Demystifying data

So, what is data? 

Different businesses will be at different stages in understanding their data and what is possible when this data is analysed. 

In practice, data is often grouped into two types. The first is quantitative: counts and measurements such as yield per field, rainfall, kilowatt-hours, visitor numbers and till receipts. 

The second is qualitative: notes and accounts that capture behaviour and context, from tenant conversations and manager logs to patterns you notice when walking the land. 

Used together, quantitative data analysis helps reveal trends and issues, while qualitative data helps explain them. This interplay makes local knowledge indispensable. The lived understanding held by land agents, estate managers and owners provides the on-the-ground context that should underpin any further data analysis. 

Estates will all be at different stages on this journey. Those just setting out may focus on ‘digitising the estate office’, getting properties digitally mapped, tenancy information catalogued, and moving accounting systems into the cloud so everything is visible and consistent. Others, further along, will use data analysis in a more strategic way, for example to forecast rental income over time or to inform asset-retention and investment decisions. 

Information Value Chain 

The Information Value Chain illustrates how information progresses from raw data to informed action. These same principles underpin an estate’s journey through data and analysis. Until it’s analysed, data remains information without meaning.  

 This chain goes as follows:  

1. Data collection and generation (sensors, maps, surveys, records).
2. Data management (storage, governance, standards, quality control).
3. Sense making and analytics (interpretation, modelling, pattern detection).
4. Decision making and application (tools and automation that support informed action).

Artificial intelligence 

AI can undoubtedly be extremely useful, and some of the things it can do are mind-blowing, but businesses are often just overselling simple machine-learning techniques that are now ubiquitous in most software packages. 

Aside from the terminology, part of the problem with AI solutions is that they are often a one-size-fits-all package. ‘Garbage in, garbage out’ is a well-understood concept, but even when the best data has been used, analysis by an adviser who can incorporate the local context and understanding is crucial. 

Good data analysed in the right way can boost the resilience of estates by providing support for complex long-term decision-making, but the human element remains vital.