In today’s job economy, building and maintaining a solid foundation of technical skills is undoubtedly crucial; however, it is equally important to recognise that impressive soft skills are an indispensable complement to your technical expertise. It plays a vital role in supporting and enhancing your overall professional competence.
In the age of Big Data and advanced analytics, Data Analysis has become integral to decision-making processes for businesses across all industries. That being acknowledged, Data Analysis within organisations is often challenging. Do you know why? While rarely acknowledged openly, the truth is that data tends to be fragmented and scattered throughout different business units of the organisation. Possessing exceptional soft skills enables you not only to locate but also gain access to such data, navigating the intricacies of data governance policies within the organisation.
What are Soft Skills and Hard Skills?
Soft skills and hard skills are both distinct categories of skills.
Hard skills refer to specific technical abilities and knowledge, acquired through education, training, or experience in a specific field or industry. They can often be quantified and demonstrated through certifications or degrees.
Soft skills encompass a range of interpersonal and personal qualities that facilitate effective communication, collaboration and teamwork, problem-solving, and critical thinking. They enable us to navigate in complex data landscapes, handle conflicts, adapt to change, and continuously learn and develop.
In this article, we will explore the significance of soft skills in the world of data analysis and how they contribute to success in this rapidly evolving field of data.
Understanding the Business
Throughout my career journey, the recurring feedback I have received is my curiosity and deep interest in understanding the intricacies of business. I firmly believe that while technology can be learned through courses and training at any time, the significance of business knowledge goes beyond immediate advantages. Unlike technology, which can be acquired through structured learning, business knowledge is a gradual accumulation of expertise that grows over time.
Gaining a comprehensive understanding of the foundational aspects of the business processes provide invaluable insights into the inner workings of the organization at its core. It offers a deep understanding of not only how the organization functions but also the detailed mechanics behind the specific product or service.
Here are some of my personal approaches to enhance business understanding:
- Get involved in the day-to-day operations of the business by actively engaging with stakeholders, attending meetings, and observing key processes. This will provide firsthand exposure to the business dynamics and help you gain insights into various functional areas.
- Collaborate with Business Stakeholders to understand their objectives, challenges, and how data analysis can support their decision-making processes.
- Active listening is an underrated skill but holds a remarkable capacity to foster profound business understanding. It enables you to ask relevant questions to understand the business objectives behind data analysis requests and dig deeper to uncover the underlying problems or opportunities. Asking insightful questions will lead to more meaningful analyses and actionable recommendations.
- I often take the time to research and study the industry in which my organisation operates, and understand the market trends, competitive landscape, and regulatory factors that impact the business. This broader industry knowledge will provide more context and valuable insights into data analysis.
- Stay updated on business concepts, principles, and practices through books, articles, and podcasts. Expand your knowledge beyond your own domain. This multidisciplinary approach will enhance the understanding of how different functions interconnect within the business.
- Lastly, a crucial practice that has consistently facilitated my growth is actively seeking feedback from stakeholders regarding the value and impact of my analysis. Understand their perspectives and use their feedback to refine and improve the work. This iterative process will not only enhance the business understanding but also strengthen credibility and manage expectations.
The Art of Communication and Data Storytelling
Data analysis is not just about crunching numbers; it’s about extracting meaningful insights and presenting them in a compelling way.
In the data landscape, where information is literarily overflowing, having a sound understanding is key, and presenting this understanding to wider teams in an easy yet compelling way is a Sherlock Holmes task.
Here are some of my favorite hacks:
- I often work with both technical and non-technical colleagues. I usually tailor my language, tone, and level of technical details based on the background, knowledge level, and needs of my colleagues.
- Data analysis frequently involves tricky concepts and technical jargon. To effectively communicate these complex ideas, it is essential to simplify and present them in easily understandable terms. Another frequent feedback I have received over the years is my ability to convey complex concepts to diverse business stakeholders in a simpler manner. How do I achieve this?
My go-to strategy is utilizing visuals, analogies, and real-life examples to make my points clearer.
- Coming from a Data Visualization background, I leverage it to present information in a visually appealing and easily understandable manner. How do I achieve this? By carefully selecting the most suitable charts, graphs, and diagrams that effectively convey the key messages and facilitate the interpretation of the data.
Trust me, this approach is incredibly useful. Not only do I create visuals for work-related matters, but I also enjoy creating illustrations for all my articles.
- As humans, we are naturally drawn to narratives, and storytelling is a powerful tool for data analysis. Visualisations are essential in storytelling as they provide a framework that helps the story unfold with context. This powerful combination of visuals and storytelling not only engages the audience but also makes the insights more relatable.
Instead of struggling to decipher the meaning from a two-dimensional visual, the use of storytelling and visuals bring the data to life and enhances the understanding.
Collaboration and Teamwork
Data analysis is rarely a solitary endeavor. Data-related projects often require the collaboration of multiple teams with diverse backgrounds and areas of expertise. These teams typically consist of individuals such as product managers, data engineers, project managers, BI developers, data analysts, and business analysts.
Here are a few approaches I find valuable:
- My absolute favorite one, without a doubt, is connecting with my teammates not just as colleagues, but as individuals too. We all experience the ups and downs of life, and considering that we spend a significant amount of time at work, it’s important to acknowledge that everyone’s capacity to handle workload fluctuates due to personal circumstances and commitments. By being mindful of my teammates’ availability and supporting them during challenging times, I can effectively redistribute tasks, ensuring that project milestones are still met in a timely manner.
- It is a fundamental principle rooted in psychology that people inherently expect that you prioritise hearing out their perspective first. My dad gifted me a copy of “The 7 Habits of Highly Effective People” during my college years. It took me an additional few years to truly internalize and incorporate this principle into my life. Habit 5, as articulated by Stephen R. Covey, remains profoundly relevant:
“Seek first to understand, then to be understood.”
This is also an effective way to engage in a conversation with business stakeholders and broader teams.
- Every project is a learning experience for all the team members. It’s not uncommon to acquire new skills to fulfill project requirements. Therefore, it’s crucial to be a little patient when someone is in the process of learning and implementing a new skill. However, it’s equally important to proactively seek help from teammates when needed, instead of waiting for them to come to the rescue. In my experience, most people are willing to jump on a Teams call to troubleshoot together, which significantly expedite the debugging process.
- Set up regular team catch-up calls for knowledge sharing among interested participants. Although it can be a bit challenging since not everyone may be interested, it is still possible to arrange these sessions. In these calls, team members can take turns every few days to teach something they are confident about to the rest of the team. For example, if someone is skilled in SQL, they can conduct a quick session for everyone on some quick tips. Likewise, if another team member possesses valuable domain knowledge or expertise for a particular project, they can share it with the wider team. This strengthens the collective knowledge base.
Critical Thinking and Problem-Solving
We often hear a lot of talk about the importance of critical thinking and problem-solving for data professionals. However, do we truly understand what are they and why are they considered crucial skills to develop? Simply reading a blog, taking a course, or solving a few problems online won’t be enough to develop these skills. It’s important to realise that passive involvement alone is inadequate when it comes to nurturing your ability to think critically.
Then what should be done? Be curious. Curiosity is a significant factor in developing critical thinking and improving problem-solving skills.
Curiosity is our natural inclination to explore, learn, and understand the world around us.
Merely activating your critical thinking abilities at work isn’t sufficient. It’s essential to integrate it into your daily routine and make it a natural part of your thought process, both in professional and personal contexts.
I have no special talent. I am only passionately curious.
~ Albert Einstein
Here are some hacks that I love:
- Ask questions. I find it valuable to question assumptions, challenge existing knowledge, and explore alternative perspectives. When you raise questions, you can delve deeper into problems and identify potential solutions. However, it’s important to keep this in mind:
There’s a delicate balance between asking relevant questions and needlessly consuming everyone’s time by raising unnecessary ones.
- Refresh your domain knowledge. Developing a strong understanding of the domain or industry you’re working in is essential. By familiarizing yourself with the specific challenges, goals, and terminology of the domain, you can ask more relevant and insightful questions. This knowledge helps you to grasp the context of the data and identify key factors that impact data analysis and in turn decision-making.
e.g. If you want to deepen your understanding of lending operations within the banking sector, try understanding its key concepts, operations, regulations, industry trends and best practices.
- Dirty Data Challenges: Before diving into data analysis, it is crucial to thoroughly evaluate the quality and reliability of the data at hand. The ultimate aim of data analysis is to convert information into actionable insights. However, this journey is rarely a linear and straightforward process. Analysing data involves scrutinizing accuracy, relevance, and sources of data, as well as addressing any potential assumptions, gaps, errors, or biases.
In reality, the value of data lies in its ability to provide answers to relevant business inquiries.
Organisations prioritize data that goes beyond visually appealing dashboards and instead addresses their specific questions and contributes to potential growth.
By leveraging data quality dimensions, you can establish a framework that ensures subsequent analysis are founded on dependable and significant data, leading to more accurate and informed insights.
- In the world of data, every problem is different, and there are no fixed rules that can be followed to solve problems easily. In my view, it is of utmost importance to articulate the problem or question with clarity right from the beginning. Equally crucial is ensuring agreement among team members regarding the problem statement and the expectations that accompany it. From here onwards, you can begin the task of identifying and gathering the relevant information needed to initiate your project.
- Embrace the possibility of being wrong. Data is often complex and messy. As we already discussed, it’s crucial to question the reliability of the data itself, especially since decision-making relies on it. During data analysis, it’s highly likely that your initial perspective might be incorrect. Accept this fact and adapt your analysis moving forward.
How can you accomplish this? By examining the data from diverse perspectives to uncover patterns, detect anomalies, and derive insights that might not be immediately apparent. This will help you gain a deeper understanding of what you’re trying to solve before forming an opinion.