GenAI has become an integral component of every stage in the data process. AI isn’t just for building models anymore. With GenAI, it’s starting to play a role across the entire data pipeline—from how data is discovered and documented to how it’s governed and used. This shift offers exciting opportunities to enhance data quality, streamline workflows, and uncover new insights. Ross Helenius, Director of AI Transformation Engineering & Architecture at Mimecast, leverages over a decade of analytics engineering & BI experience to share how to unlock the value of data for AI and AI for data.
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Table of Contents
- AI's Role in Transforming Data Management
- 4 Tips for Your Next AI and Data Initiative
- Case Study: AI-Driven Business Outcomes at Mimecast
- Embracing the AI-Powered Future of Data Management
AI's Role in Transforming Data Management
Data teams are witnessing a significant transformation in how they manage and utilize data, thanks to GenAI. Let's explore some areas in data management that are being reshaped by GenAI.
- AI-powered Observability: Moving beyond manual detection and user reports to identify data anomalies, AI now detects data pipeline anomalies automatically. This improves data trust and speeds up problem resolution.
- AI-assisted Data Modeling: AI tools are changing how we approach data documentation, testing, and discovery. They make data models easier to understand and work with, simplifying complex data structures.
- Unstructured Data Processing: Generative AI can extract value from unstructured data sources. It can synthesize data, extract sentiment, and create new features, which can be used to improve structured data pipelines.
- AI-driven Data Enhancement: Beyond traditional data modeling, AI allows teams to make predictions, classify data, and explore new possibilities that weren't feasible with conventional methods.
4 Tips for Your Next AI and Data Initiative
While GenAI offers incredible potential, ensuring the quality of data used to generate outputs is crucial. Combining traditional data quality practices with new techniques for monitoring and validating model results is essential for building trust and maximizing value. Here are some practical strategies for leveraging GenAI effectively.
1. Prioritize data quality
While GenAI offers impressive capabilities, they are not immune to the "garbage in, garbage out" principle. High-quality, accurate, and comprehensive input data is crucial for generating reliable outputs. It's essential to implement rigorous data cleaning and validation processes. This includes removing duplicates, correcting errors, and filling in missing information. Additionally, consider the diversity and representativeness of your data to avoid biases. Regular data audits and quality checks should be integrated into your workflow to ensure the ongoing integrity of your input data.
2. Choose the right model and fine-tune
The selection of an appropriate model is a critical decision that can significantly influence the quality and relevance of your results. Different models have varying strengths, weaknesses, and specialized capabilities. When choosing a model, consider factors such as the specific task requirements, the volume and nature of your data, and the desired output format. Once you've selected a model, fine-tuning becomes crucial. This process involves adjusting the model's parameters to better align with your specific use case. Fine-tuning can help mitigate biases, improve accuracy, and reduce the likelihood of hallucinations or nonsensical outputs. It's an iterative process that often requires experimentation and careful evaluation of results to achieve optimal performance.
3. Continuously monitor results
Implement robust monitoring systems to evaluate the reliability of GenAI outputs. Utilize techniques such as confidence scores, which provide a quantitative measure of the model's certainty in its predictions. Additionally, employ consistency checks across multiple outputs, track performance metrics over time, and set up automated alerts for anomalies or unexpected patterns. Regularly review and analyze these monitoring results to identify potential issues, biases, or areas for improvement in the GenAI performance. This comprehensive approach to monitoring ensures ongoing quality control and helps maintain trust in AI-generated insights.
4. Educate and build trust with users
Guide users to become comfortable with GenAI interactions and understand their limitations. Foster confidence by emphasizing data attribution and maintaining model transparency. This approach enables users to better discern when to trust model outputs, enhancing decision-making processes. Over time, users will develop a nuanced understanding of GenAI capabilities and limitations, leading to more effective and responsible use of AI-generated insights.
Case Study: AI-Driven Business Outcomes at Mimecast
AI can drive significant business outcomes when it is carefully integrated into existing workflows and aligned with specific business goals. Mimecast, a cybersecurity company specializing in email and data security, showed how GenAI can empower the sales team to be more productive, generate more pipeline, and close more business.
At Mimecast, the sales team faced challenges with disparate data, making it difficult for reps to quickly and effectively sell products into their customer base. Mimecast developed Expansion AI to enhance their sales processes, using both predictive and generative AI to assist sales representatives. By analyzing vast amounts of data, Expansion AI identifies high-potential customers and provides concise summaries of previous interactions, effectively streamlining the sales workflow and enabling more targeted outreach. To ensure widespread adoption, Mimecast integrated Expansion AI directly into their Salesforce platform. This strategic implementation allows sales teams to access AI-powered insights within their familiar work environment, eliminating the need to switch between multiple tools and boosting overall efficiency.
Mimecast closely tracked user adoption and business outcomes to ensure the Expansion AI model delivered value. This included monitoring the number of users actively using the tool, the amount of pipeline being generated, and the amount of business being closed. Mimecast met these business goals for Expansion on a faster timeline than expected.
Mimecast's success with Expansion AI relied on collaboration across different departments. Data teams worked closely with business and technology teams to ensure the GenAI model met business needs and fit seamlessly into existing processes. This collaborative approach was instrumental in ensuring the tool's successful implementation and widespread adoption across the organization.
Embracing the AI-Powered Future of Data Management
GenAI has emerged as a powerful enabler for data quality and governance. However, it's crucial to remember that human expertise remains essential in guiding AI implementations. As we look to the future, the potential of GenAI to revolutionize data management promises exciting developments in how we discover, analyze, and derive value from our data. At Select Star, we are here to support you in your journey to get your data AI-ready.
