Recession-proof your career: Here’s how to be a stronger data pro in 2023

As the new year begins, so does an opportunity for us to redefine ourselves professionally. But how do we build a sustainable personal brand in an economic environment that is full of the unknown?

Organizations are figuring out how to sustain businesses while aiming to scale their operations. Teams are downsizing due to tough revenue calls made by organizations as the demand for data-driven insights increases every day. 2023 is the year to remain consistent in the value we provide as individual data professionals.

Here are some ways for you to consider scaling up your personal brand.

Identify your niche.

Data as a professional is ever-changing with new technologies and business needs coming up consistently. As a new data professional, it might be intimidating.

The first two to three years as a data professional is the time for you to get as much data experience as possible. This means taking on data projects that allow you to perform various aspects of the front-end or back-end data process. If you have limitations on the type of projects you can work on with your employer, you could volunteer with a nonprofit or conduct independent projects to build your portfolio. You won’t know what you do not like until you actually do it.

There are various areas where you can acquire your niche. Technical analysis vs. communication management, product management vs. project management, and building a data product vs. analyzing a data set are just some of them.

Learn to constantly adapt while providing value.

As much as we might like to think otherwise, our role and our day-to-day are impacted by the markets and the economy. The problem that every company faces today is how do we keep providing value in the best customer-friendly way possible. Today the conversation is more about how can we scale up while controlling the revenue.

How can you as an individual contributor help?

• Learn about the business — Apart from the general knowledge about the product/service your employer provides, it is essential to know about what the business’s key performance indicators (KPIs) are for the year and how your role aligns with those KPIs. This will help you to get a better understanding of the outcome of the work that you do on a daily basis. Further, be curious about how your peers and other teams are aligned with the business KPIs. Having a holistic view of the future of the organization will make you a better data service provider.
• Identify areas where we can save costs, save time, or increase revenue — Once you have identified the KPIs and value of your work as an individual contributor, start detecting ways by which you can identify opportunities to save cost and cycle time to do a certain task using your data analysis skills. A typical organization is always looking for ways to save costs and time while increasing revenue, and especially so during a tough financial year. For example, say you work with the sales team. Currently, they take five minutes per person, per call to cold call. The team is looking to implement an automated CRM system that will allow each salesperson to dial a call within two minutes. This CRM system costs $10,000. Through comparative analysis, you can analyze if this new system will actually save costs in the long run.
• Be proactive to provide insights into these areas to your manager and client — After you have identified the answer to the above question about whether or not the team should implement the CRM system. Bring this information to your client or your manager and start discussing different approaches. Being proactive in providing various solutions will help you stand out as a data professional in the long run. Sometimes “good to know” information can initiate bigger conversations around cost savings.

We are currently living in a paradox with the recent tech layoffs and increasing demand for tech talent across the world. I see this as an opportunity to truly realize your niche area within data and become an integral part of the data industry.