Patterns, Dimensions and Connections: Navigating the data landscape

To understand the data landscape it makes sense to think about maps. In a basic form maps have been around since mankind needed to have a sense of orientation and to travel to places in as effective way as possible. As we humans developed, maps were carved into stones (say a Roman Forum outline) or drawn on parchment and later paper. In more recent times maps have been accessed on screens e.g. radar applications (now some 80 years old), viewed on monitoring screens (not just radar) and today as navigation systems which we use via our smart phones or on a dedicated device in the car.

As mentioned above, maps are used to guide us to travel from say A to B. But they are also used to explore and to analyse environments when making a decision:

Example 1: Where to go on holiday?

• Scenery
• Facilities
• Recreational possibilities
• Connections related to the above and to other places related to a destination

Example 2: Surveying the business environment

• Office location for any type of business
• Shopping outlet investment possibility for a retail business
• Hub connections

What the above all have in common are the interaction in a designed structure with patterns (landscapes, urban / rural, road networks etc), dimensions (scale and proportion, symbols reflecting entities in the map) and connections (for example internet links to defined entities, navigable links between the dimensions).

Furthermore, in relation to digital maps, named entities can be selected on-demand to relate to the context and the user's requirement.

The same principles as outlined above are today applied to AI text analytics and quantitative data. Patterns can be interpreted (using machine learning and natural language processing), dimensions (applying knowledge graphs / defined entities) and connections (how the entities relate to each other and change over time). Most of the features on a digital map might only change over weeks or months (except for maybe road works, accidents or weather conditions). In the case of AI text analytics (or "a text data landscape map"), the entities and knowledge graphs can change in real time as an updating stream of new events are added to the emerging intelligence picture. This adds an active dynamism to the navigation direction of travel for the user - whether requiring insights for an A - B direct journey or for exploration guidance purposes.