The　smart　building-integrated　photovoltaic　(SBIPV)　systems　have　become　the　important　source　of　electricity　in　recent　years.　However,　many　sociological　and　engineering　challenges　caused　by　temporal　and　spatial　changes　on　demand-side　and　supply-side　remain.　In　this　paper,　the　barriers　and　traditional　data　utilization　of　SBIPV　system　causing　the　above　challenges　are　summarized.　Data-driven　SBIPV　was　firstly　proposed,　including　four　aspects:　Data　Sensing,　Data　Analysis,　Data-driven　Prediction,　and　Data-driven　Optimization.　Data　sensing　goes　beyond　the　technical　limitations　of　a　single　measurement　and　can　build　the　bridge　between　demand-　and　supply-side.　Then,　the　demand-side　response　and　electricity　changes　in　supply-side　under　various　environmental　changes　will　also　become　clear　by　Data　Analysis.　Data-driven　Prediction　of　load　and　electricity　supply　for　the　SBIPV　is　the　basis　of　energy　management.　Data-driven　Optimization　is　the　combination　of　demand-side　trading　and　disturbed　system　optimization　in　the　field　of　engineering　and　sociology.　Furthermore,　the　perspective　of　data-driven　SBIPV,　technologies　and　models,　including　all　four　data-driven　features　to　make　automated　operational　decisions　on　demand-　and　supply-side　are　also　explored.　The　data　-driven　SBIPV　system　requiring　much　greater　policy　ambition　and　more　effort　from　both　supply　and　demand　side,　especially　in　the　areas　of　data　integration　and　the　mitigation　of　SBIPV　system.