Artificial intelligence has been used in the last decade in the pursuit of human-like machine learning, but AI is just now seeing a surge as companies worldwide recognize its potential. Artificial Intelligence (AI) provides natural intelligence and smartness to machines that can think, act and decide as humans do; this artificial intelligence is referred to by some businesses as "smart technologies". With successful demonstrations of this technology's capabilities over the years leading up to recent demands from many industries, it seems clear that there will be increased use of what we call “AI-led organizations”.
The perfect harmony
AI is all about data these days. Well, you would be wrong. AI is only as good as the data it uses to make decisions-- and if that data is bad or incomplete, AI can't do its job very well at all. Data management matters for AI just as much as it does for any other business function. That's because there are many facets of data management, from finding the right information to managing that information in a way that improves efficiency and quality through governance practices such as ensuring high-quality metadata (data about your data), designing workflows around best practices. Hence, people know what they're looking at when they review it, making sure there's an audit trail if someone needs to go back and find trails and focus on explainability. AI is a great way to make decisions and take action on data, but AI can't do that without high-quality data management.
Data matters because AI needs it for decision-making. Data also plays an important role in the workflow of AI systems--from finding good information through designing workflows around best practices so people know what they're looking for.
The issue at hand:
AI can't do this without high-quality data. AI needs robust, clean and structured data to make decisions, create new content or products, identify problems in systems and operations before they happen--and take action on that information quickly enough to avoid disruptions.
AI will be able to learn from mistakes when we get better at managing the quality of data. However, data management practices are the backstage work in the context of AI. For example, in a play, front artists drive an audience to cheer and come back for more--and pay for tickets to be entertained by them again. Similarly, AI needs quality data governance to make good decisions and avoid ethics and privacy concerns.
Recommended best practices:
Data governance is fundamental to AI-led organization’s data management and efficiency using the data. Master Data Management (MDM) with a controlled mechanism is essential for efficient data management. It requires technologies, processes, and people to manage and protect the confidential data from, guaranteeing the understandable, complete, secure, correct, discoverable business data. Data governance includes data architecture, data modeling & design, data security, data storage & operations, data integration & interoperability, documents & content, reference & master data, meta-data, data warehousing & business intelligence, and data quality.
The primary goals of data governance are
● implementing compliance,
● establishing data usage rules,
● minimizing risks, reducing costs,
● improving communication,
● increasing data value, and
● risk management and optimization
Some AI-driven organizations have demonstrated that quality data governance practices can lead to AI success. For instance, one of the large technology organizations has shown that it is possible to generate up to $100M in savings from AI adoption alone with the right approach and investment. This includes a focus on adopting AI for predictive maintenance and automation, as well as focusing attention on data governance practices.
Though data governance has advantages, it affects the organization at strategic, operational, and tactical levels. The first dirty and challenging task for AI-led organizations is to convince stakeholders of a data governance budget. An open corporate culture requires changes in the existing organization’s structure that may add to political issues. Lastly, the demands for flexible operations and networks within the organization are necessary for the rapidly changing business requirements that need to implement data governance standards as per the company’s business requirements.
Artificial intelligence has the potential to make or break a company. AI can enable an organization’s competitive advantage and help it maintain that edge by identifying problems before they happen--but this only works if there are excellent data management practices. Organizations need high-quality data governance practices to evolve and scale the organization and create competitive advantages.
Summary: Managing the dirty task in an AI-led Organisation:
All employees within the organization need to accept and understand the technical and business aspects of data governance and it is essential for AI-led organizations, The dirty or low ROI (in short term) tasks of data management, administration, analysis, and data strategy to ensure improved business opportunities and performance. It also needs an additional workforce with expertise in data governance, such as Master data practitioners, data stewards, governance, ethics, and data quality professionals.