The terms Artificial Intelligence and Machine Learning are often looked at as the two sides of a coin. Predominantly though, while the ML algorithms enhance AI proficiencies and enable them to do more intelligent and cutting-edge computing, there’s an additional layer of perceived impenetrability which veils the machine’s ability to analyze and arrive at impactful decisions.
There’s speculation in the industry about ML algorithms being a probable ‘Black Box,’ mainly due to the uncertainty around trusting an ecosystem that is not entirely transparent towards its data compliance and decision-making processes.
The global community of data analysts has helped design full or semi-automated analytics systems that are ML or AI-driven. However, the fundamental issue of data quality may always prevail. Additionally, there are diverse and disparate data sources, huge data volumes, as well as unstructured data types that tend to worsen the prevailing data management issues, especially those relating to data governance.
Many think that it may be advisable to practice some caution as ML gains momentum and continues to be at the forefront of changing the way companies operate. Without robust data governance processes, the keenness to allow ML to take over the decision-making process entirely may unleash some vital issues – unreliable and misleading information and unforeseen expense overheads.
So how can this be done effectively:
Should the gap between the necessity to build, organize, and implement effective and robust ML models be bridged?
Is it necessary to accommodate the highly growing demands and the need to understand and decrypt how those models work?
How do we understand what data is being accessed and harnessed by the ML algorithms? Also, what are the long term and often irrevocable consequences?
Data governance is definitely the most reasonable answer.
In any ecosystem, Data Governance as a framework defines and helps implement the overall management of the obtainability, usability, integrity, security, and effectiveness of data used.
Considering the cut-throat competition in today’s business world, every company needs a sustainable and well-designed Data Governance framework that strengthens data governance without restricting the extensive potential of machine learning.
With the ever-evolving usage and scope of AI and ML and the implementation of newer technologies, Data Governance will gain wider acceptance as well as more scope of application. Due to the recent wave of several high-security data violations, data security has become a vital part of the data governance efforts. A prime example of data governance measures is the European Union’s directive regarding General Data Protection Regulation (GDPR) that further reinforces the need for establishing more robust models.
There’s still a long way to go to discover AI and ML’s complete potential and true capabilities for an organization. And in a world of disruptive data, smart ML algorithms, and the ever-evolving AI environment, data governance is the only way to provide some much-needed security and safety.