As a passionate data scientist, it is my honour and privilege to work with some of the most intelligent, interesting and driven individuals on the planet. As a result of my recent efforts within an organisation I was offered postgraduate study at the University of New South Wales focusing on predictive analytics across a logistics supply chain.
Yet despite the calibre of the individuals I work with and my general passion for this field of work, I remain often discouraged by a lack of understanding about the field of data analytics and the tremendous value it can add to companies. This blog is my personal effort to add my voice to this commentary. Hopefully I can be insightful and interesting, at times offering advice, training and direction to what is an incredibly interesting and diverse field.
I'd like to start by laying out what I think data analytics is and why it's important.
What is data analytics?
Data analytics is about taking mountains of data and analysing it to see what insights can be drawn gained through synthesis, in-depth knowledge and the art of extraction.
Synthesis. In a a modern logistics framework, there is literally hundreds of thousands of data points per item being recorded, everything from their GPS location, to check in times at key nodes. At a very basic level, this data is pretty useless and frankly annoying/overwhelming. I mean a piece of freight is going to arrive when it arrives right?
Synthesis is about taking these data points and starting to look at them in combination with lots of other disparate pieces of data to get insights. For instance, if your logistics network was global in nature, then you might want to look at normal weather patterns and understand the impact sunny days could have on your network and optimise around that. Or you might choose to look at your workforce and understand the impact substandard equipment has on your bottom line. Or you might want to look at the maintenance cycle of your assets and change it around to reduce total downtime. The point is that in each case, the insight was gained through combining different bits of information to produce a better understanding of the whole.
In-depth knowledge. Truly effective data analysis is a combination of synthesis combined with a thorough understanding of the subject being studied. For me this was shown in my MAGNUS project. The engineering firm I was working for had massive inefficiencies generated through a stove piped information environment, leading me to be confident in achieving a good result through simple synthesis. However, it was when I befriended one of the other engineers and was able to use his expertise in their engineering decision tracking tool that the savings ramped up into the millions of dollars. This was because it was in knowing and understanding the process that we were able to see the opportunity and make effective changes.
The Art of Extraction. For me this is the true test of effective data analytics. The process of synthesis and in-depth knowledge can produce many amazing insights into what is happening in an organisation. But sometimes those insights are pretty useless. True data analytics is just as much about filtering out the noise as delivering insights to end users.
So Why is this Important?
Data analytics has the potential to really revolutionise many organisations. It's very nature is about fully understanding and quantifying the risks, rewards and opportunities inherent in a choice. As a result, when data analytics is employed correctly it provides deep insight into the impact of decisions, individuals and equipment on the desired outcome.
A great recent example of this lies in Walmarts recent decision to continue investing in streamlining their trucks and delivery network. By analysing the routes they typically drove, they were able to increase the economy of their network by about 2% by reducing wind drag on their vehicle fleet. Furthermore, by identifying where the largest hold ups were in their network, they were able to redistribute deliveries and decrease overall down time.
For many organisations this kind of insight can improve returns dramatically. It increases competitive advantage and decreases risk.
Yet despite the calibre of the individuals I work with and my general passion for this field of work, I remain often discouraged by a lack of understanding about the field of data analytics and the tremendous value it can add to companies. This blog is my personal effort to add my voice to this commentary. Hopefully I can be insightful and interesting, at times offering advice, training and direction to what is an incredibly interesting and diverse field.
I'd like to start by laying out what I think data analytics is and why it's important.
What is data analytics?
Data analytics is about taking mountains of data and analysing it to see what insights can be drawn gained through synthesis, in-depth knowledge and the art of extraction.
Synthesis. In a a modern logistics framework, there is literally hundreds of thousands of data points per item being recorded, everything from their GPS location, to check in times at key nodes. At a very basic level, this data is pretty useless and frankly annoying/overwhelming. I mean a piece of freight is going to arrive when it arrives right?
Synthesis is about taking these data points and starting to look at them in combination with lots of other disparate pieces of data to get insights. For instance, if your logistics network was global in nature, then you might want to look at normal weather patterns and understand the impact sunny days could have on your network and optimise around that. Or you might choose to look at your workforce and understand the impact substandard equipment has on your bottom line. Or you might want to look at the maintenance cycle of your assets and change it around to reduce total downtime. The point is that in each case, the insight was gained through combining different bits of information to produce a better understanding of the whole.
In-depth knowledge. Truly effective data analysis is a combination of synthesis combined with a thorough understanding of the subject being studied. For me this was shown in my MAGNUS project. The engineering firm I was working for had massive inefficiencies generated through a stove piped information environment, leading me to be confident in achieving a good result through simple synthesis. However, it was when I befriended one of the other engineers and was able to use his expertise in their engineering decision tracking tool that the savings ramped up into the millions of dollars. This was because it was in knowing and understanding the process that we were able to see the opportunity and make effective changes.
The Art of Extraction. For me this is the true test of effective data analytics. The process of synthesis and in-depth knowledge can produce many amazing insights into what is happening in an organisation. But sometimes those insights are pretty useless. True data analytics is just as much about filtering out the noise as delivering insights to end users.
So Why is this Important?
Data analytics has the potential to really revolutionise many organisations. It's very nature is about fully understanding and quantifying the risks, rewards and opportunities inherent in a choice. As a result, when data analytics is employed correctly it provides deep insight into the impact of decisions, individuals and equipment on the desired outcome.
A great recent example of this lies in Walmarts recent decision to continue investing in streamlining their trucks and delivery network. By analysing the routes they typically drove, they were able to increase the economy of their network by about 2% by reducing wind drag on their vehicle fleet. Furthermore, by identifying where the largest hold ups were in their network, they were able to redistribute deliveries and decrease overall down time.
For many organisations this kind of insight can improve returns dramatically. It increases competitive advantage and decreases risk.
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