ARE SENSORS CRITICAL TO PREDICTIVE ANALYTICS ?

My immediate answer to this is –YES! Predictive analytics and response optimization of value chain assets requires real context in terms of time and space to traditional data management.

From a design perspective it is easy to conceptually design and understand data in the organisation – but placing it in context and deriving insights from it is difficult.

I believe that in order to integrate and fully harness data across marketing, sales, operations, services, support, tactics and strategy  - you need to deploy four broad categories of sensor and data collection technologies (and this is before we talk about big data !), being:

1      - Remote sensing

      -  Non-intrusive sensing

      -  Intrusive sensing, and

      -  Digital sensing

   

Remote sensing is the ability to analyse images and extract meaningful information from it. In the following instance we created an algorithm which uses spectral analysis for feature extraction and then combining it with census data to forecast trade area success. 

So far this model has been 100% in the classification of successful or not successful trade areas.

Non-intrusive sensing deals with the collection of information in a non-identified, non-intrusive manner. Here the analysis of radio-signals from Wi-Fi sensors and cellular towers are particular useful to study the mass movement of people around points of interest. This deal particularly well with staff movement, staff capacity planning and customer journeys. This example shows a heat map of customers moving around a retail point.

Intrusive sensing deals with the tagging of physical items such as equipment, stock, or vehicles. This enables real behavior to become visible in the supply chain – here an example of optimizing cost and service coverage.

Digital sensing enables data sources in the organisation to become geo-spatial intelligent as it is captured, or in the rework of historical data. This means that any customer information, supplier information, employee information or any data with a physical location becomes geo-intelligent; meaning it can be placed on a map with all the other sensor data mentioned about so that a complete data set on a map can be used to optimize value chain assets.

When this is done, one can start asking meaningful questions from a an optimization perspective and create the traditional views and results after the big data, reduce maps, data science and visualization steps are completed.

The application area of predictive analytics and response optimization stretches across the organisation - from planning to risk management - and as such we are only limited by our imagination by what we can do with predictive analyics and sensors today !