Who the heck was Ishikawa?

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Recently, a colleague and friend of mine hit a significant work anniversary. He mentioned some of his memories of starting at SAS. First impressions included the huge range of analytical techniques available in the software, most of which he had never heard of before. He wrote, “About nine months after I’d joined [SAS], I almost left. I even had an interview. I couldn’t see any way I’d learn all this 'SAS stuff.' I was never any good at stats at school. Who the heck was Ishikawa? ...”

His comment sparked a memory and a train of thought I'd like to share. Although my friend didn’t fully appreciate my story about how I first came across Ishikawa diagrams, I thought that some of you might be interested.

Hidden Insights - Who the heck was Ishikawa

Hidden Insights - Who the heck was Ishikawa

It starts with Deming

In my very first proper job, straight out of university, I had a boss who sent me to a Deming course. For this, I am forever grateful. This course set the foundation for my understanding of the importance of using data and real-world evidence to continuously improve your business.

Edwards Deming was an engineer, statistician and business consultant. He is credited with hastening Japan’s recovery after WWII by helping the Japanese to successfully industrialise and build quality into their manufacturing processes. Ishikawa diagrams were one of the tools they used to examine cause and effect.

After encountering the work of Walter Shewhart (you've heard of Shewhart charts, right?), Deming became interested in statistical process control. Shewhart was applying statistical techniques to manufacturing processes.

As a result, Deming decided that all organisations had people and processes, inputs and outputs. Therefore, the same statistical approach could be used for business improvement outside of manufacturing. He proved this theory by using statistical process control (SPC) techniques to achieve a sixfold improvement in productivity at the US Census Bureau.

It's all about information in variation

Deming’s philosophy also demanded changes in how businesses were led and managed. According to Deming, "The central problem in management and in leadership ... is failure to understand the information in variation."

Thinking about it now, I realise that my entire career has been about helping organisations understand the information in variation. The most successful B2C organisations use the information in variation to understand and predict customer behaviour. By identifying variations in behaviour, we can segment our customers, anticipate their individual needs and preferences, identify fraudsters and manage risk.

And the range and granularity of data available across all organisations have exploded over the past few decades. This has led to the development of machine learning and artificial intelligence techniques to find the complex patterns in this tsunami of data. Arguably, this statistics-led approach started in the manufacturing industry. So, have manufacturers advanced their use of data and analytics over the years? Not as much as they would have liked to, but the tide is starting to turn. I’ll talk about this more in my next blog.

Business improvement is about understanding the causes of variation in behaviour. This is as true for manufacturers as it is for B2C organisations. Click To Tweet
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About Author

Jennifer Major

Jennifer has spent many years at SAS working as a pre-sales consultant in a range of different industry sectors including telecommunications, pharmaceuticals, media and services. A lot of her work has been in the B2C sector, with a huge focus on predicting customer behaviour to support marketing and risk management. Her work with the energy sector started off with a focus on energy retailers. But what she found particularly fascinating were the complex dynamics of having to balance reliability and cost against the increasingly urgent need to transition to renewable energy sources. The innovation required to balance increasingly volatile energy supply and demand got her seriously interested in the potential of using IoT data to help manage energy – this includes the whole ‘smart’ paradigm of Smart Grid, Smart Homes and Smart Cities. She then started exploring the whole buzz about IoT in more detail. It quickly became evident to her that the application of Artificial Intelligence and Machine Learning techniques to IoT data has huge potential to change our world for the better: not just in energy management, but across all sectors - from health care to financial services and everything in between. One particular area of focus is in Manufacturing, with the huge potential of Industrial IoT to revolutionise efficiency and reduce waste. Jennifer heads up the IoT practice for SAS UK & Ireland. Jennifer holds a Bachelor Degree in Mathematics and Drama – which she feels to be a perfect combination for someone whose job it is to communicate the power of analytics to businesses.

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