The use of data and analytics in precision horticulture can increase yield and quality, reduce waste, and improve understanding of consumer preferences. Advances in data science are helping overcome limitations of data collection, storage, and processing, which have been obstacles to the application of data-driven approaches to decision making. However, these advances are not necessarily applied to precision horticulture because stakeholders in horticultural value chains may not be aware of how the data technologies can add value. Ambiguity in guidelines around data licensing negatively impact data owners’ trust in how their data will be used and how they can retain control over it. These challenges, in addition to the complex nature of data integration across various sources, make creation of integrated data products cumbersome and costly. In this chapter, we identify six key phases that should be considered for data-driven horticulture to ensure benefits for stakeholders, address data licensing concerns, and reduce cost of data reusage. As the trend in use of big data for smart horticulture continues to grow, taking careful consideration of the six elements of data-driven precision will enhance the horticulture industry’s ability to employ big data technologies for value and impact creation across the value chain.