Speaking at a recent Computing event, a panel of experts provided their biggest mistakes made within their own organisations when attempting to launch big data initiatives.
Gael Decoudu, head of data science & digital analytics at Shop Direct, recently speaking at the Big Data and IoT Summit spoke about how his firm ended up quickly drowning in a data lake that they had created.
“The approach we took was to create massive data lake, to collect as much data as we could,” said Decoudu. ” So we invested money in that, then after couple of years realised that we couldn’t do anything with it.”
Decoudu then went on to describe the problems they had with open source software, speaking about the difficulties of knowing which supporting tools and software to use.
“We’ve slowly moved on to open source, and we’re now on AWS [Amazon Web Services], and we’re starting to use [programming language] R. One of problems with open source is it’s hard for someone who doesn’t have lots of experience to pick the right package. There are probably 20 different ways of doing neural networks in R and Python, but which is the right one? Which should you use in a business setting? Getting that wrong can cost the company millions of pounds”
Encouraging younger staff to be proactive at firms, Jude McCorry, head of business development, at The Data Lab argued
“Some companies get excited about saying they hire graduates in data science programmes, but often those graduates just sit there and wait for work to come to them. They’re supposed to be there to answer questions about the data, and be self starters”
If you can avoid these mistakes, your big data will thank you.