Current Challenges in Performance Analysis and Big Data
26 Jun, 2022
2 Min Read
In the pursuit of using data to make smarter decisions top sports organizations are learning key lessons. Applied high performance outcomes are critical.
Harvard Business Review summarizes the challenges of implementing data strategies. In 2019 they wrote how data science needed the “Art of Persuasion” to result in successful application. Whilst in 2020 they followed up with research into the best ways to approach data.
In high performance sport, many of these challenges resonate and lessons from the successful organizations can be summarized as:
Build the right team
Diversity of experience is key and so is the ability to empathize with high performance environments and support evolution rather than revolution. Listen, test solutions quickly and learn.
Create a flexible data environment
Separate data collection and processing from data analytics and visualization outputs. This way data sources can be changed with minimal effort and the future innovations in analytics and visualization software can be adopted faster to maintain a competitive advantage.
Answer current performance questions first
Don’t overthink the questions you need to answer. Stay true to your organization's goals and strategy by being very clear on the most important performance questions across Football Performance, Human Performance and Talent Identification.
Build the right team
Create a flexible data environment
Answer current performance questions first
Key Data Personas
In elite sports, one of the biggest misconceptions in building internal data strategies is the required skill-sets and personas to manage, manipulate and communicate insights that support the decision-making process.
Building the right team is key to your data strategy
Across the sports industry, many organizations are traditionally made up of the Tactical Analyst persona due to the importance of tactical insights for coaches and players.
However, many Tactical Analysts have been asked to perform the role of the Technical Analyst due to the amount of data available; which can result in manual and inefficient workflows or limited insights because of the skills gap that takes time and specific training to fill.
Over recent years, many organizations have started employing Data Scientists to solve their data strategy challenges but have then discovered that the skill-set of a Data Scientist falls short when it comes to centralizing all the data sources into a single database. This can often result in siloed data that does not match the overall vision of the data strategy.
Therefore, a dedicated Data Architect persona in your organization should not be undervalued because they play a critical role in centralizing all your data sources and ensuring they remain available to the Technical Analyst, Data Scientist and Tactical Analyst.
Take a moment to reflect on the Key Data Personas employed at your organization.
