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Blog April 9, 2024
4 min read

Empowering BI through GenAI: How to address data-to-insights’ biggest bottlenecks

Explore how integrating generative AI (GenAI) and natural language processing (NLP) into business intelligence empowers organizations to unlock insights from data. GenAI addresses key bottlenecks: enabling personalized insights tailored to user roles, streamlining dashboard development, and facilitating seamless data updates. Solutions like Tiger Analytics’ Insights Pro leverage AI to democratize data accessibility, automate pattern discovery, and drive data-driven decision-making across industries.

Adithyaselvi Nagarajan

The Achilles’ heel of modern business intelligence (BI) lies in the arduous journey from data to insights. Despite the fact that 94% of business and enterprise analytics professionals affirm the critical role of data and analytics in driving digital transformation, organizations often struggle to extract the full value from their data assets.

Three Roadblocks on the Journey from Data-to-Insights

In our work with several Fortune 500 clients across domains, we’ve observed the path to actionable insights extracted from data is hindered by a trifecta of formidable bottlenecks that often prolong time to value for businesses.

  • The pressing need for personalized insights tailored to each user’s role
  • The escalating complexities of dashboard development, and
  • The constant stream of updates and modifications required to keep pace with evolving business needs

As companies navigate this challenging landscape, the integration of Generative AI (GenAI) into BI processes presents a promising solution, empowering businesses to unlock the true potential of their data and stay ahead in an increasingly competitive market.

Challenge 1: Lack of persona-based insights

Every user persona within an organization has different insight requirements based on their roles and responsibilities. Let’s look at real-world examples of such personas for a CPG firm:

  • CEOs seek insights into operational efficiency and revenue, focusing on potential risks and losses
  • Supply Chain Managers prioritize information about missed Service Level Agreements (SLAs) or high-priority orders that might face delays
  • Plant Managers are interested in understanding unplanned downtime and its impact on production

Hence, the ability to slice and dice data for ad-hoc queries is crucial for gaining technical know-how. However, the challenge lies in catering to these diverse needs while ensuring each user gets relevant insights tailored to their roles. Manual data analysis and reporting may not pass the litmus test, as it can be too time-consuming and may not be able to provide granularity as desired by the key stakeholders.

Challenge 2: Growing complexities of dashboard development

Creating multiple dashboards to meet the diverse needs of users requires a lot of time and effort. It typically involves extensive stakeholder discussions to understand their requirements, leading to extended development cycles. The process becomes more intricate as organizations strive to strike the right balance between customization and scalability. With each additional dashboard, the complexity grows, potentially leading to data silos and inconsistencies. Dependency on analysts for ad-hoc analysis also causes more delays in generating actionable insights. The backlog of ad-hoc requests can overwhelm the BI team, diverting their focus from strategic analytics.

Managing various dashboard versions, data sources, and user access permissions adds another layer of complexity, making it difficult to ensure consistency and accuracy.

Challenge 3: Too many updates and modifications

The relentless need to update and modify the dashboard landscape puts immense pressure on the BI teams, stretching their resources and capabilities. Rapidly shifting priorities and data demands can lead to a struggle to align with the latest strategic objectives. Also, constant disruptions to existing dashboards can create user reluctance and hinder the adoption of data-driven decision-making across the organization.

Plus, as businesses grow and evolve, their data requirements change. It leads to constant updates/modifications– triggering delays in delivering insights, especially when relying on traditional development approaches. As a result, the BI team is often overwhelmed with frequent requests.

Empowering BI through GenAI

What if anyone within the organization could effortlessly derive ad-hoc insights through simple natural language queries, eliminating the need for running complex queries or dependence on IT for assistance? This is where the integration of GenAI and NLP proves invaluable, streamlining information access for all key users with unparalleled ease and speed.

At Tiger Analytics we developed Insights Pro, a proprietary GenAI platform to overcome these challenges and deliver faster and more efficient data-to-insights conversions.

In a nutshell, by generating insights and streamlining BI workflows, Insights Pro takes on a new approach. Rather than contextualizing data using data dictionary, it leverages the power of LLMs for data dictionary analysis and prompt engineering, thus offering:

  • Versatility – Ensures superior data and domain-agnostic performance
  • Contextuality – Comes with an advanced data dictionary that understands column definitions and contexts based on session conversations
  • Scalability – Spans across different user and verticals

This democratizes access to data-driven insights, reducing the dependency on dedicated analysts. Whether it’s the CEO, Supply Chain Manager, or Plant Manager, they can directly interact with the platform to get the relevant insights on time and as needed.

Empowering Data-Driven Decision-Making | Applications across various industries

Logistics and Warehousing: AI powered BI solutions can assist in optimizing warehouse operations by analyzing shipment punctuality, fill rates, and comparing warehouse locations. It identifies areas for improvement, determines average rates, and pinpoints critical influencing factors to enhance efficiency and streamline processes.

Transportation: Transportation companies can evaluate carrier performance, identify reasons for performance disparities, and assess overall carrier efficiency. It provides insights into performance gaps, uncovers the causes of delays, and supports informed decision-making to optimize transportation networks.

Supply Chain Management: AI powered BI solution empowers supply chain leaders to identify bottlenecks, such as plants with the longest loading times, compare location efficiency, and uncover factors impacting efficiency. It guides leaders towards clarity and success in navigating the complexities of supply chain operations, facilitating data-driven optimization strategies.

Business Intelligence and Analytics: Analysts are equipped with a comprehensive view of key metrics across various domains, such as shipments across carriers, order-to-delivery times, and modeling to understand influencing factors. It bridges data gaps, simplifies complexities, and offers clarity in data analysis, enabling analysts to derive actionable insights and drive data-informed decision-making.

Undeniably, empowering BI through AI can only be achieved by knocking off time-consuming bottlenecks that hinder data-to-insights conversion.

Tiger Analytics’ Insights Pro also goes a long way to combat other challenges that Generative AI has been associated with at an enterprise level. For instance, it ensures that data Security concerns are uploaded to the GPT server as data dictionaries. It also delivers an up-to-date data dictionary so that new business terms shouldn’t be manually defined in the current session.

Looking ahead, NLP and GenAI-powered solutions will break down barriers to data accessibility, automate the discovery of hidden patterns empowering users across organizations to leverage data insights through natural language interactions. By embracing solutions like Insights Pro, businesses can unlock the value of their data, drive innovation, and shape a future where data-driven insights are accessible to all.

Adithyaselvi Nagarajan

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