Generative AI is Transforming Advanced Analytics

FAQs

• What is generative AI (GenAI) and why is it considered the 'next evolutionary step' in analytics?

  Generative AI (GenAI) is a type of artificial intelligence that combines natural language processing (NLP) with deep learning algorithms. It’s considered the next evolutionary step in analytics because, unlike previous AI applications that primarily relied on human experts to interpret data, GenAI platforms can automatically generate insights that are easily understandable and much faster than traditional solutions. This represents a “fundamental shift in the industry,” according to Ali Dalloul of Microsoft, making advanced analytics more accessible and efficient for a wider range of users, moving beyond the technical expertise previously required.

• How does GenAI contribute to the 'democratisation of data analytics'?

  GenAI significantly accelerates the democratisation of data analytics by making complex data insights accessible to virtually anyone, regardless of their technical background. Historically, analytics was confined to data specialists who would interpret data for others. Even with advanced analytics platforms promising ‘data democracy’, users often still needed to query experts. GenAI, particularly through large language models (LLMs), simplifies this process by allowing users to retrieve deep and instantaneous insights simply by posing natural-language questions, much like a conversation. This removes the need for specialised technical knowledge, empowering more employees to leverage data-driven insights.

• What are the key benefits organisations can expect from implementing GenAI in analytics?

  Organisations can expect several significant benefits from implementing GenAI in analytics. Firstly, it allows them to derive greater value from ‘Big Data’ by quickly and accurately categorising, sorting, and analysing massive datasets to generate actionable insights from natural-language queries. Secondly, GenAI excels at analysing large sums of unstructured data, identifying meaningful patterns and trends that humans would struggle to process quickly. Thirdly, it leads to considerable savings in human time and resources by automating repetitive tasks and improving productivity. Finally, GenAI can enhance the quality and flexibility of analytics output, from detecting novel correlations to producing natural-language summaries of complex data relationships.

• Can you provide examples of how different industries are currently using GenAI in analytics?

  Certainly. Several industries are already seeing measurable results from GenAI in analytics:

   

  • Finance: Bloomberg developed BloombergGPT, a GenAI solution with a natural-language interface for financial data analysis. This helps finance teams save time and energy while gathering more accurate insights from vast datasets.
  • Events: Firms in the events industry are using GenAI to analyse attendee data from past events, such as activity and preferences, to gain insights for planning future events. GenAI can rapidly ingest and interpret thousands of pages of complex attendee information.
  • Retail: Retailers are leveraging GenAI for projecting customer behaviour, automatically generating market intelligence reports from machine learning models. This intelligence supports the rapid creation of marketing strategies and natural-language recommendations.
  • Human Resources: HR and management teams use GenAI to quickly analyse employee performance data and generate personalised plans for improvement, requesting suggestions based on analytics results through a GenAI interface.
  • Healthcare, Government, and Transportation: These fields benefit from GenAI’s ability to quickly create sophisticated models, providing time- and human-sensitive insights crucial for critical decision-making.
• What are the five key steps a company should take to successfully employ GenAI in analytics?

  To successfully employ GenAI in analytics, companies should follow these five steps:

   

  • Create a framework emphasising decision support: Define clear roles for individuals using GenAI tools and prioritise capabilities that best support human decisions.
  • Curate and adopt analytics with GenAI: Select or, in rare cases, build GenAI analytics solutions that allow users to rapidly generate insights from massive datasets, tailored to their specific roles and needs (e.g., executives vs. customer support).
  • Integrate GenAI analytics solutions into employee workflows: Embed GenAI into existing analytics infrastructure and develop a strategy to drive cultural change, encouraging adoption for decision support across the organisation
  • Personalise training on your new GenAI analytics investment: Tailor training to users’ skill levels and roles, providing resources to ensure fluency. Emphasise using approved internal GenAI tools to align with company priorities.
  • Continually measure the success of GenAI analytics: Establish metrics to track success across all aspects, from the initial framework to training effectiveness. Employee sentiment surveys are also valuable for gauging impact.
• How does GenAI help organisations get greater value from 'Big Data' and unstructured data?

  GenAI significantly enhances the value derived from ‘Big Data’ and unstructured data in several ways. For ‘Big Data’, it can quickly and accurately categorise, sort, and analyse vast, often disorganised, datasets. This helps bridge the gap where a company’s comprehensive knowledge might be “unaccounted for and difficult to organize and deploy.” By processing natural-language queries with contextual information, GenAI can generate actionable insights that might otherwise remain buried. For unstructured data, which is notoriously difficult for traditional systems to process, GenAI, particularly through LLMs, can parse through large volumes of text, images, or other non-tabular data to identify meaningful patterns, relationships, and trends. This capability is expected to become a significant part of text mining pipelines for many BI vendors.

• What is the role of large language models (LLMs) in GenAI analytics?

  Large language models (LLMs) are a crucial component of GenAI analytics. They combine natural language processing (NLP) with deep learning algorithms, forming the core technology that allows GenAI platforms to understand and generate human-like text. In analytics, LLMs enable users to interact with complex data platforms by posing questions in natural language, making the process of accessing deep and instantaneous insights as simple as a conversation. This eliminates the need for complex queries or data science expertise, thereby democratising access to analytics and broadening its utility across an organisation, even for non-technical personnel.

• Why is it important for companies to continually measure the success of their GenAI analytics investment?

  Continually measuring the success of GenAI analytics is critical to ensure that the investment yields tangible and sustained results. Establishing clear metrics allows organisations to evaluate effectiveness across all aspects of the implementation – from the initial decision-support framework to the personalisation of training. This ongoing assessment helps in understanding what works well, identifying areas for improvement, and demonstrating the return on investment. Furthermore, measuring success helps to reinforce the cultural shift towards data-driven decision-making and ensures that the GenAI tools continue to align with evolving business priorities and deliver value both immediately and in the long term. Employee sentiment surveys are also a valuable tool for gauging user adoption and satisfaction.