Venturing into Machine Learning – Start small and keep it real

The world of Advanced Analytics, Artificial Intelligence and Machine Learning is amazing, but also very intimidating. The big boys (Google, Facebook etc.) are doing some very clever, yet potentially unsettling things. However, I think bridging the gap between this hype and real-world applications in your business is hard!

Conversations with our clients are fascinating at the moment as they require a good understanding of both the business and the art of the possible. Typically, they are excited to get going, don’t really know where to start, and then end up boiling down into one of a few real world scenarios…:  

  1. Using anomaly detection techniques to monitor thousands of operational data feeds, KPIs or machine level outputs in real time. Many systems will use crude threshold alarms, but it is possible to be much better with models that learn ‘normal’ and then look for anomalies in real time.
  2. Extending scenario one into process optimisation. What are the factors that drive the process outcome (customer satisfaction, sales or yield)? Can these be better managed to improve performance?
    Reduce your dependency on manual checking by automating repetitive checks (e.g. invoice validation)

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Big Data London

I attended my latest ‘data’ conference recently, getting a pass from the team to go to ‘Big Data London’.

Given this was my first time at Big Data LDN (not London!) I didn’t really know what to expect, so the things I walked away with are perhaps not news to some, but they are a reflection of what I heard from speakers, observed at stands and took from chats with several folk.

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Can't believe I answered the wrong question

"I have just smashed together 14 separate data sources, cleaned up millions of lines of data and published the best looking, most whizzy dashboard the business had ever seen – unfortunately, I answered the wrong question…’’

It is all too easy to be seduced by the big idea, and then immediately start mashing data.  The work may be technically exceptional, overcoming real data issues and providing great visualisation.  But too often the answers fail to address the underlying business need…

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