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Navigating the Landscape of Data Hygiene: AI’s Transformative Role

In the realm of corporate data management, Chief Data Officers are increasingly voicing a common concern: the pervasive issue of Dirty Data. This challenge is a critical barrier to the effective implementation of evidence-based decision-making strategies. The trepidation surrounding Dirty Data often stalls potential advancements in their tracks.

Simultaneously, Chief Artificial Intelligence Officers and AI Strategists are grappling with their own dilemma: identifying impactful use cases for AI within their organizations. The menace of Dirty Data frequently hampers these efforts. Interestingly, it is in the realm of cleaning and refining this Dirty Data that AI often finds its most significant and value-adding application within many enterprises.

Consider industries like data services, natural resources, or real estate. In these sectors, the data extracted from countless pages of contracts feeds into hundreds of thousands of data record fields. A simple example is the option date in a lease renewal. A single error in such a field can have substantial financial repercussions, including hundreds of thousands of dollars of penalties or lost opportunities amounting to millions. Often, there are many errors.

Compounding this issue, companies often acquire data through mergers and acquisitions, inheriting datasets with dubious origins and no feasible way to audit them. This is where the strategic deployment of artificial intelligence becomes a game-changer.

Companies that outsource to providers like DatumNexus® (https://www.datumnexus.co/) are leveraging Large Language Models (LLMs) to reprocess their vast contract archives, cleansing and correcting their system of records. This approach not only mitigates significant risks but also uncovers valuable opportunities.

Datum Nexus’s methodology extends beyond mere problem-solving for inherited data issues. It is also employed to certify data pre-merger and acquisition, enhancing the value of the acquisition, or post-acquisition to minimize risk.

The crucial task in AI Strategy is identifying scenarios where the need is clear and the AI-driven solution is well-understood and feasible. While sometimes these solutions manifest as internal processes or software tools, the most effective approach often involves partnering with a reliable external provider.

At Responsible Solutions Ltd, we have developed a methodology for large enterprises to identify those internal opportunities and effectively capitalize on them.

#artificialintelligence #ai #chiefdataofficer #mergerandacquistion #dirtydata

Original Linkedin Article

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Written by Russell Brand

Russell has started three successful companies, one of which helped agencies of the federal government become very early adopters of open source software, long before that term was coined. His first project saved The American taxpayer 250 million dollars. In his work within federal agency, he was often called, “the arbiter of truth,” facilitating historically hostile groups and factions to effectively work together towards common goals

 

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