Artificial intelligence is rapidly transforming the way businesses harness data and analytics, taking the field far beyond what it was even a few short years ago.
At Datacom, these changes are already reshaping our team, our processes and how our customers engage with their own information. The big story here is accessibility. AI makes it possible for virtually anyone within an organisation, not just the data scientists or highly trained specialists, to quickly make sense of proprietary data and extract value in ways that were unimaginable before, often via a simple free text interface.
The shift brought by large language models and generative AI is profound. Where once a manager might have waited weeks for a new Power BI report, today’s modern data platforms – like Snowflake and Fabric – enable anyone to pose natural language questions to a data set and get instant answers and insights.
Need to know this month’s sales figures, the top performer or the margin achieved? Just ask, no need for deep technical skills or complex queries. This means timely decisions and giving new groups within the business the chance to explore, verify and act on data themselves, rather than relying solely on specialist teams.
Internally at Datacom, we use these systems to unlock insights from our own operational data. Whether it’s project forecasting, delivery management or resource allocation, the information is now available to anyone in the business who needs it and has the appropriate authorisation to access it.
A major misconception in the AI era is that the old distinctions between structured and unstructured data no longer matter. The reality is more nuanced.
Yes, AI can reach across vast stores of information, documents, email, data lakes and more, but the underlying structure, metadata and governance remain crucial. For instance, unstructured files scattered in emails, SharePoint repositories and personal laptops pose both challenges and opportunities. Datacom has invested heavily in metadata systems, ensuring we know what data we have, where it’s stored and how it can be responsibly and efficiently interrogated.
The power of AI to process and synthesise unstructured information is remarkable, but it doesn’t eliminate the need for intentional structure and oversight. There are multiple reasons for oversight and caution, including protecting sensitive data and avoiding costly bills. A number of organisations have been caught out after using AI models to churn through masses of unstructured data, failing to track their consumption costs and finding themselves landed with bills that run to tens of thousands – another reason that strategy, planning and data quality are an important foundation for AI.
At the same time, we are seeing clients saving thousands of labour hours by using AI to automate data analysis, cutting the manual and time-consuming work often involved in aggregating data and reporting business information.
At Datacom, we are using AI to automate documentation, accelerate quality assurance and streamline data mapping and migration. Our internal quality assurance (QA) agents use machine learning to validate information, speeding up lengthy reviews that once took months into a matter of weeks, sometimes days.
When organisations work with us to deploy AI, our certified team can rapidly plug into modern data platforms, delivering projects faster and critically, passing those efficiencies directly to our customers.
We’re also evolving our approach to talent. The skills needed in data analytics are shifting toward working alongside AI agents, validating outputs and thinking creatively about solving problems. Junior analysts spend less time on repetitive minor work and more time collaborating with the end users, interpreting and driving value. All of this assisted by secure, internal AI systems tailored to Datacom and our customers’ needs.
Datacom is also investing in partnerships with universities to help shape the next generation of data professionals. Working with tertiary institutes, we are challenging students with real-world projects that encourage the use of AI and data science tools, providing a pipeline of talent ready to contribute with the latest skills.
As data leaders, we advise starting small when it comes to data analytics projects, targeting specific needs and iterating quickly. AI enables fast, focused experimentation so teams can fail fast, learn and scale what really works.
The rise of accessible AI in data and analytics is not just a technical change, it’s a cultural one. It empowers everyone, not just technical specialists, to ask better questions, discover new insights and work collaboratively with data.
Datacom’s experience shows that with good governance, quality data and a willingness to experiment, AI can turn data into insights and is a true source of strategic advantage for any organisation.
AI is reshaping data analytics, making insights faster, more accessible and empowering smarter decisions across every level of business. At Datacom we’re helping New Zealand organisations unlock this potential by turning raw data into actionable insights through advanced analytics, automation and intuitive platforms that put information in the hands of everyone – not just data experts.
By combining accessibility with local expertise, we enable businesses to optimise performance, reduce costs, mitigate risks and build resilience while enhancing customer experiences and driving long-term success.
