That "quick question" over Slack has been the bane of data analysts forever. Imagine those are now handled by ChatGPT, giving quick and reliable answers to business users. Stakeholders are happy, and data analysts can focus on deeper, more impactful work. Are we about to finally see this happening?

In this episode I talk with David Jayatillake (Co-Founder & CEO at Delphi Labs) about how large language models like GPT-4 are changing the way we work with data. What does this mean for data analysts or analytics engineers, and where do these new tools fit into the modern data stack?

Key takeaways:

• A lot of tools already offer a text-to-SQL approach. While this can be very useful to increase productivity for data analysts or analytics engineers, it's problematic as an interface for business users. When the semantic layer is effectively generated on the fly with every new query, results are unpredictable and can lead to a loss of trust.
• With a semantic layer, analytics engineers and data analysts can implement business logic and and expose data and metrics to business users in a safe and reliable way. (For example, dbt offers a semantic layer, but a lot of BI tools like Looker or Metabase have their own as well.)
• Delphi builds on top of these existing semantic layers, offering a natural language interface for business users. Instead of digging through a BI tool, stakeholders can simply ask their question in Slack. The answers will be limited to what is defined in the semantic layer, therefore avoiding the risk of wrong results.
• When data analysts are freed from answering "simple" requests, they can focus on deeper, more complex work to generate insights and recommendations to the business.
• While AI might eventually be able to take over most operational tasks, David believes that strategic decision making will still require human oversight in the future.
• Besides building data tools, David is also very active in the data community. He hosts a Mastodon server for data folks, and you can find him on dbt Slack and Locally Optimistic. You should also check out his Substack where he's written a lot about semantic layers recently.

Find David and Valentin on LinkedIn.

Have you ever dreamt of being your own boss? Work on projects that you choose, on your own schedule, at your own rates? If you already have experience working with data and analytics, becoming a freelance consultant is a great way to break out of the corporate grind.

In this episode I talk with Jekaterina Kokatjuhha about how to become a freelance data & analytics consultant. She shares her personal experience and practical tips for how to get started, build a brand and audience, and overcoming uncertainty.

Key takeaways:

• Before going solo, Jekaterina worked in various data roles in several different industries. The insights into different business models she gained there are helpful for understanding her clients problems now.

• Her first freelancing client resulted from a match on the Bumble dating app.

• She started consulting with one day per week, while still being employed full-time. When this was going well she decided to leave her job completely.

• When she started looking for new clients, she focused on D2C brands. This allowed her to capitalize on her deep experience in this area and target her communication to this audience.

• On LinkedIn, Jekaterina writes about common problems these companies face (e.g. which metrics to care about). When potential clients reach out to her, she asks them what content resonated most with them, so she knows where to put her focus.

• To increase her reach on LinkedIn, she posts around 8am and aims for 100 reactions within 1 hour, to get boosted by the feed algorithm.

• She also posts selfies occasionally. While this used to make her feel uncomfortable, it's important for people to connect her face to her content.

• Going beyond solopreneur freelancing, her next step is building a data agency. Her goal is always to help businesses extract more value from their data.

Find Jekaterina and and Valentin on LinkedIn.

You've seen the pictures of people working on their laptops in beautiful, exotic locations. Exploring the world while you work – the digital nomad lifestyle is nothing new, but it's getting a lot more common, in particular since we all learned to work without an office over the last years. And the data analyst job is well suited for this lifestyle.

In this episode, I talk with Melanie Dietrich about the benefits and challenges of working as a digital nomad data analyst. She also shares her story of breaking into data, coming from a business background. And how we can work on closing the gender gap in tech (and data).

Key takeaways:

• Finding the right accommodation is a challenge when working on the go. You want a good desk and good wifi, but that's often not obvious from the descriptions on Airbnb.
• Going to a coworking space means additional expenses, but can help to connect with the local community of digital nomads.
• For internet access, it's good to have backup options. SpaceX Starlink works great for van life, but is too heavy for backpacking.
• Coming from a business background (audit consulting), Melanie wanted to move beyond Excel and taught herself data analysis with SQL and Python, using online courses.
• When looking for your first job in data, it's important to put yourself out there and demonstrate your knowledge. Networking is key.
• Helping business users solve their problems establishes your role in the team. Become the go-to person for their data questions and teach them how to use the available data tools themselves.
• Data science skills (e.g. machine learning) are often not so relevant in daily work. Data engineering skills are often more in demand.
• Melanie is a co-founder of the Women in Data x Business career network. They organize events and share experiences to encourage more women to chose a career in data.

Find Melanie and and Valentin on LinkedIn.