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Complex Data Querying
Businesses Data Querying is difficult, expensive, and slow.
Introduction
Businesses store valuable information in databases but face barriers in accessing these insights due to the technical nature of data querying.
Core Problem
Non-technical stakeholders struggle to extract insights from databases, leading to bottlenecks and delayed decision-making.
Business intelligence (BI) has become an indispensable tool for companies aiming to make informed decisions. With the advent of complex databases storing vast amounts of data, accessing and interacting with this data becomes a challenge, especially for those without a technical background:
Lack of Data Query Expertise
Traditional data querying and extraction often require knowledge of specific database querying languages, like SQL. For business stakeholders without this expertise, gaining direct access to insights becomes a challenge, leading to reliance on intermediary technical teams, which can introduce delays.
Loss of Agility
In rapidly evolving business landscapes, there are instances where decisions must be made in real-time. The delay in acquiring data-driven insights, primarily due to the technical challenges mentioned earlier, can lead to overlooking potential opportunities or making choices based on obsolete data. Such challenges, not only affect the immediate decision-making process, but can also have long-term implications for the business strategy. Companies must prioritize bridging this gap to ensure they remain agile and responsive to market dynamics.
Complexity of Data Formats and Interpretation Barriers
Even when data is extracted, it's often in digital formats that aren't digestible without data pre-processing. Stakeholders might receive a spreadsheet full of figures but lack the tools or expertise to interpret it effectively, leading to potential misinterpretations.
Custom Queries Requirements
Different stakeholders might need insights at different levels of granularity. For instance, a CEO might want a high-level overview of sales performance, while a department manager might need detailed metrics about their specific domain. Traditional querying methods can be cumbersome when tailoring the granularity of results.
Natural Language Ambiguities
Businesses often want to frame queries in natural language, but human language is inherently ambiguous. For instance, "Show me last year's performance" could refer to various metrics (sales, engagement, profits). Without smart interpretation, direct natural language querying can lead to unclear or incorrect results.
Integration with Multiple Data Sources
Modern businesses often utilize a variety of tools and platforms, each generating its own set of data. Stakeholders might want insights that merge data across these sources (e.g., correlating marketing spend with sales performance). Manual querying across multiple platforms becomes complex and time-consuming.
Resolution
The NLP for Business Intelligence tool democratizes data access. Users can query databases using natural language, eliminating the need for complex SQL commands or intermediary data teams.