Companies Using Generative AI Need Clean, Trusted, and Actionable Data

In many cases, companies are not managing data for best results.

Generative Artificial Intelligence (AI) has captured the imagination of people around the world. Organizations are adopting the technology at varying levels across nearly all industries and markets, disrupting business models and creating value. Its usage will only grow as businesses seek to develop strategies to take advantage of its uses.

The management consulting firm McKinsey & Company estimates its potential economic benefits at $7 trillion, explaining its productivity impact by sectors: “About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D.”

By combining large amounts of data with fast, iterative processing and intelligent algorithms, AI software learns and responds automatically from patterns or features in the data inputs to make predictions, decisions and carry out a myriad of tasks. For AI to function properly, companies need to have a foundation of clean and actionable data. Without this foundation, they risk incorrect outputs and lose out on the insights and benefits that AI holds in its potential.

The state of data management in companies varies widely, with outdated, unstructured, and unreliable data often the rule. Bad data is often described as “dirty” data, containing errors such as: spelling or punctuation faults, outdated data, incomplete data, incorrect data associations, and duplicate instances in the database. This type of poor quality data often results in biases, errors and so-called hallucinatory AI results, a pattern termed “garbage in, garbage out.”

The professional services firm Deloitte, in a recent report, warned that AI depends on a solid data structure to reach its potential. “For AI to succeed, organizations should address data challenges and fix bad data, applying principles to better manage, clean, and enrich it so broader AI ambitions can be met. But most haven’t reached a level of maturity in data management capabilities, and about a third of AI programs fail as a result,” the report said.

To be sure, companies have access to very large amounts of data, but in many cases, they are not managing it for best results. The importance of data as a strategic asset has been apparent as companies focused on data-centric strategies and data driven growth. But with massive amounts of data appearing from disparate sources and of varying types, they are increasingly challenged to properly manage it.

To be sure, companies have access to very large amounts of data, but in many cases, they are not managing it for best results.

“While vast amounts of data are available to organizations, it is rarely interconnected or integrated to realize its benefits,” Deloitte added. “This hurdle can make it more difficult for organizations to leverage not just their own internal data but data from external sources. In addition, important insights can be missed due to lack of complete or standardized data, and this can produce inaccurate analysis and reports.”


Data is the foundation of models used by sales and marketing

In fact, accurate, controlled, validated data is fundamental to executing effective campaigns and expanding the use of AI-driven technology. Data is the foundation of models that sales and marketing and other groups are using in their activities.

Data governance plans are necessary to institute policies and adhere to data quality standards in order to stop the hemorrhage of substandard data into data assets. Data on customers and suppliers can be prioritized, mastered, and governed, and organizations can utilize commonly accepted definitions and attributes across all platforms, enabling them to talk to each other.

Dun & Bradstreet’s Master Data solutions can help enable teams across companies to drive growth and manage risk with a trusted data foundation based on a single source of truth. Organizations can connect disparate data siloes by leveraging pre-mastered data, and AI to speed-time-to-value and gain trusted views of business relationships, improved end-to-end processes, and visibility to improve risk management.

With these benefits, organizations can:

  • Accelerate Time to Value – Drive workflows through automation, powered by AI/ML, to help increase productivity and efficiency across teams and help reduce costs across the organization.
  • Manage Data Silos and Access a Single Source of Truth (Data) – Rely on trusted data from the Dun & Bradstreet Data Cloud to deliver data and insights into organizations’ ecosystems utilizing the Dun & Bradstreet D-U-N-S® Number as a unique identifier to provide data across applications and systems for one source of truth across the enterprise.
  • Create Trusted Views of Strategic Business Relationships – Develop views of complex business relationships – hierarchies and corporate linkages – for a comprehensive view of customers and suppliers to help prevent damaging risk scenarios.

For example, with Sales and Marketing teams, Dun & Bradstreet’s Master Data solutions can deliver trusted insights to help prioritize the best leads to go after and ultimately help to drive more sales and growth. Among the strategic benefits:

  • Segmentation and targeting
  • Customer acquisition/retention
  • Opportunity identification
  • Account/lead routing

Dun & Bradstreet’s Master Data Management Solution, D&B Connect, enables teams to leverage industry-leading pre-mastered data, AI, and master data subject matter experts to help accelerate the time-to-value process.

Discover how Dun & Bradstreet can help your enterprise strengthen your master data strategy for AI. Learn more!.