Analytics Anonymous

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?

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.

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.

It's easy to get started with SQL, but mastering it requires a deep understanding of database architecture and query optimization, which takes time and practice to develop.

In this episode I talk with Michaël Scherding (Analytics Engineering Manager at SFEIR) about his journey to becoming a SQL expert. We also dive into the unique challenges of scaling an analytics platform to more than 100,000 employees.

Key takeaways:

You need to understand the business to be successful as a data analyst. But how do you learn this? And how can you best support a business at different stages?

In this episode I talk with Olivia Höwing about her experience working as a data analyst at Project A. She gets to work with many different portfolio companies and learn about different business models and tool stacks. This position offers a unique vantage point and a great learning curve for a data analyst.

Key takeaways:

How do you break into a data career? What are the skills you need to learn? Where do you find your first job? There are many different paths that people take on this journey. And in the end, we often face the same challenges. And we can learn by listening to each others stories.

In this episode I talk with Cynthia Ovadje (Junior Data Analyst at 1&1 Mail & Media) about how she realized her plan to become a data analyst, and the lessons she learned along the way. I had met Cynthia some years ago, when she was just starting out in this field. And I was really happy to hear how she's found her way into her current role since then.

Key takeaways:

• The "analyst" title is used together with many different roles. Cynthia started out as an inventory analyst, making sure the right items were in the right warehouse at the right time.
• Looking at job offers and applying even if you don't meet the requirements can give you valuable experience and opportunities to learn about the job. Cynthia didn't let herself get discouraged by unsuccessful applications, but instead kept learning from the feedback she got.
• You can learn a lot of the technical skills with online courses. After several months of learning SQL and Tableau she landed her first job as a junior data analyst.
• Besides technical skills, strong communication is crucial for the data analyst role. The ability to translate business needs into technical solutions, and then explain complex analysis results in clear and simple language allows you to deliver real impact.
• Understanding how the business works gives you superpowers as a data analyst. You can learn this by joining meetings with many different stakeholders and building relationships with people from other parts of the business.
• A more established company (and data team) can be a better place to start at as a junior data analyst. There are usually more resources available to support you in your professional development compared to a startup.
• Working at personal projects can be super helpful to showcase your skills when you don't have the experience on your CV.

Bonus topic: Cynthia shares her experiences in moving to Germany from Nigeria, and the unique challenges of living and working here as a woman of color.

When business users complain about the data, that's a good sign! It means they actually want to use it.

In this episode I talk with Uzi Blum (VP Data at Taxfix) about the four layers of data quality.

Key takeaways:

1. Three steps to quickly gain trust with your stakeholders: (1) Show them you understand their problems, (2) deliver results quickly (within weeks, not months), (3) focus on getting the most important metric right first.
2. Weekly active data users (WADU) in the organization is a good proxy metric for the trust people have in data. An aspirational metric might be share of decisions taken that are based on data.
3. Data quality can be measured by the share of incident-free days (reactive), or the share of data assets that are compliant with your quality standards, have monitoring in place, and are covered in the glossary (proactive).
4. To ensure quality on the row layer, we can use unit testing (to cover expected cases) and monitoring (to cover unexpected cases, e.g. with Great Expectations).
5. To discover problems before your stakeholders do, it can be effective to have a data team member on call to check data quality issues in the morning and give a "green light" when it's good to use.
6. Having a glossary with aligned definitions of all metrics can go a long way. Ideally, this is linked to your BI tool, to help users with the right context.
7. Guidelines for creating effective dashboards can also help with providing context (e.g. having clear titles and labels, highly visible filters and consistent color codings).

For more on this, check out these blogs by Uzi and the Taxfix team:

How much time will you need for this new analytics project? You might want to underpromise and overdeliver.

Wouldn't it be great if you had only that One Metric That Matters? So clear. So simple. Turns out life – and product management – is not so simple, and we need to navigate this complexity.

In this episode I talk with Jonas Adrian Eriksen (Group Product Manager at Gorillas) about North Star metrics and data-informed product development.

Key takeaways:

1. Northstar metrics provide focus, but create the risk of tunnel vision. They're best used on a team level to align on yearly or quarterly goals. They need to be leading indicators, not lagging.
2. On a company level, we should consider a constellation of multiple "star" metrics and need to understand their interdependencies.
3. The best setup for product analytics is to have data analysts embedded in the cross-functional product teams. The analyst can assume the role of "chief of staff" to the product owner who understands the business problem and is proactive in defining the goal and finding solutions.

Bonus topic: We talked about the "Great Resignation", and both our personal stories of why we have quit our jobs (twice) in the past months. Caring about your mental health plays a big role here.

That "quick question" over Slack? Yeah, that's probably not the highest value your analytics team can deliver.

In this episode I talk with Elizabeth Press (Head of Business Intelligence at Gymondo) about using DataOps to make analytics work more efficient, reliable and valuable.

Key takeaways:

1. With DataOps, we want to move away from "quick questions" over Slack and piles of ad-hoc requests or self-inflicted "emergencies". It all starts with gaining an overview of our tasks and projects (for example with a Kanban board) and establishing healthy communication patterns.
2. We can use product thinking or design thinking to understand customer needs and context. The task of the data or analytics team is then to design good solutions.
3. It's important to measure the quality of our work as data or analytics team. How much value are we delivering, how much are we held back by fixing broken systems?
4. Analysts are the face of the data organization. Their strength is in understanding the data, the customer needs and context, and then telling effective stories to drive better decisions.

I hear all the time how "dbt changed our life". When was the last time so many people were so excited about a data tool?

In this episode I talk with Sowmia (Head of Analytics Platform at TIER Mobility) about data democratization – and dbt plays a big role in that.

Key takeaways:

1. dbt is a game changer. Making use of the power of the data warehouse, and based almost purely on SQL, it has become the foundational platform for all other data products. It also allowed the team to grow from 5 to 60 people quickly.
2. Analytics engineering is the name of the game. AE do the work previously done by DWH + BI engineers. They focus on data modeling, and are often embedded in business functions. (In contrast to data analysts or scientists, they don't work on storytelling or generating insights.)
3. Looker works great for enabling self-service access to data assets (created via dbt). It allows for the definition of metrics in version-controlled code. Analysts will only create a few "one-stop" dashboards, with business users doing most of the ad-hoc analysis and report building.
4. The analytics team is running data literacy onboarding and training programs, and measuring adoption and success also via regular surveys. As next steps, they're looking into bringing in a data catalog tool to further help business users make sense of the data.

What is the sexiest job in data right now?

In this episode I talk with Tiffany Valentiny (Data Lead at Trade Republic) about roles and careers in data & analytics.

Key takeaways:

1. Analytics engineer might be the sexiest job in data right now. They empower the whole organization to make better use of data.
2. Finding good analytics engineers is hard. It can help to give candidates very clear expectations of what that role entails.
3. Remote work allows more flexibility in hiring, but might not be ideal for more junior candidates because "learning on the job" is more difficult in this setting.
4. Increasing diversity in the team allows for a wider variety of perspectives and ultimately better solutions. And having diversity attracts more diversity.

When should you use a 3D exploding pie chart?

In this episode I talk with Tobias Hazur (Senior Product Analyst at Mobimeo) about data visualization.

Key takeaways:

1. Think about the story you want to tell with the data. Don't let the tool dictate what you show.
2. Eliminate everything that doesn't add value. Create clarity, not confusion.
3. Observe how people use and interpret your charts. Learn from feedback and iterate.

Are you struggling to measure the value of analytics? You're not alone.

In this episode I talk with Irina Ioana Brudaru (Head of Data & Analytics at Finlex) about how to show the value of data & analytics.

Key takeaways:

1. The goal of analytics is to help organizations make better decisions, faster. It can be useful to break down the decision-making process into smaller iterative steps and continuously integrate new information.
2. To get buy-in and build trust with your stakeholders, find low-hanging fruits and prove value quickly. Start small and then keep building on that.
3. Align with the business goals and measure the impact on KPIs that are driven with your analyses or models. Internal metrics can include adoption of your self-service program as well as data quality and reliability.

Bonus topic: To improve the diversity in your organization, give women a platform to showcase their work and create positive role models.