4 skills the next-gen data scientists need

The world of data science is no longer confined to building complex algorithms and churning out intricate visuals.

Given the increasing reliance on data and analytics across diverse industries, from agriculture to manufacturing and healthcare to financial services, it is evident that the next generation of data leaders will play crucial roles in influencing strategy, decision-making, operations, and numerous other functions.

While technical skills remain foundational, the next generation of data scientists must hone their ability to identify the right problems.

Here, we explore the four critical skills that are becoming increasingly essential in the data science toolkit.

1. The keen eye: Identifying the underlying problem

In a world overflowing with data, the first challenge is often not the lack of information, but the ability to identify the real problem.

Aspiring data scientists must develop a keen eye to see beyond surface issues and delve deeper. This requires a blend of analytical thinking, business acumen, and a healthy dose of curiosity.

Here's how this translates into action: Imagine a retail store experiencing a decline in sales. A traditional data scientist might analyse sales figures and identify a drop in specific product categories.

But a next-gen data scientist would go further. They might ask questions like:

• Are there any changes in customer demographics?
• Have competitor marketing campaigns shifted?
• Is there a disruption in the supply chain?

By actively seeking a deeper understanding of the situation, they can uncover the root cause – maybe a change in consumer preferences or a logistics issue.

2. Sharpening the focus: Defining the problem with precision

Once the underlying problem is identified, the next step is defining it with precision. This is similar to drawing a clear perimetre around a crime scene in a detective story.

A well-defined problem statement acts as a roadmap, guiding the entire data analysis process.

Instead of a vague statement like "increase sales," the goal could be "increase online sales of organic produce by 15% within the next quarter for customers residing in the city center."

The definition should specify how success will be measured. In this case, it's a 15% increase in online sales.

The problem definition should consider the available data and its limitations. By crafting a clear and concise problem statement, the data scientist ensures everyone involved – from business stakeholders to technical collaborators – is on the same page.

3. Steering the investigation: Problem oversight and communication

Data analysis isn't a solitary pursuit. Often, it's a collaborative effort involving data engineers, analysts, and domain experts.

The next-gen data scientist plays a crucial role in overseeing the process– providing regular updates, seeking feedback, and ensuring everyone remains focused on the defined problem.

This requires strong communication and collaboration skills. Here are some of the best practices:

• Schedule periodic meetings to share findings, challenges, and potential next steps.
• Encourage open communication so team members can raise concerns or suggest alternative approaches.
• Embrace feedback. Data analysis is an iterative process. Be receptive to feedback from stakeholders and use it to refine the analysis.

4. Cracking the case: Communicating solutions for impact

The culmination of a data scientist's work is presenting the solution in a way that resonates with the audience. Gone are the days of using technical jargon and complex visuals.

Today's data scientist needs to be a master storyteller, translating complex findings into a clear, concise, and actionable narrative.

Here are some key elements of effective solution communication:

• Know your audience by tailoring the communication style and level of technical detail to the audience's expertise, speaking in business terms for stakeholders and using more technical language when addressing fellow data professionals.
• Focus on the impact by highlighting the implications of the findings and how they translate into actionable recommendations.
• Use visual storytelling to support your narrative.

By becoming effective communicators, data scientists ensure their insights are not just understood but also acted upon, creating a real-world impact.