by Obaid Motiwala, on Jan 7, 2025 6:20:08 PM
Key takeaways
- The convergence of Business Intelligence, Copilot, and GenAI unlocks significant data value.
- It helps tide over the issues related to analyzing data in a timely manner.
- It allows business users to go beyond queries and talk with their data in simple, natural language.
The Business Intelligence landscape is fast changing. New Artificial Intelligence (AI) driven technologies, such as Copilot and GenAI, augment the Business Intelligence Cloud platforms to unlock the immense potential that is latent in enterprise data. This technology convergence enables business users to talk to their enterprise data in simple natural languages, such as English or any other language, and see beyond lengthy tables and figures sans the structured query language. The technology confluence offers a powerful synergy that tides over the challenges encountered in analyzing high data volumes. It helps to accurately identify trends and patterns in the data, oblivious to the human eye, and make accurate decisions in a timely manner.
What are the challenges in enterprise decision-making?
Businesses face a myriad of problems during decision-making. Some of them are –
- Complex datasets: Businesses usually have composite data sets that are usually not usable and require curation to extract meaningful information.
- Lack of data accessibility: Business users usually lack direct access to data, which is a must for making informed decisions.
- Structured language querying: Business users route their information requirements through technical users with data access. They use structured query language to retrieve answers to queries.
- Lack of data governance: Data is stored passively as an afterthought during work processes. It is seldom controlled or audited.
- Lack of actionable information: Business users do not have instant access to real-time actionable information that hampers timely decision-making.
- Lack of real-time decision-making: It leads to opportunity loss and stagnates business operations and revenue growth.
What is the importance of automated actionable insights in business scenarios?
Business Intelligence solutions enable businesses to stay in control of their enterprise data through real-time monitoring. Some of the important aspects of modern Business Intelligence solutions are –- Instant report generation: Business users generate quick reports through self-service.
- Better decision-making through collaboration: They offer real-time access to business insights for informed decision-making.
- Democratizing information access: They help to transcend protocols within RBAC frameworks and access real-time information.
What is the importance of AI-led Business Intelligence in today’s business environment?
The convergence of Business Intelligence and AI technologies, such as Copilot and GenAI, has transformed the business intelligence landscape. It brings forth many new-age capabilities –
- Conversational User Interface (CUI): It allows querying in natural languages, such as English, through simple prompts. It leverages NLP and NLG to tap into enterprise data repositories and retrieve fast answers.
- Analysis at scale: The unique technology convergence allows analyzing complex data at scale to bring out fast actionable information in different visual formats without using Structured Query Language. This technology convergence effectively analyzes patterns, sees beyond, and identifies outliers better than contemporary solutions.
- Analyzing complex datasets: It analyzes complex data and offers insights within a fraction of the time required by contemporary BI solutions.
- Greater insightful depths: It helps to analyze data to retrieve actionable insights with greater depth and accuracy.
- Control over data: It helps to exercise greater control over data and establish data governance with RBAC frameworks.
AI-led Business Intelligence use cases
- Customer Service: It allows agents to dynamically generate to-the-point information for customer queries in an agile environment. It helps them to handle high call traffic better and increase customer satisfaction.
- Quality Analysis: It allows supervisors to analyze the agents’ conversations with customers, decide the agents’ individual development plans, and schedule training. It helps the supervisors move beyond sampling towards the complete quality analysis of the customer conversations handled by the agent fraternity.
Simply put
Analysis of complex data poses a myriad of problems. The Business Intelligence, Copilot, and GenAI convergence allows business users to overcome such problems and unlock the immense potential that is latent in enterprise data. It allows talking to the enterprise data without the Structured Query Language protocols that lie with the select few in the organization. The synergy helps tide over the problem of managing huge data volumes to generate meaningful and actionable business insights.
