Business Intelligence and Machine Learning
Decision Support in Information Systems using Business Intelligence and Machine Learning
Information systems can streamline and accelerate daily decision-making processes by processing, highlighting, and pre-filling critical information. Known as decision support, these systems gather and prepare crucial information for informed decision-making.
Decision support in information systems shows up in different forms, like a desktop interface, pointing out issues, or making automatic suggestions and tasks using rules or machine learning. These systems spot risks but let the user decide. This saves time and lets people focus on important tasks. Decision support systems help make fast decisions, making it easier for organisations to use their experts' time well, improving productivity and skills.
We recommend developing decision support for activities and situations like:
- Frequent yet simple decisions, e.g., categorizing invoice items under expense groups.
- Time-consuming information comparison and processing, e.g., detecting violations based on historical data.
- Situations needing constant plan revisions due to rapid changes, e.g., production planning or logistics tasks.
- Rare events requiring quick response, e.g., preventing risks in emergencies.
- Finding a specific record among many irrelevant ones, e.g., a client who ordered a product with a specific component last year.
- Issues arising from multiple adverse factors, e.g., preventing increased accident risks due to environmental conditions.
- Comparing information from multiple data sources, e.g., viewing consolidated similar information across group companies; ongoing projects, pending orders, and invoice payments; aggregate information from multiple factories.
- Manual regular data and metric consolidation for reporting, e.g., monitoring budget adherence; monthly or annual performance reports; environmental impact indicators.
- Organizing large amounts of information, e.g., constantly updated datasets naming the same object differently from three sources.
To incorporate decision support into an organization, we suggest three main options:
To choose the best solution, we initially carry out an analysis to identify:
- What decisions are to be supported in the future?
- Which roles and processes are related to these decisions?
- What data is needed for decision-making?
- In what form and systems do the relevant data reside?
- Is data organization or consolidation needed?
- Is there a need to collect additional data?
- What solutions will support these decisions?
- Is data mining, machine learning algorithms, or metric calculation needed?
- What risks should be considered in creating a new solution?
- A project plan or roadmap for implementing the solution.
The objective of this stage is to develop a solution in line with the functional order outlined in the project plan. This may involve activities such as:
- Enhancing existing software for the new solution to work in previously created software.
- Developing new software with new capabilities.
- Enhancing or creating a data warehouse or database.
- Establishing data exchange between different systems.
- Creating visual data representations and calculations with data processing.
- Developing and testing new machine learning models.
- Integrating existing machine learning platforms with existing software.
- Testing and evaluating the accuracy of calculations and predictions.
- Developing functionality for user feedback and ratings on machine learning models, aiding in training the machine.
- Creating additional data description functionality, simplifying the organization's data management (master data).
This phase is vital for the success of the project. It facilitates the implementation of the solution and the transition to the improved workflow. Tasks involved may include:
- Training and guiding the use of the solution.
- Creating various support materials.
- Helping understand the ethics principles arising from using the solution.
- Evaluating the real-world performance of machine learning models.
- Supporting and monitoring the technical performance of the solution.
- Enhancing the system with additional requests.
In our blog, you'll find insightful posts on how to navigate the world of data analytics, including key concepts, data migration, and cross-usage. We offer recommendations for preparing for ESG or sustainability reporting and meet Kaur Kivirähk, the founder and CEO of Trinidad Wiseman's machine learning and business analytics center.
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