Data Lakes in Supply Chain

Data lakes are data storage technologies intended for bulk reads and bulk writes. This is where the first stages of the data preparation are performed. They are particularly well suited to address supply chain challenges, because many situations require an inspection of the company’s entire history of orders and stock movements.

William Edwards Deming once famously said that “without data you’re just another person with an opinion”. In this episode of LokadTV, we find out why he is absolutely right and tackle the subject of data lakes.

We try to understand why many companies are failing to consider both how and where their data is stored, and instead are putting huge amounts of resources into expensive Business Intelligence tools that don’t always do the job properly.

Here we discuss the mulitple drawbacks of some of these tools and try to comprehend just why they are still so commonly used across the industry despite their shortcomings. So what exactly are data lakes and why should companies be that much more interested in them?

Around 20 years ago the trend of data warehouses was sweeping the world of technology, but it sadly didn’t live up to expectations. However, things have since vastly progressed and this is now something that is ready to be put in place properly. We investigate what exactly has changed and why now is the right time to revisit this topic. We try and understand how the mass of technological advancements is making it easier for companies to keep track of their processes.

Finally, we discuss the history and downsides of Business Intelligence systems and why they might not be as efficient as they initially appear. We learn how easy it is to implement a data lake into an organisation and discover in more detail the various technical challenges that can be encountered when implementing a data lake.


00:08 Introduction

00:34 What is a data lake?

02:24 Around 20 years ago, we had the trend for data warehouses; what has changed?

04:11 What changed in the mindsets and what changed in the way we are using data lakes?

06:29 What about today?

07:29 How do you know that the data you are actually using is good data?

13:12 Why should a big multinational company be interested in a data lake?

16:40 How easy is it to implement a data lake?

18:23 Why are data lakes something that hasn’t been widely adopted by companies at present?

20:05 How about the future, what’s next?