How Strong Data Governance Accelerates GenAI Success
A sound data strategy is critical to fully realize the benefits of AI solutions. This Research Note demonstrates the value of data designed to deliver AI outcomes through the lens of an internal WWT project, the RFP Assistant.
Why is data governance important?
Good data governance can significantly improve the day-to-day operations, efficiency and effectiveness of a team or program in a number of ways:
- Higher data quality: With good data governance practices in place, your data is more accurate, consistent, reliable, available and trustworthy.
- Better data accessibility: Clear guidelines and processes for data access and sharing means the right people have access to the right data at the right time, enabling teams to collaborate more efficiently and effectively.
- Stronger data security and privacy: Data governance includes measures to protect sensitive data and ensure compliance with relevant regulations and policies. By implementing proper security controls and privacy measures, teams can mitigate the risk of data breaches and maintain the trust of customers and stakeholders.
- Easier data integration and interoperability: Good data governance promotes standardization and consistency in data formats, definitions and structures. This facilitates data integration across different systems and enables interoperability between teams and programs. As a result, teams can more easily share and combine data, leading to improved coordination and streamlined operations.
- Data-driven decision-making: Effective data governance establishes clear processes for data collection, analysis and reporting. This enables teams to leverage data insights to make informed decisions, identify trends and address issues proactively. Data-driven decision-making improves the overall effectiveness of a team or program and helps drive continuous improvement.
All these benefits underscore the many reasons that data governance is foundationally important to your AI development plans and long-term success. To illustrate what this value looks like in a real-world scenario, let's examine the role data governance has played in WWT's development of its AI-powered RFP Assistant tool.
What is the RFP Assistant?
The RFP Assistant is a generative AI (GenAI) tool developed internally at WWT to support our Proposal Team. Our AI team has developed the RFP Assistant to quickly search through multiple databases for relevant information, shortening the time it takes the Proposal Team to review incoming opportunities and produce proposal content via the tool's auto-generated response capabilities.
Data governance's impact on AI assistants
A history of solid data governance
The Proposal Team has put in significant effort over the years to maintain and update their data sources, ensuring their data is current and accurate, and conveys WWT's competitive value in a compelling manner. They are heavily involved in regularly updating data and ensuring good data governance when it comes to the digital assets they maintain across multiple applications.
The Proposal Team facilitates an ongoing data cleaning process within WWT's customer relationship management (CRM) application to make sure the relevant data fields are standardized and up to date. This has allowed the team to create the necessary tools to confidently automate some of their processes.
The team has also established an overarching data governance framework and strategy, involving the right data governors, data owners, and data strategy around the data in question. They maintain the data and ensure the right framework is in place.
Data designed to deliver. . . RFP responses
The volume, veracity and variability of the data means the information generated by the RFP Assistant is much more likely to be accurate. The RFP Assistant crawls WWT's multiple data sources, including the Content Management System (CMS) which houses thousands of pieces of content, to generate responses based on queries the Proposal Team submits. These functions include:
- RFP intake: The tool automatically scans and parses RFP documents, extracting key requirements and criteria, and creating a summary of the project scope and objectives. This summary allows the response team to begin crafting a proposal response faster.
- RFP qualification: The tool compares the RFP with an internal database of similar projects to assess past RFP success metrics, fit and feasibility. The information could include client industry, technology solution, required service, recency or a combination of these attributes. These data points help those involved evaluate the new RFP and make informed decisions and recommendations based on past results.
- RFP research: The tool searches for and retrieves relevant information from past proposals that will help the team craft win themes and identify case studies and other relevant information to build a winning proposal.
The RFP Assistant is expected to automate up to 75 percent of the review and qualification groundwork required to assess, classify and assign incoming RFPs. It is estimated that the digital assistant should help qualify a large portion of the RFPs the team responds to each year.
Prioritizing the RFP Assistant as an AI use case for WWT
WWT's AI Center of Excellence — a cross-functional team involving leadership, business consultants, data science, internal IT and software engineering — is responsible for evaluating and prioritizing use cases for AI development.
To qualify and prioritize GenAI use cases, the team follows a structured process. For each potential use case, the team estimates the AI solution's possible value to the business by analyzing internal and external data sources, processes and systems, and assessing an application's potential impact on areas like client experience, risk mitigation, cost savings and competitive advantage.
The technical and operational feasibility of each use case is also evaluated. This includes factors such as the time, resources, and costs required to collect, clean and store data, and the availability and quality of data.
The RFP Assistant was one of the earliest prioritized GenAI use cases at WWT because it met three criteria:
- There was a tangible business value that could be calculated and measured.
- The data was available.
- The data was of good quality and in the right format for GenAI.
All these factors contributed to accelerating WWT's ability to develop and roll out an AI-powered RFP Assistant.
Conclusion
It's clear that good data governance plays a central role in building more effective AI tools, and our experience in developing internal AI tools confirms that attention paid to data governance is well worth the effort.
When starting a new data governance initiative, it's critical to focus on aligning data governance capabilities to business outcomes. The RFP Assistant project at WWT is a prime example of how the hard work of thoughtful data governance pays off.
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