From a developer's perspective: High-quality data is always at the foundation of the best AI solution.
06 | 2025 Antti Rintala, Data Architect, Partner at Kipinä
There is a lot of talk about AI solutions, but less about what they are actually built on. The answer is simple: data. And not just any data, but high-quality, reliable, and business-driven data.
In his blog series, Kipinä consultant Antti Rintala outlines the foundational requirements for leveraging artificial intelligence. This summary compiles key perspectives to help build a robust foundation not only for AI solutions, but also for sustainable, data-driven business management.
1. Data quality matters most – not quantity
The success of an AI solution begins with ensuring that the available data accurately, timely, and logically reflects reality. While data volume is an appealing metric, without quality it can lead to misleading or skewed analysis.
Key quality dimensions include:
Timeliness: Is the data real-time, or is quarterly updated information sufficient?
Accuracy and integrity: Does the data truly reflect what it is intended to represent?
Consistency and error-free status: Do different systems contain conflicting information about the same subject?
Contextual validity: Is the data relevant for the specific purpose it is being used for?
Crucially, quality is not a standalone technical metric, but a shared organizational objective. Without it, AI models may still make decisions – but based on flawed premises.
2. Trust is built on sources, transparency, and documentation
High-quality data is one thing – but reliable data is entirely another. Data reliability is built upon multiple factors: its origin, processing chain, and how openly the data is managed and documented.
Key perspectives include:
Source credibility: Do you know the origin of the data, the reputation of its producer, and their level of expertise?
Data lineage: Can you trace how the data has flowed and transformed through various stages?
Transparency: Is there sufficient background information available regarding data processing and decision-making for non-technical stakeholders as well?
When the data processing workflow is transparent, an organization can not only enhance its own expertise but also demonstrate external accountability – an increasingly vital competitive advantage.
3. Effective governance bridges strategy and data
Data is often handled in isolation from business objectives, which strips away its actual value. At its best, data governance aligns directly with the company's strategic priorities: what is measured, why it is measured, and under what conditions.
Data policies should reflect business strategy – rather than merely fulfilling regulatory compliance.
Enforcement can involve technical mechanisms, not just guidelines.
Relevance does not emerge from data on its own, but from business needs: what supports decision-making, and what does not?
Data governance is not just risk management – it is building a competitive advantage. When business and data are aligned, a shared language and better decision-making are established.
4. Information security and regulation – design proactively, not reactively
Information security and privacy are not features that can be bolted onto the end of a project. They must be factored in right from the foundation of your data and systems – otherwise, you risk accumulating technical debt and business liability.
Key takeaways:
Cybersecurity is much more than a firewall – it requires continuous planning and risk management.
Data protection regulations (such as GDPR) are constantly evolving – robust documentation and clear governance minimize risk.
Collaboration between the legal department and the data team must not be left to chance.
Adopting artificial intelligence can mean handling sensitive data in entirely new ways – which is why proactive preparation is critical even before writing the first line of code.
5. AI is never finished – it is a continuous evolution
Artificial intelligence is not a "set-and-forget" solution. Models change, data quality fluctuates, and the business environment shifts. Without ongoing monitoring and maintenance, systems will inevitably begin to produce flawed inferences.
Why does this matter?
Model performance degrades over time (e.g., model drift) if systems are not regularly updated.
Ethics and transparency require human oversight in decision-making – even when AI provides recommendations.
Bias and discrimination can emerge unnoticed if training data is unbalanced or if rigorous testing is absent.
AI governance is not merely a technical issue, but a fundamental business and ethical responsibility.
The true value of an AI solution is engineered before the code
Companies that invest in a high-quality, transparent, and strategically aligned data foundation do not just extract more value from AI – they future-proof their operations. Data quality, governance, and reliability are not isolated steps, but part of a continuous strategic endeavor.
A successful AI solution does not start with code – it starts with the question: do we trust our own data?
Antti Rintala, Data Architect & Partner, Kipinä
Antti Rintala is a software developer and solutions architect with nearly 20 years of experience spanning cloud services, IoT solutions, and diverse technologies. He combines deep technical expertise with a business-driven mindset, specializing in Azure environments, system performance, and user experience. Antti is driven by a passion for solving complex business challenges through technology – practically and with measurable impact.