Four areas for AI revolution
At the DataIQ 100 Summit, Diana Schildhouse, Chief Analytics and Insights Officer, Colgate Palmolive discussed her four-part strategy for leading the AI revolution:
- A business-first approach.
- Communicating a vision through a concise strategy.
- The promotion of data literacy.
- Creating value.
A business-first approach
With a business-first approach, data leaders and the wider organisations can benefit from several advantages, including the prioritisation of customers, first-mover advantages if the business is able to execute strategies ahead of competition, and a financial advantage as the financial focus of investments and commercial decision-making are front of mind.
The core four points made by Schildhouse were complemented in the presentation by Shashank Kadetotad, Global Director and Head of Data Sciences, Mars Wrigley. Kadetotad delved into how the data team at Mars Wrigley took a “business problem first mindset” to tackling value creation with AI.
Kadetotad explained that there are numerous wins to be had with AI, but these can only be achieved with a connected core foundation. “Data strategy should be core to any company in this digital age,” said Kadetotad, and the connected data foundation is led by this digital strategy.
The Mars Wrigley team can understand and make connected decision with a clear sight into different impacts through a connected data foundation; this in turn generates interconnected insights. Following this, the Mars Wrigley data team then creates an enterprise level analytics platform and a genAI engine.
Vision communication
Clear communication of a vision is essential, particularly as non-data professionals can often find the idea of data and analysis complex and off-putting. A data vision needs to be set in motion and this can be done in different ways depending on the size, legacy and ambitions of the organisation, so careful consideration is needed from the data team.
Schildhouse described that she works to the idea of a one-page strategy to act as the North Star for the data vision. This couples nicely with ongoing conversations within the DataIQ community about data storytelling and narration as clear communication is central to the development of data culture.
Additionally, Schildhouse emphasised that there is a need to consistently repeat the core messages of the data vision and focus on why it is needed, not what is needed. Over time, this vision will evolve and the buy-in from other departments will improve, but for organisations at the earliest stages of their data journeys and needing to communicate their visions, this is a great foundation to build from.
Promoting data literacy
Schildhouse explained to the attendees of the DataIQ 100 Summit that promoting data literacy is crucial. Data literacy is an ongoing project for most businesses and data leaders should make this as inclusive as possible by avoiding jargon and simplifying information. This will help to drive engagement and adoption, as well as continuing the evolution of data literacy rate and data culture maturity throughout an organisation.
This can be achieved through improved storytelling skills – a key tool in the arsenal for data leaders. In a recent DataIQ report, 49% of respondents actively disagreed or had no opinion that individuals in the business have the appropriate skills to interpret data and draw valid conclusions. This highlights the necessity for improved data literacy skills through education and consistent communication
Take the DataIQ Culture for Teams Assessment for data literacy to help understand the level of data literacy that currently exists within your organisation. The assessment uses the ten DataIQ culture dimensions outlined in The Ten Pain-Points of Culture report.
Value creation
Creating value is the final piece of the puzzle, according to Schildhouse, which entails articulating the value of what you do and telling the story internally, as well as identifying your priority areas so that you can focus on achieving your goals. Be sure to share the glory and the story of how this has benefitted the business, she stressed.
Schildhouse stated the importance of quantifying value, but emphasised that data provides insights, not actions: “Our goal is to deliver impact, not outputs. Ask what actions were taken based on the recommendations and aim to quantify the expected benefits, ensuring alignment with the derived value.”
Data leaders need to keep in mind that just having data does not create value automatically, data leaders need to start with questions around where and how value needs to be created in the business, and then move to data.
Other things that need to be addressed to ensure the best value creation is achieved includes having the right data strategy in place to align efforts and support business goals, as well as having the right technology foundations and architecture to manage and operate the data challenges. This last point can require investment and time-consuming platform shifts for organisations that are still early in their maturity development, but the foundations are essential to achieving long-term, flexible success.
The above learnings came from keynote presentations and discussions at the 2024 DataIQ 100 Summit about the ongoing AI revolution and how data leaders can achieve success before being left behind by competitors. DataIQ events provide incredible insights into how leading data professionals have approached a multitude of topics. The DataIQ 100 Summit is the premier face-to-face opportunity for attendees to learn from, connect with, and develop lasting relationships with some of the most prominent minds in the industry.
To register for the 2025 DataIQ 100 Summit, click here.
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