Augmented Analytics

Automated insight generation and machine-assisted data analytics

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Why Augment Your Analytics

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Automated Tasks

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.

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Enhanced Insight Discovery

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.

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Conversational Analytics

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.

Sample Use Cases

Select an industry.

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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.

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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.

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Boardwiser NLG

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.

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Topic Modeling

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.

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Ask the Expert

Eszter Windhager-Pokol

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.

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Predictive Maintenance in Manufacturing

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.

Demand Forecasting with Latent Matrix Factorization

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.

Customer Segmentation at an Energy Company

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%.

Predictive Maintenance in Pharmaceuticals

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.