Data-driven working with RPA

For many of our customers, data is now the primary fuel for their business processes. However, with the growing amount of data, gaining the right insights becomes a challenge, and ensuring data quality is crucial. In situations where spreadsheets aren’t enough, dashboards don’t provide the necessary information, and relevant data is available but not in the right place, a combination of RPA and an advanced data analytics tool can provide the right solution.
We chose Alteryx because we have experienced with our customers how powerful the combination of UiPath and Alteryx can be. Whether it concerns financial processes or the service domain, they complement each other excellently.
And various options for linking the tools make seamless integration possible. An additional advantage for our customers is that the principles of using both tools are very similar. In other words, an experienced RPA Developer can get started with Alteryx right away with minimal training.

How does Data Analytics make our process optimization smarter?

Alteryx is a platform for data preparation and advanced data analysis. It offers the opportunity to quickly gain insight into the causes of changing data or to efficiently prepare data for further processing.

Data Preparation:

• Explore, clean and transform raw data.
• Integration of data from different sources.
• Handling missing data and unstructured data.

Advanced Analysis:

• Building predictive models and machine learning workflows.
• Performing statistical analyzes and advanced calculations.
• Quickly gain insights into the cause vectors of changes.
• Creating spatial analyzes and visualizations.

Workflow automation:

• Automating repetitive tasks and processes, real-time and directly from the data source.
• Merging data processing and analysis into streamlined workflows.
• Replacing scripting and programming functionalities.

Location Intelligence:

• Analyzing geographic data and location intelligence.
• Visualizing spatial patterns and trends on maps.

Collaborate and share:

• Share the analysis of workflows and results with others in the organization.
• Collaborate on projects and analyses.
• Publishing data analyzes and dashboards.
• Transparency in data processing provides insight for auditors.

Connectivity with data sources and dashboarding tools:

• Integration with various data sources, including databases and Big Data platforms.
• Use of predefined connectors for popular data sources.

RPA integration:

• Integration with Robotic Process Automation (RPA) for automated business processes.
• Improving end-to-end automation by combining data preparation and analytical tasks with RPA processes.