This is an unmissable conversation packed with innovative perspectives where Aaron Phethean and Bethany Lyons dive into the intricate world of what “full stack” genuinely means in the data realm. Armed with facts and humor, they explore the vital role of trust, and the complexities of data reconciliation. Their discussion also ventures into the dynamic startup landscape, stressing the urgent need for creative solutions to the persistent challenges in data management and analytics that so many face. With their fresh insights, this conversation is a must for anyone looking to understand the future of the industry.

Takeaways

Be a full stack data person to have a bigger impact.

Data is a digital twin of real-world processes.

Understanding data representation is crucial for business insights.

99% of data work involves plumbing, not just visualization.

Trust in data is essential for effective decision-making.

Reconciliation of data is a complex and painful process.

Startups must solve specific problems for individual users.

Data analytics is not an assembly line; it's iterative.

The future of data solutions lies in addressing unsolved problems.

Navigating the startup landscape requires understanding customer needs.

Titles

Data: An Unsolved Problem

Innovating in the Data Space

Sound Bites

"Be a full stack data person."

"Data is just a digital twin of the process."

"How do we enrich the data?"

Chapters

00:00

Introduction

00:34

Introduction and Background

03:31

Sales Dynamics in Startups

06:25

The Importance of Trust in Data

09:39

Challenges in Reconciliation

12:32

Navigating Startup Challenges

14:44

Understanding Data Challenges in Organizations

17:46

The Importance of Data Representation

20:37

Navigating Data Complexity in Business

23:39

The Role of Data Teams in Organizations

26:46

Shadow IT and Data Solutions

30:04

Broadening the Data Skillset

31:03

The Concept of Full Stack Data Professionals

In this episode of Data Matas, host Aaron Phethean and his guest Stéphane Burwash dive deep into what it takes to build a true data-driven culture. Recently promoted to Data Engineering Lead at Potloc, Stéphane shares his thoughts on building trusted analytics, where quality data is at the foundation.  The conversation digs into the hot topics of AI and self-service analytics - and questioning their relevance - as well as the application of modern technologies such as Meltano and BigQuery and "the separation of church and state" in the data space. Not only that but the two touch on the importance of the people element and emphasise the need for open and honest stakeholder management in an organisations journey to data excellence.

Takeaways

• Stéphane started his data engineering journey alone, relying on community support.
• Building a community is crucial for learning and growth in data engineering.
• Potloc evolved from a market insights company to a data-driven organization.
• Navigating data engineering challenges requires asking questions and seeking help.
• Stakeholder management is essential for successful data projects.
• Technologies like Meltano and DBT are integral to Potloc's data stack.
• AI is being leveraged to improve data quality and analytics processes.
• Self-service analytics can empower users but requires careful governance.
• Data quality issues often arise from a lack of awareness and communication.
• The role of a data practitioner is to maintain a big picture perspective.

Sound Bites

• "Ask questions, don't be afraid to learn."
• "Everybody has been in that position."
• "We shouldn't be trying to do the custom solution."

Chapters

00:00
Introduction and Background of Potloc

04:43
Role of a Data Engineer at Potluck

06:34
Data Sources and Technologies Used

09:58
Balancing Complexity and Impactful Work

15:30
Working with BI Analysts and Data Modeling

23:46
Focus on Data Quality and Maintenance

25:42
Challenges of Data Quality and Data Integrity

36:12
The Importance of Stakeholder Engagement

41:14
The Concept of Self-Serve Analytics

43:25
The Value of a Holistic Understanding of Data

47:14
The Role of Data Practitioners

48:15
Introduction

49:24
The Value of Online Communities and Asking Questions

50:22
Overcoming the Fear of Feeling Lost

50:48
The Generosity of the Data Community

52:10
Networking and Learning at Meetup Events

53:21
Building Connections and Getting Insights

CitySprint is one of the largest same-day courier providers in the UK, with a strong presence in London. They operate a UK-wide network and offer same-day logistics services. The company relies on a fleet of couriers who use various modes of transport, including bikes, to quickly deliver parcels. CitySprint's goal is to move away from investigating data challenges and focus on building trust in the accuracy of their data. They are working on modernizing their infrastructure and implementing a new data management system to improve data quality and reporting. The BI team at CitySprint plays a crucial role in analyzing data and providing performance stats to different teams within the company. The team is also responsible for bridging gaps in the existing systems and ensuring the data remains current and relevant. The project aims to streamline the BI stack, create a single version of the truth, and enable faster reporting in smaller time windows. The challenge lies in managing the people side of the project and helping the team adapt to the new ways of working. In this conversation, Aaron and Joe discuss the legacy technology stack at CitySprint, including BI visualization tools, ETL tools, and the transition to Snowflake and Power BI. They also touch on the potential of AI in the business and the importance of embracing change. Joe emphasizes the need for data managers to straddle the technical and business perspectives and build strong stakeholder relationships.

Takeaways

• CitySprint is a leading same-day courier provider in the UK, with a strong presence in London.
• The company is focused on improving data quality and reporting by modernizing their infrastructure and implementing a new data management system.
• The BI team at CitySprint plays a crucial role in analyzing data and providing performance stats to different teams within the company.
• Managing the people side of the project and helping the team adapt to the new ways of working is a key challenge. CitySprint had a legacy technology stack that included BI visualization tools and multiple ETL tools before transitioning to Snowflake and Power BI.
• AI is a buzzword in the business world, and CitySprint is exploring its potential in areas such as customer sentiment analysis.
• Embracing change is crucial for success in the data field, and building strong stakeholder relationships is essential for effective communication and collaboration.
• Data managers need to straddle the technical and business perspectives to bridge the gap between technical experts and business managers.
• The ability to adapt and embrace change is key in a rapidly evolving technological landscape.

Titles

• The Role of the BI Team at CitySprint
• Improving Data Quality and Reporting at CitySprint Exploring the Potential of AI in Business
• The Importance of Adaptability in the Data Field

Sound Bites

• "CitySprint has a fleet of couriers who use everything from large vans to bikes to quickly deliver parcels all across the UK."
• "The measure of success is moving away from investigating challenges and issues within the data."
• "CitySprint has a fleet of couriers who use bikes to quickly deliver parcels in London."
• "What are the kinds of technologies that CitySprint had as legacy?"
• "We had to rewrite the whole process for getting the data out of the business systems into Snowflake"
• "AI is buzzword everywhere, isn't it?"

Chapters

00:00
Introduction to CitySprint

03:14
Data and Analytics at CitySprint

05:03
Modernizing Management Systems

16:29
Exploring AI at CitySprint

32:36
The Importance of Data Quality and Trust

34:11
Innovative Reporting and Test-Driven Development for Data Quality

35:36
Shifting Mindset and Processes for Data Quality

38:56
Building Relationships with Stakeholders in Data Management

43:16
The Role of People Management in Data Management

44:07
Designing KPIs: Balancing Behavior and Culture

Jessica Franks shares her experience of joining Not on the High Street as an engineering manager for the data team. She discusses the challenges of starting a new role in a new country and managing a team with diverse skill sets. Jessica explains how she tackled the lack of data strategy and created a visual representation of the data architecture using a Wardley map. She emphasizes the importance of simplifying infrastructure and improving data quality before diving into AI and ML projects. Jessica also highlights the need for clear communication and collaboration with stakeholders to ensure successful data initiatives.

Takeaways

• Starting a new role in a new country can be overwhelming, but with the right experience and skills, it can be managed effectively.
• Creating a visual representation of the data architecture, such as a Wardley map, can help communicate the complexity and prioritize projects.
• Simplifying infrastructure and improving data quality are crucial before diving into AI and ML projects.
• Clear communication and collaboration with stakeholders are essential for successful data initiatives.

Titles

• Navigating a New Role in a New Country
• Prioritizing Infrastructure and Data Quality

Sound Bites

• "Everything new sounds like you set out on an adventure and you got what you asked for. Was it scary though? Was that like, oh my god, what am I going to do?"
• "Having a picture that everyone agrees this is what it is. You know, there's all too often like 10 little things in the corner that nobody really understands that that's crucial to the way things operate."
• "Using a Wardley map was new to me and I instantly loved it. The way it laid it out, the way it communicated to everyone what was visible and important, but then not visible and important was sort of also, you know, that I think as a person running an engineering team is often really hard to explain that this crucial piece that no one can see, we need to do something about."

Chapters

00:00
Introduction and Setting the Scene

02:13
Navigating a New Role and Team

11:06
Visualizing Data Architecture with Wardley Maps

19:53
Prioritizing Infrastructure and Data Quality

28:11
Challenges of AI and ML in Data Initiatives

32:05
Conclusion and Key Takeaways