woensdag 30 oktober 2019

Enterprise Architectures for Artificial Intelligence (III)

Taxonomies of Artificial Intelligence

There are at least five ways to position AI in the enterprise landscape:
  1. By processing method: batch, micro batch and real time
  2. By algorithm type: pattern recognition, clustering, associations, scoring, predictive, classification, text, speech and image mining, …
  3. By data type: high vs low dimensionality, graph data, self-describing data vs structured schema data, machine vs human sourced data, mediated data registration vs direct data registration,…
  4. By data behaviour: volatile vs stable data values, long vs data persistency,
  5. By analytics goals and or business process: churn prevention, prospect qualification, complex evaluations of loan applications, CVs, customer feedback, basket analysis, next best action proposals, fraud detection …

The enterprise architect will choose the relevant combinations between these taxonomies to produce a coherent end-to-end vision on the architecture. A possible selection criterion is the governance model used in the organisation. In a business monopoly analytics goals will be leading and combined with algorithm type. In an  IT monopoly processing methods combined with data behaviour is the most probable direction and in a duopoly, well,… that depends.

Let’s do an exercise and suppose this is the outcome of a duopoly governance model: combining the processing method with the algorithm type to indicate which processing method is most suited for the chosen algorithm type. Using this schema may help to manage expectations between the business and the IT people better.

Micro batch
Real time
pattern recognition
Ideal method for large data sets
Suited for simple patterns
Only as a binary in/out of pattern decision which implies a large (batch) training set
Ideal method for large data sets
Suited for simple clustering criteria
Only as Y or N adherence to an existing cluster which implies a large (batch) training set
Ideal method

Hardly possible
Develop a base line
Adjust the base line
Score against the base line
Develop a base line

Adjust the base line
Match with the trend
Train the dataset

Classify new data
Simple classification
text mining
Train the dataset
Reveal polarity, topics, etc…
Deliver alerts
speech mining
Train the dataset
Reveal polarity, topics, etc…
Deliver alerts
Image & video mining
Train the dataset
Classify images
Deliver alerts

From this crosstab, it becomes possible to position the concrete algorithms, the data sets and their life cycle management, the ingestion volumes, timing and the technology to deliver on the various promises made.
Other methods will give you paths to the same end result: a coherent and methodical inventory of the landscape, linking business processes to AI and data mining initiatives and routines as well as the data and the applications to deliver the goods. Based on a gap analysis, the enterprise architect can develop a roadmap that communicates with all parties concerned.