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1 posts from February 2019

22 February 2019

Gettin’ vizzy with it - where to start with Data & Analytics

by Will Dupré Data & Analytics Strategist

Rewind about three years and I’d never heard of Tableau, or Alteryx, or for that matter, Power BI. A colleague dropped out of a data visualisation training course being run by PwC, and nominated me to go in his place. If I reflect on my where my career is now, I’d say this unintentional opportunity was the start to my own career disruption. This internal two day face-to-face training (and the six week “sprint” that followed) was a great experience, and I’ve not looked back. The course focused on Tableau and was in part delivered by Tableau consultants. Because of this training, my interest was very much piqued to begin to pursue work in the Data & Analytics field.

I now work within PwC’s Technology & Investments team, with responsibility for adoption of, and engagement with, Data & Analytics. The role can be very rewarding; I speak to people across the business every day to discuss new ways of working, and hear a hugely encouraging number of good news stories. Working in this environment also encourages me to do my best to keep my own training and understanding up-to-date, and to really grasp the opportunities available to us, both within PwC and for our clients. In my role, I advocate the use of Data & Analytics daily – and whilst I don’t in any way predict the demise of Excel – there are a number of tools available to help us to augment our offering and break free from some of the constraints of traditional software. Below I’m going to introduce you to some favourites briefly. Take a look:

Data & Analytics tools – where should you start?

Tableau was the tool that initially got me hooked on data visualisation. The ability to present datasets in a visually-compelling and clear manner, in a predominantly drag-and-drop environment, while also having the ability to be creative – (one might say: artistic) – at the same time is what got me phenomenally excited. Tableau encouraged me to speak to internal teams about their datasets, and interrogate the challenges their clients were struggling with – I knew we could do more to tap-in to the value of the data and provide deeper insights to our stakeholders.

Experience level: General Consultant / Data Analyst - you can get started on Tableau with little or no prior experience, although an understanding of data analysis concepts would be useful.

Ease of Use: Easy – at least to start with! The more you use it, the more knowledge you’ll gather, and the more you’ll realise what it can do.

Online resources: You can download a trial version to see what you think. Tableau also has some great learning resources online to get you started.


Alteryx was just as mind-blowing, but in a slightly different way. Almost anything you ask of it, it can do. Text to columns, removing whitespace, dealing with null values, performing calculations, sampling, joins and unions, filtering, and of course analysis of data. With Alteryx, not only is the original data itself left unaffected (which is crucial from a data governance perspective), but with every tool you add to the workflow, you can see exactly what your data looks like both before and after the process you’ve implemented. Added to that, the visual interface is clear and you can easily add annotations and tool containers to make your workflow even easier to read.

Experience level: Data Analyst – with a basic knowledge of data-sets and processes, you’ll see the value immediately.

Ease of use: Pretty straightforward! You’ll need to get to know the built-in tools, and will quickly come to rely on a few favourites, but some of the configuration as you get more adventurous will need a bit of work.

Online resources: Like Tableau, Alteryx has a trial version available. The Alteryx Community also provides interactive training.


In other areas, quite rightly there’s a drive to get people coding – and with Python and R relatively easy to pick up and perfect for statistical and data analysis, these are great places to start for those looking for more technical interpretations. It can be perhaps a surprisingly creative endeavour.


Experience level:  Data Analyst / Data Scientist - Python is a general-purpose coding language that has become very popular recently. You don’t need experience of any other coding languages to start learning Python.

Difficulty to use: If you come from a programming background, you probably already know about Python. Those who already know another coding language are likely to find Python easy to learn. ‘Packages’ such as NumPy, Pandas and Matplotlib are available to import for data analysis.

Online resources: Python does have its own tutorials, however Python courses are also available through many third-party training providers, for example Udemy, Pluralsight and Data Camp.



Experience level: Data Analyst / Data Scientist - Similar to Python, R is a coding language, however it is more focussed on statistical analysis and graphical representation. You don’t need experience of any other coding languages to start learning R, although a knowledge of statistics would be advantageous.

Difficulty to use: Like with Python, if you come from a programming background, you probably already know about R. A huge number of packages are available, but users should be cautious before using code downloaded online.

Online resources: R provides manuals to help, but similar to Python, courses on R are also available through many third-party training providers, for example Udemy, Pluralsight and Data Camp.


Robotic Process Automation (RPA) – where a computer is programmed to perform a repeatable task for you – has also been getting a lot of airtime recently, is growing in popularity and is relatively easy to learn and deploy. Tools such as UI Path and Blue Prism are beginning to really democratise access to the benefits these kinds of approaches can take. If a robot could perform the tasks that drain your time and motivation, just think how much longer you could spend on what really energises you – and adds the most value.

Ultimately, it won’t be long at all before Data & Analytics isn’t seen as the whizzy new technology on the block, but core to how we work; the data-driven mindset will be fundamental to every task and engagement we’re involved in. Tools like Alteryx, Tableau, Power BI, UI Path and more will begin to replace more traditional software. So, in the immortal words of The Doors, the time to hesitate is through – Data & Analytics offer you and your team so much opportunity, and before you know it, you’ll wonder how you ever did without it. Get involved!

If you would like to discuss these issues, or the impact of emerging technology or data and analytics on your industry, then contact our Data & Analytics team.


by Will Dupré Data & Analytics Strategist