This new decade signals the end of understanding data’s impact and the dawn of using data to propel innovation. Businesses are shifting from simple prediction models towards robust machine learning algorithms to obtain insight and keep up with the advances in technology. Thus, data literacy is required to be competitive in the job market. Data literacy means the ability to look at data and make inferences from that data set. This also means understanding the characteristics of the data as well as how to use the tools required to analyze it. You might not be expected to create the next Alexa or Siri, but everyone needs to know where to obtain data, the way to analyze data, and how to extract actionable insights from the data.
This expectation is not just for entry-level people in the workforce, but rather it will be the standard at every level of the corporate ladder. It’s even more crucial for those who are managers, directors, and C level execs understand the meaning of the data. If you’ve watched the movie Moneyball, which shows the story of a general manager for the Oakland Athletics, he revolutionized baseball via data-driven decisions. One of the most intriguing parts is where the general manager selects players to be on a roster by using the scouts for the other coaches. Many of them were in baseball for decades, so they all utilize their experience in industry and intuition to pick the right players. The general manager throws them a curveball by expecting them to rely on player data for decisions. Of course, they were strongly opposed to this idea of using data and felt insulted.
The goal of using data is for the data to be turned into info., and for the info. to become insight.
That situation has occurred numerous times in the corporates world — there are executives who believe that their experience is more important than relying on a data literacy campaign and using objective methods to solve problems.
Per the Data Literacy Index from Qlik, about ninety three percent of companies have decision-makers who believe that their employees should be data literate; however, fewer than one third view data literacy as significant to having a successful economy. With the advances in technology shaping our world, it’s vital to start making moves to ensure you are comfortable living in a data-driven world.
How to Become Data Literate
Although a lot of people have difficulty “speaking data”, there’s a tons of resources at your fingertips to help you get started in your learning journey: apps, sites, online courses and an array of materials. The key is to remember that there’s no one, ultimate resource to become data literate. Similar to learning any foreign language, data literacy is a combo of practice and learning, so don’t be scared to look review an assortment of different resources until you find what works for you. Down below, I have made available several starting points that I suggest. These recommendations have worked wonders from my experiences, but they’re only a few out of many ways to begin.
1. Learn How to Code
It’s a typical misconception that coding is for employees of big tech firms or for entry-level engineers. However, coding can be vital and useful for those seeking to develop an edge on the corporate ladder. Possessing a basic understanding of how to program will aid entry-level employee looking to be more competitive. For managers and up, programming bridges the knowledge gap that exists between them and software developers — it’s similar to captains understanding how their ship functions. Even for those convinced to study coding, they might panic after seeing the plethora of languages from which they can choose. Rather than picking the most popular language, the key is to you start with a relevant programming language for your industry. Understanding what is commonly expected for your company and industry can help you narrow down what to learn.
2. Massive Open Online Courses (MOOCs)
With everything becoming available online, it’s easy to discover a course on the internet about what you’re interested in learning. Sites such as Coursera, edX, and Udemy host online courses and offer specializations brought to you by the elite universities and top businesses. Thousands of people can become students together in the same courses, which builds an amazing community of learners with which to interact. Also, if you pass your course, you can earn a certificate from it to present to your employers and recruiters. There’s so many options for courses or programs, but the ones that stand out are Machine Learning by Stanford University, The Data Science Course 2020 on Udemy, and the Data Science Professional Certificate from Harvard University. These ones not only offer an understanding of data, but also show you the way to apply what you learned.
3. Read, Read, Read
There’s some cool books about programming, the world of data, and so much more. Whether you are just starting to learn about data or you want to become more literate with data, I suggest picking up a copy of Data Science from Scratch by O’Reilly and reading it. This book offers an excellent bird’s eye view of data science’s principles, and even shows you some Python programming to aid you in beginning your data journey. Another awesome book is Storytelling with Data by Wiley. This book was written for those who want to understand how to display data in a bunch of different ways. Data Visualization is a crucial skill for everyone to know because it’s used to find the hidden patterns within data.
4. Company Sponsored Programs
For those currently employed, check if your company hosts data literacy seminars or lectures online to help you in your pursuit of becoming more data literate. Companies are realizing the value of having a data literate organization, and per a report from Gartner, eighty percent of companies have initiatives for data competency to deal with the need for data literacy and lack of it. If your company or organization does not have one, then you should start one. This highlights your interest in becoming more data literate and shows off your determination to spearhead an initiative like this at work.
5. Start a Side Project
If you talk the talk, do you also walk the walk? Some people read books and take online courses, but the best method for keeping all that info in your head is to actually make use of it. Whether you do a project to visualize data, a project on analytics, or even work on a Machine Learning project, nothing compares to playing around with data and understanding what it can and cannot tell them you as well as understanding the tools at your disposal. Sites like Kaggle have so much data from various topics. Find data on a topic you enjoy and begin to analyze it. You’ll never know until you try what you will discover.
Achieving data literacy doesn’t occur overnight. Be prepared to deal with concepts and ideas you haven’t seen before and know that it’s ok if you do not understand it at first glance. As you keep diving into data, you’ll soon be discussing complex topics and take the lead on data-driven initiatives.
 Data Literacy Index (2018), Qlik Data Literacy Program
C. Pettey, How data and analytics leaders learn to master information as a second language (2018), Smarter with Gartner