Data literacy skills are more important than ever. Organizations are beginning to adopt a data literate culture that focuses on data integration, analysis, and insight. No longer are data scientists and other data analysts the only ones who should be fluent enough to discuss data as a second language.
As data analytics becomes a core part of digital business, employees must be able to access, prepare, and act on their business data. This is where data literacy comes in.
The ability to “speak data” is now an integral aspect of most business roles.
What is data literacy?
Data literacy is “the ability to read, write and communicate data in context.” This includes an understanding of data sources, constructs, analytical methods, and applied techniques.
Data literacy is an underlying aspect of “digital skill,” an employee’s ability to use existing and emerging technology to deliver better business outcomes.
Data literacy skills include:
- Performing complex, meaningful analysis using different forms of data
- Using data as a means to communicate important ideas about new products and services, strategies, and workflows
- Understanding various data visualization dashboards
- Making well-informed, data-driven decisions rather than relying on intuitive gestures
The Importance of Data Literacy in a Data Illiterate World
The ever-growing volume and variety of data require employees to utilize better critical thinking, computational, problem-solving, and analytical thinking using data. Data illertacy. is no longer an option.
According to a recent survey by Accenture:
- 75% of employees are uncomfortable working with any form of data
- One-third of employees took sick days after working with data
- Data illiteracy costs employers five days of productivity—and billions of dollars in lost productivity
Data is the backbone of any organization, and, with data, top management can make better, more accurate decisions. However, if employees are not comfortable reading data or access data structures, then it can prove counterproductive for an organization’s growth.
7 Data Literacy Skills All Business Analysts Should Know
It’s well established how important it is for data analysts to have the right skill sets to survive in today’s fast-paced world. Data literacy is and will continue to remain one of the top priorities for organizations and enterprises alike.
The true benefit of data literacy is when employees can visualize data, locate important insights, and establish well-defined trends. Let’s unpack 7 data literacy skills that your business teams should know.
- Microsoft Excel
- Statistical programming tools
- Data visualization
- Presentation skills
- Machine learning
- Critical thinking
1. Microsoft Excel
Excel is the bread and butter of every organization, whether for technical or non-technical teams. Of course, the 1,048,576 rows can hold limited data, but even those rows are an excellent choice for people who want to store their data in spreadsheets.
While R, Python, and other related tools can significantly aid in manipulating and transforming large datasets, Excel works quite well with its inherent macros and VBA tools. Think automation, and the first thing that comes to mind is using Excel and VBA in conjunction with each other to automate daily repetitive tasks.
Over the years, Microsoft Excel, Google Sheets, and other spreadsheets have remained afloat with their limited maintenance and intuitive features.
Note: If you deal with large datasets, you might be better off learning a statistical language to access some of the best data wrangling techniques.
2. Structured Query Language
Structured Query Language, or SQL, is an omnipresent industry-standard database language. The language is often thought of as the “graduated” version of Excel since it can handle large datasets that Excel can’t.
With large datasets comes the need to query them. As large volumes of data get added to data warehouses daily, SQL has become the preferred querying language for SMBs and large organizations alike.
Business and data analysts equipped with SQL can successfully manage and store data and relate databases like Amazon Redshift, Snowflake, and many more. If Big Data is on your learning to-do list, then it’s time to learn SQL first.
3. Statistical Programming Tools
Statistical tools like R and Python step in where Excel’s functionality ends. These statistical tools can do what Excel does, but ten times faster. Predictive analysis, forecasting, advanced analytics…you name it, and these two tools can do it.
Python and R have become the new-age industry standards, and the journey towards full data literacy is incomplete without learning these two multi-faceted languages. Use these tools for extensive coding, analysis, visualization, and ad-hoc analysis, to improve the quality of your outputs.
Note: SAS is yet another tool from the suite of statistical languages. However, SAS is a costly system with limited functionalities, whereas “the sky’s the limit” with Python and R.
4. Data Visualization
We wouldn’t be fully explaining data literacy without the mention of data visualization tools like Looker, Power BI, and Tableau. These tools offer intuitive dashboards for everyday use.
They also help you tell a compelling story with your data to captivate your end-users—whether they be your executive team or customer base. Map your trends, locate pain points, and show up-and-downs with eye-catching graphics. Data may be crucial, but what’s even more critical is how it is presented.
5. Presentation Skills
Data visualization and presentation skills go hand-in-hand. But these skills don’t come naturally to everyone. Some of those who are data literate and can access, prepare, and act on their data confidently cannot successfully present their data.
Data presentation is about sharing the correct facts and highlighting what’s important in the eyes of the end-users. For example, a CEO doesn’t need to see raw data sets pulled directly from your data warehouse. On the contrary, he’d find more value in high-level numbers that may explain why the marketing department cannot bring in leads.
6. Machine Learning
Machine learning, artificial intelligence (AI), and predictive analytics are hot topics as of late, and you can’t overlook them on the journey to data literacy.
If this is your field of interest, you should have your statistical knowledge in place before jumping in completely. Check out an out-of-the-box tool like Orange, which can give you an insight into the building blocks of machine learning models.
7. Critical Thinking
Having the right technical skillset is half the battle. To win the remaining half, you need to develop your thinking patterns and adopt critical thinking. In other words, how will you leverage, apply, and act on your data?
Critical thinking and technical knowledge go hand-in-hand. To hone your critical thinking as it applies to data, set some ground rules like:
- What’s the issue at hand?
- What’s the nature of data you are dealing with?
- What is the expected output?
- Who’s the end-user?
- What tools can you use to manipulate, transform and present the data at hand?
Asking such questions will go a long way in developing your data literacy skills.
Honing these data literacy skills will ultimately help you make better-informed, data-driven decisions to grow your business. However, we know that not all business analysts can learn all of these skills.
Those without the right data literacy skills can still make the most out of your data, especially with a platform like Kloudio. Kloudio allows you to pull data from your SaaS applications, data warehouses, and other databases directly into Excel or Google Sheets, so your business teams can work with the data they need where they’re comfortable. Create a free Kloudio account to get started.