While browsing LinkedIn today we came across a very practical article titled 'The trillion GBP reality check: Why clean data matters more than AI'. The article highlights the importance of having clean data when planning artificial intelligence projects.

While the examples cited by the author Kieren Exley are from the construction industry, they are relevant to many other sectors, including our focus area of logistics.

Here are its key points:

1. Significant amounts of company data will be difficult to use due to its siloed storage, messy organisation and trapped in PDFs or spreadsheets, meaning it can't support good decisions.
2. Poor data quality is expensive. Approximately 95% of construction data is unused and has to be reworked which costs the industry billions annually.
3. Artificial Intelligence highlights data flaws, not solutions. Feeding your AI poor historical data simply produces faster, more confident errors.
4. Historical 'dusty data' has real value. If it's cleaned and structured your old data can become a strategic asset.
5. The winners will prioritise data discipline. Structured data capture, a focus on high-value datasets and development of user-friendly tools is essential.

The full article is well worth reading and you can find it by clicking here.