Episode 38: Close and recap of data series
- Embedded IT
- May 12, 2025
- 3 min read
Updated: Jan 15
Data sits at the heart of every modern organisation, and it is a critical prerequisite for any artificial intelligence project. This recap brings together the core themes of the recent data discussions, offering a clear and practical overview for anyone involved in technology procurement or IT strategy. The focus is on understanding the basics well enough to engage confidently with stakeholders, suppliers, and technical teams without needing to be a data expert.
If you’re new to the topic, start with our introduction to the data series.
The importance of data readiness for AI
A key message throughout the series has been simple: no AI project will succeed without the right data in place. Before approving contracts or procurement activity, it is essential to ask whether the organisation’s data is in a suitable state for use. Poor quality, poorly structured, or poorly governed data will undermine even the most ambitious AI plans.
Understanding different types and sources of data
The recap highlights the distinction between structured and unstructured data. Structured data is usually easier to work with, while unstructured data adds extra complexity.
Data also comes from a range of sources. Internal data tends to be simpler and lower risk, whereas external data often involves additional cost, licensing considerations, and dependency on third-party accuracy.Sensors and Internet of Things devices add another layer, generating streams of data both inside and outside the organisation.
How data is collected
Data must be gathered before it can be used. Common methods include APIs and web scraping, which bring information into the organisation so that it can be processed and managed effectively.
How data is stored
Once collected, data needs to be stored securely. Storage decisions often involve cloud platforms, which introduce important commercial considerations around data privacy, data protection, and long-term cost. These are essential areas for anyone in technology procurement to understand.
Data processing and analysis
Processing tools help transform raw information into clean, usable data. Analysis tools then make it possible to draw trends, identify patterns, and generate forecasts from that information. These stages are where data becomes genuinely valuable.
Data visualisation
Visualisation is not only about generating graphs. It is about telling a story with data in a way that is easy to understand and meaningful to stakeholders.
Data governance, security, and integration
The series also covered the importance of data governance and quality management, ensuring that data is accurate, protected, and responsibly handled. Integration tools help different platforms and systems exchange information so that data can be used consistently across an organisation.
Why procurement professionals benefit from this understanding
While procurement specialists do not need deep technical knowledge, understanding terminology such as ETL (extract, transform, load) helps them ask sharper questions. It strengthens conversations with stakeholders and suppliers and ensures that technology procurement aligns with real-world needs.
The link between data and artificial intelligence
Data is the fuel for every AI system, whether it is a model like ChatGPT, a code-generation tool, or emerging entrants such as DeepSeek. Without high-quality data feeding these systems, the output will fall short.
For organisations planning AI initiatives or improving their approach to data, get in touch.
