Exclusive Trusted Magazine Q&A with Robert Barbaro, Digital Data Architect.
How could you describe your career path in a few words?
Pivoting from an international career in Opera and the Performing Arts for 10 years to Data Architecture has been full of unexpected twists and turns. My first dive into data was in data analysis, however, my curiosity for business improvement steered me towards roles in business analysis in the data domain. Whether developing a new set of business reporting resources or leading a team implementing services such as a call centre, a passion for balancing the needs of all stakeholders emerged. By the same token. whilst heading a team into supply chain and financial data, I discovered a real fascination and knack for drafting data models, data lineage documentation and architectural solutions. Overall, experience in different industries, regions, and landscapes (data, business, application and digital) enables me to progressively optimise data teams in their ways of working and designing and implementing effective data solutions and improvements enterprise-wide. The fast pace of the tech industry excites me because it is constantly evolving but at the end of the day, essentially relies on the combination of expertise, skills, thought processes and respectful collaborations for a common goal.
What were the highlights of the key digital innovation trends for 2022? Can you give us some major examples?
This answer will be based on the perspective of 'the organisation' with a focus on the data domain. 2 important trends come to mind: Generative AI Platforms and Customer Insights and Analytics which portray a narrative.
Trend 1 : Generative AI Platforms
Trend 2 : Customer Insights and Analytics which portray a narrative
Based on your experiences, what are the impactful trends in digital innovation that are becoming more important in the context of 2023?
Integrating Generative AI for developers and other documentation Developing new app features is a fast-paced environment. This leaves limited time to optimise performance; resolve bugs and malfunctions; efficiently validate current live code and data pipelines; and scope out expected time and resources needed to complete projects and iterations. Repository resources like Github enable development and engineering teams to build on past code and architectures and learn from others. Now more than ever, there is pressure on these teams to deliver more features and optimisations in digital environments. These resources can now leverage generative AI tools to not only kick-start scoping but also speed up and improve validation of the development and testing stages. In my architectural workspace, I have found generative AI tools can assist in building models and editing documentation: AI tools can be that ‘little helper on the shoulder’ boosting your ability to navigate workflows and tasks and free you up to apply your expertise and learn more.
API Security
In the digital space we are seeing an increased integration with third-party vendors providing more comprehensive service and product delivery to customers. More integration tends to mean building and expanding API infrastructure. Therefore, on the horizon for any organisation is the need to protect data flows, monitor and prevent vulnerabilities which may occur throughout the API landscape. Governance, metadata maintenance, education, vigilant adherence to industry best practices and business processes will continue to be imperative themes to be mindful of.
Data Lakehouse
As an architect, there is a constant re-examination of how data flows and pipelines operate – data lakes, data warehouses, reporting platforms should seamlessly ‘liaise’ with the needs of the business. As data and its uses become more nuanced and valuable, there must be a clear understanding of where data is stored and processed: data centralisation, or commonly referred to as ‘one version of the truth’ is the natural step many organisations have either achieved or are trying to achieve. The next step is arguably to take a data lakehouse approach: this is where business intelligence teams, business stakeholders, data scientists, IT operations, data engineering resources can access data in all forms, structures, sources and purposes in one place. In many ways, data lakehousing is a destination to aspire to -a 'way of thinking'. Of course, the size, age, infrastructure and data maturity of an organisation will dictate how far its adoption can go. I encourage those interested in this topic to consult literature on 'data lakehouses' and 'data mesh' because they have the benefit of not only optimising data estates but can also significantly influence business architecture and team structures.
In your opinion, how can they create high value for organizations?
In the case of AI, there has been sufficient time to observe the incredible benefits of AI integration and output vs the limitations, data quality risks and impacts to society and education. If we are talking about utilising AI tools and platforms to commence, produce and/or edit business documentation, clearly the quality and usability of the results rests in the authorship of the queries and the level of scrutiny and validation accompanying the output. In some instances, tasks may be delegated to AI-assisted platforms and in others, people need to embrace ownership of their research, experience and analytical and communitive skills: after all, people are still 'doing the work'. By the same token, where time was once lost to mundane and repetitive tasks, time has been recovered to enable better planning, sufficient thought and futureproofing.
Overall, there is a movement towards embracing more powerful and nuanced data tools and platforms. Such tools and platforms may be used in two fundamental contexts: 1) to understand if operations and service/product delivery are acceptable at the current scale and 2) to add new offerings to the customer. The challenge remaining is to not let the latter compromise the former
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