What does it mean to be an analytics consultant? Tools, Training, and Tips
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After 10 intensive weeks of training our first set of analytics consultants have successfully graduated from Concentra Analytics Academy. They have gone through a rigorous combination of consulting, technology and analytics education and join our other 10 qualified consultants in Tableau Desktop and 11 qualified in Alteryx as well as learning client management and problem solving skills.
I sat down with "TTRON" our self-named set of graduates (Timothy Wintle, Tim Day, Ryan Smith, Oscar Della Casa and Nicolas Voirin) to find out about the peaks and troughs of their 10 week boot camp and what it takes to be an analytics consultant.
1. Tell me about the Analytics Academy. If you could summarise in a few sentences, what have you learned?
Ryan: I learned an eclectic blend of technical, business intelligence, and consulting skills. We were taught to use market leading tools like Tableau, Alteryx, Microsoft Excel, and SSIS but also core business skills, such as client engagement, project management, and stakeholder communication. In a real analytics project, it's not enough to just know how to use the right technology. You need to have a good grip of a client's needs, how to tailor a solution that fits the business context and how to present it back to the client effectively.
2. Tell me about your favourite project during the program!
Oscar: It would be our final project. We had to build analytics dashboards for Concentra's marketing team to help them assess the effectiveness of their marketing initiatives, e.g. social media activities and blogging, in engaging customers. It was both fun and challenging because it gave us insight to how a real analytics project is executed, end-to-end. It's more than just churning out pretty dashboards and slides. We kicked-off the project by discussing with the marketing team about the performance metrics they wanted to see. Using these answers we then pulled out the relevant data and built prototype dashboards together with them. As we were dealing with real complex data, we had the hands-on experience in collating, cleaning and transforming dirty data. Finally, at the project completion, we presented our findings, data-driven analysis and recommendation for improvement to the marketing team. It's hugely satisfying to see the fruits of our work being put to real use.
3. What do you think will be some of the biggest challenges when you work with real clients?
Tim: I think there are two main challenges: dealing with poor data and ensuring you follow best practice. First, 99% of the time data will exist in poor format – either missing, dirty and or stored in multiple systems. Second, making sure you're doing best practice in every aspect of your work, especially under a tight schedule is challenging. One valuable lesson I learned is to consult experts in specific areas whenever you're in doubt, as best practice may vary according to the sectors and types of project you're working on. The projects and assignments we did during the program are really useful as they encapsulated these challenges.
4. There has been a lot of hype about the user-friendliness and intuitiveness of self-service analytics tools and how they have empowered people with minimum IT or programming background. Speaking from your experience, how true is this?
Nico: I would say very true. For example, to be able to use classic tools like Microsoft Excel and SSIS (more specific to data warehouse) you need to know the right formulas and shortcuts, which can take time to master. But, self-service tools like Tableau and Alteryx are saving you effort by integrating SQL language into their systems. Knowledge in programming will still be beneficial, but Tableau and Alteryx do most of the legwork for you. They have simplified the process of data cleansing, querying, and visualisation through the use of a drag and drop button. Also, they have a wealth of free online resources to get you started. You can become proficient with these tools through self-learning, but to step up your game and really learn the best practice, you still need insights from experts in the company.
5. How can you make learning data and analytics fun?
Timothy: Learning analytics can be daunting at times. You have to find clever ways to make the learning as fun and relevant to you as possible. For example, we did an analytics blog competition around our work which was a fun and productive way to learn. During the program, we were given the challenge to write 20 tips and how-to blogs on using Alteryx and Tableau. We could choose any topics we love, while practicing our skills in creating more difficult charts in Tableau, such as Sankey, waterfall, etc. From soccer to US congress to superheroes and rainfall in France, we had the freedom to be creative in choosing the data we wanted to visualise. Even after the blog challenge, we now continue to create dashboards to visualise our favourite topics for fun. For example, we now take scores and analyze our performances in the weekly Concentra 5-aside football games.
How to get unstuck
Having graduated from the Academy, the grads came up with three tips for those who are just starting with analytics:
1. Enjoy the ride
It's like when you have a new camera. Just play with it, explore, and ask others for tips. You will be surprised at how fast you will learn when you make your practice relevant to what you love. Regularly analyze and visualise datasets on topics you are passionate about. Ask questions, get on-the spot advice and don't be afraid to get feedback from people in your team. Google is great but shared experience is hugely valuable.
2. Stick to best practice throughout your work
First, the importance of documenting your code cannot be overstated. Make sure it's in a clear and proper format that can be easily shared and audited. Second, best practice can vary according to the sectors and types of project you're working on, so when in doubt consult experts in specific areas. Utilise people around the company who know the tools by heart.
3. Don't fit the data around the questions
One of the greatest traps in doing analytics is to approach datasets with a selection bias. Often it is tempting to reinforce a set of mistaken assumptions by fitting the data around the questions. The process should be reversed: find the data first, critically evaluate the patterns and answer questions about it.
So if you're about to start your journey in analytics, think of how the insights and tips above may help your learning. By approaching data in a creative and strategic way, you can overcome many of the barriers thought impossible to break.