Data science in retail: it’s as much about people as science

Data science in retail: it’s as much about people as science

Article by Peter Thomas

The need for data science in retail

Considering we started talking about AI almost two decades ago, it’s perhaps surprising that it is only just starting to make an impact on enterprises today. Certainly, this is the case with retail — today’s omnichannel shopping environment has placed a premium on efficient and relevant interactions with brands.

Retailers recognise that AI, and specifically machine learning, has the ability to handle vast amounts of data and is able to use that data to identify patterns and to make decisions with minimal human intervention. In today’s market conditions, this is an extremely appealing proposition; to be able to deliver more relevant shopping experiences whilst increasing operational efficiency at the same time. However, in many cases the anticipation of AI is still greater than its actual impact on day-to-day life for the vast majority of retailers.

There is a reason for this. While there’s no doubt that sound data practices are at the heart of AI’s success, too many fast-track the foundations of data science, and leap straight into AI, expecting to plug it in and for the algorithms to deliver out of the box. A black-box thrown into the e-commerce tech stack can certainly do a lot of automated heavy-lifting, but there is still a need for human intervention to guide what the algorithms are trying to achieve as well as augmenting their outputs with human ingenuity and inspiration. Delegating this responsibility to an opaque black-box to make all the decisions is short sighted – as the algorithm is only a part of the process. It cannot define what data to assess, how that data should be featured and the interpreting of the results in-line with commercial goals.

Considering we started talking about AI almost two decades ago, it’s perhaps surprising that it is only just starting to make an impact on enterprises today. Certainly, this is the case with retail — today’s omnichannel shopping environment has placed a premium on efficient and relevant interactions with brands.

Retailers recognise that AI, and specifically machine learning, has the ability to handle vast amounts of data and is able to use that data to identify patterns and to make decisions with minimal human intervention. In today’s market conditions, this is an extremely appealing proposition; to be able to deliver more relevant shopping experiences whilst increasing operational efficiency at the same time. However, in many cases the anticipation of AI is still greater than its actual impact on day-to-day life for the vast majority of retailers.

There is a reason for this. While there’s no doubt that sound data practices are at the heart of AI’s success, too many fast-track the foundations of data science, and leap straight into AI, expecting to plug it in and for the algorithms to deliver out of the box. A black-box thrown into the e-commerce tech stack can certainly do a lot of automated heavy-lifting, but there is still a need for human intervention to guide what the algorithms are trying to achieve as well as augmenting their outputs with human ingenuity and inspiration. Delegating this responsibility to an opaque black-box to make all the decisions is short sighted – as the algorithm is only a part of the process. It cannot define what data to assess, how that data should be featured and the interpreting of the results in-line with commercial goals.

Considering we started talking about AI almost two decades ago, it’s perhaps surprising that it is only just starting to make an impact on enterprises today. Certainly, this is the case with retail — today’s omnichannel shopping environment has placed a premium on efficient and relevant interactions with brands.

Retailers recognise that AI, and specifically machine learning, has the ability to handle vast amounts of data and is able to use that data to identify patterns and to make decisions with minimal human intervention. In today’s market conditions, this is an extremely appealing proposition; to be able to deliver more relevant shopping experiences whilst increasing operational efficiency at the same time. However, in many cases the anticipation of AI is still greater than its actual impact on day-to-day life for the vast majority of retailers.

There is a reason for this. While there’s no doubt that sound data practices are at the heart of AI’s success, too many fast-track the foundations of data science, and leap straight into AI, expecting to plug it in and for the algorithms to deliver out of the box. A black-box thrown into the e-commerce tech stack can certainly do a lot of automated heavy-lifting, but there is still a need for human intervention to guide what the algorithms are trying to achieve as well as augmenting their outputs with human ingenuity and inspiration. Delegating this responsibility to an opaque black-box to make all the decisions is short sighted – as the algorithm is only a part of the process. It cannot define what data to assess, how that data should be featured and the interpreting of the results in-line with commercial goals.

Article from: https://www.information-age.com

Contact DNS Industries to discuss your retail goals. Our DNS Industries consultants are experts in the design and build of displays and can advise you on the best use of materials and product that represents your brand the way you envision.