What is Data Literacy And Why Data Literacy Matters

Like the phrase computer literacy is not limited to the limited ability to switch on, switch off a computer to simply use a program like Microsoft Word, the phrase data literacy has no well defined limit. What is Data Literacy? In simplest words, data literacy is the ability to understand and utilize data effectively in the context of data collection, data sharing and analysis. Data literacy is just what it sounds like: familiarity with data and its uses. For various reasons, practically all of the citizens, students, computer users should pose a minimum data literacy. However, in the context of professional statistical and data analysis works; data governance and data literacy are building blocks in the knowledge base of the professionals. In this article, we will discuss minimum for the future information professionals who are likely to be involved in data quality and research data management.

Table of Contents 1 Introduction 2 Definition of Data Literacy 3 Why Data Literacy Matters 4 Strategies For Building Data Literacy 5 Conclusion

 

What is Data Literacy : definition

 

Unlike gaining generalized knowledge by a lay, a professional needs to consider matters like transparency of the processes, cost reduction, policies, standards, decision rights; accountabilities and methods. As with any knowledge, literacy is the starting point but fluency is the goal. This is especially true in terms of data literacy, because data is the most important asset to many of the companies of today. Data leads to insights that directly inform decision making and strategic planning. Said differently, data fluency empowers companies to act quickly, confidently, and correctly more of the time. We can use the phrase data literacy in the context of :

1. Academic/scientific data of statistical works
2. Educating a population
3. Discussing skills of employees and employers

So in professional practice, data literacy refers to a professional’s ability to understand and utilize data effectively. This term was largely unknown even two decades ago. However, now it is one of the most important skill both for the individuals and the enterprises to have. Building a data literate workforce is not easy, and advancing onto data fluency is not either so. However, the skill-sets are essential for any company which hopes to stay competitive and relevant in the maret. Here are some strategies for building a company-wide culture of data literacy.

Graphics created by Kanarinka, for Creative Data Literacy at Tapestry Conference 2017.

 

Why Data Literacy Matters

 

Based on the existing literature and expert input where data literacy pointed towards the ability to collect, manage, evaluate and apply data in a critical manner, we started from the definition From that point of view, it becomes a required ability in the knowledge-­based economy. Our decisions are now lead by data. The process how to collect, manage, evaluate, and apply the collected data to support an evidence based decision making increasingly being required in many jobs. Also, data literacy reveals the ability to recognize the importance of ethical guidelines in personal behavior on web, real life. Unfortunately, current education system is inconsistent in the context of delivering data literacy across the twelfth-grade to graduate institutions. Our considerations of developing a working knowledge of  data literacy and apply the skill-sets appropriately at present is specialty specific.

The emergence of data and analytics capabilities, including artificial intelligence, requires creators and consumers to “speak data” as a common language.

– Gartner

Data literacy skills may include:

1. Understanding underneath information a data set represents
2. Ability to understand graphs, charts, and visualizations
3. Ability to judge data sources, accuracy, and methods
4. Identifying type of data
5. Judgement of impact the data in real life

 

Strategies For Building Data Literacy in Corporate Setup

 

We can deliver education in various methods including hands-on learning in workshops and labs, module based learning, successive or iterative learning. Project-based learning may be useful to implement a successive learning approach. We can think to cover the below topics :

1. Fundamentals of statistical practice
2. Fundamentals of practice in data sciences including the vocabulary
3. Comprehensive knowledge on data visualizations
4. How data is used to support arguments
5. Landscape of Big Data
6. Personal data management (social media shares, likes and malicious data collection)

Invest in Education and Training

It is important for the students/employees to understand why data is so important if a data-driven culture is going to take over. Education and training helps to underscore this message while developing important data literacy skills across the workforce. It is unrealistic and unreasonable to expect the students/employees to pick these skills up on their own, which is why it has to be an enterprise-wide initiative.

Create a Single Source of Truth

Data is often vast, varied, and disconnected, which leads inevitably to confusion and conflict. In order for everyone in an organization to be data literate, they must rely on the same information. Otherwise data is different to different departments/students/employees. Integrating all data on a single platform ensures that the students/employees know where to find information and feel confident that information is complete.

Develop a Data Dictionary

Every company defines what data, metrics, opportunities and obstacles are important for themselves. A data dictionary defines what these priorities are so that misinterpretations don’t hold back communication and collaboration. Once developed, a data dictionary serves as a one-stop reference that helps to homogenize data-driven initiatives across an organization.

Prioritize Accessibility

Historically, only a selected number of specialists and executives had access to critical company data. This is the part of why data literacy now suffering. But also the fact is that, now data is more accessible and better understood, it is important for wide swaths of users to have data at their disposal. Relying on data-exploration tools like relational search ensures that average users do not become overwhelmed with the details of data science.

Hold Decision-Makers Accountable

Data literacy is only valuable when it leads to more objective and empirical decision making. Unfortunately, people who are set in their ways will continue to rely on assumptions and intuitions instead. Holding decision makers accountable creates a lot more onus to act with caution and certainty. Over time, data becomes an asset that these decision makers are eager to understand and utilize.

 

Conclusion

 

For the benefit of the employers (and the society), data literacy must be  recognized as a basic skill. Elements of data literacy is necessary in undergraduate curriculum for the sake of consistency in basic knowledge. If there is any doubt that data literacy is a mission critical skill, we need to consider the recent surveys of the executives. More than 80 percent of the respondents had launched an analytics or big data initiative. By all accounts, this is the skill that will either drive future success or cause future failure. If we want to be on the former half of the equation, data literacy is essential.

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