Andy Baillie, VP, UK &Ireland at Semarchy, looks at how AI can be used as a catalyst for effective master data management.
Businesses today collect more data than ever before in a constant quest to elevate the efficiency and accuracy of their operations. Master Data Management (MDM) sits at the heart of this initiative, acting as a single source of truth for decision-making and strategic insights.
Introducing artificial intelligence into MDM offers unprecedented opportunities for accessible and actionable insights for all employees. Analyses of industry practices reveal how AI’s role transcends traditional boundaries, offering bespoke solutions and safeguarding data integrity across various sectors.
However, the route to a rewarding AI integration must begin with a firm commitment to data quality.
Prepare a data foundation first
AI systems thrive on quality data. Before contemplating the adoption of AI within your organisation, ensure that your data is well-organised, accurate, and actionable. This will lay the groundwork for AI to enhance, rather than complicate company processes.
Preparing a data foundation requires four main steps:
- Audit and cleanse company data: Implement rigorous data cleaning processes to ensure accuracy, consistency, and reliability. Flawed data can lead to poor AI performance and decision-making.
- Invest in master data management: Incorporate an MDM solution to create a single source of truth where AI can access and analyse data consistently.
- Establish clear data governance protocols: Create a clear set of rules for collecting, storing, managing, and protecting data to make sure it meets all compliance and regulatory standards.
- Secure and protect your data: Prioritise cybersecurity measures to protect against breaches that can compromise the data’s integrity and the trust in AI systems.
The alignment between master data and AI uses, such as quality assurance and customer experience, is paramount for truly actionable, empowered, and enriched data.
Closing the AI expectation-reality gap in data management
Research highlights a discrepancy between employee expectations and the efficacy of data tools integrated into their workflows. Only a fraction of employees find the information surfaced during their work tasks to be actionable. Therefore, it’s essential to focus on several key aspects to bridge the gap between expected results and what AI actually delivers.
First, design AI systems that align with users’ daily tasks and goals to ensure they seamlessly fit into existing work routines. Second, prioritise the quality and relevance of data over merely collecting large quantities – users need accessible, actionable insights rather than a vast volume of data. Centralised data repositories enable efficient management of vast datasets, which is vital for accurate AI-based decision-making.
Education serves as a cornerstone to successful AI adoption. As AI is projected to automate a significant portion of human tasks by 2030, workforce re-skilling becomes imperative. So, the next step is to invest in educating and training the workforce on how AI works and its limitations to empower them to utilise AI tools fully. Lastly, develop transparent AI practices that users can understand and trust, as this clarity is essential for closing the expectation-reality gap.
Key use cases for AI in data management
To realise AI’s full potential, deploying it as a transformative catalyst across stakeholder personas such as business users, data stewards, and app designers is crucial. Data stewards benefit from enhanced data governance capabilities, while app designers see improvements in app generation efficiency.
It can also revamp the quality assurance process by automating, significantly refining data integrity with minimal human input. For example, in customer experience, AI analyses data to predict consumer behaviours and tailor personal experiences, providing actionable insights.
Predictive maintenance is another area where AI shines, spotting potential system and process breakdowns early to prevent downtime. For supply chain management, AI’s ability to detect inefficiencies, forecast demand and adjust resource allocation in real time makes it an indispensable tool for business resilience and continuity.
In addition, using AI’s data-driven insights to inform product design can steer development towards more successful outcomes based on real-world usage patterns and customer feedback.
Implementing AI in data management workflows
Organisations considering AI and MDM integration must start with a focused deployment, following an incremental approach targeting specific areas where AI can bring immediate value and slowly build on small successes.
Crucially, AI should augment and enhance human workflows rather than replace them completely. Integrate AI with employees’ current tools to minimise resistance and accelerate adoption. Furthermore, develop AI tools tailored to the unique needs and use cases of different stakeholder groups within the enterprise to boost relevance and efficiency. Foster a culture of continuous development through regular user feedback and be ready to refine and enhance AI functions to align more closely with user needs and company objectives.
The potential for inaccuracies and data breaches will always exist; address this head-on by using external benchmark data to train AI in low-risk settings before full deployment. Such an AI co-pilot model allows for a gradual evolution of AI strategies, ensuring that the technology delivers on the promise of smartly harnessing data for better business outcomes.
AI as an integral ally for the future
Adopting AI technology should be a considered, phased, and human-centred approach. Organisations must reinforce their underlying data structure, simplify user tasks, and create trust in AI technologies by showcasing their logic and effectiveness. Pursuing this pragmatic approach will allow AI to transcend its role as a companion for data management and become a driver of innovation and improved decision-making, guiding organisations towards an era marked by seamless, data-driven excellence.
The true enabler of leveraging AI’s potential is the groundwork laid by leaders who invest in superior data systems upfront, carving the way for AI to serve as a faithful ally in master data management. Modern MDM solutions can help overcome technology obstacles in AI deployment by providing a low-code, intuitive environment that streamlines adoption and fosters innovation without compromising data integrity.
