Understand the value in unstructured data
Prior to the advent of GenAI, it was difficult, time-consuming, and in some cases prohibitively expensive for organizations to track and manage their unstructured data. Now, GenAI can automatically ingest the entirety of a company’s unstructured data, intelligently sort the content, and summarize key details and relationships. Plus, it’s possible to merge that data with structured data, giving the organization a multidimensional view that can allow for more strategic decisions.
What does the data management process look like in practice? So many ways exist to use GenAI technology, depending on the organization’s strategic needs. Maybe it would be helpful to aggregate product requirements from a variety of different communications. Or maybe the company’s leaders need to better understand the intricacies of legacy contractual agreements without the need for manual review. Or it might be valuable to identify recurring customer issues and sentiments by analyzing voice and chat logs. What’s possible depends on the business’s specific datasets, goals, and strategies.
The true value in using GenAI comes when a company can interact with and query all the unified data through conversational AI interfaces. Instead of struggling to write complex database queries, teams can simply ask questions in natural language and receive highly contextualized insights pulled from structured and unstructured sources in real time. This self-service analytics capability can accelerate data-driven decision-making without adding more pressure to internal resources.
However, unlocking the full potential of unstructured data is not as simple as plugging it into an out-of-the-box AI tool. Thoughtful curation, training, and governance are critical for accurate and reliable results.
Appoint a knowledge curator – or curation team – to stay organized
With all the hype around GenAI, business leaders might think it’s so intelligent that they can simply ask it questions and receive accurate answers. However, this view overlooks a key limitation – while AI can understand context from unstructured sources, it struggles with understanding context in structured data sources. It’s not enough to simply deploy GenAI models. Those models must first be trained on the specific data, business terminology, and desired outcomes for each use case.
To bridge this gap, an organization needs to appoint a knowledge curator or curation team that can provide the critical context and metadata training that can help GenAI models properly interpret and analyze unique data assets. This might involve engineering precise prompts, adding contextual metadata to raw data, and fine-tuning GenAI models to understand how to answer desired questions accurately.
Implementing a knowledge curator function also helps enable the self-service analytics capabilities previously mentioned. Without it, the company risks receiving inaccurate or misleading outputs from the GenAI models, because those models are operating on data they don’t fully comprehend. With a knowledge curation layer, GenAI models can be trained to answer analytical queries more reliably by properly accounting for all relevant contexts.
Take advantage of new tools to manage data
The cornerstone of AI is data – but the traditional approach to data management can be a cumbersome and time-consuming process. Businesses typically construct extensive enterprise data warehouses (EDWs) to consolidate and manage data from various systems, which involves gathering information from multiple sources, transforming it, and integrating it into a data warehouse. However, this can be a multimillion-dollar, multiyear effort, and in the time it takes to build the EDW, the initial requirements might already have changed.
Businesses can navigate around these cumbersome processes with GenAI and modern data management tools. These tools allow a company to aggregate and interact with data where it resides, without the need for lengthy transformations or waiting periods for centralized warehousing. The key to using these tools is to make sure the organization’s data is cleaned up and prepared for analysis.
These data management tools can make a difference in both day-to-day tasks and enterprise-level events. For example, a company might try to aggregate data in its EDW for a potential sale. An EDW houses a lot of information, but it doesn’t necessarily have every single bit of information needed for the sale. To help accelerate the reporting process to pull together complete information for the sale, the company might have to manually review a variety of contracts, including long-term agreements with suppliers, which can be incredibly time-intensive and prone to human error. With the GenAI tools now available, those contracts could already have been integrated into the dataset, allowing for a faster and more complete review.
Unleash GenAI’s potential with data lakehouses
Another way to use GenAI to maximize data, whether the business has already invested in an EDW or not, is by migrating to a data lakehouse, allowing the organization to bring together structured and unstructured data from various sources without extensive up-front modeling and transformation. This flexibility can help the business find value in its data in a faster and more efficient manner by focusing on immediate business needs rather than spending years developing and building the perfect data model. A data lakehouse also can allow an organization to apply structure, cleansing, and transformation efforts in parallel as its priorities shift, rather than having to complete these tasks up front for the entire dataset.
An organization also can use a data lakehouse to take in raw data as is and build analytics solutions with what it already has. Then, as requirements change, the business can easily incorporate new data sources without needing to adjust architecture or wait for data to be integrated into existing warehouse pipelines. Data is a constantly evolving asset, making it a challenge for a company to predict all its needs years in advance. The data lakehouse approach can help company leaders bridge the gap between what they know now and what might change in the future.
As GenAI capabilities continue to evolve, organizations have to be prepared to adapt and take advantage of new opportunities as they arise – especially when it comes to managing data. To stay ahead in this shifting landscape, it can be useful to work with a third-party team that has deep AI expertise and rich data management experience and can help companies determine which tools and technologies are best for their specific business goals.
