Trusted AI starts with trusted data
Leaders require clarity, strategy, and safeguards to build AI solutions on a foundation of credible, high-quality data. This toolkit is your guide to confident, compliant innovations covering everything from understanding key concepts to evaluating data partners.
5-piece toolkit
Five essential pieces—from glossary to governance—designed to help leaders build smart, transparent AI with data they can trust.
1
A visual overview of the challenges and opportunities in sourcing credible data for AI
2
An in-depth exploration of trends, risks, and strategies for using high-quality data in AI.
3
A 10-step checklist to ensure AI initiatives are aligned with business goals and powered by quality data.
4
A 10-point guide to assessing data providers for AI quality, ethics, and compliance.
5
A glossary that demystifies key terms in AI and big data for business leaders.
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Harnessing Data for AI Innovation
10 ways your company can exploit ai’s opportunities and manage its risks with high-quality data
The proficient use of AI technology is widely regarded as a key determinant of a company’s future successes, with predictions suggesting it will change the world. In April 2024, LinkedIn founder Reid Hoffman forecasted that AI will have the “same level of impact on the transformation of society that the steam engine had.”
Unsurprisingly, major companies are exploring how to transform their key business functions by harnessing AI technologies. From machine learning to predictive analytics, and from natural language processing to generative AI, these technologies are revolutionizing various aspects of business and society.
A 2024 survey by Wavestone found that 87.9% of executives consider investing in AI and data a top organizational priority. Yet the stark reality is that the vast majority of AI and big data projects fail– corporate projects fail at a rate as high as 80%, according to a recent Harvard Business Review article.
Moreover, the hurried adoption of AI can expose companies to new risks, from algorithmic bias to the use of inaccurate or unethically collected data. The question now being asked in boardrooms around the world is:
How can we gain a competitive advantage by successfully implementing AI while mitigating risks?
This ebook will demonstrate that a foundation of high-quality, trusted and enriched data is a necessary condition for a successful corporate project involving AI, generative AI or big data analytics. It suggests ten ways companies can avoid the risks of AI and use the technology to transform their business with support of trusted data from LexisNexis® through its leading API Solution, Nexis® Data+.
Why AI and big data projects fail
“Move fast and break things” was Facebook’s early motto. As CEOs watch their competitors bringing in data and technology, their instinct may be to move as quickly as possible to catch up or pull ahead. But this excitement should be tempered by the unfortunate fact that as many as four in five AI projects end in failure. Understanding why they fail can help firms to put in place a strategy to avoid a similar fate. Typical reasons include:
- Poor quality data
An AI project can only be as effective as the data powering it. Yet many companies focus more on acquiring the technology, with data an afterthought. Data that is inaccurate, unprovenanced, biased, outdated or partial will replicate all these problems in AI’s outputs. - Insufficient thought to integration
Big data projects often bring in data from various sources, using a confusing mix of bulk data deliveries and APIs. Moreover, each dataset may be structured (or unstructured) in a different way and require significant work to clean and ensure it can be used in the company’s chosen analytics software.
