Paul Laughlin

About Me

Paul Laughlin is Founder and Managing Director at Laughlin Consultancy Ltd that helps companies generate sustainable value from their customer insight, His speaking focuses on topics including Customer Insight, Leadership, Data, Analytics, Data Science, and Research & Database Marketing. Follow Customer Insight blog.

How Data Science teams can be more methodical – Part 1

Do you see a need to have more methodical processes in Data Science teams? Does your team have the common methodology, process or workflow it needs? A tip for success with your Data Science team is to be more methodical. By this, establish and use a consistent methodology, process or workflow. This will enable repeatable results, simpler collaboration & knowledge transfer. If it is a well–designed methodology, it should also ensure appropriate QA stages and reduce the cost of rework.

More Data Science methodology options – has much changed? - Part 2

Data Science methodologies is definitely still an evolving field. Data Science teams may routinely need to draw data from a variety of database structures including column and even graph databases. They may also need to use their wider data access to load data into Data Lakes and spend longer on sourcing data than previous generations. For all these reasons, it is not surprising to see an explosion of more varied options. It is impossible to be comprehensive in this post, but let me share some exemplars that typify different approaches to this challenge.

How to help a non-technical audience understand their readiness for Data Science

This post helps you consider how to communicate Data Science through your internal events. It shares the content choices to answer challenges needed to communicate about Data Science to a non-technical audience within your business. Understanding the breadth of Big Data types and their potential relevance and challenge for legacy systems is also important. Non- technical audience need to carefully manage their relationship with the IT department and ensure they have clarity on goals to achieve, and the software/tools that Data Scientists need.

The Harvard Innovation Lab

Made in Boston @

The Harvard Innovation Lab


Matching Providers

Matching providers 2
comments powered by Disqus.