Poor data literacy is one of the major blockers to creating a data-literate workforce- says Gartner.
There’s plenty of misinformation floating around data literacy, what is data literacy, how to build data literacy, data literacy meaning, and so on.
Most organizations connect data literacy with employing a team of data nerds. Some say data literacy is the same as deploying expensive technology, and for others, it means conducting a one-off data literacy campaign. Well, neither of these is completely true.
In this blog, we debunk the top 5 data literacy myths that organizations should stop believing if they wish to lead in a data-driven world.
Here we go-
MYTH#1 Data literacy is ALL NUMBERS
Numbers are nice, but actionable insights are everything!
Numbers should not just appear big on-screen but also lead to bigger actions. Data literacy is the ability to see through numbers, connect the dots and create value out of those numbers. It’s about digging deeper and telling stories with numbers.
While organizations might have huge, impressive figures, without accompanied data storytelling, they’re of little use. When backed with compelling storytelling and actionable insights, statistical data gives businesses a holistic view and leaves no room for intuitions or gut feelings.
Also, numbers are cluttered and insufficient in certain aspects. So, counting on them wholeheartedly is a bad idea.
Takeaway- Telling stories with numbers is an important component of data literacy. By acting as a bridge between data insights and interpretation, data storytelling builds effective context setting and leads towards smarter decisions.
MYTH#2 Data Literacy Means Hiring More Data Scientists
Another misunderstood aspect of data literacy is that it requires everyone in the organization to be a data guru.
On the contrary, data literacy= creating data awareness and fostering a data-driven culture.
Accenture’s data literacy survey stated that only 21% of 9,000 employees were confident in their data literacy skills.
Gone are the good old days when data science was considered a job. It still is a job but on the technical front.
In the era of data literacy, you don’t need enough coders, you need people who are passionate about using data in their daily operations.
The idea is to invest in a data-smart workforce who can rationally interpret, communicate and collaborate with data (to some degree). These people should have at least basic data interpretation skills that let them excel at their jobs.
Data literacy requires people to become change champions- the ones who infuse, embrace and support data-based decision making. Data literacy is about a data-driven mindset where people (from the frontline to the C-suite) speak the same language!
MYTH#3 Data Literacy = Analyzing Only Structured Data
Sorry to burst the bubble but data literacy isn’t limited to understanding only structured data. It applies to working with all data types depending upon the task at hand and decisions to be made.
Understanding the difference between structured and unstructured, reasoning with data, classifying, categorizing, comparing data, and identifying distinct patterns to further tap into those insights is the road map to data literacy.
“Without clean data, or clean enough data, your data science is worthless.” — Michael Stonebraker, adjunct professor, MIT
So data literacy includes areas like data analytics, data visualizations, data storytelling, understanding technologies, and mastering the language of data to communicate with decision-makers.
MYTH#4 Data Literacy Equates to Training
Upskilling is crucial, we get it! But Data literacy is not just enrolling in courses or setting up workshops.
It’s breaking down silos and allowing data to flow freely in the organization.
It’s about building a data-driven culture- one that happens from the ground up and is embraced across various functions.
Even the finest training materials won’t make businesses data-literate unless a data-driven culture is instilled. Data literacy is all about change management. It has to be integrated within the business model. Organizations need to be introspective and make a sustained effort to strengthen their data culture.
MYTH#5 Data Literacy, Alone, is Enough to Build A Data-Driven Company
Data literacy alone cannot lead the organization to be data-driven. There are other factors involved too-
Data Maturity of the Organization- To simply put, data maturity defines the measure to which an organization leverages the data it collects. To reach a high degree of data maturity, data must be ingrained and integrated into all decision-making activities of your business. The higher the data maturity score of an organization means the better prepared it is to identify trends and loopholes. As data evolves, organizations too progress on the maturity spectrum.
Data-Driven Leadership — The days of relying on data specialists for solving data queries have long passed. To flourish in today’s constantly changing business ecosystem, CMO’s and executives need to take ownership, become more analytical, and incorporate data into the company’s DNA.
Data Culture- Every individual in the organization needs to be passionate about data. They should be eager to use data to solve their most complex challenges. People at all levels should trust data. Giving people the data they need to fulfill an organization’s goal is the first step towards building a data culture. Of course, data culture isn’t a nocturnal event, but when done right, fuels better decisions.
“In a world of more data, the companies with more data-literate people are the ones that are going to win.” — Miro Kazakoff, senior lecturer, MIT Sloan
Wrapping it Up!
Which myth have you encountered the most?
What’s stopping your organization from being data-driven?
Share with us your top challenges and we’ll help you understand how using data effectively can have a profound impact on your business. Let’s talk!
Data -based Decision Making Teaser Video
Originally published at https://knolskape.com on January 3, 2022.
