AI & DATA ANALYTICS data sources , before spending too much effort on operationalising a wider variety of data sources ,” says André Balleyguier , DataRobot ’ s Chief Data Scientist for EMEA . “ Once business ideas are clearly scoped and road mapped according to their value and feasibility , the next step is to prioritise which data sources to focus . This prioritisation goes hand-in-hand with the development of a data strategy that scales to an increasingly large variety and volume of data sources needed to solve the business problems .”
Data expectations While such unified analytics has revolutionary potential , it ’ s not a fix-all , as Spooner explains : “ Raising expectations too high at the outset can lead to overhype . Similarly , failing to explain what is possible can lead to indifference and a poorly supported project . Select the right use cases to start and then expand . The initial use cases should be of high value , achievable , near term and data ready .” The real benefits come through standardisation . “ Scaling often means adopting standard processes and platforms for data cataloguing and management that are both flexible and replicable , to avoid unnecessary manual customisation for each data source ,” says Balleyguier .
It ’ s also a question of ensuring that the correct stakeholders have access to the relevant data . “ The other challenges that need to be addressed relate to how these data sources are made available for consumption by the business : are
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