FEATURED STORY OF THE WEEK
Generative AI is Transforming Advanced Analytics
Ever since ChatGPT was released to the public in 2022, the world has become fascinated with generative AI (GenAI). But as the broad hype surrounding GenAI stabilizes, enterprise software providers and their clients are beginning to explore its business potential in a more concerted way.
Ali Dalloul, VP, Azure AI Customer eXperience Engineering at Microsoft, believes GenAI and large language models (LLMs) will become so common in enterprise companies that AI assistants will become commonplace for data workers, TechTarget reports.
Indeed, data analytics is one of the most sensible applications for GenAI. While analytics was once esoteric and confined to data specialists, the democratization of data analytics has become a consistent trend; the accessibility of GenAI and its capacity for data analysis stand to accelerate democratization.
Even so, success with GenAI in advanced analytics is not a foregone conclusion. As with any new software investment, business leaders must determine their goals and specific need within their own organizations, and which emerging analytics-based GenAI technologies can deliver the best results in those contexts.
In this article, we explore recent progress with GenAI in analytics. We provide insights into its emerging benefits and use cases, as well as five steps any company can use to employ GenAI in analytics successfully.
The Rise of Artificial Intelligence in Analytics
Relatively speaking, artificial intelligence (AI) is not new to analytics. Advanced analytics platforms on the market today leverage AI for multiple functions, including pattern recognition, predictive insights, natural-language processing (NLP), and even video or image analysis. Most of these applications continue to rely on human experts to parse data and data sources for any meaning or trends.
But generative AI technologies offer an entirely new layer of analytics capabilities. By combining natural language processing (NLP) with deep learning algorithms, these platforms can generate automated insights virtually anyone can understand, and at a much faster pace than traditional AI-based analytics solutions.
“The architecture and the science [of GenAI] have caught up to the hype,” Dalloul describes. “They have multipurpose, rich models that in and of themselves are platforms. It is, in our view, a fundamental shift in the industry.”
Why Generative AI is the Next Evolutionary Step in Analytics
Analytics technologies have already witnessed a lifetime of changes over the past two decades. Today’s solutions evolved from business intelligence (BI) technologies originally designed for data scientists and technicians rather than everyday users. Much like the original computers, employees who wished to enjoy the benefits these technologies provide were forced to query data experts who subsequently retrieved and processed the insights they needed—a slow and arduous process.
Even before generative AI, this had begun to change. Advanced analytics platforms promised data democratization, where “a data democracy simplifies the data stack, eliminates data gatekeepers, and makes the company’s comprehensive data platform easily accessible by different teams via a user-friendly dashboard,” as IBM describes.
GenAI in analytics seeks to broaden access to analytics even further with the aid of large LLMs, which can make accessing deep and instantaneous insights from analytics as simple as posing a natural-language question anyone can understand.
Now, GenAI is driving meaningful change in real-world analytics use cases as software providers scale out new GenAI-based solutions. These new tools promise remarkable boosts in efficiency and broader support for business decisions at all levels of the organization—indeed, at any level where even non-technical personnel can benefit from data-driven insights.
Emerging Benefits of Generative AI in Analytics
There are countless benefits both organizations and individuals can enjoy in terms of outcomes, both for the company and within individuals’ everyday workflows. Here are some of the broader benefits organizations are beginning to realize today:
- Getting greater value from Big Data. As Harvard Business Review describes, “A company’s comprehensive knowledge is often unaccounted for and difficult to organize and deploy where needed in an effective or efficient way.” Gen AI can help bridge this gap by quickly and accurately categorizing, sorting, and analyzing large datasets to generate actionable insights based on natural-language queries featuring contextual information provided by users.
- Analyzing large sums of unstructured data. GenAI can help virtually anyone parse through large, unstructured datasets to identify meaningful patterns, relationships, and trends. Forrester predicts “most of the BI vendors [will] rapidly shift to leveraging LLMs as a significant part of their text mining pipeline.”
- Saving human time and resources. In a recent survey of developers about their use of one GenAI tool to build analytics models, there were “88% of respondents indicating they are more productive and 96% indicating that they are “aster with repetitive tasks,” Forbes reports. As “conversational” interfaces for GenAI become more common across otherwise complex applications, employees across teams will realize more substantial results.
GenAI also has considerable potential to improve the quality and flexibility of analytics output, from detection of novel correlations to natural-language summaries of complex data relationships.
How Industries Can Use Generative AI in Analytics Today
Enterprise companies, many of them household names, are already implementing and even building their own GenAI analytics technologies that are driving measurable business results. Here is a closer look at some of these exciting developments.
- A natural-language interface for financial data analysis. Bloomberg created its own GenAI solution, BloombergGPT, which features a natural-language interface for financial data analysis. “In total, Bloomberg’s data scientists employed 700 billion tokens, or about 350 billion words, 50 billion parameters, and 1.3 million hours of graphics processing unit time” to train its own solution, Harvard Business Review describes. With decades of financial data already available and new data arriving everyday, this solution is helping finance teams save time and energy while gathering more accurate insights.
- Analyzing activity among conference attendees. In the events industry, “Firms have used generative AI in analyzing attendee data from past events to gain insights for future events,” VentureBeat reports. These data are particularly complex since attendee activity is so varied and difficult to track. In these contexts, “humans simply cannot interpret thousands of pages of information at a rapid pace; AI can not only ingest it in an instant but interpret key points and metrics to deliver immersive data insights for users to consume.”
- Projecting retail customer behavior. Similarly, retailers need immediate insights into customer behavior to better project future needs. AI-driven predictive analytics has already helped retailers, but GenAI offers even greater potential to automatically generate reports that offer market intelligence from powerful machine learning models. With this intelligence, brands can quickly generate marketing strategies, for example, with the support of natural-language recommendations from GenAI.
- Recommending employee performance improvements. Employee performance and potential are determined by a combination of contributing factors, including skill sets, personal habits, attitudes, and environments. With the support of GenAI, HR and management teams can now quickly analyze employee performance data and generate personalized plans for improvement; they can use a GenAI interface to request suggestions based on analytics results.
In addition, creating more sophisticated models quickly can be extremely valuable in fields such as healthcare, government, and transportation. where time- and human-sensitive insights are paramount.
How Your Company Can Employ Generative AI in Analytics
GenAI should not be an afterthought in your next analytics investment. “Depending on the analytics maturity and business priorities, organizations can decide on a roadmap of whether and where they would leverage generative AI,” as Forbes describes. The following are five steps you can take to employ GenAI in analytics successfully:
1. Create a framework emphasizing decision support. Analytics accomplishes nothing without the human decisions it supports, no matter its level of sophistication or democratization. A well-crafted framework for decision support, including clearly defined roles for the individuals in the organization who will use GenAI analytics tools, can help determine which capabilities to prioritize in new analytics solutions.
2. Curate and adopt analytics with GenAI. Once your framework is in place, curate and adopt (or in rare cases, build) your GenAI analytics solution. Your solutions should help users rapidly generate insights from massive datasets, unique to each of their roles. Executives whom you hope to empower with GenAI-based analytics will have different needs than customer support representatives, for example; prioritize solutions that accommodate all parties, ideally each with their own role-based credentials and interfaces.
3. Integrate GenAI analytics solutions into employee workflows. GenAI should be embedded into your existing analytics infrastructure, so ensure that all relevant employee workflows are integrated with the tools. This will allow users to access insights more quickly and accurately than ever before. You should develop a strategy to drive cultural changes within the organization, encouraging adoption for decision support.
4.Personalize training on your new GenAI analytics investment. Tailor training to your users’ skill levels and unique roles in the organization. Invest in resources that enable employees to quickly gain fluency with GenAI analytics tools, so they can seamlessly adopt them for more informed decision-making. (GenAI itself might help in this area.) Emphasize the importance of using approved GenAI tools as opposed to publicly available tools (e.g., ChatGPT), where results may not align with your company’s own priorities and knowledge assets.
4. Continually measure the success of GenAI analytics. Establishing metrics that measure success across all aspects of the organization is critical, from the initial decision-support framework to the personalization of training. This will help ensure that your investment in GenAI analytics yields tangible results both now and in the future. Employee sentiment surveys are also a worthwhile approach.
Begin Mapping Your Analytics Future
This is an exciting time for technology and business—not to mention, human society. “As generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation,” says McKinsey.
Generative AI can be intimidating, but the potential rewards are significant. With a thoughtfully crafted approach, you can put GenAI analytics to work for your business right away.
Partner with Uvation for Generative AI Analytics
Uvation is an industry-leading software integration partner and consultant, servicing some of the world’s most innovative companies today. Contact us directly to learn more about how Uvation can help your company transform its analytics capabilities and future business success.