3 Fundamental Lessons as an Analyst from a Non-IT Background
Every beginners doing analytics need to know these lessons.
If you are looking to understand how analytics works, you are on the right page.
In today's data-driven world, analytics has become an invaluable tool for individuals and organisations alike. Whether it is a business looking to gain insights into customer behaviour or an individual seeking to make informed decisions, the world of analytics holds immense promise. There are many different type of analytics roles in the business industries such as data analyst, business analyst, strategy analyst and more.
Amidst the widespread adoption of analytics, the real challenge is differentiating ourselves than others. I have worked in multiple domain industries as an analyst, from think tank to public administration to currently in the strategy world. Coming from a non-IT background myself, here are three pivotal lessons I learned to leverage our analytics career.
1. Understand that Analytics is a Process
The most critical lesson I have learned as an analyst is that the process is as critical as the output.
Analytics is defined by the scientific process of discovering and communicating the meaningful patterns which can be found in data (Techopodia, 2023).
Why understanding the analytics process is important?
Understanding our process will inform our datasets context — from why such data is captured, how is it important to our analysis, what kind of analysis do we need to produce. When we gain a comprehensive understanding of our datasets, it facilitates our tasks in finding the relationship of our variables and most importantly writing our analysis later on.
Our process is the first layer of shield to our analysis defence. Often times the user of our analytics outputs are people who are not in data. Communicating our process with the the non-technical stakeholders is definitely not easy. But if we are confidence in our process, it would help a lot. It is very common that they would questions on how do we get the outputs, and do not get panic. That’s where we need to explain our process.
There are times that we produce inaccurate analysis or missing the context needed. My experiences taught me that this is where having a thorough understanding of our process is important. If we are still in learning phase, in order for us to get the right answer to our work while seeking help, we do need to explain our process a lot, especially to our bosses.
2. Knowing Fundamental Concepts of Data is a Value-Added
Knowing fundamental concepts of data does provide a high leverage on how we do analytics because it will translate to the level of how we design our process and how we manage our data.
In a summarised process, generally we will go through of doing analytics in the following:
At data collection stage, we need to know the following:
Data sources - Data can be collected from numerous sources such as databases, websites, sensors and more. Knowing our data sources help us to understand if our data is relevant and significant for our analysis.
Data types - Data comes in various forms and it will affect how we process our data. Generally, we need to see if we will be handling structured data or unstructured data, and quantitative data or qualitative data or mixed. In the real world, it is typically mixed types of data that we need to manage.
At data cleaning and processing stage, we will mainly involve in:
Tools and software - When we have identified our data sources and types, it will facilitate us to determine what kind of tools that are relevant to use or the easiest to work with our data. There are many of them out there, popular ones are Excel, Power BI, Tableau, Python and R. Each of them has different purposes and layers of functionality and sophistication. I have always advised people who are starting venturing into the data world to start with Excel because it helps building our design and logic thinking with data.
Finally, at analysis and reporting stage:
Analysis approach - Approach to doing analysis may vary according to our needs. In my previous cases, I have produced analysis for clients and often times they already have set of metrics that they want to see. That situation is more straight forward. However, if there are no predetermined variables for the analysis, I would need to do an exploratory approach. There are many ways of doing this too, one of it is to identify our hypothesis and problem statement. Other instance is I need to do correlation analysis and model my data.
Visualisation - Visualisation is very important! Because that is the final output the audience will see and interpret. If they cannot understand our visualisation, our analysis will be obsolete. We do want our works to be appreciated right. So, knowing to choose the right visualisation is pivotal, and often times I would try to put myself in the non-technical audience shoes and ask question if they would understand my visualisation.
3. Soft Skills are as Critical as Technical Skills
Doing analytics extend beyond the technical skills. Part of analytics also is the ability to communicate our findings effectively across different audiences. There are times it require us to collaborate with diverse teams and even understand the needs of other people for analytics.
I have spent at least half of my working hours in a week communicating with my stakeholders whether it is in a form of meetings, presentations, calls and even emails. We do not work in silo because our analytics works is for the people who need it. So we need to spend ample time understand their requirements. This also goes back to our first lesson which is we need to be able to convey our process that requires soft skills too.
When we can do both soft skills and technical skills excellently, it elevates our value in the company as an analyst. Employers want their staffs to be able to communicate effectively.
In conclusion, this holistic approach to analytics, emphasising process, fundamental data concepts, and the symbiosis of technical and soft skills, offers a strategic advantage in a landscape where analytics has become ubiquitous.