Data Agents Boosting data-driven decision making

One of the key success factors of a modern organization is the ability to make data-driven and fast decisions throughout the organization.

Traditional data analytics solutions

Dashboards and reports are a critical starting point for any organization in their data journey. However, they have certain limitations in terms of flexibility, distribution and timeliness.

• Flexibility: Reports have a fixed format optimized for answering a set of predefined questions. They can become confusing if overloaded with information and details. As a result, reports are always a trade-off between providing details and presenting the big picture. In practice, they cannot be designed to answer “everything.”
• Distribution: Reports are built into specific apps such as Power BI, and users are required to navigate in these apps specifically. Often users have several reports they use and must find the correct one to answer the question at hand. There is always friction when navigating e.g. from the typical MS Office environment to external app.
• Timeliness: Due to technical limitations in reporting solutions, they typically contain data from the previous day backwards and do not reflect the current situation exactly. For very operational needs, this is not sufficient, and users need to rely on getting data directly from ERP or other IT systems to understand what is happening right now.

Data Agents unlocking new value in data

To boost data-driven decision-making, especially in more operational, fast-paced roles, organizations can start to harness the benefits of AI-driven Data Agents.

A Data Agent is a Large Language Model-powered intelligent application equipped with a set of critical data analytics tools. In practice, users chat with the Data Agent in Teams using natural language and can ask detailed questions about their daily operations. Think of a Data Agent as a supercharged, personal data analyst always at your disposal.

Example:

Under the hood, the Data Agent transforms the user’s question into database queries, executes them, and finally translates the numerical results back into natural language. By combining the unprecedented flexibility of Large Language Models, traditional code execution, and the wealth of data in the organization’s data platform, we can achieve amazing results.

Every operator, manager, and leader in the organization can get answers to their questions related to the organization’s operations in seconds instead of hours or days. Data Agents democratize data utilization in the organization and provide a massive boost towards holistic, data-driven decision-making at all levels.

Data Agents complement traditional dashboards and reports by offering much-needed flexibility, timeliness, and ease of use. When a prompt answer to a specific question is needed, a Data Agent is always ready to help.

Implementing Data Agents into production-level use cases is not science fiction. They are fully within the grasp of every organization, and use cases are plentiful—from Purchasing to Customer Service to Sales and beyond. The question is not whether your organization will start utilizing Data Agents, but when. The most significant competitive advantage can be achieved by adopting them now.

 

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The blog author is Antti Luoto, an experienced Data Architect well versed in technologies such as Databricks, Synapse, Data Factory, Delta Lake, relational databases, Power BI, Azure functions and Azure OpenAI Service.