As the temperatures rose, so too did the value of data

As much of Europe experienced another spell of exceptionally hot weather, organisations across the Continent were making hundreds of decisions that were influenced, directly or indirectly, by the weather forecast.

Retailers are adjusting stock levels as demand for seasonal products changes. Energy providers are forecasting spikes in electricity consumption as cooling systems work harder. Logistics companies are reviewing delivery schedules, while transport operators monitor infrastructure under increased pressure from extreme temperatures.

Weather has always influenced business. What has changed is the number of decisions that can now be informed by data.

As the temperatures rose, so too did the value of data

Until recently, a weather forecast might have been used by a planner to make a handful of operational decisions. Today, that same forecast can be combined with inventory levels, historical sales, staffing availability, traffic information, supplier lead times, sensor data and energy prices. Artificial intelligence can analyse those data sources simultaneously, identify emerging patterns and recommend actions in a matter of seconds.

The forecast itself has not become significantly more valuable. The value comes from the context surrounding it.

AI has dramatically increased the number of questions organisations can ask of their existing data.

• Which stores are likely to see the biggest increase in demand this afternoon?

• Which delivery routes carry the greatest risk of delay?

• Which assets are most likely to fail if temperatures remain high for another three days? Where should additional staff be deployed?

• Which suppliers are most exposed to disruption?

Many of these questions could have been answered before, but doing so often required significant manual analysis. AI makes it possible to analyse far more variables, far more quickly, allowing organisations to respond while events are unfolding rather than after they have happened.

This is one reason why data quality has become such an important business issue.

AI models rely on accurate, timely and well-connected information. If operational data is incomplete, duplicated or out of date, the recommendations generated become less reliable. The sophistication of the model cannot compensate for weaknesses in the underlying data.

As organisations continue to invest in AI, many are discovering that their greatest asset is not necessarily a new model or application. It is the operational data they have been collecting for years. Sales transactions, customer interactions, maintenance records, inventory movements, production data and supply chain information all become more valuable when they can be analysed together.

Extreme weather provides a timely illustration because its effects are visible across almost every sector. Similar principles apply when organisations respond to changing consumer behaviour, supply chain disruption, fluctuating energy prices or unexpected market events. Each creates new questions, and AI expands the speed and scale at which those questions can be explored.

Whilst the most recent heatwave has passed, the growing importance of data and AI in day-to-day decision making is likely to remain.

Perhaps the most interesting development is not that AI is generating new sources of information. It is helping organisations extract more value from the information they already possess.