Advancements in artificial intelligence (AI) have led to questions on how it will affect jobs, including those important to companies that have a data-driven culture. For example, data analysts are mentioned as roles that could be potentially affected by increasing AI adoption. However, the U.S. Bureau of Labor Statistics projects a 36% growth rate by 2033 for data analyst roles, which is well above average for all occupations.
So, why is there a positive forecast for data analyst jobs? Here are four reasons for the increasing demand:
- Data-Driven Decision Making: Companies across all industries (sports, healthcare, marketing) rely on data to make more informed decisions.
- More Big Data Access: We have more data now than at any point in history, which exponentially increases demand for experts who can organize, manage, and analyze data.
- Company-Wide Demand: From marketing to finance to operations, every department within an organization needs accurate data to optimize processes, identify trends, understand customers, and keep a competitive advantage.
- AI Integration: AI doesn’t and shouldn’t do all your data tasks, but it has enhanced the tools, data outputs, and context when it comes to analyst skills like SQL and programming.
So, how can your organization capitalize on this?
Despite the growing demand and need for data analysts, companies are still making the mistake of only analyzing the data and not also taking action on the data insights.
Everyone wants a '360-degree view,' but most entertainment data operations are flawed. Whether you have an overworked marketing manager ignoring dashboards or a massive, siloed analytics department, these disconnected processes stop raw data from turning into actionable revenue.
The most profitable teams aren’t the ones with the best dashboards, they’re the ones with the quick ability to take action. That’s why companies shouldn’t just hire a data analyst; they need a data doer, someone who will quickly take action on the data.
What are your next steps?
The truth is that knowing who is likely to buy a ticket is useless if you can’t instantly message them. Most platforms stop at the descriptive insight: telling you what happened. Start measuring your time-to-action: how long it takes you or your team from establishing the data insight to sending external messaging.
Test your team. Give a scenario and see how quickly you and your team can take action.
Scenario: It starts raining 2 hours before kickoff or curtain rise. You need to creatively pivot to capitalize on the unexpected weather.
📝 The Old Way: create a report for the VP that analyzes historical attendance drops during rain. Have them analyze. It’s too late, the game is over before you can take action.
💻 The Data Doer Way:
- Open the data platform.
- Filter “fans within 20 miles” + “has purchased merchandise before.”
- Use that list and send an SMS message saying, “Don’t let the rain stop the fun. Enjoy 20% off ponchos at the team store + free hot chocolate if you scan this text at the gate.”
- Potential result: $5,000 in merchandise sales in 15 minutes.
Your fans live on their phones, not in your data warehouse. The winning strategy is consolidating your data and the tools necessary to take action in one tool. Because with the right platform, you empower your data team and other departments to become data doers.