Advanced analytics allows you to use the future’s insights today. Every company has a treasure of data in their hands, and many already do something with this data to generate some value. However, only few know how to scratch deeper than the surface and maximize the value gained from analytics. Using advanced analytics, such as machine learning, artificial intelligence and deep learning models, we can simulate your data or forecast your future business. For the future’s insights, today.Start Analyzing
What is Advanced Analytics?
Advanced Data Analytics is the set of tools and techniques that help you understand not only the current situation of your business (‘regular analytics’), but also the future state. Next to analyzing the future, Advanced Analytics allows you to dive deeper into ‘why’ certain things happen and if it’s predicted to happen again. Regular analytics or business intelligence focuses on when a machine failed and what was the issue and can plot these KPIs over time. Advanced Analytics however allows you to see why this machine failed (root-cause analysis), if the machine will fail again (predictions) and what can be done to prevent this in other scenarios (simulation).
Benefits of Advanced Analytics
Advanced Analytics goes beyond regular business intelligence tooling. It allows for a wider range of applicability and better accuracy, but also:
- Deeper insights; allowing for more on-the-money initiatives and interventions.
- Accurate forecasting; predicting future trends to .
- Anticipate problems and opportunities; better planning of transport and stock management
- Simulating and testing solutions and scenarios; better accuracy on todays interventions.
What is Advanced Analytics used for?
Advanced Analytics is applied on a very broad range of questions and in many environments, and it is continuously developing. However, there are some fields where Advanced Data Analytics is already applied very often.
- Fraud detection and risk management
- Quality inspection
- Marketing and customer lifecycles
- Maintenance (failures and planning)
- Customer satisfaction
How is Advanced Analytics applied?
As said, Advanced Analytics is the whole spectrum of tools and techniques that allow for advanced data manipulation, simulation and analysis. Therefore, it is important to select the right tools and techniques for the right situation. An Advanced Data Analytics track always starts off with a specific question; how to decrease transport costs without losing on the on-time and in-full delivery rate, or which factors have the most effect on customer satisfaction. From your question our experts can advise you on which best-in-class tooling to use.
DataLane has implemented numerous use cases at a wide range of customers and industries. In this section you can find a variety of use cases. Click to learn about the challenges, solutions and business value that we have been able to create for our clients.
Mining Multiple Processes
DataLane implemented eight core processes and provided profound insights and improvement opportunitiesRead use case
Mining Web Analytics
Process mining was applied to get insights into customer behaviour by analyzing the the visitor’s web journey on its company websitesRead use case