Guest Post by Steve Tadeo; Feature Photo by Markus Spiske on Unsplash
The topic of Data Literacy is getting a great deal of attention these days. The concept has been around for a long time, but now it has a name. Individuals and businesses are recognizing its value, so it’s gaining traction. Companies are focused on developing Data Literacy skills across all functions and job roles. Accordingly, there has been an increase in the number of learning and development programs available for companies to deploy to develop this competency. As a learning manager I recently launched several data literacy initiatives utilizing a blend of externally available and internally developed content.
I consider myself a strong advocate for Data Literacy, but it’s been a journey. It started a few years back when my focus was learning and development for Data Science. I developed curriculums addressing all subject areas that comprise data science. I conducted several webinars and engagement efforts to drive awareness of Data Science and promote Data Science learning and application. I believed the goal was to have every employee AI/ML trained and on a path to become a Data Scientist or any Data discipline. The approach to this data-centric, data science organization would have all employees:
- Complete 3 – 4 courses in Statistics
- Study a wide range of machine learning algorithms
- Learn Python or R or both
- Make a lifelong commitment to Data Science
Essentially, I envisioned a company of Data Scientists, with Python becoming the new corporate language. I say this a bit jokingly but I thought that a best-in-class data driven company meant having the highest concentration of Data Science talent possible.
The Data Science learning program was successful, and the volume of learning continued to grow. While the program was popular, I noticed that the number of new or first-time data science learners was hitting a plateau. It was becoming clear that my vision of a “Data Scientist” company was not going to materialize.
Why was such a large portion of the company not very interested in Data Science?
I started researching what training the “non-Data Scientists” were consuming. I discovered that they were developing knowledge and skills closer to their business domain. They were developing competencies in finance, negotiation, sales, and product management but not so much in the data and analytics space. It seemed that the data science vs non-data science job roles, were progressing down two divergent paths and were becoming more and more separated. One path dedicated to a specific domain and the other centered around analytics. Once I accepted that a company of Data Scientists was not possible, I started to question if this concentration of focus and talent was optimal. Was I pursuing something that was not only unachievable, but also unneeded? And that is when I realized that what was really needed was a diverse pool of talent and expertise, but with the ability to work together. What was missing was a path to connect these teams and allow them to work collectively and collaboratively towards common goals and objectives.
Data literacy the bridge to connect isolated silos.
The very nature of an organizational structure can create domain excellence but also business silos. These silos can prevent cross organizational collaboration, setting limits to the business value that can be created. Through data literacy organizations can take full advantage of the knowledge and skills that exist but may be distributed across the company. Breakthrough innovation can occur when these isolated groups align their talent and focus on shared goals.
I believe this is how breakthrough applications such as Amazon’s recommender system are born. It was the collective work of many different organizations in the company providing their input and working toward a common goal. Collaboration requires communication and so Data Literacy is the path or the language, by which information can flow across the organization.
10 Signs of Data Literacy
So, if data literacy is the answer, what does it look like? How do you know when you are data literate? What does a data literate person look like? Here are 10 signs that illustrate a data literate mindset:
- Questioning not simply accepting – Your credibility using data begins with your understanding of the data itself. Is the data valid? There are many questions that test validity and data quality. Are you comfortable enough with data fundamentals to ask basic questions? How was the data collected, processed, and presented? So, data literacy is a lot about asking questions.
- Being curious about data – Are you generally curious about data? When presented a data set or visualization, do you want to have even more information? Do you enjoy metrics and analytics and view data as a door to more insights and possibilities? Data literacy is about exploring, investigating, and discovering.
- The ability to see data in context(s) – Data alone is just a collection of facts. Context brings data to life and creates relevance, value, and impact. As mentioned before organizations are siloed and therefore data can also be siloed. Can you see the data beyond its silo? Can you see relationships?
- Understanding that data can create opportunities –Data literacy is seeing that data can create opportunities. Every company now has some form of a recommender system. Viewing customer data as a possibility and opportunity to drive a very different level of customer engagement. Instead of responding to orders, companies can used data to drive new business, new markets, and new business relationships.
- Able to explain, communicate, and argue with the credibility of data – A data literate person uses data to explain and communicate decisions. Effective communication has many benefits. Effective communication creates champions and advocates. A data literate person can drive consensus and alignment.
- Using data to drive future actions rather than justify past decisions – This can be a big challenge because we often seek quick decisions. Using data to evaluate multiple possibilities is a sign of data driven decision making. A data literate person considers multiple options to help minimize quick decision biases.
- Balancing data, experience and critical thinking – A data literate person sees data as an important tool for decision making but it doesn’t replace or override experience and critical thinking. Data and analytics are limited to the scope and depth of the data presented. Many other factors may come into consideration and so a data literate person understand and uses multiple influencers in decision making.
- Understanding and appreciating statistics – The heart of data analytics is math and statistics. The foundation for many advanced analytic models is math and statistics. A data literate person is comfortable with distributions, aggregations, correlations, data types and other data fundamental terms. This does not mean that data literate is being able to perform these statistical calculations, although that is possible, but it’s about having a discussion in a data context with someone more advanced in this field of study.
- Assuming everyone is data literate – At some point a data literate person stops questioning their data competence and the competence of others. Using a language analogy, it’s when you stop learning French and you move to France. A data literate person has moved from having “data” conversations to just conversations that naturally include data references.
- Being an advocate for data literacy – Being an advocate means promoting Data Literate actions. Use data to make decisions and require data credibility from decisions made by others. It’s about sharing learning experiences and promoting data literacy learning and development. It’s about rewarding and recognizing best practices.
Final note is that data literacy is not binary, and so what you know, what you do and what you think is on a continuum. It’s also not fixed. Data literacy is evolving and so if you are committed to being data literate know that it’s an ongoing journey and you must keep working and evolving. Below are a few free resources to help you develop your data literacy knowledge and skills.
A review of suggested learning offers. The 8 Best Data Literacy Courses and Online Training for 2021 (solutionsreview.com). Also Tableau and the Data Literacy Project are making great contributions by offering free training: Data Literacy for All (tableau.com) and The Data Literacy Project – building a data-literate culture for all.
Steve Tadeo is a data and analytics consultant who is committed to a data informed culture. Steve has designed and implemented several data science and data literacy programs throughout his career.