Knowledge Base | Starschema

Knowledge Base

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.

Introducing the Stack of the Future for Modern Data Leaders

Fast unobstructed access to data and time to insight matters more now than ever. In these quickly changing times, businesses must innovate and implement a ‘Stack of the Future’ to be able to make accurate, data-driven decisions in minutes, not hours or days. The potential value of data is well known but in the new environment, the ability to easily share and collaborate on data is a competitive differentiator that will be leveraged by forward-thinking companies

COVID–19 Data Set Modeling and Analytics

During times of crisis, companies must look at the available data — both internal and external— and try to understand how that data can be used to determine how the business is currently being impacted, how it is likely to be affected in the future, what are most likely scenarios that will play out, what can be done to counter those scenarios and take advantage of hidden opportunities in this rapidly changing environment. The Starschema COVID-19 dataset ingests reliable data from multiple sources and makes it analytics-ready so it can be easily accessed and used.

Working with the COVID-19 Data Set

During this time of crisis, everyone is searching for answers. Governments, healthcare institutions, non-governmental organizations, and businesses large and small urgently need to make decisions about their future. We believe they should be armed with accurate, easily accessible, analytics-ready data. That’s why we collated, curated, and unified the most credible and reliable public data sets into a single source of truth data set.

Data Quality & Data Enrichment in the AI Era

In the age of artificial intelligence and machine learning, the standards for data quality have risen but machine learning can aid in cleansing and enriching data. In this webinar, Tamas Foldi, Starschema CTO and Tableau Zen Master presents the latest trends in data quality and enrichment and demonstrates how AI is helping to make cleaning and enriching data easier.

Managed Data Services

Today’s data platforms are complex, dynamic environments critical to the success of your organization. Starschema’s managed data services ensure high availability and peak performance while reducing operational costs of your data pipeline.

Starschema HealthLake

Healthcare Information Technology (HIT) is an indispensable part of managing and delivering healthcare services but patient data handling is highly regulated, presenting a challenge to practitioners. Learn how Starschema HealthLake can ensure compliance with the U.S. Health Insurance Portability and Accountability Act (HIPAA) while streamlining analytics.

Building AWS based Enterprise Data Lakes

Forward leaning companies are harnessing the power of the cloud to consolidate data silos into data lakes. Amazon Web Services has multiple services that can be used individually or in collaboration to kick-start the building of a data lake for any organization.

SAIL: A Scalable Architecture for Inference and Learning

The Standard Approach to Inference and Learning (SAIL) is a fast, efficient and scalable way to facilitate rapid, efficient and agile machine learning as well as inference.

Five Ways to Leverage AI and Tableau

In this webinar, Tamas Foldi, Starschema CTO and Tableau Zen Master along with Kristof Csefalvay, Starschema’s VP for Special Projects present how ML/AI, natural language generation (NLG) and other goodies can be used to reveal hidden insights to drive bigger business impacts.

Advanced Techniques for Innovative Data Visualizations

In this on-demand webinar, Ivett Kovacs, Tableau Ambassador, shares advanced techniques used to build innovative visualizations with Tableau and how to apply these techniques in real life. She covers drawing with numbers, applying geometric functions, densifying data, and layering.

Machine Learning for Business Leaders

From recommender systems on streaming services to personal assistants like Siri or Alexa, ML is nearly everywhere today. This white paper demystifies machine learning for non-technical business stakeholders to better collaborate in, and derive more value from, data science initiatives.

Palette Rescue

With Tableau’s Disaster Recovery method, large deployments can take 10+ hours to backup and same to restore. Even if you back up daily like you should, if disaster strikes you’ll lose a full day of productivity, and a full day of data. Palette Rescue ensures that your users are never interrupted by a server crash.

Opening the Black Box

Machine Learning models can be so complex that they seem like a magical black box with inputs and outputs, but little understanding of how the outputs are derived. In this white paper,

Eszter Windhager-Pokol, Starschema head of data science, explains the concept of interpretability and several methods to address the problems posed by black box models.

Palette Insights

Our client, a Fortune 100 company spanning multiple industries, operates a Tableau environment with thousands of users. To ensure optimum performance and provide great user experience, the client needed to gain a deep understanding of how their Tableau Server resources were being utilized.

Data Platform Migration

When large organizations merge or divest, then new entity has business transformation thrust upon it. Our client, a power generation manufacturer, divested from a larger organization to become a brand-new company. With the looming deadline of a cut-over from the existing data platform, the client engaged Starschema to validate and test the existing technology stack and determine the best way to move forward.

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.

Tableau Solutions

Hundreds of companies — Fortune 10 through startups — rely on Tableau and Starschema to see, understand, and use their data to drive business results. Our Tableau solutions are engineered to bridge the gap between the out-ofthe-box Tableau capabilities and the needs of our Fortune 500 clients. These solutions are cost effective, and can rapidly and easily be implemented in the enterprise environment.

Tableau License Manager

In large Tableau deployments, accounting for and maintaining licenses can be challenging for various reasons. However, our custom application produces a consistent and reliable database of users who currently holding a Tableau License Key.

Azure Data Lake

We design and implement data lakes to analyze data in distinct ways, gain insights and create value out of the data your organization generates and imports. Our standard BI Data lake solution implemented on Microsoft Azure platform is based on Lambda Architecture providing flexibility to process either structured data coming from traditional SQL databases or semi/non-structured data ingested from IoT devices, logs, documents.

Palette Trust

With Palette Trust Tableau Server administrators can communicate outage notifications, including direct communications and automated outage notifications directly on Tableau Server. Palette Trust automatically polls Tableau Server for internal process statuses and creates notifications based on predefined rules. No more automated messages sent through distribution lists to interrupt and be ignored.

Patient Selection Algorithm Development for Cardiac Resynchronization Therapy

Cardiac Resynchronization Therapy (CRT) can be a life-saving therapeutic option. However, the intervention itself has considerable risks, carries significant expense and its benefits to heart failure patients are limited to a small group of suitable patients, making patient selection critical. The right algorithm can determine patient selection criteria to identify patients most likely to benefit from CRT and least likely to suffer pre-operative or post-operative complications.

Detecting MRI artifacts Using Deep Convolutional Neural Networks

Magnetic resonance imaging (MRI) artifacts, distortions or false signals that affect image quality, may adversely affect diagnostic quality, resulting in potential diagnostic errors and the need for costly and time consuming repeat examinations that may delay timely treatment. Detecting these artifacts with convolutional neural networks can save costs and improve outcomes.

Starschema Antares iDL™

A fully automated, compliant-by-design intelligence data lake architecture with real-time ingestion and best-of-breed standardization and audit features.

Starschema Inverba NLG™

As humans, we think and communicate in narratives. Numbers and visualizations help, but taken on their own, they often don’t tell the full story. A good dashboard may be useful for showing trends and conveying metrics, but it’s words that resonate with decision-makers and provide them with important context and situational awareness

Dashboard Collaboration for Tableau

At the heart of data driven organizations lies the concept of data democratization: making data more accessible to everyone and making it easier to share, discuss, and act upon. Data visualization tools like Tableau help companies view, understand and leverage their data to make better decisions about their business.

Large Scale Data Replication Deployment

Our client, a global manufacturer in the power generation industry, faced challenges with an aging reporting environment based on multiple Oracle ODS systems and reporting straight from source databases.

Getting to a Single Source of Truth

Large enterprises acquiring companies often face challenges integrating their financial systems. Over the last several decades, our client has acquired dozens of businesses, each with its own ERP system and data warehouse solution.

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.

Palette Insight

See exactly what’s happening inside your Tableau Server, optimize performance and maximize investment. Palette Insight enables the Tableau administrator to ensure fast and reliable performance across all dashboards, exceed expectations of internal customers and allocate costs to business units.

Optimizing Data Visualization

Great visualizations can make data come alive, leading to better insights and decisions. Data consumers of every kind rely on dashboards to perform their work, but they have a common complaint, slow loading times.

Using Data to Improve Global Supply Chains

Over 60% of the world’s global seaborne trade is shipped using intermodal freight containers, and the ports that manage them serve as central points for supply chains – over 90% of global trade is conducted through ports.

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%.

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