Build and Beyond: Transforming Your Teams With Data
Welcome to Build and Beyond, where we feature perspectives from our EngineeringX community members on key topics around software engineering. EngineeringX is a dynamic global community of engineering leaders who are passionate about driving developer productivity and innovation.
In this series, Nick Durkin , Harness Field CTO and Head of EngineeringX, interviews community members to dive deeper into their experiences and insights, giving you an inside look at the trends shaping our industry.
The most successful engineering teams today don’t just rely on intuition – they rely on data. For engineering leaders, data-driven decision-making is crucial for optimizing workflows, tracking performance, and identifying areas for growth. By leveraging data effectively, teams can enhance performance and make smarter, faster decisions that lead to real change, innovation, and competitiveness in today’s fast-paced industry.
I sat down with Suyog Iswalkar , Senior Director of Software Engineering at Deluxe Media Inc., an entertainment services company based in Burbank, CA. With over 20 years of experience, Suyog has led teams through significant initiatives such as seamless cloud migrations and developing microservices, transforming the technology landscape in the entertainment industry. In this interview, he shares how data has evolved into a central force in decision-making, how to begin this transformation, and what to watch out for when transitioning to a data-driven approach.
How has the role of data evolved in software engineering teams over the past few years?
Back in the day, data was like a supporting actor rather than the star of the show. Data collection processes were slower, and we didn’t have the computing power or tools to analyze large datasets quickly.
Remember those clunky ETL processes? We’d use them to clean up data, mostly creating reports for customers and higher-ups. Decisions were made based on gut feelings and experience. Data was used as a backup, confirming what we already knew rather than leading us somewhere else. Today, data is at the heart of everything we do in software engineering. We use it to guide product decisions, predict outcomes, and solve problems more efficiently. The rise of specialized roles like Data Engineers and DataOps Specialists, along with advancements in AI and cloud technology, has transformed how we process and use data. We can now analyze real-time data to continuously improve our systems, making data-driven decision-making an essential part of the workflow.
Here are some ways we use data in software engineering:
This trend of data-centricity will only intensify, especially as AI becomes more integrated into our processes. Data literacy and ethical considerations around privacy and security are becoming more critical for everyone involved in software development.
Data has gone from being an afterthought to the cornerstone of innovation and decision-making in software engineering. As we move forward, our ability to leverage data will be a key factor in the success of our projects and organizations.
What steps did you take to integrate data-driven decision-making into your teams?
We started small by picking a specific project and team to test our new data-centric approach. The idea was to choose something with visible impact to quickly show the value of what we were doing. This pilot was our chance to figure things out before we rolled it out across the whole organization.
Next, we considered our objectives and ensured they aligned with broader business goals. We set specific, measurable targets, determined what data we needed to track our progress, and reviewed and updated them regularly.
We realized people are the key to our success, so we invested as much as possible in them. We provided data analysis and visualization training and brought in consulting specialists to refine our capabilities and navigate our initial learning curve. We also invested in user-friendly business intelligence and data visualization tools.
Data governance was a major focus. We set up clear policies for data access, security, and privacy and defined who is responsible for managing data.
We also needed to address the challenge of breaking down data silos. To solve this problem, we built systems to make it easier to share data across departments and created a central data repository. We pushed for cross-functional collaboration on data projects.
Our biggest challenge was creating a data-driven culture. We led by example, encouraged our teams to back up their ideas with data, and celebrated the wins from data-driven insights. We measured improvements in cost reduction, time to market, and customer satisfaction and regularly reported these improvements to our stakeholders.
One thing we were careful about was balancing data with human judgment. We used data to inform our decisions, not to make them for us. We combined data insights with our expertise and intuition, always thinking critically about what the data was telling us.
We also worked hard to ensure the high quality and reliability of our data. We set up processes to validate and clean our data, and we regularly audited our data sources.
Lastly, we fostered a culture of experimentation. We encouraged A/B testing and created a safe environment for data-driven experiments. We learned from our successes and failures, using these insights to keep improving.
This has been an iterative journey. We're constantly learning and refining our approach. By starting small, learning from our experiences, and continuously improving, we've effectively leveraged data to enhance our decision-making processes across the organization. It's been challenging at times, but the improvements we've seen have made it all worthwhile.
What are some common challenges teams face when transitioning to a data-driven approach, and how have you overcome them?
We found ourselves dealing with two types of challenges: technical and people.
On the technical side, figuring out where to get the right data was a challenge in itself. We had to make sure the data was actually good (accurate and not full of noise). Processing all this data efficiently was also a challenge. We needed it to be fast enough to make timely decisions, which meant figuring out which steps or processes could be automated.
Choosing the right tools and tech was another major hurdle. It felt like a new “must-have” data tool was available in the market every week. We needed to be careful about what and where we invested our initial limited budget.
To solve this, we invested heavily in building a solid data infrastructure, invested in the right tools, and established clear processes for managing and maintaining our high-quality data. It was a lot of work, but technical challenges are somewhat straightforward to address.
The people side of things was a different ball game. Changing the way people think and work is never easy. We encouraged everyone to shift from relying on gut feelings to making data-based decisions.
It was crucial to get buy-in from our executive team and my peers, some of whom were pretty resistant to change. We needed their support to drive this transition and obtain the needed resources.
We had to prove that this new approach was worth it. That meant carefully choosing projects that would benefit from being data-driven. We had to make sure the data we collected was actually tied back to our company's goals and key results.
To do this, we focused on communication: listening to everyone’s concerns and feedback and finding champions within the company who were excited about this change and could help spread the word.
It was important to show early wins. Once people started seeing the benefits in action, it became easier to get them on board.
Looking back, finding the right balance was the key to making this transition work. Good technical solutions were important, but we also needed thoughtful change management. Both our systems and people needed to be aligned with our new way of doing things.
What advice would you give to other engineering leaders looking to transform their teams with data?
As an engineering leader, I’ve learned that transforming your team with data is much more about people than technology.
The first and most important thing is finding an executive sponsor. You need someone at the top who believes in what you’re trying to do and can advocate for you. Trust me when I say it makes a world of difference.
Next, look for champions within your organization. These are your allies on the ground. They can be enthusiastic, data-savvy engineers or new employees who’ve seen the power of data-driven decisions in their previous roles. They will help spread the excitement and keep the momentum going when things get tough.
Third, build a core team that is truly passionate about this mission. You want people who are not just skilled but who believe in the power of data to transform how you work. This group will push through challenges and inspire others.
Finally, be patient and persistent. Transformation doesn’t happen overnight. You will face many setbacks, and that’s normal. Keep pushing forward, celebrate small victories, and always keep the long-term vision in sight.
How do you measure the impact of data-driven decisions on your team's performance and productivity?
We have to measure impact, not just business outcomes. Here are some key metrics we track:
Other metrics to consider are sprint velocity, cycle time, code review efficiency, mean time to recovery, and deployment frequency. These can all provide insights into our team's improving performance and productivity.
All these improvements must be tied back to our organizational objectives. We should be able to demonstrate how our enhanced team performance translates into business value.
Remember, this is an ongoing process. As we refine our data-driven strategies, we'll discover new ways to measure and improve our team's performance and productivity.
As we wrap up our conversation with Suyog, here are a few more fun facts you should know:
What is one thing you are most proud of?
I volunteer to teach individuals from less fortunate backgrounds. It allows me to empower them through education and foster personal growth.
What are you reading or listening to right now?
Storyworthy by Matthew Dicks
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Technical Business & Data Analyst |Product Manager | CSPO®) | TPM | QA | ISTQB | AWS Cloud Practitioner | MBA-Finance | Project Execution
3moGreat read, Suyog Iswalkar! Wishing you continued success in your journey!