Mark Kelly
Futurist, Board Advisor and AI Thought Leader
In the AI era, data has become companies’ most valuable asset. As AI technologies advance, the demand for high-quality, well-organised data intensifies.
Paradoxically, while data generation soars, we face scarcity as large language models (LLMs) consume information at unprecedented rates.
Strategic data management
Interviews with over 600 AI leaders reveal a crucial insight: properly tagged data is essential for AI success. Companies failing to organise their data risk falling behind in AI-driven innovation.
JPMorgan Chase exemplifies this approach. Their CEO recently highlighted how AI is used across the business, from content to contracts. By investing heavily in data infrastructure and creating a centralised data lake, the bank has developed AI-powered fraud detection and personalised financial advice services, showcasing the value of well-organised data in finance.
From raw data to actionable intelligence
AI-driven data analysis tools are streamlining decision-making processes by automatically identifying meeting attendees, discussion points and next steps. Unilever has leveraged similar AI-powered analytics to process customer interactions, social media and market trends, enabling rapid identification of consumer preferences and agile product development strategies.
Companies that recognise data’s true
value and implement robust strategies
to harness its power will thrive.
The promise of synthetic data
Generative AI offers a solution to data scarcity through synthetic datasets, complementing existing proprietary data and opening new avenues for innovation. Moderna’s rapid development of a COVID-19 vaccine, partly facilitated by synthetic data in modelling and testing, demonstrates how this approach can accelerate critical research and development processes.
Getting started on the data journey
For businesses embarking on data transformation, a structured approach can yield significant results:
- Conduct a data audit
- Define clear objectives aligned with business goals
- Start with small, scalable pilot projects
- Invest in talent and upskilling
- Leverage cloud technologies for enhanced data capabilities
This targeted approach yielded insights that informed product development and personalised marketing, boosting customer engagement and sales.
Best practices for the data-driven future
As businesses navigate this data-rich landscape, key best practices emerge: implement meticulous data tagging systems; seize opportunities to collect relevant data; ensure regulatory compliance (eg. EU AI Act); prioritise privacy-by-design in data governance.
In this new era, data isn’t just an asset — it’s the lifeblood of AI-driven success. Companies that recognise data’s true value and implement robust strategies to harness its power will thrive. As AI reshapes industries, effective data management and leverage will be crucial differentiators in the competitive landscape.
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