Generative AI will be the dominant analytics trend in the first half of 2023

Generative AI has been the dominant analytics trend in 2023 so far. However, the first half of the year was just the beginning of how generative artificial intelligence will be used to transform data analytics.

While many vendors have announced plans to integrate creative AI across all of their platforms, few features have made it beyond anticipation.

There have been many theories about how generative AI could revolutionize analytics, making it accessible to more than just data experts and easing the burden on those who oversee the analytics functions of these organizations. But few tools have been launched.

Those that have been deployed are still largely hidden. “Generative AI announcements in the analytics/BI market are still mostly in development or private preview,” said Constellation Research analyst Doug Henschen. “Overall, I’d say we’re past the idea/theoretical stage. But we’re not seeing a market-proven improvement in productivity and understanding.”

But generative AI hasn’t been the only analytics trend in 2023 so far.

Cloud costs have become an issue as organizations move more of their data and analytics operations to the cloud, so finding ways to manage costs is a growing trend. Furthermore, the emphasis on data quality becomes even more critical as the volume and complexity of data increases.

The State of Generative Artificial Intelligence

Generative AI has become the dominant analytics trend so far in 2023 because it has the potential to transform the way organizations work with data.

Data is and has long been largely the domain of a small group of experts within an organization.

Data is complex and analyzing it beyond just looking at the basic numbers takes practice. However, training is time-consuming and expensive. As a result, many organizations lack information literacy.

Analytics platforms are complex and require the most code to query and process data. Even those designed for self-service users with low-code/no-code functionality and additional intelligences such as natural language processing require some expertise.

As a result, while technology has evolved to include no-code skills and natural language understanding of some commands and questions, the number of data users in organizations has remained constant for decades at about a quarter of all employees, according to several studies. including one in 2022 from the Eckerson Group.

If executed correctly, generative AI can change that. Large language models (LLM) have large vocabularies that understand free-form natural language, not just specific business terms and commands. Additionally, their automation capabilities can translate typed words into code that computers can understand and then translate that code back into natural language that all business users can understand.

Therefore, generative AI has the potential to enable all business users to work with data and empower data experts by reducing the amount of code required for their work.

But as of mid-2023, generative AI in analytics is still in the potential stage, not the production stage, according to people in the industry.

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“The current hype is causing a lot of… product announcements. But when you look beyond marketing and look at the fine print, a lot of it isn’t available to customers yet,” said James Fisher, chief strategy officer at Qlik. “That means things are moving fast and customers are eager to explore opportunities. .”

Since OpenAI released ChatGPT in November 2023, which marked a significant leap in LLM capabilities, many analytics vendors have implemented plans to add generative AI to their platforms.

Sisense was among the first to reveal an integration with ChatGPT in January 2023 to develop tools powered by generative AI. Since then, companies like Amazon QuickSight, Microsoft Power BI, Qlik, Tableau and ThoughtSpot have announced plans to add generative AI.

In addition, some BI users have found ways to add generative AI by waiting for commonly available tools from vendors.

For example, Fisher noted that Qlik customer Harman International, a global automotive, audio and lighting technology company, built an application with ChatGPT and Qlik that uses natural language to generate insights through Qlik’s analytics engine. Meanwhile, some ThoughtSpot customers are using Sage, the vendor’s AI-powered generative search platform, in beta testing and are seeing benefits, according to Cindi Howson, the vendor’s head of data strategy.

Henschen added that many companies are in their testing phase to figure out what they want from generative AI and which vendor tools might best meet their needs.

“I’m seeing a mixture of excitement and caution from the [managers] we’re talking to,” he said. “Everyone is working on strategy and kicking the tires on what different providers are offering. Initial tests usually focus on internal and developer functions. There is a strong interest in developing custom designs that are specific and protected for organizational use only.

Top benefits of generative AI for businesses.

Trends within a trend

While generative AI has generally been the dominant analytics trend in 2023 so far, the trends will feed into the overall trend.

According to Donald Farmer, founder and director of TreeHive Strategy, vendors take two approaches to incorporating generative AI.

Some integrate with LLMs, such as ChatGPT and Google Bard, and use LLMs as natural query interfaces and to create data stories.

For example, Sisense—perhaps the first analytics vendor to announce plans to add generative AI capabilities to its platform—is doing so by integrating ChatGPT. Also, ThoughtSpot, which already has existing LLM capabilities, builds on top of OpenAI’s GPT-3 integration in Sage.

Still others are building their LLMs, which they plan to use as a foundation for their AI skills. One example is Salesforce, which is developing Einstein’s GPT to generate new insights. Others include AWS with Bedrock, Google with Bard and Microsoft with Copilots.

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“The market is already starting to segment, and that’s important,” Farmer said. “It shows that there is something real here.”

Those who develop their own LLMs tend to be larger vendors, including technology giants that can use generative AI not only in their analytics and data tools, but also in their cloud computing platforms.

Farmer further noted that others, including ThoughtSpot and Domo, do more than integrate with LLMs, but don’t go so far as to fully develop their own.

“You see a segmentation between those who actually have LLMs they can rely on and those who use third-party LLMs,” he said. “And somewhere in the middle is ThoughtSpot, which is trying to think about the implications of LLMs without creating an LLM. House is doing something similar. These are signs that [generative AI] is a real market.”

ThoughtSpot’s Howson also noted that there are differences in how vendors approach generative AI. In most cases, this is related to the concentration of sellers.

For example, ThoughtSpot’s platform has always been built around the concept of natural language search. Therefore, the seller uses generative artificial intelligence to make the search platform more intuitive and easy to use.

In addition to analytics, vendors are developing industry-specific LLMs, Howson noted. Bloomberg is developing an LLM in financial services; Truveta builds for such treatment. “Vendors take very different approaches,” Howson said, noting, for example, that vendors also focus on their own segments, such as ThoughtSpot for analytics and insight, Atlan and for metadata generation, and SnapLogic/GPT for data usage. .

Other analytical trends

While much of this year’s product development has focused on generative AI, there are other trends as well. According to Henschen, one of the key emerging trends is an emphasis on cost control.

Cloud service providers usually bill according to consumption. Customers must pay for the computing power used and the time spent on it. While the costs of cloud computing seem cheap at mere hundredths per minute, they quickly add up when organizations are dealing with massive amounts of data and hundreds—perhaps even thousands—of employees working on that data.

An important development stage is that customers want to control analysis costs.

Some vendors, such as Tibco, have implemented management capabilities that allow organizations to see when they are using the most computing power to reduce power bills during downtime.

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Others enabled organizations to implement stricter access controls to prevent users from making ad hoc requests and raise costs for organizations. Still others have implemented internal analysis of the database, so users no longer collect data transfer costs.

“The number one non-generative AI trend in the first half of 2023 was the rise of cloud cost optimization,” Henschen said. “The ground zero for this trend was the cost of cloud data storage. Customers are demanding better cost and workload management and analytics from platform providers.”

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He added that some users are moving some of their data back on-site to reduce costs.

Similarly, Howson cited an emphasis on cost management in cloud computing as an important analytical trend.

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“Some of it is changing the way the cloud world behaves — like streaking servers — but also vendors are giving customers more visibility and control,” he said. In addition to cost control, a key trend is continued investment in data quality and other core analytics needs, according to Fisher.

Data quality is critical because organizations need accurate data to make accurate decisions. When decisions are made based on bad information, the results can be disastrous, depending on the importance of the decisions.

History is full of examples of how bad information has led to unintended consequences. In business, decisions based on bad data can lead to unwanted costs, lost opportunities and a general lack of trust in data as a reliable decision maker. Organizations are collecting more data than ever before to make real-time decisions – and from more diverse and complex sources, such as IoT devices, so ensuring data quality is critical.

“Organizations have rethought the way they access and transform data in the cloud to remove … significant barriers to real-time decision-making,” Fisher said. “This required continued investment in data pipelines, data integration and data quality solutions. Once organizations acquire these building blocks, they will use them to make decisions closer to real time.”

The perspective of the other side

As it has done so far in 2023, generative artificial intelligence will be the dominant analytics trend for the rest of the year, experts said. But it is evolving.

The first half of the year was full of product development plans. According to Henschen, the first wave of generative AI capabilities will likely hit the market in the second half.

This in turn leads to imitation as sellers see what their competitors do best and become fast followers. “As new features are released across many classes of software, we see copycats and creative extensions as each wave of functionality is introduced,” Henschen said.

“It’s a fast-paced environment where developers and data analysts are the first user groups to see increased productivity and deeper insights.”

Howson also said the rest of the year will be marked by generative AI innovation.

Organizations that are already thinking about how they want to adopt generative AI and planning when the tools to enable that adoption will be released have an advantage over organizations that wait to use the tools before developing a generative AI strategy.

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“We’re seeing a lot of innovation and it’s an exciting time,” Howson said. “Customers who educate themselves about the options and use them wisely have an advantage. The difference is isolating what works at scale and reliably.”

At the same time that generative AI-enabled tools are being released, the focus, Fisher says, is on generative AI management — primarily data management, since LLMs are built around data.


“While generative AI has the ability to deliver value across a broad spectrum, it must be used in a way that protects your data assets,” he said.

This could lead many organizations to stop using public LLMs and instead develop custom LLMs trained only with their own data, Fisher continued.

“For language models large and small running behind a firewall or in a private cloud, we see longer-term use of generative AI because then the enterprise can ensure data quality, governance and lineage,” he said.

The final months of 2023 will also see the first significant failures of reproductive AI, Farmer predicted. The resulting analytical trend is therefore a reality check for generative AI.

LLMs have a “hallucinations” problem. In other words, not all of their answers are correct. They also have security issues. Suppliers and companies implement administrative measures to protect proprietary information. However, there may be errors in data transmission. Data can be leaked.

“Generative AI has some bugs that can be noticeable,” Farmer said. “It could be something that significantly damages a company’s reputation because AI did something.”

He went on to say that some generative AI users are already unhappy with the limitations of its text generation features and auto-generated images.
“It’s only natural that generative AI loses its luster,” Farmer said.

“This means that by the end of this year and the beginning of next year, we will focus on real-world use cases. Then we will have a much better understanding of the future direction of generative AI.”


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