By Lokesh Bhagchand, COO, Agilisium
Modern day businesses increasingly rely on AI-driven insights making the accuracy, consistency and reliability of the data very critical. Quality data forms the foundation for deriving actionable insights and accurate decision-making processes.
However, organisations have long struggled with data quality issues, resulting in inaccurate analyses and misguided conclusions. This highlights the importance of clean, accurate data in ensuring effectiveness of AI driven insights.
Enhancing data quality through GenAI: Opportunities and challenges
The unprecedented computing capabilities of Generative AI (Gen AI) offers a transformative approach and several opportunities for enhancing data quality as follows.
Error detection and correction: By leveraging Natural Language Processing (NLP) and contextualised Machine Learning (ML) algorithms, Gen AI can assist in detecting anomalies and can automatically flag inconsistencies thus increasing accuracy levels.
Data lineage and transparency: Another area where Gen AI can assist is tracing the origin of data sources and data lineage. This transparency becomes crucial in ensuring data integrity and for meeting regulatory requirements, more so in highly regulated industries like Life Sciences.
Speeding up Data Processing: Leveraging Gen AI for improving data quality processes helps ensure that the accuracy of actionable insights is improved while reducing time to insights, hence enabling faster decision making.
While Gen AI offers innumerable opportunities for enhancing data quality, it also presents unique challenges.
Data hallucination: Gen AI may generate incorrect data, leading to false insights.
Bias in AI models: AI models may amplify existing biases if trained on unrepresentative data.
Complexity in implementation: Integrating Gen AI into existing data systems can be complex and resource intensive.
Given these opportunities and challenges, data quality needs to be looked at with renewed focus to ensure that AI models are trained on clean, accurate, and representative datasets. The following framework can be leveraged to master data quality in the age of Gen AI:
a. Integrating data quality considerations into the AI model development lifecycle, from data collection and preprocessing to model training and evaluation
Gen AI can’t fully benefit organisations without integrating data quality into every stage in the AI model development cycle since poor data quality can result in inaccurate models and faulty insights. During data collection and pre-processing, Gen AI can be leveraged to perform data cleansing, removing duplicates, correcting errors and filling in missing values. Equally important is ensuring that the data is representative of the real-world scenarios the model will encounter.
During the model training phase, Gen AI can be used to continuously assess the relevance and accuracy of the training data and identify any biases that may exist. By addressing these issues early on, organisations can develop more robust models that produce reliable outcomes.
b. Integrating Advanced Tools and Techniques into data management practices to address common challenges and improve overall data quality
Organisations can leverage Gen AI capabilities to apply advanced tools and techniques to tackle their unique data quality challenges. In the pharma industry, for example, where data is often non-digitised and scattered across logbooks and paper records, AI can play a role in digitising and standardising the information. In addition to improving data accessibility, this digitisation also lays the foundation for more sophisticated analytics and insights generation.
Leveraging AI powered tools
Data Cataloguing is one such AI powered tool that can automate the classification and organisation of data which is a powerful way to streamline data management processes and improve accessibility to data assets across teams. Data Lineage Tracking is another critical tool which provides visibility into data flows, ensuring transparency and accountability.
Leveraging Cloud Solutions and Accelerators
Current trends also indicate a growing adoption of industry-specific cloud solutions as organisations seek to overcome challenges related to unstructured, siloed data. Cloud based data environments enhance data quality, streamline operations, and drive more accurate, data-driven decision-making. Industry tailored solutions and accelerators available in the market can also accelerate the timeline of improving data quality.
Data Observability is one such solution available in the market that offers code-free implementation for your existing data stack. It proactively detects and resolves data anomalies in near real-time, enhancing the accuracy and trustworthiness of your data for analysis, decision-making, and downstream processes.
c. Establishing effective data governance frameworks that ensure data quality and consistency across the organisation
Data governance is an essential component of effective data quality management. As organisations increasingly use AI to automate tasks, data governance frameworks must incorporate principles for ethical data usage, privacy protection and regulatory compliance. Establishing data stewardship procedures, enforcing data quality standards
and defining clear roles and responsibilities are all essential parts of an effective framework.
Cloud migration and technology – Companies stand to benefit by migrating their data to cloud environments that offer advanced tools and services which validate, clean up and standardise datasets to maintain uniformity and precision. By leveraging cloud technologies, organisations can maintain robust data governance frameworks, ensuring that data remains accurate, secure, and compliant with regulatory standards.
Automated data validation – Gen AI can be trained with pre-defined rules and guidelines, and after that, data validation can be automated. This will reduce the burden on data stewards to ensure that data quality is consistent across the organisation.
Culture of accountability and transparency – Using Gen AI, organisations can continuously monitor any deviations from compliance requirements and pro-actively act to ensure the data sets remain trustworthy.
d. Adapting data quality management practices to keep pace with technological changes and emerging best practices.
Agility with AI driven tools – To gain a competitive advantage and accelerate time-to-insights, organisations must remain agile and stay up to date with technology innovations. By implementing AI-driven tools early in the data lifecycle, organisations can respond faster to new opportunities and issues.
Advancement in data operations – Gen AI is making significant strides is the automation of Data Operations (Data Ops). As per Forbes ~73% of organisations are investing in Data Ops. The future of data operations will see intuitive platforms offering seamless, end-to-end solutions for building, migrating, and managing Data
Infrastructure, enabling Data-as-a-Service. The next gen AI will deliver a single platform for automating Data Store Operations with streamlined automation and real-time Insights.
Leveraging synthetic data – Another crucial component for training AI models, is the use of synthetic data (data generated through simulations and algorithms that mimics real word data), as organisations are realising it is faster, more flexible, and a scalable solution. The benefits of synthetic data are plenty. To name a few – its unbiased, free
from personal data, is of better data quality, is more cost effective and allows for faster development and control.
Leveraging specialised providers – Outsourcing data challenges to specialised digital transformation service providers offers organisations several key benefits, including prior experience in data transformation and deep domain expertise. This approach reduces the need for large in-house data teams and infrastructure, cutting costs related to hiring and maintenance. It also allows organisations to focus on core activities while leaving technical complexities to specialists. While choosing a provider, organisations stand to benefit from choosing one with industry-specific solutions to ensure AI models are suited to their niche requirements.
Conclusion: A new era of data quality management
Mastering data quality in the era of Gen AI, is not just about adopting new technologies but also integrating them into a comprehensive data quality strategy. Every stage of the data lifecycle must ensure data quality is prioritised. Well thought out data governance frameworks built in with compliance to regulatory requirements must also be implemented to truly unlock the full potential of Gen AI. Outsourcing data challenges to specialised data analytics providers can accelerate data transformation projects, enabling quicker decision-making and faster time to market for new products and services. This holistic approach will not only accelerate the time to insights but also enhance the accuracy of the AI-driven insights making it more actionable and impactful.