Generative AI in data analytics - how AI is making it easier to access data

Unless you have been living under a rock buried under 10 feet of dirt, you are well aware of AI’s potential to reshape our world. While you may have ideas of how AI will impact how we work, you need to be a time traveler to predict every aspect of its impact. But we can assume that people, automation, and governance will be key players in the AI-driven future

AI is already impacting how humans manage and interact with data, turning it into actionable insights. AI can also serve as our reliable copilot, managing the underlying data that supports these insights. It can also operate independently to ensure the trustworthiness of the data we rely on for critical decision-making.

To harness the full potential of AI in data access and management, humans must remain at the center of the process. This approach means close monitoring and alerting, and appropriate training and retraining are crucial for the seamless integration of AI, making it a powerful ally in navigating the data-driven landscape.

AI’s role in streamlining data consumption and analysis

AI and, more specifically, large language models (LLM) are taking center stage in helping analysts and decision-makers get the data they need in a consumable format to support quick but thorough decision-making. Text to SQL technology reduces the technical barrier between analysts, data, and insights, enabling analysts and decision-makers to query databases without SQL knowledge. New LLM models automatically create SQL queries based on a common language. If a sales manager is interested in sales by region and market segment, they can define the parameters using common business terms to pull the needed data.

AI is not just simplifying data access but also transforming the presentation of data insights. AI powered data visualization copilots automate the process of building complex charts and graphs, eliminating the back and forth with data analysts. Decision makers can effortlessly ask an AI assistant to create a chart instantly and, if needed, instruct the chatbot to tweak the visual, which can be done in seconds. This advancement accelerates data formatting for consumption, eliminating the need to learn how to use multiple BI tools and platforms.

AI’s role in data management and governance

While AI has great potential to help fetch data for decision-makers, data quality is paramount for meaningful outputs. Luckily, AI extends its capabilities beyond retrieval, finding a wide variety of applications in data management, governance, and data quality.

AI is being applied in data governance as a copilot or recommendation engine. Moreover, the future holds the promise of autonomous data governance, where AI takes the lead in ensuring data quality, paving the way for more reliable and trustworthy insights.

Data Tagging

AI tools are now integral to data governance platforms, streamlining the process of exposing higher-quality data for analysts and decision-makers. Specifically, the technology is becoming an essential tool in managing data catalogs, aiding in greater data discovery and governance. For example, AI supports data governance by helping analysts tag sensitive data, such as personally identifiable information (PII), by predicting potentially restricted data columns based on past characteristics.

Data Documentation

In collaboration with data stewards, analysts, and engineers, AI plays a crucial role in classifying and documenting data assets, improving discoverability for data consumers. To help standardize business terminology and concepts, AI suggests the most appropriate terms to describe data in a data glossary. Similarly, AI can help document data assets by recommending the best way to describe them.

Data Access

A copilot can also play a key role in shaping data access control rules. AI can suggest user authorization based on individual users’ characteristics and profiles. Conversely, AI can also flag inappropriate access, enhancing security. This capability empowers more authorized users to leverage vast enterprise data to generate business value.

Data Validation

AI-powered suggestion engines or copilots contribute to robust data governance by ensuring valid data inputs. Models can learn to identify input errors based on what the AI expects to see as input, flagging discrepancies outside a specific range. Real-time suggestions enable error correction before they enter the database, preventing downstream issues and promoting data accuracy.

Strategies for better AI training

The foundation of AI models lies in the quality of the data used for their training. Poor data leads to confusion and hampers performance, especially in the case of Generative AI, where identifying the effects of bad data is much more challenging due to its opacity.

Given this fact, prioritizing the highest quality data for AI training platforms is paramount to producing quality downstream AI models. It is key that data practitioners work closely with AI-assisted processes to teach them to monitor and scrub data correctly and more autonomously. This proactive approach ensures better data quality and enhances the overall performance of AI models.

Move documentation closer to data

As data practitioners tag data, valuable insights inform future tagging suggestions. Ensuring that the right people conduct the process of data tagging and asset documentation will have compounding effects down the road. Practitioners must effectively tag PII data so AI will learn and flag such data accurately in the future. Continuing to teach AI through appropriately approving or denying AI suggestions for documentation also helps AI grow smarter and more effective over time. Incorporating line-of-business managers and professionals close to data collection ensures accurate documentation, reflecting the contextual nuances of data collection.

Granular tagging

Implementing tagging data at a more granular level enhances AI model performance, delivering more precise results. With richer granular metadata, AI gains more differentiated data that can support more specific rules. For example, AI can suggest rules that pertain to single columns within a table or tailor rules that apply to particular personas. This granular approach enables a more nuanced approach to authorizing access to data, providing greater insights to more decision-makers.

Shift metadata management and governance left

Data quality issues often arise during data ingestion or asset creation, impacting AI performance and overall organizational competitiveness. Taking a proactive approach through data validation is key to eliminating issues down the road. The timing of when AI is incorporated into your data governance process can also influence the outcome.

Leveraging AI to support data quality and governance protocols the minute it hits your systems can limit the risk of poor data degrading your models. By shifting data governance and data quality checks to the left and integrating AI-driven quality checks earlier in your process, a larger number of stakeholders participate in ensuring high-quality data for AI model training. Also, by integrating AI into your data management workflow, people can collaborate with AI, ensuring real time improvement in quality and governance without the need of stepping out of your workflow or revisiting data quality issues after the fact.

Advancing Toward Autonomous AI

Integrating AI into your data governance process and ensuring thorough AI training with clean data opens doors for AI to take a more active role in your data governance strategy.

Well-trained models instill confidence in their ability to handle tasks typically performed by data practitioners. AI has the potential to learn to create data lineage automatically or automate essential aspects of proper data governance, marking a significant step towards achieving greater autonomy in managing and optimizing data processes.

Automating Anomaly Detection and Correction

AI emerges as a powerful ally in autonomously enhancing data quality by automatically identifying anomalies in your data and fixing errors. AI is proficient in identifying patterns in large data sets and can efficiently pinpoint large and small anomalies. Through predictive modeling, it can predict what data points should be and adjust a data point that deviates from expected patterns with minimal human intervention. With proper training, AI can scrub data sets, find and fill in missing values, or correct inaccurate or inconsistent data. AI can also standardize data into standard formats, transforming, for example, state abbreviations into the traditional two-letter form or standardizing different address formats.

With more sophisticated training, AI can be trusted to autonomously create its own data quality rules or create metadata to organize data more effectively. By integrating AI chatbots to work with humans, models can learn rule structures and parameters and create frameworks to govern their processes. Similarly, AI can generate metadata and documentation to build richer context around data, making it more usable. One example is identifying PII data, such as a social security number in unstructured data, and tagging it as a sensitive data point.

These processes not only save time but also minimize the risk of sensitive data exposure while making less sensitive data more accessible to decision makers.

Monitoring your models

While well-trained AI models play a significant role in automating data governance processes, human involvement remains indispensable.

Even if your models are currently performing well, there is no guarantee of consistent performance in the future. Things change, models drift, and biases can emerge over time. To mitigate potential risks, mechanisms must be implemented for humans to monitor AI for errors and performance degradation. This might include tasks such as comparing an AI model output with real data to ensure accuracy and alignment with expected results. Human oversight ensures ongoing model effectiveness and adaptability to evolving circumstances.

Structuring your strategy for optimal AI productivity

Organizational success in AI strategies hinges on effective structuring. It is crucial to place professionals closest to the data and its context at the forefront of training data governance models. When training AI, the more granular the data, the better. So, encouraging practitioners to provide feedback to models fosters improved model performance.

Aligning line-of-business professionals with IT will be essential to an effective training process. IT can test models and implement training processes to ensure optimal performance while business leaders continue to integrate feedback into their workflows. This constant training and retraining cycle will mitigate risk while improving data accessibility.

As models improve, they will become more precise and capable of building greater context around data sets. With greater precision and contextual understanding, this data becomes much more valuable in driving decision-making and business strategy. Those who refine their strategy and decision-making process in tandem with AI advancement will retain a competitive advantage in the marketplace.