Insight-Driven Automation
Our insight-driven approach to automation is designed to turn operational data into valuable, actionable insight, and then use those insights to automate functions that result in better outcomes. This approach ensures that our clients are automating the right processes while building better organizational data literacy and maturity. We call this approach “Augmented Analytics.”
Get to the Point with Intelligent Automation
Augmented Analytics solutions enable decision makers and analysts to quickly get to the point of their analysis and automate ‘next-step’ actions using intelligent automation, which is the convergence of Artificial Intelligence (AI), Business Intelligence (BI), Machine Learning (ML) and Robotic Process Automation (RPA) technologies. Following this methodology has made a huge impact on our clients by:
- Improving the speed and quality of decision making
- Automating required next steps and action items
- Embedding AI, ML, and RPA into business intelligence (BI) apps to pinpoint areas of focus
- Aiding in rapid discovery/revealing hidden areas requiring deeper analysis
- Automatically correlating and delivering relevant data
- Making data science more accessible to a wider range of users and decision-makers
- Identifying the highest ROI automation candidates to maximize the impact of automation projects
Accelerate Data Discovery
Augmented Analytics automatically correlates relevant data to aid rapid discovery, reveal concealed areas requiring deeper analysis, and deliver those insights to decision-makers faster than traditional BI. The integrated technologies can pinpoint hidden events or conditions such as fraud, customer churn, health conditions, or component failure.
Using machine learning approaches to identify these events allows decision makers to act on them immediately, effectively eliminating discovery efforts to allow more time for addressing the root cause. Additionally, Augmented Analytics’ use of conversational analytics and natural language processing (NLP) make data science more accessible to a wider range of users and decision makers.