With the ongoing rapid change in the data and technology landscape, businesses and professionals continue to feel the pressure to adapt their skill sets to cope with new requirements. Traditional roles of data engineers and analysts are expanding to include new competencies as modern technologies are included in existing business architectures.
A forward-thinking team
Realistically, we need a team of people who can incorporate new focus areas and technologies into their existing arsenal of capabilities. Understanding the company’s technology strategy, as well as the existing and planned architecture, is crucial to building a forward-thinking team. The timeline for change will determine the speed at which the team needs to shift and adopt new thinking and approaches.
To find out more about how Calybre can help you unlock the story in your data, visit us at www.calybre.global
The pressure to include AI solutions in business is real. We are seeing that most leaders have at least one AI-related KPI on their scorecard. The translation of that objective into business-relevant workloads must be accomplished before you change your team structure.
The importance of keeping the lights on
Changes to existing architectures take time. Keeping the lights on and running “business as usual” processes has as high a priority as experimenting with leading-edge tech. People in your team may experience frustration at having to work on older or less-exciting solutions, so it is important to give as many staff as possible the opportunity to work on the newer tech, rather than running the risk of losing people. Assuring people that they have the opportunity to gain experience in cutting-edge technology will increase the likelihood of them sticking around to maintain older systems.
Reuse and recycle
Skills learnt on older technologies form a solid foundation for future-relevant skills, and industry and company-specific IP and mileage can be invaluable when implementing new systems. Many principles used in modern solutions have been around for decades; it’s the way we apply those principles that has changed so dramatically.
I won’t need people in the future. I’ll replace them with AI.
There are, without doubt, situations where AI can perform tasks that were previously done by people. Increasing confidence in AI tools has resulted in AI assistants being used extensively to perform routine tasks. The chances of AI completely doing the data team out of their jobs is not on our foreseeable horizon, so before you get too carried away in this future fantasy, spend some time understanding the true nature of the data work that still needs to be done.
What happens if I can’t afford the change?
Finding the right balance between powerful AI capabilities and cost efficiency is key. It is important for a business to spend time defining which questions must be answered to improve their operations and then strategically select the most impactful. New architecture costs must be weighed up against the potential benefits and businesses with tighter budgets should not exclude the option of using smaller, tailored AI models that are efficient and meet their specific needs.
The ideal futureproof data team
A full-fledged large data team could include the following roles:
- The engineers: Beyond the traditional data engineer who builds and maintains the data pipelines, there are now requirements for machine learning engineers to develop and optimise LLMs and other AI models, AI engineers who specialise in enhancing AI models, and cloud engineers who implement scalable solutions using cloud platforms such as Amazon Web Services, Azure and Google
- The analysts: There is still a requirement for business analysts to ensure that business objectives are aligned with solution outcomes. The analyst family of roles also includes data quality analysts who focus on the accuracy, reliability and quality of data used in AI models, and data analysts or scientists who analyse data trends and refine models to produce actionable, relevant
- The developers: Developers will often specialise and follow either an engineering or analytical path. Current data teams need AI agent developers, who build and implement AI agents that can perform tasks autonomously, and automation specialists who are experts in automating processes and integrating AI agents into existing workflows.
- Team leadership: Enterprise and solution architects and project managers may be supported by data product managers who treat data as a product, creating strategies and road maps and overseeing the data lifecycle. Ethical AI specialists align to regulatory compliance teams and ensure that AI solutions adhere to these requirements. AI Infrastructure Architects are specialised in designing the infrastructure that supports AI workloads, including computation and storage needs.
The need for a Swiss Army knife
Realistically, finding all these skills in a single individual with the level of depth you require to implement best-practice data solutions, is probably an outside chance. Partnering with specialised service providers during critical growth phases is a good option to ensure that you keep your legacy systems and processes up and running, while you shift your chosen solutions to more modern architectures or platforms.
Calybre’s Data-Analytics-as-a-Service offering is designed to be the Swiss Army knife needed to support you on your data and AI journey, providing you with a scalable, affordable solution – the right data professional at the right time with a simple subscription service.
About Calybre
Calybre is a data consulting company based in South Africa and the UK. To find out more about how Calybre can help you unlock the story in your data, visit us at www.calybre.global.
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