In anticipation of joining the Data School, before I started training I was looking for all the information I could find to help me along my journey. Common advice included to become comfortable with statistics, SQL and presenting. Now that I’m almost finished my training, I wanted to share some of the lessons I’ve learnt that I didn’t see anyone mention.

 

Lesson 1

Data Modeling is the key to getting the data to do what you want.

It is no coincidence that four of the blogs I have written have been on data modeling. While I had no idea what data modeling even was before I started my training, I now firmly believe that data modeling is the secret MVP behind all complex data analytics and visualisation.

To use any table with certainty, it is fundamental to understand the grain, or the lowest level of detail. You may have a table of sales data, but does each row represent an individual sale? An individual item per sale? An individual sale followed by a confirmation or cancellation status? Understanding the grain will lead to finding (or oftentimes creating) a primary key for the table.

Once you have the primary keys of the various tables you are using, you can join them appropriately. It is just as important to understand the cardinality of the relationships between tables, as many-to-many relationships should be resolved with bridging tables. Unfortunately, this is not always a straightforward process. In one of my projects, missing data meant we had to create our own keys to guide the tables to join conditionally.

Another consideration is date tables. Creating and associating date tables with your data may be necessary to create a chart that requires more date values than is strictly contained in the data. In fact, while Tableau is fairly good at interpreting and organising dates, Power BI requires a date table to perform any relatively complicated analysis with dates.

 

Lesson 2

Colour is an extraordinary tool for storytelling.

A dashboard is really just a bunch of interacting numbers and categories put together to deliver insights. As a dashboard becomes more complex, it can very quickly become overwhelming and difficult to decipher, and this is where colour can really simplify things.

Firstly, you can utilise colour to demonstrate the relatedness of different categories, for example representing different degrees of value intensity with different shades of the same colour, positive vs. negative values with contrasting colours, and neutral values with neutral colours.

Additionally, if you are repeatedly representing the same measures across different charts, it can be helpful to build a narrative by using the same colour every time you represent the same measure, like blue for sales and orange for profit.

Finally, there are many times where the purpose of a chart is to emphasise a particular instance, like an unusually high profit value or an underperforming region. To highlight this, it is often effective to colour the instance of interest in a strong colour like red while all the other instances are coloured in grey. This easily draws the user’s attention towards the point you are trying to give focus to.

 

Lesson 3

Actively managing user requirements is necessary to produce quality work under budget and on time.

As data analytics consultants, our job is just as much analysing and visualising data as it is providing advice and guidance to our clients for how to best define the scope of a project. The project management triangle diagrammatically represents the factors of a project that are in constant conflict with each other, always pulling in different directions: quality, cost and time. The resulting area within the triangle is said to represent the scope of the project. In the context of our one-week client projects, the cost and time are fixed, and hence the only variable is the quality. The only way to control this is through actively managing the scope.

The earliest step to take to set yourself up for a successful project is to break the requirements down into specific, achievable deliverables. The client may provide you with a scoping document and they may have even converted their expectations into digestible tasks but most often they won’t, and even if they do it is important for you to rewrite them in the context of your own work to ensure you properly understand what they are after.

The next step immediately after is to understand from the client how to categorise these deliverables from highest priority to lowest. While you may want to complete everything they ask you to, it is usually unlikely and puts unnecessary pressure on yourself to deliver, especially if you misestimate the time it will take you. Having a prioritised list of requirements enables you to work your way down the list methodically and hence contribute most value.

One last thing that I would describe as easy to ignore but is really crucial is to communicate with and provide possible solutions to the client as soon as any unexpected issues arise – and they always do. There is nothing worse than running into problems and keeping them to yourself, and then eventually revealing them to the client when it’s too late. You have robbed them of the opportunity to present an alternative and it just reflects poorly on you. Usually clients will be understanding and reasonable about it and will be provide you with a solution, whether it’s supporting you to try to resolve the issue or deciding to remove the task from the list of requirements.

 

Upon reflecting on my 4 months of training at The Data School, these are the three lessons I learnt along the way that I found most unexpected yet essential. Having said that, you are likely to find three of your own most important lessons from your own journey. While there is so much technical knowledge to absorb from the training, it is this wisdom through experience that is most valuable and difficult to discover any other way. I have thoroughly enjoyed my time during training and cannot recommend it more highly for any aspiring data analyst.

 

Image by Manfred Steger from Pixabay

Hunter Iceton
Author: Hunter Iceton

Hunter Iceton is an enthusiastic and positive individual. He graduated from Sydney Uni in 2017 with a Bachelor of Commerce (Liberal Studies) majoring in Finance, Marketing and Quantitative Business Analytics. For the next few years, Hunter spent his time creating and releasing music, while tutoring primary and high school students in Mathematics and Business Studies. Hunter is now excited to be joining The Data School, looking forward to approaching analytics with a creative perspective. In his spare time, Hunter enjoys continuing to create music, reading philosophy and cooking plant-based dishes. Otherwise, he can usually be found at a restaurant, a bar or an art gallery.