The volume of data available to businesses has been growing at lightning speed: indeed, IBM estimates that 90% of the data in the world today has been created in the past two years. To the prepared, this data is a source of massive opportunity.
However, with this opportunity come new pressures to effectively capture, manage, and derive value from the data that businesses are now confronted with every day. This means that data fluency is now one of the most sought-after and valued skills for those entering the workforce.
Like it or not, all businesses today are in the business of data science, and job trends are starting to reflect it: The US Bureau of Labor Statistics recently projected that the number of data-related jobs could increase by more than 30 percent in a matter of years. Likewise, according to research published by Northeastern University, 70 percent of U.S. executives say they will prefer job candidates with data skills by 2021.
In response to these trends, corporate training programs focused on fostering data security, fluency and management skills are on the rise. Higher education is also doing its best to fill the gap. At the university level, there has been a spike in the number of programs and certifications related to working with big data. At the heart of these programs are hard skills and technical training/knowledge, like:
- Structured Query Language (SQL)
- Microsoft Excel
- Critical Thinking
- R or Python-Statistical Programming
- Data Visualization
- Presentation Skills
- Machine Learning
But many argue that demand is such that traditional teaching approaches are struggling to keep up, exposing business to a ‘data science skills gap’. As Ruth Krumhansl, director of the Education Development Center’s Oceans of Data Institute explains, meeting the data needs of today and tomorrow will require a more fundamental shift in how we teach data science, and to whom:
“Basic skills in working with data, which every person should have, are not being taught in K-12 school, so they’re lacking at high levels in data-driven professions.”
What is needed is a way to foster data awareness, fluency, and basic data management and interpretation skills in all business learners – not just those who specialize in data science. Across the board, business education needs to find more ways to incorporate and instill some of the more general, soft-skills learners need in order to become truly data fluent and data-driven in their careers.
As business educators aim to ‘democratize’ data fluency in this way, they will look toward teaching tools and learning environments that are able to instill data-related skills in individuals and teams. Simulations can help. When done right, simulation games provide a learning environment where the basic tenets of data fluency can be instilled and refined. Here’s how they can help:
Instilling the importance of a systematic and quantitative approach to decision-making
As learners move through the rounds of a simulation game, the consequences of competitive decision-making are clear. And just like in real life, basing decisions on your gut or on a whim won’t get you far. The need to systematically analyze historic data to both understand your current position, and to determine how to move forward becomes essential to game succes
Providing an opportunity to practice analyzing virtual datasets to assist with decision making across multiple business functions
Depending on the type and the rigor of the simulation, virtual datasets that mimic the kind of datasets that real businesses rely on every day can be fundamental to game-play. For example, the HFX simulation, The Strategy Game, includes 4 years of historic demand data across 3 product lines, as well as granular financial data covering all aspects of the business over the course of the game. As part of the game, there are supplementary excel templates that assist participants in conducting demand forecasts, production planning, and cashflow and profitability analyses. These datasets and the supplementary worksheets are not only essential for successful decision making within the game, they also give students first-hand experience in what it is like to interpret and develop a response to incoming commercial data.
Enhancing the understanding of some of the challenges around data management, visualization, & security
Similarly, simulations typically require participants to work in teams in a competitive environment. To play effectively, they need to effectively delegate data-related tasks, and each participant needs to communicate their findings with clearly visualized analytics. Finally, these datasets need to be shared in such a way that they remain confidential to other team members, lest a competitor is able to use the data to their advantage. Thus simulation provide a small but significant step toward gaining experience in data management, visualization, and security practices – all increasingly important skills to master.
Ultimately, the data science skills gap is something that most, if not all, businesses will grapple with in one way or another. Investing in the data fluency of all employees will not only be a part of filling the gap, but also of remaining relevant and competitive in a data-centric world. As a result, business training and education – both in school and on the job – will increasingly lean on tools like simulations that allow for both hard and soft data-related skill development.