Updated: Oct 24, 2021
Data literacy is the ability to read, analyze, and communicate to others with data in context, including an understanding of data origins and constructs. In this golden era of digital and data, we do not need to be Facebook or Walmart to build a data lake in our company. With the advancement in both hardware and cloud-based systems, this opportunity is now readily available to every company for a modest cost.
As organizations amass much more data at an unprecedented rate, it becomes increasingly important for all employees — and not just data scientists — to be data literate so that we can contribute better in our roles and help our companies sharpen their competitive edge in today’s aggressive global economy. According to a survey sponsored by Qilk, 94% of respondents indicated that data literacy is key to developing professional credibility to boost career growth.
Analytical self service tool without data literacy is like going to a war with cutting edge weapons but do not know where and how to hit your enemy
Whether you love or hate numbers, the relentless creation of data and the growing importance of the ability to extract insights from data are here to stay.Therefore, data literacy is critical if you want to differentiate yourself from others in your professional career. And for an organization, data literacy is a critical piece of the puzzle to set you apart from competitors.
Data Literacy, the Evolution and Demand for Different Kinds of Data Skills
Over the past decade, we have seen an evolution of data skills. In the early days of data science, many companies looked for candidates with strong programming skills such as R, Python, and SQL, with good fundamentals in mathematics or statistics. During that period, programming and statistics was a clear differentiator. As more people jump onto the bandwagon of coding and statistics, those skills have become a commodity today.
In addition, many of the algorithms and technologies are embedded into data platforms, which further lowers the barrier to entry into the data science arena. Today, companies are looking for a different set of data skills: Employees need to understand the workflow of data mining and be able to challenge the output of algorithms, not just simply accept and assume that system decisions are always right.
In the near future, every employee in every functions within an organization should possess some data-related skills
A recent study conducted by Harvard Business School found that many data teams are not suffering from a lack of technical skills; rather, they lack skills in data-driven problem-solving. I can clearly identify and empathize with the above study, especially in a commercial (non-tech) organization. Some of the skills that are lacking in commercial and data professionals are:
1. Know how to ask the right questions about the output of algorithms
2. Understand which data is relevant for their business, separating lagging and leading indicators
3. Design A/B tests and conduct structured experiments to test their hypothesis
4. Know how to interpret the analyzed data from the data team and link it back to their business
5. Create data-driven, compelling stories and business cases to communicate to business leaders
1. Know how to ask the right business questions
2. Translate business problems into a data mining problem with reasonable assumptions in place
3. Sound business and financial acumen to run analysis that is relevant for the business
4. Create easy-to-digest presentations and visualizations for business leaders to understand
What Can You Do to Improve Your Data Literacy?
Digital leaders will emerge from enterprises that embrace and extend analytics throughout their organizations. However, low data literacy is holding them back. It is time to get serious about improving our data literacy.
Just working on and rolling out data projects does not equal being data literate. A data scientist also does not necessarily mean that he or she is data literate.
Recognize that data literacy is important for your professional career growth
A quote from Jim Rohn: “If you really want to do something, you’ll find a way. If you don’t, you’ll find an excuse.”
It is important to deeply recognize that data literacy has become very important, not just for data professionals but for everyone. Based on the earlier discussion, you also know that companies, especially non-tech companies, need more people with the ability to interpret data, draw insights, and ask the right questions for the business. Data-driven decision-making markedly improves business outcomes.
Data literacy is not exclusive to data professionals. It is the new core business skill that future business leaders or managers should possess. While data literacy is a skill that anyone can develop, it takes time, persistence, and practice. Unless you are convinced that data literacy can help you become a better employee, manager, or leader, you’ll find excuses to delay your learning.
Continuously learn and pick up that technical skill if you need it
You may not need to be a master in analytical tools. However, to have fruitful discussions, ask meaningful data-related questions, and perform certain data-related tasks, you must “know enough”. Extracting insights from a vast amount of data is a great challenge for many employees. According to a study by Accenture, an eye-opening 74% of employees reported feeling overwhelmed or unhappy when working with data. This has a negative impact on their performance: 36% of those overwhelmed employees spent at least one hour a week procrastinating on data-related tasks and found alternative methods to complete the tasks without using data. Another 14% avoided the task entirely.
Marketing managers can assess their data literacy level when they understand at high level the flow of the analysis and feel confident enough to challenge the output of the analysis from whatever algorithms
Excel is still the most common and useful tool for data analysis. If you want to feel in control of your data or get familiar with the data you have, you should learn Excel. You can refer to this infographic for the 100 most useful features of Excel and learn those that are relevant to you.
Improve business and financial acumen
Business acumen is the ability to understand business situations in order to make good judgments or decisions for the company, while financial acumen is the ability to evaluate the impact of business decisions on financials in the short and long term.
Data professionals can measure how data literate they are when organization is confident that they can be the second lineup as marketing manager in the commercial unit
Business leaders faces trade-offs in every business decision: balancing short- and long-term gains, tangible and intangible gains, etc. Analyzing the data with a solid understanding of the business and the financials will make your analysis much more relevant to your senior business leaders.
The best way to improve your business and financial acumen is to ask for job rotations within the company, if possible. If you are a data professional, don’t be afraid to move to the commercial department and learn more about business and vice versa. If job rotations are not possible, then look for cross-functional project opportunities that allow you to work side by side with your business or data colleagues.
Get the real feedback that you need in your analysis
Have you been in situations in which you thought that senior leaders loved your presentation and analysis but your recommendations were not implemented? If that is the case, you might need to get a senior leader who is willing to invest time into sharing with you the real feedback on your analysis or presentation. That direct feedback will allow you to understand where and how you can further improve on your presentation or analysis.
I believe only people who truly wants you to improve will give you candid feedback
I hope that the above discussion is good enough to show that data literacy will be a very important skill for future leaders and managers. For those who are preparing for data science interview in a non-tech company, I have prepared a video on what data science manager look for in a candidate in this link.
The opinions expressed in this article are my own and I do not represent or speak on behalf of any organization. If you enjoyed reading this and would like to have a conversation on this topic, please feel free to reach out to me on LinkedIn or Instagram.