The Value of Data
In our final part of blog 6 in this series, we round up with some examples of data’s value in urban planning, in predictive analytics and in automation and AI.
We asked our team to share what the value of data means to them, in their experience. Some contributions are personal, others are more general. Nine of the team have shared their experience in parts 1,2, and 3.
Here are the final 3 contributions from James Grace, Anthony Wright and YY Tsang.
1. James Grace (BI Consultant DBI)
Urban Planning
City planners use data to improve infrastructure, optimise traffic flow, and design urban development.
In a university research project I am involved with, we aim to gather data on walking patterns and identify environmental features that influence emotional well-being. This data is essential for uncovering how specific surroundings—such as green spaces, urban structures, or social hubs—impact mood and mental health. By analysing this information, we can propose actionable changes to public spaces, encouraging individuals to incorporate routes that enhance emotional well-being into their daily routines. The implications of this research extend beyond individual health, potentially shaping urban planning and public policy to create communities that foster psychological resilience and overall happiness.
2. Anthony Wright (Senior BI Consultant DBI)
Predictive Power
Data enables predictive analytics, where organisations can anticipate trends, potential risks, or upcoming demands. This is useful in fields like finance, healthcare, and supply chain management.
A major retail project I worked on involved integrating and processing large volumes of disparate retail data which had been fed back to a wholesale organisation so that it was in the correct, standardised format required by another third-party application. This enabled the wholesaler to use predictive intelligence to make daily forecasts for their retailer customers about which products they needed to purchase to fulfil demand.
3. YY Tsang (Senior BI Consultant DBI)
Automation and AI
Data powers machine learning and artificial intelligence models, which can automate repetitive tasks, streamline customer service, and even power autonomous systems like self-driving cars.
AI and machine learning are extremely effective in revealing insights from various data sources. While generative AI like ChatGPT is the most hyped, quantitative AI works powerfully in the background on many healthcare or other real-world data challenges. Quantitative AI involves using machine learning algorithms to process and analyse large amounts of numerical data.
Dedicated analytics platforms such as the Diver Platform have automation features that reduce much of the manpower required to constantly update and manage various data sets. This streamlines the processes involved and results in less time being spent as well as greater accuracy. AI can thus equally be used for example to forecast volumes of patients and wait times in ER or estimate with accuracy inventory levels and sales patterns.
