Machine learning and artificial intelligence – pure hype (?) part 2
I hope you enjoyed our first article in our series about Machine Learning. If you missed it - no problem. You can read it here.
In the second part of this blog series we focus on the following topics:
- Introduction: From global galactic issues to audit, ICS, data analytics and risk management
- Building bridges: why initial disappointment may set in – from your data to machine learning algorithms
- Content is king (here too): Creativity comes later – only actual use will create added value.
- Three examples of use: a – Old wine in new bottles; b – A new approach; c – Doing things differently
2 Building bridges: why initial disappointment may set in
Although machine learning algorithms aren't entirely new, the subject's current topicality as mentioned in the introduction means they are currently attracting huge interest. Many software manufacturers and IT companies are responding with corresponding products. Amazon is offering AWS (Amazon Web Services) with relevant tools and services and associated suitable algorithms.
We, too, are working intensely with the ACL Robotics solution from Galvanize. It recently incorporated the K-MEANS algorithm as a clustering process (in addition to the train and predict commands, which themselves use a set of algorithms). In this context, our colleague Moritz has also written a series of articles with an integrated workshop, here you will find part 1.
But why did I write “why initial disappointment may set in” in the header to this section? Let’s look at an example: After the K-MEANS algorithm was introduced, customers contacted us who, without further ado, wanted to perform analyses and wanted to know if they can use this analysis to cluster their business partner master data, text-based descriptions of their audit findings or accounting transactions, and what the result looks like. An initial disillusionment then set in as they realised that the K-MEANS algorithm requires numeric attributes as a basis.
A transformation process is therefore needed in order to be more flexible when it comes to database matters. As a first necessary step: Depending on the planned intention and the selected algorithm, you need to convert the data. We can assist you with such transformations: the analysis is no longer based on tables and fields, but on “derivatives”. They contain the information which is contained in selected fields, but that can be directly used for ML algorithms. This information is thus almost totally detached from the semantics of the original data and is optimised for use by special algorithms.
In summary, you need to be aware that you may not be able to use your data directly in machine learning algorithms. If you have the right know-how, you can perform the necessary transformation yourself, or you can use our pre-configured content. As well as being optimised from a technical perspective, the content has also been tailored and optimised to specific applications.