Data Teams Must Prove Business Value
Data teams are facing increased pressure to demonstrate their return on investment as corporate budgets tighten. An industry podcast argues that after a period of rapid expansion, data functions must now pivot from building infrastructure to supporting revenue-generating activities. Experts advise data professionals to focus on quantifying the business impact of their work.
- Following a pandemic-era hiring boom, the tech sector has seen significant job cuts, with an estimated 200,000 U.S. tech workers laid off in 2023 and around 95,000 in 2024, contributing to the pressure on departments to prove their value. - A 2023 Gartner survey found that 69% of data and analytics leaders report struggling to deliver measurable return on investment, a key factor driving the current scrutiny on their budgets and performance. - Companies are increasingly adopting a "data-as-a-product" strategy, where data is managed like a commercial product with dedicated teams, clear ownership, and self-service access for business users, aiming to make data more reusable and directly tied to use cases. - The role of the Chief Data Officer (CDO) is evolving from a cost center focused on compliance and governance to a profit center expected to generate new revenue streams through data monetization and strategy. - To better demonstrate financial impact, data teams are being urged to adopt metrics familiar to finance departments, such as Return on Investment (ROI), Internal Rate of Return (IRR), and impact on Earnings Per Share (EPS), alongside operational metrics. - The skill set in demand is shifting from purely technical proficiency to a hybrid model that blends data science with business analytics; business analysts often use SQL, Excel, and Tableau to solve immediate business problems, while data scientists may use Python and machine learning for predictive modeling. - Data-related spending for a mid-sized organization can exceed $250 million annually, covering areas like third-party data sourcing, architecture, and governance, which is why leaders are now focused on cost-cutting measures like auditing data subscriptions and eliminating redundant reports. - New roles are emerging within data teams to address the growing complexity of the field, including AI Governance Specialists and Data Product Architects, reflecting a shift from generalist data roles to more specialized functions.
