I was fortunate enough to attend and speak at Data Day Texas 2024. If you’re not familiar with the conference, Lynn Bender has been organizing it in Austin since January 2011 as one of the first NoSQL / Big Data conferences. Data Day Texas attracts speakers and attendees from around the world. I was so impressed with the breadth of presentation topics (it was tough to choose which sessions to attend!), engaging networking, and the fun energy of this close-knit conference. It’s not often you see folks passionate enough about data to spend a full Saturday together sharing stories and learning. 

We’ve put together a helpful list for those looking for more data conferences to attend (both in-person and virtual).

I’ve recapped some more details of the day below, but I noticed a few major themes that came. As I reflect on this conference, I know I will be thinking more about these topics as I head into the rest of 2024:

• The job market remains difficult and is changing the industry: From Lynn’s opening remarks to several sessions throughout the day, this topic kept coming up. Folks are feeling the pressure of the market at all levels (from ICs to senior roles), and there was a lot of discussion about navigating this and setting yourself apart if you’re looking for a role. The requirements for senior-level career growth are changing, and being more strategic, focusing on your communication skills, and building empathy for executives to meet them halfway were some common pieces of advice.
• Reflecting on the past as a guide for the future: In comparison to fields like software engineering, many “modern” areas of discipline in data are still relatively new. There were a few sessions that looked back on history to inform where we are now and where we’re going. I liked the macro approach to navigating where we are along the journey.
• Measuring the business value of data functions is still an elusive part of our jobs as data people: This is a topic that never seems to be too far from folk’s minds, but given the economic climate this is more top of mind than ever. Data teams that can’t measure their value are labelled as cost centres, and cost centres are the first things to go when the going gets tough. Data teams need to think about controlling their costs and keeping them in line with the value they deliver, and having small, iterative approaches to delivering value that are easier to get buy-in for.
• AI is the future of business, and AI can’t happen without data: I keep seeing this topic coming up as well, that regardless of how advanced LLMs and generative AI get, the limiting factor and major opportunities lie with how much clean and accurate data we can provide to these models. So data teams are more critical than ever in this process, and we need to help businesses understand this before they attempt to dive head-first into AI initiatives.

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An Energetic (and Cathartic) Keynote

We kicked off the day with an incredible keynote session from Sol Rashidi: Practitioner turned Executive; lessons I learned about how decisions are really made with data ecosystems. In this session, Sol shared some of the pivotal moments in her career journey, from being a hands-on data practitioner to moving into executive-level roles (she was the 1st Chief Data & AI Officer appointed in 2016), and the learnings she has had along the way. Sol is such an engaging speaker and hearing her unique perspectives was both validating and inspiring. Data roles are inherently political and often lead us to get caught up in the challenges of change management. Being reminded of this in such a relatable way helped me realize I’ve fallen into this trap a few times along the path in my career. One specific comment Sol made that stood out to me was that data people need to “decide whether you want to be a thermometer (someone who measures) or a thermostat (someone who drives change) in our organizations”. I think this is such a critical question for data people to be asking themselves, especially because driving change requires so much more than just building out your technical skills. Focusing on soft skills, building relationships, and learning how to sell yourself are critical to ensuring your data initiatives aren’t cut from budgets (especially in the current economic climate).

Sol summarized her learnings for data practitioners considering a path in executive leadership:

1. Progress over perfection: businesses often don’t know what they want or need. Ship something, get feedback, and iterate.
2. Relationships over recognition: data isn’t about being right at the expense of others, and if you fail to build relationships, you will fail. Prioritize relationship building and don’t make other execs look bad when you are right.
3. Preservation over pride: You may have to swallow your pride for the sake of playing the long game and building adoption. Just because you built something does not mean they will come (to use it).
4. Collaboration over control: Slow is smooth, and smooth is fast. Focus on getting alignment with your team(s) and push for collaboration over forcing people.

This was such an impactful keynote that speakers and attendees were talking about it for the rest of the day. If you ever get the chance to see Sol speak live, don’t pass it up!

A Day-Packed with Insights

The Past, Present, and Future of Data Catalogs

After Sol’s keynote, I caught Juan Sequeda's summary on the Past, Present and Future of Data Catalogs. Juan’s presentation broke down the past and historical context about how humans have always had a natural inclination to organize and document information, and with the creation of the World Wide Web, we saw the origins of knowledge graphs and how that was really a precursor to present-day data catalogues. In that not-so-distant past, cataloging had been a very broad exercise, but in our current state, we now take a very narrow focus (“Can I find my data”?). Juan argued that we must “build on the shoulders of giants” as we move into the future of data catalogs, and that the opportunity lies with being very broad again, cataloging and documenting everything in your company, connecting LLMs with knowledge and context of the organization to really unlock their true potential.

Driving Positive ROI with Cost Containment

After Juan’s session, I presented my own session about Cost Containment: Scaling your Data Function on a Budget. I wrote an article about cost containment as well. In this session, I shared my learnings from being the first data hire at a few startups and running a data team on a tight budget. With the economic climate we’re currently in, data teams who are not cost-conscious run the risk of being labelled as a cost centre (and cost centres are the first thing businesses cut when their outlook gets worse). I shared some practical methods of how to measure and reduce costs across your data stack (measure, set up feedback loops, and make costs visible and accountable to data producers and consumers).

Finding Common Ground between Execs and Practitioners

After grabbing a quick lunch, I caught another session with Sol as a speaker, and this time she was joined by Joe Reis to discuss Bridging the Gap: Enhancing Collaboration Between Executives and Practitioners in Data-Driven Organizations. It was an engaging dialogue and the session helped to highlight the often-overlooked dynamics within organizations trying to become more data-driven, focusing on the need for mutual understanding, strategic alignment, and the crucial role of communication in bridging the gap between vision and execution. Once again, growing soft skills are a non-negotiable aspect of making this type of career progression.

You Need to be More Strategic

The next session I caught was one with Aaron Wilkerson: You need to be more strategic - The mantra for data leader career growth. I enjoyed this session as I feel like I’ve spent such a good portion of my career trying to convince more people that data is a strategic function. Hearing this perspective from Aaron and some of his own experiences and insight on the topic was refreshing and validating.

Some of my main takeaways from that session:

• There is often lots of great technical leadership, but not as much strategic leadership (many business leaders aren't sure how to be strategic)
• Becoming more strategic as a data person is a huge advantage in your career
• Concerning growing your career and becoming more strategic, consider what is driving you? Money, job title, roles and responsibilities?
• Decide how you want the company to view you as a data person
• Get people's attention (be present in meetings, contribute, turn on your camera)
• Don't end up being the “ideas person” → ensure you can execute
• Work with your stakeholders regularly to truly understand business value
  

The Past, Present, and Future of BI

Next up, I headed to Ryan Dolley’s session on Business Intelligence in the age of AI, which focused on how we can expect the practice of BI to change in the face of advancements. Ryan is the VP of Product Strategy at GoodData and a host of Super Data Bros Podcast–check out my recent episode with them!)

Similar to Juan’s session earlier in the day, Ryan took us through the historical context of how business intelligence began, what our current state is, and what we can expect as we move into the future. It was a really helpful summary to see how far we have come, and how things are coming full circle as we begin to realize the future of AI-driven BI requires more robust data governance and control (which were characteristics of early BI solutions).

Ryan broke his presentation down into three eras: the Enterprise reporting era (1998-2010ish), the self-service era (2010-2024ish), and the AI era (2024ish and beyond). He argued that we’re embarking on the new AI era, and shared some thoughts on what BI needs to embrace to stay relevant in this new age (mainly that BI professionals would transition to focus more on metrics layer definition and management, rather than dashboarding and visuals). My favourite part of this session was the concept of BI using a “metrics backflow” to ensure that insights from the BI/analysis layer get pushed back into the metrics layer to be defined. This serves as a kind of prototyping for metrics and will help connect domain ownership of data to the governance of a metrics layer.

How to Evolve as a Data Scientist in the Age of AI

After Ryan’s session, I caught Megan Lieu’s presentation on Evolving as a Data Scientist in the age of AI. Megan’s talk described how folks in data science roles can navigate the changing (and potentially threatening) job market with the arrival of LLM. She offered advice about how to adjust by either:

1. Moving your skills to the market (by upskilling)
2. Moving the market towards you (by growing your personal brand)
3. Doing nothing (taking a bet that maybe we’re in another AI hype cycle and this will blow over)

She shared her first-hand experience about how she was unfortunately laid off in a data science role, but how she quickly overcame this minor setback because she had been investing in growing her LinkedIn presence with content creation (she shared more about this in her recent episode on the Women Lead Data podcast with me). This helped her get a new role very fast (an example of moving the market towards you). I thought this was a great talk from Megan with some unique advice to help folks in a difficult job market.

A Closing Town Hall for the Books

Similar to the beginning of the day, Data Day Texas ended with an energetic and cathartic session, this time it was an open mic format where folks in the audience could pose a question to the group and anyone could share their thoughts, advice, or further questions to add to the discussion. It was so interesting to hear how folks are managing through many of the same challenges. Here is a summary of some of the topics discussed:

• What is the value of data?: A classic discussion for data folks, but I think my favourite comment was that organizations don’t ask these same types of questions about software (e.g. what is the value of software?)
• Sol made a great point that the goal should not be to replace critical thinking and decision making, but to augment it with more information–positioning data in this way will make execs and leaders more responsive to your initiatives
• My favourite quote from the session: “The real world leaves a shadow and the shadow is data”

Reflecting on the Experience

Overall, Data Day Texas was a super valuable day of knowledge sharing that was more than just technical knowledge–delving into the strategic, communicative, and collaborative skills essential for data professionals in today's fast-evolving landscape. Even though it was just one day, there were recurring themes about how to make career transitions to broaden your levels of impact, and emphasis on being a proactive agent of change ('thermostat') rather than a passive observer ('thermometer'). It underscored the evolving need for data professionals to not only manage and interpret data but also to drive strategic decisions to generate business value and foster a data-centric culture within their organizations.

To be quite honest, I’m still not sure how I discovered this conference, but I’m so glad I did. Lynn focuses on building a diverse slate of speakers (see speaker list), and I was grateful for the opportunity to join this group. Thank you so much to Lynn Bender and Alex Law for organizing–this is absolutely a conference I would recommend and hope to return to in future years!