Tech companies being focused on platforms, AI and big data are missing the human side in their technology-driven outcomes and aspire to “deep-tech models”.
Meanwhile, market research companies focused on measuring human aspects of opinion, emotions and behaviour during purchases, consumption and societal contexts are missing the tech scalability. Both need to structure “humanised data models” while coming from these different angles in order to support ”knowledge-based” transformation strategies.
Today’s modern brands and organisations will reach exponential growth if they can leverage the full value chain of “deep-tech” or “humanised data”. Indeed, they are rebuilding their foundations to compete in the era of data and advanced analytics, defining their new business opportunities with the rise of AI at their core, and shifting from AI tactics to full, AI-driven data transformation, in which changes in technology and the nature of business competition connect. But organisations must accelerate on this path: a recent MIT and BCG 2019 study reveals that only 20% companies have completed their data transformation (with both teams in AI and some AI products at scale), accounting for a mere two-point increase on 2018.
In this way, there is a critical need to create competitive advantage and value beyond algorithms. We must avoid getting stuck with algorithms and legacy around the algorithms that revolve around a “so what” issue, disconnected from business people within the organisation, from customers and citizens in markets and society, and from the human dimension.
The challenge for data and AI is not to develop the next AI feature, but to move from tactical goals to strategic objectives, from capturing and targeting customers to holistically understanding and predicting customers, from fragmented data and AI projects to embracing critical mass and making the real change within the whole organisation.
In this context, market research and data insights companies have a responsibility to shift to a humanised data model, to accompany private and public organisations in their data transformation with a people centric approach (consumers, customers, citizens, employees).
Ipsos is highly involved in building a humanised data model within its “Total Understanding” global transformation programme. We are leveraging data from society, markets and people and pioneering in market research (MR) with a new Global Science Organisation established in September 2019, committing experts in data science and AI, academics and partnerships to be a part of the whole research value chain.
So how do we humanise data in a tech environment? I would like to share a synthesis of thoughts based on my experience within Ipsos, conversations with many clients from different industries, exchanges within a tech company roundtable I participated in at Websummit 2019 on “making the most of data” and from a workshop I animated at the Corporate Innovation Summit on “what is human data”.
- Humanised data requires to create a specific company environment, skillset and culture.
- Humanised data is about integration, hybridation, the connection between data and knowledge about business/people.
- MR agencies are today in a unique position to act on humanized data because of their original skills in the knowledge about people, citizens, consumers.
[WEBINAR] Future of Fintech: What to expect in 2020
March 30 - The blistering pace of innovation in the fintech space - driven by continually evolving consumer expectations, complicated by competitive interactions as well as synergies between incumbents and various challengers, and fueled by ever-increasing funding (witness the explosion of unicorns!)