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to gain competitive edge and increase sales volumes. Karvitz (2019)
indicated that in Toulouse in France, Naio technologies developed a

robot called Ted to assist the farmer to increase pruning precision

within a vineyard. Ted sends live data to Naio technologies. Ted works
under soil to cut out weeds under vines, thus reducing the need for

water and nitrogen competitiveness. Ted seeks vineyard row and plot

consistency (important for water and growth conservation). In Spain

a robot vineyard project named Vine Scout analyses data points from
canopy temperature, nitrogen levels (obtained by 3d stereoscopic

machine vision system, lidar and ultrasound sensors).

Tandon (2018) claimed that the benefits of AI are: cost savings and
increased efficiency. If organisations implement AI, this creates value

added work, increases employee flexibility and work life balance.

However, AI is costly to implement. To increase the efficiency of AI,

organisations should prioritise ease of accessing data and automation
capability, a Return On Investment (ROI) and cost saving.

Contradictory is Zillani and Leva (2020) who indicated that BD impacts

upon organizations operationally and strategically. Organizational

benefits of BD include flexible, adaptable and computerized processes.
Wang (2017) stated that over 73% of BD projects are not profitable.

1.3. Research purpose

The dissertation aims to compare and contrast the application of BD,
AI and Analytics within the full wine life cycle covering vineyard

industry (Viticulture and Oenology) to sales for the UK and Italy wine

organisation to identify the supporting contribution. The dissertation

composition applied primary and secondary data to support the
research objectives. The primary data will be derived from key

vineyard management stakeholders both from the UK and Italy. The

secondary data will be extracted from academic literature.

1.4. Limitations of study

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