Separating hype from reality can be a full-time job in today’s market. And even though the public discussion of generative AI tools like ChatGPT may seem overheated at times, it’s clear that business leaders believe there is a fundamental shift underway.
A major Salesforce survey of IT leaders, for example, found that 67% were prioritizing generative AI for their companies over the next year — and a third said GenAI was a top priority.
To make those plans a reality, businesses need to leverage the most complete, accurate, timely data available, delivered in flexible formats and powerful computing environments.
But first, companies must ensure they’re far enough along the curve of data maturity to give their AI investments a fighting chance. Here’s how to assess your company’s data maturity, and the steps leaders must take on the path to AI-driven growth.
The Data Maturity Arc
Every business has unique challenges, but there are some common ways to determine where your company sits on the spectrum of data maturity. We like this four-part rubric, inspired by Google’s data maturity benchmark.
Nascent
Businesses in the nascent stage deal with disjointed operations, siloed data, and a weak or non-existent data-centric culture.
Developing
Isolated parts of the business are able to drive value from data, but not in a way that makes a significant difference. Leaders are at least aware that data maturity is important.
Mature
Data-informed decision-making is common across the business. Data-fueled tech has largely been adopted, but it’s most commonly used for one-off projects.
Leading
Businesses in the Leading stage see data as the foundation for strategy. Upheld by a data-centric culture, they experiment, innovate, and are a leader in AI integration.
To move up the data maturity curve, data and go-to-market leaders should typically prioritize several key factors. Here’s how to put them all into action to assess your business’ readiness for AI-fueled growth.
Defining the Business Use Cases for Data
Before launching an investment in AI — and the data and systems it takes to ensure success — it’s helpful to consider which of the main data use cases your projects will address.
Operational
Key Question: How can AI and data maturity be used to streamline internal processes?
Examples:
- Supply chain management
- Automated billing systems
- Predictive data analytics
- Automated data management
Analytical
Key Question: How can AI and data maturity unlock deeper insights for actionable business intelligence?
Examples:
- Market trend analysis
- Competitive analysis
- Financial performance forecasting
- Analyze customer interaction data
Customer-facing
Key Question: How can AI and data be used to enrich the customer experience?
Examples:
- Highly personalized marketing campaigns
- Customer service chatbots
- Client relationship management
- Enhanced customer support platforms
Once defined, sourcing and enriching data for a superior data-driven foundation is the next key step.
Sourcing and Enriching Superior Quality Data
By weaving together data from direct customer interactions with insights from broader market sources through third-party data providers, businesses can gain a holistic, actionable view of their customer base and TAM.
For example, ZoomInfo Copilot uses multiple high-quality data sources to provide sellers with a comprehensive view of their key accounts, including intent spikes, earnings calls and regulatory filings, and ZoomInfo’s unparallelled contact and company data.
With AI-guided prospecting, sellers are able to target accounts based on fresh buying signals, including new investments and key personnel changes. Copilot’s AI email generator creates a personalized email in seconds, referencing relevant account information to increase engagement and reply rates.
This comprehensive perspective is crucial since it enables an in-depth understanding of customer behaviors and preferences. It’s also key to crafting personalized campaigns that deeply resonate with target customers.
Adding multi-vendor data enrichment transforms your decision-making process by layering rich context and detail and equipping leaders to make nuanced decisions with more impactful outcomes — supplying the crucial data foundation for any AI-fueled growth initiatives.
Prioritizing Data Granularity and Relevance Over Volume
To truly hit the mark, growth strategy must be fueled by data that is both minutely detailed and expansively relevant — not just large in volume. Difference-making data dives deeper into specifics, including:
- The true shape and size of companies: Where are they located? How many employees do they have? What’s their annual revenue and growth compared to five years ago? What industry are they in?
- Corporate hierarchy structure: How is the business structured? Who is the Ultimate Parent, and what are the subsidiaries within those? Where is each of those located?
- Contact data with added context: What’s your contact’s job position and seniority level? What’s their place in the company hierarchy in relation to its buying committee?
- Tech stack insights: Layer in data points about the technologies a company employs. What technologies have they installed or uninstalled recently? Who are we competing with? Who are we complimentary with? What’s our market penetration compared to tech serving this particular industry?
Such granular data enables sales efforts that are not just personalized, but strikingly on target. It provides a transformative foundation upon which companies can build sophisticated, finely tuned ML models and train Large Language Models, the foundation of generative AI tools.
Eliminating Silos by Centralizing AI-Ready Data
Data is created in many formats, runs through many systems, and is used by disparate downstream teams. Whether it’s structured, unstructured, or semi-structured data, the resource-intensive task of preparing data sets for each use case is a true roadblock for businesses everywhere. Data leaders have silos to thank for that.
The need for easily accessible data has led companies to embrace automation with the expectation of a centralized, fully managed platform — the new pressures of adopting AI are only increasing the urgency. However, there’s still a significant gap between expectation and reality.
Too many operations teams are only going as far as enabling sellers, with the help of filters, to figure out who they should be reaching out to. This approach still means sales and marketing teams are the ones taking the time to fulfill that task manually — when they could be using those resources to sell instead.
The solution summarized in one word? Centralization.
Centralizing data in modern businesses involves:
- Adopting a unified data platform that integrates and normalizes diverse data sources
- Introducing modern data warehousing solutions with cloud technology for scalability
- Standardizing data governance processes across the organization, including setting common data formats and usage guidelines
- Using data integration tools like APIs to enable seamless data flow between systems
- Automating data management processes to reduce manual errors
Deploying and Establishing a Data-Literate Culture
A truly data-centric culture happens gradually by compounding several cultural improvements:
- Start with Stakeholder buy-in: The foundation of a data-driven culture is top-down support, which means showing the tangible value of data to stakeholders is key. Through clear examples and success stories, leaders can illustrate how data-driven decisions positively impact the business, securing ongoing commitment to data-centric practices.
- Data maintenance and management is everyone’s responsibility: Raise awareness across all company levels by embedding a consistent message of shared data responsibility. This responsibility extends beyond individual departments, individual teams, or employees. Instead, data needs to be framed as the common thread that connects multiple functions within the company.
- Data literacy accelerates data maturity: Businesses need to ensure widespread data literacy by providing accessible resources and training to support employees at all levels. Add regular data literacy assessments to help track company progress and identify areas for improvement. This continuous learning approach helps your workforce keep pace with evolving data practices.
Iterating and Creating Feedback Loops
A robust feedback mechanism is essential to fostering and maintaining data maturity. This involves setting up routine sessions to meticulously analyze performance data. Such reviews should not be mere formalities but actionable discussions that drive the business forward.
- Designing performance indicators and assessing data influence: Crafting clear and measurable performance indicators is crucial. Metrics like sales conversion rates, customer engagement levels, and operational efficiencies are not just numbers but rather reflections of the impact of data maturity and AI initiatives on the business. These indicators serve as tangible evidence of progress and areas needing attention.
- Gathering feedback from key stakeholders: Collect opinions from diverse perspectives to identify areas for improvement. This feedback, when integrated thoughtfully, can lead to significant refinements in strategy and operations.
Prioritize AI-Ready Data Maturity
The unprecedented enthusiasm for implementing new AI tools is a timely reason for businesses to prioritize data maturity.
ZoomInfo offers the best B2B commercial data, delivered on your terms — accessible, flexible, and primed to accelerate your business. Capitalize on C-suite interest to drive the data maturity conversations and initiatives that will set your company up for the next generation of growth by contacting a ZoomInfo data specialist today.