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:
Enhancing an existing custom information system with decision support
Developing a new information system equipped with decision support
Creating a separate dashboard for decision-making, gathering data from existing systems.
Analysis Phase
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.
Development Phase
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).
Implementation Phase
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.
Success Stories of Clients Who Have Benefited from Business Intelligence and Machine Learning
Automating Feedback Analysis with Artificial Intelligence
We assisted Rimi Baltics in enhancing their feedback analysis process through the use of artificial intelligence. This advancement has enabled Rimi Baltics to efficiently and effortlessly analyze customer feedback, allowing them to quickly identify and address any areas of concern. Consequently, this proactive approach ensures an improved user experience for Rimi’s customers.
Automating financial analyses through the use of RoiBot
In the beginning of 2022, Flexa Estonia started using the RoiBot Financial Planning and Analysis solution that we have developed, which makes budgeting and reporting much easier than it has previously been. The solution enables companies to quickly make changes to their budget, thus ensuring they can operatively react to market demands or general economic changes.
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Business Intelligence and Machine Learning
Machine learning and business intelligence (BI) are our backbone, supporting our projects in data cross-utilization and interface creation, data storage, and systematic mining. We analyse data to identify patterns that contribute to increasing the efficiency of business decisions and optimizing system processes. We use state-of-the-art machine learning algorithms and analytical methods, allowing us to process large datasets. This enables us to gather essential information about customer behaviour and market trends, and to provide our clients with even more effective and relevant solutions.
Kaur Kivirähk
Intelex Insight CEO, partner
Kaur Kivirähk is the founder and partner of Intelex Insight, with over 20 years of experience in data analysis and consulting. He holds a master's degree in economics and has helped many companies and government agencies optimize strategies and analyze business processes over the years. Kaur's extensive knowledge and long-term experience make him a valued specialist who can offer practical and innovative solutions to complex challenges in data analytics and artificial intelligence. In addition to his work at Intelex Insight, Kaur has been a guest lecturer at both the University of Tartu and Tallinn University of Technology, teaching economics and sharing his knowledge with the next generation.
If you wish to discuss your project or have questions about the work being done at Trinidad Wiseman, please feel free to get in touch with us.
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