As a PE business, monitoring owned businesses with highly complex datasets can become a highly complex task. Answering new critical questions with data-backed answers is still a time-consuming activity that requires weeks of data analyst and engineer time. LLM models such as ChatGPT 4 integrated into various business systems will be the next advancement in business, especially in the data analytics space. The latest showcase of where the market is headed with AI-led analytics is the introduction of Power BI Copilot.

Natural Language Interface to Databases (NLIDB) or Natural Language Querying (NLQ) system. These systems use Natural Language Processing (NLP) techniques to understand and interpret the user’s queries and then translate them into SQL or other query languages that the database can understand. Then this data can be summarized in graphs, tables, and written reports by interacting with the system in natural language.

There are 3 ways this will affect PE Businesses:

1. Centralized Analytics for Owned Businesses:

NLIDB systems can serve as a powerful tool for centralizing and streamlining analytics across a portfolio of owned businesses. By connecting to multiple databases and data sources, these systems enable PE professionals to access critical financial and operational data from various portfolio companies in a unified and coherent manner.

This centralized analytics approach offers several benefits:

• Consolidated Insights: PE firms can effortlessly gather data from different businesses and create consolidated reports and dashboards. This provides a comprehensive view of the portfolio’s overall performance and financial health.
• Benchmarking: NLIDB systems allow for easy benchmarking of key performance indicators (KPIs) across portfolio companies. This aids in identifying underperforming or overperforming businesses and implementing strategies accordingly.
• Risk Assessment: PE professionals can quickly assess the financial risks associated with each portfolio company by comparing financial data and market trends. Early detection of potential issues can lead to proactive risk mitigation.
• Efficiency: Centralized analytics reduce the time and effort required to compile and analyse data from multiple sources, enabling faster decision-making and more efficient allocation of resources.

2. Democratization of Analytics Across the Business:

NLIDB systems empower not only data analysts but also non-technical professionals within the PE firm to access and interpret data. This democratization of analytics has several advantages:

• Self-Service Analytics: PE professionals, including investment managers, can formulate complex data queries in plain language without relying on data experts or IT support. This promotes self-service analytics.
• Data-Driven Decision-Making: With easy access to data and insights, decision-makers throughout the organization can make more informed and data-driven investment decisions.
• Reduced Bottlenecks: By reducing the dependence on a limited number of data experts, NLIDB systems eliminate bottlenecks in data access and analysis, leading to faster response times.
• Cross-Functional Collaboration: Democratized analytics encourages collaboration across different departments, as professionals from legal, finance, and operations can all engage with data to contribute to investment strategies.

3. Portfolio Monitoring:

NLIDB systems significantly enhance portfolio monitoring capabilities for PE firms:

• Real-Time Insights: These systems can provide real-time updates on portfolio company performance, financial metrics, and key events. PE professionals can quickly react to changes and make timely adjustments to their investment strategies.
• Alerts and Notifications: NLIDB systems can be configured to send alerts and notifications based on predefined triggers. For example, deviations from financial targets or significant market developments can trigger automatic alerts, ensuring timely attention to critical issues.
• Predictive Analytics: By leveraging historical data and predictive analytics models, NLIDB systems can help forecast future performance and potential challenges within the portfolio. This foresight enables proactive decision-making.
• Customized Reporting: PE firms can create customized reports and dashboards for each portfolio company, tailoring analytics to specific business needs and objectives.

In summary, NLIDB systems offer private equity businesses centralized analytics capabilities, democratized access to data and insights, and advanced portfolio monitoring features. These benefits collectively contribute to more effective decision-making, risk management, and overall portfolio performance.

When are NLIDB systems joining the workplace and what are the challenges?

There are challenges in the implementation of AI systems in the workplace. Data focused AI systems are going to be mostly constrained by long standing problems in data quality, modelling, resource scalability and cataloguing that plague businesses.

At DVS, our capabilities lie both in the identification of business applications or NLIDBs and the development stages of the infrastructure pieces needed to deliver them. Natural Language Annotations, Metadata and Semantic Enrichment will be key to the integration of NLIDB systems to existing databases, enabling AI to find and understand how the underlying data can be used to answer our questions.

References:

• Alshammari, A., & Alshammari, M. (2021). A Review of NLIDB with Deep Learning: Findings, Challenges and Open Issues. In S. K. Singh & S. K. Singh (Eds.), Advances in Data Science and Management (pp. 217–228). Springer Singapore¹
• Alshammari, M., & Alshammari, A. (2021). A Survey of Natural Language Interfaces to Databases: Challenges and Opportunities. In 2021 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 1–8). IEEE²
• Alshammari, M., & Alshammari, A. (2021). Natural Language Interfaces to Databases: A Review of the State-of-the-Art Techniques and Open Challenges. IEEE Access, 9, 101156–101178³