Data Governance: The First Step in Implementing Artificial Intelligence
Engaging with a great artificial intelligence (AI) application is like watching a professional athlete in a game. The greatest of athletes make their efforts look so easy that we often forget we’re seeing the result of decades of hard work. Just like athletes, established corporations that have repeatedly collected the same data over years also make their AI solutions look effortless. But, in most cases, their AI is using data that has been carefully refined over decades of transactions and is enabling machine learning and statistical analysis.
Machine learning and statistical analysis are two of the most popular AI techniques to identify patterns and hidden correlations in large data sets, potentially offering significant cost savings, which is especially alluring for resource-strapped federal programs. However, the most important ingredient of machine learning to induce quality is not processing power, but data governance. While higher processing power can train a machine faster, it won’t change AI quality.
Federal programs should begin with these five data governance tenets to evaluate their readiness for AI solutions:
Availability: Is the data the AI needs accessible to the application?
Usability: Is the data structured in the format the AI needs and is it complete?
Integrity: Is the data accurate and consistent across systems?
Lineage: Can the data sources be clearly identified and explained?
Security: Is the data protected against unauthorized use and loss?
Data governance sets the stage for AI with clearly defined data policies, data standards, and data processes. It includes up-to-date data profiles that efficiently address data problems, including inconsistencies between systems and protocols for communicating changes between data owners. Federal programs need good data policies to steward data management, to implement data standards, to document data lineage, and to maintain processes that ensure high data quality and sustainable results.
Using these five areas, programs can develop a comprehensive Data Governance Strategy to support AI solutions.
AI techniques, AI resources, and AI infrastructure are all accelerating. In the beginning of any AI effort, most of the cost will be in the time spent to cleanse and integrate data. Only by strategically addressing their most valuable assets via data governance will federal programs realize the full benefits of Artificial Intelligence.
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