Data is at the heart of decision making today, and graphs are firmly embedded in the modern data stack. From fraud detection and drug discovery to market and supply modelling, graphs enable previously unachievable insights. However, while graph analytics platforms are increasingly used across the industry, most applications and solutions overlook a crucial element. Time. Current solutions focus solely on the latest version of the data - missing out on how it has arrived at the state it is today. Temporal graphs embed the full history of data, keeping track of every change that has ever occurred. This enables organisations to understand the order of interactions, compare the evolution of the network over time, and control for risk or prepare for future trends. In social networks, this can help us understand the outreach of marketing campaigns or, contrarily, control the spread of fake news. In finance, it helps identify emerging markets and allows for a timely response to financial crime. In supply chains, it brings to light highly coupled dependencies and how service outages may impact delivery. The problem? Graph analytics today is already computationally hard. Imagine what happens if you are now looking at 100,000 different versions of your network across time. In this talk, we introduce the temporal graph model through Raphtory (our open-source tool). How users can leverage time to produce previously untapped, actionable insights. How to scale to billions of records in minutes (full Bitcoin blockchain ingested in 49 minutes, loaded back in 47 seconds) while seemingly shrinking the search space with novel time-respecting queries (following money thousands of hops deep). Finally, we end the presentation with how we are bridging the gap between graph systems and other data science tooling.