Dashboards or conversational analytics?
A production planner using AsscherAi's conversational analytics can ask what orders are pending, on which production line, and what materials are required, then adjust the schedule while the materials are still available, reducing the likelihood of delays and improving overall production efficiency and resource allocation.
A category manager comparing two suppliers using AsscherAi's conversational analytics may find it easier to ask follow-up questions, but a shift supervisor checking the maintenance log on a dashboard can quickly see the status of all equipment at a glance. This difference in use case is crucial when deciding between dashboards and conversational analytics for an organisation's data querying needs.
What each option genuinely commits you to
Dashboards commit you to anticipating the questions that users will ask, and designing a visual representation that answers those questions. This requires a significant amount of upfront work to determine what metrics are important, and how they should be displayed. Conversational analytics, on the other hand, commits you to allowing users to ask unanticipated questions, and relies on the system being able to understand and answer those questions in real time.
This difference in approach can have a significant impact on the overall design of the system, and the resources required to implement and maintain it. For example, a dashboard may require a team of developers to create and update the visualisations, whereas conversational analytics may require a team of data scientists to train and refine the natural language processing algorithms.
The case where the other one is right
There are cases where dashboards are the better choice, such as when the questions being asked are well-defined and don't change often. For instance, a shift supervisor checking the maintenance log on a dashboard can quickly see the status of all equipment at a glance, and take action accordingly. In this case, the dashboard provides a clear and concise visual representation of the data, and allows the supervisor to make informed decisions.
On the other hand, conversational analytics may be the better choice when the questions being asked are unanticipated, or when the user needs to ask follow-up questions. For example, a category manager using AsscherAi's conversational analytics to compare two suppliers may find it easier to ask follow-up questions, such as "what is the lead time for this product?" or "what is the cost of shipping?". In this case, the conversational analytics system allows the manager to ask questions in a more natural and intuitive way.
The cost that only shows up later
One of the costs of implementing dashboards is the ongoing maintenance and update of the visualisations. As the organisation's needs change, the dashboards will need to be updated to reflect the new metrics and data sources. This can be a time-consuming and resource-intensive process, and may require significant investment in developer resources.
In contrast, conversational analytics systems may require ongoing investment in data science resources to refine and improve the natural language processing algorithms. This can be a significant cost, especially if the organisation has a large and complex dataset. However, this cost may be offset by the increased flexibility and usability of the system, as users are able to ask questions in a more natural and intuitive way.
A way to decide without a six month evaluation
To decide between dashboards and conversational analytics without a lengthy evaluation process, organisations can start by identifying the key use cases for the system. For example, will the system be used primarily for reporting and compliance, or will it be used for ad-hoc analysis and decision-making? By understanding the key use cases, organisations can determine which approach is best suited to their needs.
Organisations can also consider consulting with a specialist, such as Harns Technologies, to help determine the best approach for their specific use case. Additionally, organisations can start by implementing a small-scale pilot project, such as using AsscherAi's conversational analytics to answer a specific set of questions, and then expand the system based on the results. For more information on how to get started, visit our website at AsscherAi.
What this does not do
This article does not provide a comprehensive comparison of all data querying systems, but rather focuses on the specific trade-offs between dashboards and conversational analytics. It also does not provide a detailed analysis of the technical implementation of these systems, but rather focuses on the practical considerations and use cases.
Furthermore, this article is not intended to be a definitive guide to implementing dashboards or conversational analytics, but rather a starting point for organisations to consider their options. By understanding the key differences between these approaches, organisations can make informed decisions about which system is best suited to their needs, and start to explore the possibilities of using
- conversational analytics
- dashboards
Frequently asked questions
What are the main differences between dashboards and conversational analytics?
Dashboards require anticipating questions and designing visualisations, while conversational analytics allows users to ask unanticipated questions and relies on natural language processing algorithms.
When are dashboards the better choice for data querying?
Dashboards are suitable when questions are well-defined and don't change often, providing a clear visual representation of data for informed decisions.
What are the ongoing costs of implementing dashboards?
Dashboards require ongoing maintenance and updates to reflect changing metrics and data sources, which can be time-consuming and resource-intensive.
How can organisations decide between dashboards and conversational analytics?
Organisations can start by identifying key use cases, consulting with specialists, and implementing small-scale pilot projects to determine the best approach for their specific needs.