Using data science to come out of Covid-19 stronger than the competition
If it isn’t difficult enough emerging from the current crisis and recovering your business to pre-pandemic market trading levels, there is a new threat to your business. Some of the smartest companies, as well as the emergence of some new start-ups, are using data science to help them reduce cost, automate processes, make more intelligent decisions and therefore emerge from this situation stronger and fitter than before. If you don’t accelerate your data science capabilities, there’s a good chance you’ll be left behind post Covid-19. Here are 7 steps to ensuring your data project helps you compete:
Simon Asplen-Taylor recognised as one of the top 100 most influential people in data 2020
Simon Asplen-Taylor, Founder DataTick explains how he became one of the most influential people in data in the UK in 2020.
Data is societies next crude oil
A lot of data professionals are claiming that data is the new oil. It’s a compelling concept but a lot of individuals struggle to articulate how you do the mining, extraction, and refining of data in the same way you do it with oil. Explaining this idea in simple terms is something Simon Asplen-Taylor, DataTick Founder, is an expert at and he shared his thoughts on the podcast.
‘Sometimes people struggle with this analogy because it’s about bringing together all the elements of data. If you look back a few years at the types of roles data professionals did, they were always a sub-set of a technology department.
The issue is: technology can only steer data down one path. Technology is about building things and delivering functionality. That’s only a small part of data. I think very rarely has there been a chance for a Chief Data Officer role to bring those things together all at once.’
The 7 habits of good data scientists
Simon Asplen-Taylor, DataTick Founder, calls out the 7 habits of good data scientists in his published Forbes article.
There’s one sure thing you can say about data science — it’s a lot of things. Data science is not necessarily one single discipline, skillset or methodology. This is why data science is always said to be an ‘interdisciplinary branch’ of science that combines mathematics, human behavioral analysis and workflow studies, flexible use of logic systems and a core employment of algorithms.
This makes being a data scientist pretty hard work, as if algorithmic logic wasn’t already pretty tough.
6 experts reveal how to ensure your data strategy doesn’t fail
Read the article with a contribution by DataTick founder Simon Asplen-Taylor in Information Age
An effective data strategy will allow organisations to harness their most valuable asset and drive transformation across the business, for both the employee and consumer.
However, too often, an organisation’s data strategy will fail. There are a number of reasons for this and within this feature, six data leaders will explain these challenges and how to overcome them.
Why building a case for data must begin in the boardroom.
Unless you get boardroom buy-in the data activities will remain tactical and the true value of data will remain untapped. It will also be less satisfying for the data teams.
Single Customer View (SCV) - dull or shiny?
As a seasoned CDO, it seems sometimes that people are too busy chasing shiny new ideas to address the things that are important, hard to do, and that on the surface appear be a little dull. One of those is the Single Customer View (SCV). A SCV is one of the most critical data components in any organisations data strategy. All of the best customer focussed organisations have one. In this blog I’ll take a look at why a SCV is so important.
Why a data strategy matters
In the world of Chief Data Officers there are three groups of people; those that have a data strategy, those that talk about having one, and those that are oblivious to the idea of having one. Of the data strategies I have seen many are sadly lacking and not much better than not having one.
Here’s why ...
Making the business case to exploit the value of data
I often get asked the question “Where should I start my data initiative?” So, in this blog I’ve tried to explain how you can get better engagement, more investment and exploit more value from your data initiatives by starting with the business case to generate value.
Understanding where the value of data is found is the cornerstone of any data strategy.
In the previous blog, I looked at making the business case to exploit the value of data in your organisation. This naturally leads us to seek an understanding of where the value is, which is the subject of this blog and a cornerstone of any data strategy.