AI BI is a rapidly evolving field that combines the power of artificial intelligence (AI) with traditional business intelligence (BI). It helps companies make (better) data-driven decisions. This combination offers unprecedented opportunities to gain deeper insights, predict trends and optimize strategies. How SIENN tackles this? You can read about this in detail in this blog.
Automating data analysis with AI technologies
In the world of data analysis, automation is playing an increasingly important role. Thanks to AI technologies, companies can not only process data faster and more efficiently, but also gain deeper and more valuable insights. It offers advanced data analysis solutions the business-critical applications can improve through visibly better insights and clear predictions.
AI technologies such as machine learning (ML), deep learning and natural language processing (NLP) make it possible to automate data analysis. This is done by using algorithms and models that can process data, recognize patterns and make predictions without human intervention.
- Machine learning: these algorithms learn from historical data and recognize certain patterns.
- Natural language processing: NLP enables AI BI tools to analyze unstructured data, such as text from reports, emails or social media.
Predictive Analytics and Machine Learning
By using Machine Learning models you can not only understand what happened in the past, but also predict what will happen in the future. These predictive capabilities are extremely valuable, but how does such predictive analysis work and how is machine learning used to predict future trends? Especially in an IT landscape with legacy systems and others business-critical applications this can be challenging. We are happy to explain it to you.
- Collect data | This first step also applies to traditional ones business intelligence processes and revolves around collecting large amounts of relevant historical data. This can range from sales figures and customer behavior to operational data and market trends.
- Preparing data | The collected data needs to be cleaned and prepared so that it can be run by an AI model interpreted. That includes removing incorrect or irrelevant data, dealing with missing values, and normalizing the data for consistency.
- Model selection & training | Machine learning models are selected based on the specific needs of the analysis. These models are then trained using the prepared historical data. Commonly used predictive analysis models include: linear regression, decision tree, random forest and neural networks. We often start with smaller pilots to gain insight into the value of AI business intelligence.
- Evaluation | After training the model, its accuracy is evaluated using a portion of the data that was not used during training (validation set). The goal is to ensure that the model is robust and accurate.
- Prediction & implementation | Once the model has been evaluated and optimized, it can be used to make predictions based on new, incoming data.
User-friendly dashboards and data visualization
SIENN your partner at developing customizable and intuitive dashboards for all your business data. We are happy to help you manage and interpret your data efficiently. Consider:
- Accessibility of data: intuitive dashboards make it possible to make data accessible to different users. This makes complex data easier to understand for your employees in a visual and attractive way.
- Improved decision making: dashboards allow you to quickly identify and interpret critical information, empowering you to make better decisions faster.
- Real-time insight: this way, the current information is visible, allowing you to respond immediately to changes in the market or, for example, to internal processes.
- Data visualization: intuitive dashboards use various visualization techniques such as graphs, tables, maps and interactive elements to present data in a clear and insightful way.
Privacy & system integration
The integration of AI Business Intelligence (BI) with existing IT infrastructures brings visible benefits, but also poses complex challenges for companies. And especially in the areas of data privacy and system integration. In an age where data drives decision-making, ensuring the privacy of sensitive information is crucial. The need for good security measures is really important because AI BI systems collect, analyze and interpret a huge amount of data.
In addition, many companies already work with a wide range of IT systems and applications, including legacy systems that are often difficult to integrate with modern technologies. AI BI solutions must be seamlessly integrated with these existing infrastructures to minimize disruption and ensure continuity. This requires careful planning and a deep understanding of both the existing systems and the new AI tools.
At SIENN we have extensive knowledge of these tools and how we can integrate them into your current systems without disrupting their operation. This can be achieved by using interoperable technologies and offering customized solutions that take into account the unique needs and constraints of your business. Contact us for more information.
A step-by-step plan towards AI Business Intelligence
With this summary roadmap from SIENN, you can build a solid foundation for using AI BI, leading to better decision making and improved business performance. Don’t hesitate to contact us for more information.
- Define your business objectives & identify problems and opportunities: what do you want to achieve with AI BI? Maybe you want to improve customer service or optimize the supply chain. Always do this with the SMART method: Specific, Measurable, Acceptable, Realistic and Time-bound.
- Evaluate your data infrastructure: do this via a data audit. See what data you currently collect and how it is stored. Also assess the infrastructure so that current systems are compatible with the new AI BI tools.
- Choose the right AI BI tools: look at different AI BI software and tools that best suit your needs. Consider certain aspects such as cost, ease of use, integration options and support.
Develop skills and expertise: Invest in training for your team to ensure they have the skills to work with the new AI BI tools. You can also hire external specialists, such as a SIENN team to share knowledge within your team. - Implement and integrate the tools: of course ensure a phased introduction of the tools. It is best to always start with the departments and/or projects where the greatest impact is expected.
- Monitor and optimize: use dashboards and reports to monitor the performance of your AI BI systems. Are adjustments needed? Then implement these to improve the tools even more.
- Scale and expand: is the integration a success? Then expand to other parts of the organization. Of course, always keep an eye on the latest developments in AI and BI to continue innovating to strengthen your competitive position.
Need help with AI Business Intelligence?
The transition to AI Business Intelligence can be complex and requires specific knowledge and skills. At SIENN we are ready to support you in every step of this process. Let SIENN be your guide towards a data-driven future. It is crucial for you as CEO to make decisions quickly and accurately. Real-time data analysis plays an essential role in this by providing companies with the most up-to-date information, leading to faster and more effective decision-making. We would therefore like to discuss the need for real-time data analysis and how solutions such as our SIENN Dashboarding and DataSync can contribute.
- SIENN Dashboarding: the SIENN Dashboarding solution offers a clear and intuitive interface where companies can monitor real-time data. This dashboard uses AI BI technologies to analyze and visualize data, giving users quick insights into key business indicators. With the ability to receive real-time updates, companies can respond immediately to changes in the market or internal processes.
- DataSync: DataSync from SIENN is an advanced data integration tool that ensures all business data is synchronized in real-time and available for analysis. DataSync connects different data sources and ensures a seamless data flow, which is crucial for accurate and up-to-date analyses. This enables companies to gain a holistic view of their operational and strategic data, leading to more informed decisions.
Curious about more details? Contact us!
