Matt Aslett's Analyst Perspectives

Analytics software is used by business analysts and decision-makers to facilitate the generation of insights from data. It encompasses business intelligence and decision intelligence software, including reports and dashboards as well as embedded analytics and the development of intelligent applications infused with the results of analytic processes. Analytics software enables enterprises to improve business outcomes by operating more efficiently, accelerating product development and enhancing customer service. Success with analytics is not guaranteed, however. Despite the focus on data-driven decision-making in recent years, only one-third of participants in Ventana Research’s Analytics and Data Benchmark Research are satisfied with their organization’s use of analytics and data. Our research illustrates that perennial complaints about analytics and business intelligence include challenges integrating with business processes, a lack of adaptability to change and complexity accessing data sources. Additionally, preparing data for analysis and checking data quality remain too time-consuming. Meanwhile traditional reports and dashboards are often static, and actionable insight depends on the user to interpret tables and visualizations and make intelligent decisions. Exacerbating these issues is that engagement with analytics software is too often limited to those in data analyst roles rather than business decision-makers. Only 15% of participants in Ventana Research’s Analytics and Data Benchmark Research are very comfortable providing business users with self-service access to data. With that in mind, it is understandable why there is huge interest among both enterprises and software providers in the potential for generative artificial intelligence to improve data democratization and lower the barriers to working with analytics software. I assert that by 2026, one-third of enterprises will replace legacy business intelligence tools with analytic platforms that are collaborative and utilize GenAI to inform and guide business professionals. There are multiple reasons why GenAI holds so much potential to drive significant change in the analytics sector. Traditional reports and dashboards provide users with data and charts to be queried. By providing natural language interfaces that are intuitive to use, GenAI facilitates data literacy and enables data democratization by presenting users with narratives and recommendations which they can more intuitively interpret to accelerate business decision-making. GenAI tools provide consistency regardless of the user’s knowledge or skill level and business users no longer need to be experts in query languages and analytics and BI tools to generate business value from data, and GenAI tools provide consistency regardless of the user’s knowledge or skill level. Natural language processing and natural language generation are by no means new to the analytics sector. They are already key elements of augmented analytics that we assessed in our Analytics and Data Buyers Guide. However, pre-GenAI implementations of NLP and NLG were often complex and required a lot of work from BI development and IT teams to model potential use cases. It was also time-consuming to create and maintain databases of synonyms required to convert natural language questions into analytic queries. That is perhaps why only 17% of participants in our Analytics and Data Benchmark Research currently use natural language query capabilities. Adoption of NLP and NLG is likely to accelerate rapidly, given the excitement about GenAI. Analytics software providers and users are moving quickly to take advantage of the work done by others, creating large language models to convert natural language questions into analytic queries as well as automatically generating summarizations and recommendations from data and charts. Additionally, GenAI also has significant potential to unleash the value inherent in unstructured data—something that many enterprises have previously struggled to capitalize on. Potential use cases include analysis of audio, video and images as well as sentiment analysis of social media content and interpretation and summarization of written documents. In addition to improving data democratization by lowering the barriers for business users to access and work with data, GenAI also has potential efficiency benefits for data analysts. This includes automating routine and time-consuming tasks such as data preparation, cleansing and transformation. ISG’s AI Buyer Behavior Study indicates that analytics is at the forefront of AI adoption, with 87% of participants indicating that their organization is using AI for analytics and BI, well ahead of other application areas such as customer engagement (55%) and content management (48%). For key analytics use cases, 87% of participants in ISG’s AI Buyer Behavior Study have seen positive outcomes from natural language queries, while 87% have seen positive outcomes from the interpretation of data. The addition of GenAI-based NLP and NLG capabilities to existing analytics and BI software products promises to automate and accelerate the work of data professionals. It does not necessarily mean that existing analytics and BI software products will instantly become suitable for use by business professionals. People need to be trained to interpret dashboards and charts but written and spoken language narratives and recommendations can be understood intuitively without specialist skills. More widespread interaction with analytics is likely to be triggered by the development of entirely new products designed with GenAI interfaces as the primary means of interacting with data, supported by charts and tables, rather than GenAI interfaces being bolted onto or alongside the charts and tables delivered by traditional reports and dashboards. New GenAI-first interfaces will not be enough to guarantee successful widespread use of analytics, however. As more business users begin to interact with and analyze enterprise data, the greater the need for agreement on data definitions, reinforcing the importance of semantic data modeling to standardize metrics and definitions. And while natural language interfaces powered by GenAI reduce the need for technical and domain expertise to query data, they do not reduce the value of domain expertise in interpreting results. This reinforces the need to incorporate driver-based planning capabilities into decision-making processes to evaluate the requirements for and implications of recommendations in making intelligent decisions. My colleague David Menninger has previously argued that decision intelligence should be based on a combination of historical business intelligence; driver-based planning; AI-driven descriptive, predictive and prescriptive analysis; and optimization capabilities. The rapid evolution of GenAI does not alter that advice. I recommend that enterprises evaluating analytics software include GenAI-based capabilities in the evaluation criteria while also being mindful that the benefits of data democratization may not come from adding GenAI capabilities to existing analytics products but from the next generation of products developed with GenAI-based NLP and NLG as the primary interfaces for accessing and analyzing data. Regards, Matt Aslett

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