AI, Machine Learning, advanced analytics in the enterprise environment Part 2 (theory)

There is a lot of discussions the past years about the revolution predictive analytics will bring to the businesses. But, predictive analytics is not something new. It used to be a part of statistics! Corporations with the resources were doing it for decades. Nowadays, however, it is more accessible, due to technological advancements in the areas of computing power, software and storage, the abundance of big datasets, and advances in algorithm research. Any student can now use the cloud to perform an experiment that a few years ago would have been possible to be performed only by large corporations or government agencies.

The challenge is how can we apply advanced analytics, and AI in an enterprise environment, efficiently and effectively.

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AI, Machine Learning, advanced analytics in the enterprise environment Part 1 (a personal story from the 90s)

The first time I saw predictive analytics live, in praxis, to provide useful information was in the mid-90s. As a student and aspiring engineer, I was enrolled in a Total Quality/Six Sigma class. During a field trip we visited a Japanese run, car manufacturing plant where, to my astonishment, blue-collar workers were applying regression in order to predict when to change tools!

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IT Project Management in practice



Currently, there are 2 prominent project management methodologies/theories. Waterfall and agile. Most common is the Waterfall based upon PMI (but also Prince2), and agile based upon Scrum (but also Extreme Programming, Crystal Methods, Kanban etc).

Please note that very few organisations employ a pure methodology as described by the corresponding theories. There are 2 main reasons for this:

  1. Corporations are seeking for a competitive advantage. If all were using the same methodology then they wouldn’t have an advantage. Therefore they always explore further, and as a side-effect theories are further improved.
  2. There is always a gap between theory and praxis. Simply speaking usually there is either a lack of education or resources (time, money, manpower …) in order to implement the corresponding theory. Or simple enough the theory does not fit our case.

Personally, I do not find anything more practical than a good theory! The familiarity, and ability to implement various theories is a great asset to tackle problems.

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Time Series Analysis Part 5 (All models are wrong)

“Come and join us, and we will show you how to predict the future”.

I tend to get invitations like this all time. Friendly people from marketing want to meet me and eventually sell tools or services to predict the future. So, it makes me wonder, if they actually have a machine to predict the future, why on earth are they spending their energy selling software and organizing events? Perhaps, you could pause for a second and consider what would you do if you had such a machine?

Although artificial intelligence has led to remarkable achievements in recent years, expectations for what the field is able and will be able to achieve in the next decade tend to run much higher than what will actually turn out to be possible. While some world-changing applications like autonomous cars are already within reach, many more are likely to remain elusive for a long time.

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