work with data
Understanding what technology is available and what best suits your needs
Determining the amount of training employees will need for their job role or function
Creating or investing in a data literacy training program

Without clear goals, it can be challenging to put together a comprehensive data literacy training plan. Goals need to include things like:

Defining how different employees with different job functions will work with data
Understanding what technology is available and what best suits your needs
Determining the amount of training employees will need for their job role or function
Creating or investing in a data literacy training program

Without clear goals, it can be challenging to put together a comprehensive data literacy training plan. Goals need to include things like:

Defining how different employees with different job functions will work with data
Understanding what technology is available and what best suits your needs
Determining the amount of training employees will need for their job role or function
Creating or investing in a data literacy training program

This can take multiple forms and is certainly not one-size-fits-all. A training program could include internal subject matter experts holding classes on a regular basis, online or e-learning courses, or something entirely different. Recognizing where your own workforce stands in regards to data literacy skills is necessary to developing and investing in a data literacy training program that will work.

This can take multiple forms and is certainly not one-size-fits-all. A training program could include internal subject matter experts holding classes on a regular basis, online or e-learning courses, or something entirely different. Recognizing where your own workforce stands in regards to data literacy skills is necessary to developing and investing in a data literacy training program that will work.

Building data literacy skills in your organization isn’t a one-and-done solution. As the use of data continues to grow and change, so must our data literacy skills. That’s why it’s even more important to begin the process of becoming a data-literate organization now. Creating a data culture is a continuous process, and involves recognizing and understanding how your employees see and use data, developing goals and objectives, and investing in the right data training.

Building data literacy skills in your organization isn’t a one-and-done solution. As the use of data continues to grow and change, so must our data literacy skills. That’s why it’s even more important to begin the process of becoming a data-literate organization now. Creating a data culture is a continuous process, and involves recognizing and understanding how your employees see and use data, developing goals and objectives, and investing in the right data training.

If you’ve made the decision to invest in data literacy training, it’s important to understand where organizations are falling short or running into barriers. There are a few themes that repeatedly emerge but three of the primary obstacles are a lack of training, lack of support, and lack of resources.

If you’ve made the decision to invest in data literacy training, it’s important to understand where organizations are falling short or running into barriers. There are a few themes that repeatedly emerge but three of the primary obstacles are a lack of training, lack of support, and lack of resources.

Despite three-quarters of employees who reported that they wanted company-sponsored training, less than half of the employees surveyed in the Forrester report had been offered training by their organization. This may be because the company might continue to limit participation to only those in traditionally data-related roles, rather than a broader audience. Alternatively, many decision makers feel that employees should improve their data skills on their own, rather than provide the training. A lack of company-wide training can have a huge impact on your organization’s decision-making, productivity, and employee satisfaction.

Despite three-quarters of employees who reported that they wanted company-sponsored training, less than half of the employees surveyed in the Forrester report had been offered training by their organization. This may be because the company might continue to limit participation to only those in traditionally data-related roles, rather than a broader audience. Alternatively, many decision makers feel that employees should improve their data skills on their own, rather than provide the training. A lack of company-wide training can have a huge impact on your organization’s decision-making, productivity, and employee satisfaction.

This comes in a few different forms. Executives and leaders may still rely on gut instinct, rather than data, which relays the idea that they don’t think data literacy is valuable. Your organization may defer training to individual departments, leading to poor organizational support. For example, the Forrester survey found that the majority of both basic (74%) and advanced (91%) data training originated from within specific departments or teams. Regardless of reason, when you don’t have company-wide support or buy-in from leaders, it can be challenging to adequately provide data literacy training.

This comes in a few different forms. Executives and leaders may still rely on gut instinct, rather than data, which relays the idea that they don’t think data literacy is valuable. Your organization may defer training to individual departments, leading to poor organizational support. For example, the Forrester survey found that the majority of both basic (74%) and advanced (91%) data training originated from within specific departments or teams. Regardless of reason, when you don’t have company-wide support or buy-in from leaders, it can be challenging to adequately provide data literacy training.

It could be that your organization doesn’t have the subject matter experts needed to teach classes or champion data literacy. Or perhaps your organization finds it challenging to make room in the budget for comprehensive training. In fact, decision makers cite the lack of skilled staff to lead training and lack of budget as two of their primary challenges. When it comes down to resources, it’s necessary to prioritize data literacy training in order to fully realize the impact data-driven decision making can have.

It could be that your organization doesn’t have the subject matter experts needed to teach classes or champion data literacy. Or perhaps your organization finds it challenging to make room in the budget for comprehensive training. In fact, decision makers cite the lack of skilled staff to lead training and lack of budget as two of their primary challenges. When it comes down to resources, it’s necessary to prioritize data literacy training in order to fully realize the impact data-driven decision making can have.

Key Takeaways

Our collection and use of data has grown exponentially over the past few years

Data used to be siloed and hard to access unless you were part of the data team but a shift toward data-driven decision-making and data democratization has started to change that
Data literacy has become a core competency within organizations because of this shift
Increased data literacy positively impacts both organizations and employees
The vast majority of executives believe that data literacy will be vital to their organizations but, despite the obvious importance of data literacy, employees aren’t receiving the training to become confident in their data skills
Building a data-literate workforce means: cultivating a data culture, assessing current skills, developing goals and objectives, investing in data literacy training, and iterating
Organizations are falling short due to a lack of training, support, and resources but it is necessary to get past those barriers

Our collection and use of data has grown exponentially over the past few years

Data used to be siloed and hard to access unless you were part of the data team but a shift toward data-driven decision-making and data democratization has started to change that
Data literacy has become a core competency within organizations because of this shift
Increased data literacy positively impacts both organizations and employees
The vast majority of executives believe that data literacy will be vital to their organizations but, despite the obvious importance of data literacy, employees aren’t receiving the training to become confident in their data skills
Building a data-literate workforce means: cultivating a data culture, assessing current skills, developing goals and objectives, investing in data literacy training, and iterating
Organizations are falling short due to a lack of training, support, and resources but it is necessary to get past those barriers

Our collection and use of data has grown exponentially over the past few years

Data used to be siloed and hard to access unless you were part of the data team but a shift toward data-driven decision-making and data democratization has started to change that
Data literacy has become a core competency within organizations because of this shift
Increased data literacy positively impacts both organizations and employees
The vast majority of executives believe that data literacy will be vital to their organizations but, despite the obvious importance of data literacy, employees aren’t receiving the training to become confident in their data skills
Building a data-literate workforce means: cultivating a data culture, assessing current skills, developing goals and objectives, investing in data literacy training, and iterating
Organizations are falling short due to a lack of training, support, and resources but it is necessary to get past those barriers

Data Literacy as a Core Competency: A Comprehensive Guide for Organizations

Intro

Data is all around us and in everything we do. It’s generated every time we use a computer, smartphone, or other device. While data has become critical in all aspects of human life, the workplace is certainly no exception.

Data is all around us and in everything we do. It’s generated every time we use a computer, smartphone, or other device. While data has become critical in all aspects of human life, the workplace is certainly no exception.

What’s changed?

What’s changed?

What’s changed?

The Past

The Past

The Past

The Present

The Present

The Present

According to Amplitude, there are three key principles an organization needs to embrace in order for data democratization to happen:

According to Amplitude, there are three key principles an organization needs to embrace in order for data democratization to happen:

Most importantly, employees need to be able to feel confident in their data skills in order for true data democratization to take place. For organizations to build a strong data culture and truly become more data-driven, there must be a company-wide focus on foundational data skills.

Most importantly, employees need to be able to feel confident in their data skills in order for true data democratization to take place. For organizations to build a strong data culture and truly become more data-driven, there must be a company-wide focus on foundational data skills.

Data literacy as a core competency

Data literacy as a core competency

Data literacy as a core competency

What is data literacy?

What is data literacy?

What is data literacy?

As access to data continues to expand and the use of data to make decisions continues to grow, data literacy has become an essential core competency within any organization.

As access to data continues to expand and the use of data to make decisions continues to grow, data literacy has become an essential core competency within any organization.

Why is data literacy needed?

Why is data literacy needed?

Why is data literacy needed?

It’s clear to see that there are enormous benefits of investing in data literacy for organizations but that isn’t the entire picture. Upskilling in data literacy also positively impacts employees.

It’s clear to see that there are enormous benefits of investing in data literacy for organizations but that isn’t the entire picture. Upskilling in data literacy also positively impacts employees.

Who needs data literacy?

Who needs data literacy?

Who needs data literacy?

Kevin Hanegan

Kevin Hanegan

Chief Learning Officer at Qlik

Chief Learning Officer at Qlik

Where are we?

Where are we?

Where are we?

While leaders estimate that 55% of their workforce is data literate, the reality is that that estimate is not a reflection of how their employees gauge their own data literacy skills.

While leaders estimate that 55% of their workforce is data literate, the reality is that that estimate is not a reflection of how their employees gauge their own data literacy skills.

Do you believe that basic data skills are the most important skill for success at your organization?

Do you believe that basic data skills are the most important skill for success at your organization?

Do you believe that basic data skills are the most important skill for success at your organization?

Based on survey data from Forrester.

Based on survey data from Forrester.

Where do we need to be?

Where do we need to be?

Where do we need to be?

How do you build it into an organization?

How do you build it into an organization?

How do you build it into an organization?

Encourage and begin building a strong data culture.

Understand the workforce’s current skills and abilities with data.

Develop goals and objectives.

Invest in data literacy training.

Encourage and begin building a strong data culture.

Understand the workforce’s current skills and abilities with data.

Develop goals and objectives.

Invest in data literacy training.

So how do you do it?

So how do you do it?

So how do you do it?

Cultivate a strong data culture

Chantilly Jaggernaugh