Generative AI for data analytics: the future of enterprise sense-making

Breakthroughs in generative AI (ChatGTP, Dall-E) are already manifesting their potential in disrupting legacy content and media creation. In the case of enterprise data analytics, generative AI has the power to overcome key bottlenecks that limit what a person or team can accomplish while working on a problem within a finite amount of time. 

Human ideation is inherently limited by cognitive bottlenecks and biases, which restrict us in generating and testing ideas at scale and high throughput. We're also limited by the speed at which we can communicate. We don’t have the capacity to read and comprehend the thousands of articles and patents published every day, not to mention the amount and variety of data a typical Fortune 200 company constantly ingests.

What’s more, the questions we ask are biased by our experience and knowledge, or even our mood.

When exploring useful patterns in data and knowledge, a data scientist, researcher or analyst only have time to conceive, engineer, and evaluate a limited number of distinct hypotheses, leaving many areas unexplored. 

Generative AI helps bridge these gaps, bypassing our own biases and offering alternative ways to leverage data in order to explore, react to and shape our business realities.

Leveraging generative AI for analytics means that the machine creates and tests hypotheses based on all available data sources, generating specific business insights along with overall reports, and updating the business insights over time and as data changes. What's more, generative AI can bridge the gaps in the data itself – creating new data sources by collecting unstructured data into new structured sources, data which is critical to the business.

As big data and analytics continue to play a critical role in strategy and business automation, AI is becoming embedded in the sense-making process of the enterprise, turning vast amounts of data into actionable insights and unlocking impact potential.

Anyone who’s played around with ChatGPT, the artificial intelligence chatbot from OpenAI that’s making waves, knows something: What you get out of it depends on what you ask it—and on how you craft your question. According to billionaire entrepreneur Mark Cuban, “time productivity will be defined by how well you can ask the right questions to get the appropriate answers from your models.” 

Drawing from one of the largest libraries of curated functions, the Discovery platform uses generative AI to produce and interrogate four million hypotheses per minute. This offers teams a technology to work through millions of good and bad ideas every second, evaluate the strongest ones, and then combine it with their domain knowledge to create a qualified impact.

One example is that of India’s largest bank, which leveraged SparkBeyond’s technology to deliver cohesive personalized experiences across 10+ channels for its 45M customers. By using advanced driver discovery techniques, the enterprise tested hundreds of millions of combinations in minutes, surfacing strong, nuanced microsegments ready for operationalization. 

What’s more, the platform uses generative AI-GTP to present the drivers and segments in natural language, enabling cross-functional validation with its high explainability. For the Indian bank, this generated rapid business value, as the hyper-personalization led to a 4-9x jump in conversion rates across all of the bank’s retail products in months.

So instead of thinking up one idea at a time and testing it, generative AI allows a machine to generate millions of ideas automatically. What next?

This same machine can then proceed to autonomously test and rank the ideas, discovering which are better supported by the data. It could also identify the type of data that could refute one’s theories and challenge existing practices.

Often, there are gaps within an organization’s own data. This internal data may only reveal part of the story, whereas augmented external data sources can provide valuable contextual information. The machine’s Generative AI is able to identify relevant data sources to complete the picture, then generate composite hypotheses that take into account the influence of external factors, such as weather and local events, or macroeconomic factors and market conditions. 

One of the Discovery platform’s key features is its ability to analyze data from multiple sources across complex data ecosystems. It can integrate data from various systems, such as databases, spreadsheets, and cloud-based data storage, and analyze them together. This allows companies to gain a more comprehensive view of their data and identify patterns and connections that would be missed if the data were analyzed in isolation.

In addition to analyzing multiple sources of data, the machine can ingest multiple dimensions of data, such as text, images, geo-specific, and time-series data, scanning vast amounts and identifying patterns and connections that would be difficult or impossible for humans to find on their own. 

For example, global energy giant Equinor partnered with SparkBeyond to create digital twins of its oil wells, significantly improving its well placement and production performance. Using partial data from multiple sources, the platform helps identify the difference between drilling an underperforming and problematic well, and one that can pay out hundreds of millions of dollars over its lifetime. 

Generative AI is incorporated into the foundation of the Discovery platform, but its evolving maturity has enabled the machine to provide recommended actions, in the context of the business KPI. Each recommendation is based on commercial need, drawn from web mining of similar studies that involve this KPI, and generated in plain English for immediate understanding.

‍

The Discovery platform’s generative AI algorithms and its ability to analyze data from multiple sources, perform deep learning, make predictions, and provide a user-friendly interface, empowers enterprises with a radically new approach to extracting value from their complex data ecosystems.

Extending far beyond narrow AI solutions, SparkBeyond’s generative AI technology acts as an auto-analytics layer that augments all existing data products in the enterprise stack, improving performance and creating new opportunities. Corporate clients use the engine to make novel use of their data: dramatically reducing operational costs, effectively counteracting fraud and customer churn, predicting and preventing supply-chain challenges, and gaining new understanding of audience microsegments, their needs and their expected behaviors. 

Currently used by best-in-class enterprises for a wide range of use cases, the Discovery platform powers high-stakes business processes at major financial institutions, CPG conglomerates, life sciences corporations, energy companies, and top consulting and legal service firms.