This Learning Sprint was designed for a dozen data visualization team members of a large FMCG multinational to build Structure Query Language skills in order to construct viable pipelines to dashboards and reports for data visualization in Power BI. These rigorous and thorough sprints prepare them to add big data pipeline and query skills to their current business intelligence toolkit.
Since the rise of Big Data, companies are challenged to transform this massive information into insights that fuel data-driven decisions and insights that drive quick solutions (Grover et al., 2018). Top-performing organizations use analytics instead of intuition five times more than low-performing ones as they would always have an idea of what happened before, what is happening now and what will happen next (La Valle, 2010). It predicts customer behavior—making personalized recommendations, identifying and overcoming roadblocks, and fine-tuning internal processes (DalleMule, 2022). So, data analysis must always be interwoven with a company’s business strategy in order to get the most out of the available data and drive growth (Duan & Xiong, 2015).
Across three days in mid-February 2023, Eskwelabs facilitated an SQL Learning Sprint. The program was implemented, like other Eskwelabs sprints, through the participation of corporate professionals as project coaches, career mentors, and instructors.
SQL dashboards as learner projects involve creating dynamic visual representations of data through a combination of SQL queries, PowerBI, and data visualization tools. This type of project allows learners to apply their SQL skills in a practical way and develop their data analysis and storytelling skills.
The program attained a Net Promoter Score of 58%, demonstrating the enthusiasm of learners for both the sprint’s practical pedagogy & apprenticeship-based learning model, as well as the accessible but powerful technical skills of data analysis and storytelling.
“Despite just having three days of sessions, I learned a lot throughout the SQL sprint! It's incredibly effective and efficient the way our instructor and our mentors teach us SQL, especially with the examples they provide. Those concepts are definitely applicable to our upcoming reports and dashboards.”
- Nestle Employee
The gradual upskilling of existing business intelligence teams in more advanced data engineering and data science topics has several core benefits:
Eskwelabs' Learning Sprint provided dashboard team employees at Nestlé Business Services, its Shared Service Center, with the opportunity to develop their SQL skills and connect it with their work in data visualization and dashboarding, ultimately allowing them to be instrumental in the making of data-driven decisions in their company. This follows up from other learning sprints that Eskwelabs carries out to build dashboarding skills for data analytics teams and for non-data teams. Eskwelabs had previously trained members of client reporting teams in the partner organization in advanced data analytic techniques including segmentation analysis, reflecting Nestle’s focus on shifting all teams to higher value-added analysis and data science.
The continued development of new powerful tools that make all aspects of data work more accessible enable teams around the world to acquire new capabilities that can be quickly developed through upskilling. This allows teams to carry out more advanced work as well as frees up data science and engineering resources to work on other projects.
We’ll be deep diving into our impactful partnerships through the future issues of FutureNotes. To learn more about our learning sprints and how they can benefit your institution, we encourage you to download our Sprint Catalogue below. If your organization is driven by education innovation, collaborate with us and help create a future-ready workforce. Together, we can bridge the skills gap and prepare future generations for the changing nature of work.
Grover, V., Chiang, R. H. L., Liang, T., & Zhang, D. (2018). Creating Strategic Business Value from Big Data Analytics: A Research Framework. Journal of Management Information Systems, 35(2), 388–423.