Saturday, June 28, 2014


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 !

Wednesday, June 18, 2014

Business Architecture on the Move

Business data, process data, planning data, strategic data – mostly all of this data in the context of a business architecture is static. At Visualitics we aim to optimize value chain assets through four core drivers – “proximity”, “visibility”, “attractiveness”, and “movement”.

In the following example we were asked to assist in capacity planning of emergency services using data from the operations call center. Using our easycodeproduct together with a spatial quadrant cluster algorithm we were able to make this data geo-intelligent as in the picture.

Spatial quadrant cluster output

Geo-Intelligent Data

In this context business events can be modeled and planned within the context of demographic and geographic data  – simply put, we can place the event (planned/unplanned) on a map, measure the shortest distance to the event, and also deduct demographic risk factors from it.

This is what I call “Business Architecture on the move” – data in context of the real world for real decision making.

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