What soft skills should data reporting professionals prioritize?

To build your career you need to understand the business, business strategy and the key performance indicators that help inform strategy achievement. Aligning your work with data with this context demonstrates your value to the business.

Although there are many crucial skills but I think the data story telling skill, the way you interpret your results to both business users and technical users, this is important and critical to me. Most of the time, business users just wanted to know another prospective about the data which he can get very easily via data elaboration story.

Learning the business is one of the important skills for data analysts. Having business acumen helps data analysts to come up with high priority KPIs to provide recommendations for stakeholders and leadership to improve the business

Be close to the businesses areas, everything happens there. Try to understand or even have a full picture how your company makes money; where the competitors are; which products are more profitable and so on. Having this business background certainly can help you to be more assertive. Once you are a professional who can connect technical aspects + business needs.

I would say 2 things. - Communication with business clearly. To help them understand tech and vice versa. - Product understanding

Have empathy. The person listening to you is not you. And you can, with a little bit of effort make it easy for them to understand what you're trying to communicate. Here are some practical tips for this 1. Keep it simple - Someone may understand deeply technical terms while others may not. It's important to understand the background of your audience 2. Provide examples - It can be easier for people to grasp something new if they can associate it with something they already know 3. Have patience - Listen to their questions, notice the cues of the areas where they may be lacking understanding

Effective data communication is vital, whether you're addressing a board or technical experts. Empathy plays a crucial role in understanding your audience's perspective. Data can appear cold and uninviting, but it's important to humanize it before sharing results and reports. Data storytelling with simplicity and clarity, using visual aids and a clean design, is essential for concise presentations that deliver insights. It can be challenging to simplify complex data, as we often want to showcase all the intricacies. At the board level, you might have just one slide and 60 seconds to convey your message, with the detailed data available for answering questions after you've captured their attention.

The value of strong communication skills for data reporting professionals lies in their ability to transform intricate data into easily digestible information, making it accesible to non-technical individuals. This skill empowers stakeholders overall.

There are two ways of communication even in data storytelling: 1. Top- down (management communicates their requirements for decision making and data analysis) 2. Bottom -up (analyst does data analysis, makes visuals or dashboards and presents to management) Most of the times I have seen that top-down communication failures lead to bottom-up communication failure. Poorly explained requirements (like.. "do some kind of analysis and let me know") leads to confusion, loops of data analysis, and most of the times silence in the bottom-up communication. This happens in organisations with uninformed management. I think, one of the most important skill that management has to develop is communicate requirements clearly to data analytics team.

Data engineers must have the ability to comprehend and communicate effectively with non-technical stakeholders to understand their business requirements and goals. This is essential for designing data solutions that align with the organization's strategic objectives. Effective communication allows data engineers to translate complex technical ideas into terms that others can understand, ensuring alignment between technical and business teams.

Critical thinking is arguably the most crucial skill for a proficient data professional, alongside their understanding of the business and company culture. It involves rapidly comprehending concepts, identifying inconsistencies through a systematic and logical approach, exploring root causes, and cross-referencing relevant literature. These tasks are the everyday work of a content and fulfilled analyst, who also relies on preferred data manipulation tools and thrives in an environment of trust and wisdom, welcoming feedback from colleagues and business partners.

Here are some tips that can help 1. Challenge your own assumptions - Instead of assuming that you're correct, ask yourself what you're missing on, what have you not considered. 2. Let others challenge your solutions - It is very important to not get attached to your solution. Be attached to solving a problem, and not your solution. 3. Take feedback - Find colleagues and mentors who can provide you with feedback on your thinking process and/or work. 4. Review - Once your work/solution is complete, revisit it and note down what you could've done differently. It's a bit difficult to admit your mistakes but it's a very useful process to develop critical thinking.

Critical thinking allows data engineers to break down complex issues into smaller, manageable components and develop logical, systematic solutions. It helps data engineers identify data quality issues, anomalies, and inconsistencies.

Surprisingly, creativity often thrives when you're genuinely enjoying your work, having fun, even in fields like statistics, algorithms, and mathematics. In my experience, some of my most significant breakthroughs and accomplishments have emerged when I found enjoyment in what I was doing. This is particularly evident when you're part of a positive collaborative environment characterized by diversity in roles, educational backgrounds, nationalities, personal experiences, languages, and more. When you infuse a healthy dose of divergent thinking and embrace quick feedback loops in such an environment, it becomes a fertile ground for creative thinking and innovative solutions.

Creativity can help data engineers think outside the box when it comes to transforming and cleaning data. Finding unique ways to structure, cleanse, and transform data can lead to more efficient and accurate results.

Creativity skills enables the exploration of innovative visualization representations, makes for an awesome storytelling. This makes the reporting engaging and insightful. This is wgat ultimately makes an impact and aids conveying complex information more effectively to diverse audiences.

One must be careful to not get swept into their own creativity. While trying to solve a problem, it is critical to use the right tools for the job. This might mean using boring old bar chart to communicate with others. Yes, the same can be done via other means but should you? It's important to remember to not let one's creativity get in the way of communicating the message in the best possible way. There's a balance that is required here. Your creativity will help you uncover new things and present information better but if you leave it unchecked, you'll soon find yourself straying far away from solving the problem.

Data professionals are never complete with only the technical skillset, having good soft skills to represent your data solutions is of utmost importance. Storytelling is one of the most underrated skill for Data reporting professionals. It is also essential to present data insights in a compelling and meaningful way. Crafting narrative stories around the data, professionals can engage stakeholders and help them understand the significance of the insights. By delivering clear, concise, and engaging presentations, professionals can capture attention, and drive data-driven decision-making. Storytelling involves identifying and delivering key messages using anecdotes or real-life examples to make the data relatable and memorable.

A data scientist needs soft skills like willingness to research, team collaboration, and communicating complex topics simply. Research skills are critical to fill knowledge gaps. Curiosity and enjoyment of learning indicates fit with the evolving field. Cross-functional collaboration integrates their specialized skills into broader initiatives. Avoiding jargon and using analogies makes concepts intuitive to non-technical audiences, just as doctors explain to patients. Key soft skills make data scientists' hard skills more impactful.

I would add something of a subset of communication: Listening. If you ask most people if they listen, they will say yes. But we often don't really listen to people, we speak, and we don't hear it as they mean it. Taking the time to listen to the person and hear their problem is the absolute starting point to being able to solve the problem.

Data Engineering is some of the most collaborative practice in the Data field. A good engineer leverages all of their soft skills to collaborate with partners, peers, stakeholders, clients, and even vendors. When done right its leads to high adoption of your end product and its the most satisfying feeling.

In my opinion, this article focuses rather on data analysts than on data engineers. Of course, all these mentioned soft skills are also an asset as a data engineer, but not strictly necessary. As a data engineer, you're mainly responsible for technical tasks and optimizations. So, if your company has a solid data strategy and responsibilities are clearly distributed, I'd say that the data engineer doesn't necessarily need to know about all the business details. However, if you want to maximize your impact and want to get into a management position in your career, then you should definitely know about those as well.