As a firm in strategy consulting and data science training, we often work with a variety of data sets with clients and to solve different issues. We are also seeing increasing interests from organisations and individuals keen to understand data analysis applications and develop data proficiency levels, so that they are better equipped to interpret and analyse the data they collect.
In a recent coffee chat with Dr Li, Principal of Data Group at Future-Moves Group, we asked about her views on what should really matter when dealing with data. She shares three key views.
I think one of the first important considerations is having the right mindset and attitude when dealing with and analysing data. This requires us to take a bigger picture view of how data can serve its meaningful purpose to provide evidence, information and insights. We need to invest time to understand what the data means and how it could be useful to make better decisions and implement changes.
Data analytics helps to unfold the patterns and trends hidden in the mess and massive volume of information. To make full use of data analytics, besides the normal procedures of thorough data cleaning, pre-processing and appropriate analysis and modelling, it’s also useful to emphasise the following three analytical aspects.
a) Adopting an Ecosystem Approach in Designing the Analytical Framework: The entire data analytical framework should be well-designed in the context of the business issue or problem statement and taking into account the various stakeholders involved or impacted. It is important to understand the relationship among the stakeholders and how the data analysis might offer a solution to one but also give insights on how it could affect others in the chain. This is something we need to consider when drawing insights and recommendations from the analysis output.
b) Having a Strategic Perspective to Resolve Identified Issues or Problem Statement: Data users need to go beyond the data modelling and analysing by questioning the desired objective in solving issues and being able to generate insights and meaningful recommendations. This is a crucial step often overlooked by data users. Data analytics and visualisation must be purposeful, with the aim of answering business problems and taking the appropriate actionable steps.
c) Taking the Storytelling Approach with Data: The storytelling approach is important because it bridges the different components within your analytical framework right from the business problem statement, modelling and analysis, data visualisation and to the generated insights and recommendations. This storytelling style will help you present the entire analysis process in a logical and easy-to-understand manner to the audience. It will also facilitate the right narrative that can be used to communicate with different stakeholders for effective decision-making and future action.
The second point I’d like to make is, like any other subjects, “practice makes perfect” also applies to learning data analytics. That’s also why we adopt a ‘Practical’ and ‘Customised’ approach in our training philosophy and when designing our Data Science Programme (DSP).
For instance, the hands-on components within our training programme, allow participants to put their learning into practice during and after the course. By using content or cases that are contextualised to the nature of their jobs, it will help participants draw a direct link between the techniques learnt and problems they want to solve at work. During the group exercises, we also encourage our participants to think through the design of their analytical framework by considering the context of the problems, insights, and recommendations. With this, they are guided to communicate their insights in a logical and clear narrative.
Feedback from our participants who come from different backgrounds and varying levels of data skills proficiency have been extremely encouraging. Many shared that understanding the key concepts in data analytics is not as difficult as they perceived, with the right learning pace, techniques and guidance from our trainers.
The last point but certainly not the least is, I believe that as with any skills we want to acquire and master, the learning never stops. It will always be continuous learning journey. As an analyst and trainer in this fast-moving data science landscape, my team and I must make a point to read widely, be innovative to achieve feasible real-life solutions and stay in touch with the evolving data and industry trends. This is so that we are able to continuously improve ourselves and think ahead of the possible applications of the data skills and techniques in our work.