Foremost when it comes to data, we must learn to collect all the relevant data, then you should read, analyse, segregate, sometimes you have to delete it too. Apart from all, I personally see a few more steps to know and work more in data. When I started thinking about data, I see how to read the data and what to do further. To give a relevant example, when I started learning the R programming language, I collet the datasets to upload in R to Run. In the very initial moment, when I start downloading the dataset, I don’t know what to do. Then I started to keep looking a few datasets and started adjusting the values, filling the blank spaces, and even if I don’t know I delete the particular row or column too.
So here the data literacy plays. I started searching for the importance of data and how to read the data. Then I likely able to know the data literacy through one of this article. Once, if you started knowing how to read and execute the data. The next step would be yours.
I will paste source link down below. I sincerely encourage you all to visit further.
Walk into a company embracing digital transformation and you’ll find monitors displaying colorful charts and pie graphs decorated with numbers. These dashboards show information about the business meant to assist employees in prioritizing work, seeing new opportunities, and driving efficiency.
An important question remains, though: “How many employees trained on a business’ dashboards know how to use the data and analytics to make them better at their jobs?” asked Jordan Morrow, Global Head of Data Literacy at Qlik, a visual analytics and digital transformation company, in a recent DATAVERSITY® interview. This question has led Morrow on a journey where he finds himself “leading a revolution around teaching people how to use data analytics better in a world taken by a data literacy storm.”
Why should a business care about data literacy when its employees can get by with some basic reports? Morrow responds that leading organizations like Amazon, Facebook, and Netflix go a step beyond by being extremely good at utilizing data, following trends, and analyzing that information. He explained:
“Upon talking to many individuals and organizations from every single continent, except Antarctica, they realize data is one of the best assets to harness. They know the key to success in what has been described as fourth industry revolution requires harnessing data’s power within an organization.”
Morrow firmly believes if an organization does not embrace this data and analytics upheaval, then it will not survive, “because the majority of organizations want in on the action.” However, based on a worldwide study with over 11,000 participants, Morrow found that only one out of every five people felt they were data literate. The results of the study have been published in a Data Literacy Index. “So, people have a software product positioned at their finger-tips where the greater majority do not have the data and analytical skills to use it well,” muses Jordan.
All People Need to Be Data Literate
Think of data literacy as a spectrum of related skills. Raul Bhargava and Catherine D’ignazio from MIT and Emerson College define data literacy as the ability to read, work with, analyze, and argue with data, notes Morrow. He elaborated:
“A data scientist uses the scientific method with data, a career path for a few people. But for organizations that want to utilize data, (1) reading, (2) working with, (3) analyzing, and (4) arguing with data form four key characteristics [of Data Literacy]. People can develop skills in these components to become better in this digital economy.”
For many organizations, nourishing data literacy skills in each employee will be well worth it. Morrow looked at 600+ public companies across the world. This assessment was commissioned with the Wharton School of Business and Qlik. Through statistical analysis, Morrow found that organizations with the top tier of data literacy had a greater enterprise value of three to five percent. This translated to hundreds of millions of dollars of value and better return on equity. Top tier data literate organizations also had a better return on sales and a faster time to market.
Getting Started with Data Literacy
Morrow believes the number one thing impeding data literacy is where to start. He has found “great enthusiasm towards data literacy and a bewilderment on where to begin.” He noted:
“In a world full of hyped up technologies – Big Data, Machine Learning, Artificial Intelligence – the majority of an organization’s employees are not going to tip-toe into these areas. However, most people in a company will use data. They forget this. In addition, people get stuck in an old-school way of doing thing, preventing them from embracing this technological era. A new approach to things is needed to get the DNA to flow through the organization.”
To give people a digital literacy starting place, Morrow has developed a strategy and framework enterprises can adopt. His Adoptive Framework outlines six steps towards a Data Literacy Program, a workforce assessment characterizing data literacy strengths and weaknesses, and suggested roadmaps towards learning data literacy. Morrow is happy to speak with companies that have questions about the framework or that would like help implementing it.
“Companies need to know how to drive a data literacy strategy in house, whether it’s for ten employees or 100,000 employees. This Adoptive Framework that I’ve built does that. The Adoptive Framework describes not how to change a company’s culture, but to evolve a culture towards being more data-informed in its business. I have companies run the framework’s six-step approach every three to six months to bring in more employees. You need to evolve your program.”
Morrow emphasized that his framework builds curiosity, skills on how to ask questions, and the mindset of how to be data literate. He advocates that if everyone were data literate, “fake news” would not exist.
Qlik has helped launch the Data Literacy Project built from this work within data literacy. Morrow directed Qlik’s own Data Literacy Program, developing over the course of two and a half years. Everyone has access to a “nice, robust set of online courses that help [people] understand this world of data and how to use it,” said Morrow. With this program, the Qlik team can to speak to data literacy, not just sell and support a product. Morrow remarked:
“Qlik as a whole strongly believes in bringing resources, materials, and things organizations can use easily to make the technology work. When you think of technology, one of the biggest hindrances in the world is poor adoption. Why? It is not the technology. People don’t know how to uses it, and this impacts access to a world of data.”
Qlik’s work with data literacy emphasizes theory and context so that the user can better understand data. Morrow rarely uses set formulas in his data literacy teaching, as that will do nothing. He travels the world, meeting with customers, prospects, and individuals constantly, speaking often about the Data Literacy Project. This community group links partners like Accenture, Cognizant, Experian, Pluralsight, the Chartered Institute of Marketing, and Data to the People as well as academic thought leaders.
The Data Literacy Project aims to ignite discussion and develop tools to shape a successful data literate society. From this ongoing dialog, industry leaders, Big Data customers, and students can advance their own and spread data literacy. As an open source project, the Data Literacy Project will continue to be developed and foster collaboration. This will get people in the mindset and mentality of using data more and using it better.
Jordan Morrow has seven takeaways for the reader who wishes to continue his or her data literacy journey:
- To stay competitive in this digital world we’re living in, a company must utilize its data, its asset.
- Organizations that have high data literacy are well ahead in leveraging their data compared to other organizations.
- “One of the greatest ways an individual can start to get better with data and analytics is to become curious – start asking the question, why?”
- Own the chart data in the dashboard that is put in front of you. Figure out why the data appears and where comes from.
- Experiment with questions of the data to figure out questions that work and do not work.
- When finding questions about the data that work or do not work, ask why that is the case.
- Start reading books about data. To start, peruse Factfulness by Hans Rosling.