Automated insight generation and machine-assisted data analytics
Machine-learning-driven data preparation, insights and visualizations give analysts a head start.
This drives productivity and innovation as experts can focus on higher value-added tasks.
Context- and usage-aware insight and data source curation direct human attention to actionable data.
This improves decision quality at all organizational levels with complete and accurate insights.
Intuitive, human-like interfaces drive engagement with data and help users of get to insights faster, regardless of technical proficiency.
This increases platform engagement and scalability both horizontally and vertically.
Enable predictive maintenance by automatically analyzing data from sensors and machines to forecast breakdowns, schedule repairs and minimize downtime.
Optimize performance by identifying bottlenecks and waste with collated data on your workforce, equipment, shipping timings, warehouse capacity and more.
Click this tile to access a white paper on predictive maintenance in manufacturing.
Automatically analyze customer acquisition cost, lifetime value and product trends.
Detect fraud faster and with greater accuracy with context derived from more holistic data.
Analyze customer trends to forecast demand and optimize assortment, pricing and promotion strategy.
Optimize inventory by automatically tracking movement and warehouse data.
Automatically analyze patient data in medical records and identify vital sign trends to achieve better health outcomes faster and at reduced cost.
Optimize resources by finding connections between bed occupancy rate, length of stay, treatments and other factors to improve cost-effectiveness.
Click this tile to learn more about predictive maintenance in pharmaceuticals.
AION combines AutoML and data science to offer customers a robust platform for applying machine learning to real-world problems. It provides customers with the capability to rapidly build prototypes for solving business problems.
Best-in-class machine learning and natural-language generation (NLG) ensure that relevant driver factors behind KPI changes are detected and communicated without the need for manual analysis. It automatically and accurately identifies report-worthy outliers and summarizes them for easy subsequent analysis and reporting.
A flexible solution for the analysis of large volumes of text-based data to unlock insights that are otherwise difficult to extract and understand for humans. Read this white paper to get a working understanding of topic modeling and get implementation tips.
Head of Data Science
Eszter has years of experience supporting data-driven decision-making as a consultant, researching collaboration filtering and developing user behavior analytics products. As the head of data science at Starschema, she has overseen projects implementing augmented analytics solutions at a variety of large organizations.
White Paper
Data scientists and manufacturing specialists can examine production lines to determine what data can be used to predict future failures and how to best collect it. A recent (2018) report on the manufacturing industry saw 31% of manufacturing CEOs expecting artificial intelligence and machine learning to contribute to a reduction in operating costs. Learn how predictive maintenance helps make this reality.
White Paper
For many businesses, demand planning is essential. Profitability, cash flow, and customer satisfaction and retention all hinge on getting this right. This white paper will introduce latent matrix factorization to model demand curves and discuss how it can be used to achieve these outcomes.
Case Study
Introducing a machine learning based segment view about your customers gives the opportunity to improve conversions and lower customer acquisitions. Learn more how a sophisticated segmentation model, based on your existing data, can increase the number of customer purchasing and your campaigns' success rate by 40%.
Use Case
Producing high-value, high-volume pharmaceuticals on a 24/7 operational schedule means that breakdowns are time-consuming, interrupt production and are often extremely costly. Fortunately, data scientists and manufacturing specialists can examine production lines to determine what data can be used to predict future failures and how to best collect it.