Leverage Altair Data Analytics for Optimizing Preventative Churn Strategies

To successfully operationalize machine learning (ML) algorithms, data science teams must be able to continuously update their models so they can improve their predictive performance. The champion/challenger approach is a well-documented method for optimizing models and making adjustments that accounts for changes in the nature and quality of data inputs. Altair’s ML products make it easy to incorporate champion challenger processes into data analytics workflows.

Altair’s data analytics platform helps major banks and nonbank financial institutions transform the mortgage servicing process. Altair tools handle every phase of the data lifecycle, eliminate data silos, and provide decision-makers with excellent visibility into risk and profitability. Click here to explore high impact Altair Mortgage Data Solutions.

Altair® Monarch® makes it easy to extract directly from Snowflake data warehouses. You can then perform a variety of data preparation functions on the data, combine it with data from other sources, and export it in different formats to other systems. Try Monarch today, for free - click here to start trial. This video was produced using Monarch 2021.0.

One of the essential problems involved in managing large datasets is ensuring they’re complete. Many use cases, including materials databases, can use machine learning (ML) and artificial intelligence (AI) algorithms to accurately identify and fill gaps with data extrapolated from other data in the set. The datasets might contain time series data which, for example, may track the movement of components through a supply chain and/or static data like a parts inventory or test results. Altair’s data science tools are well suited to this task.

Altair’s artificial intelligence (AI) and machine learning (ML) software helps materials scientists understand how to best fill gaps in their material databases, even when it’s impossible to test all possible variants. These advanced tools also optimize testing programs, improve efficiency, and reduce the time required to complete materials testing.

Human resources (HR) is tasked with recruiting the best talent and keeping culture at the forefront while staying on top of payroll, performance, and many other responsibilities. What if you could save time getting these tasks done while your human capital is ahead of the curve to meet your organization’s business goals?

Altair Monarch’s burster node enables you to unbundle files and distribute the segmented results into separate files and locations. For example, a file containing multiple reports can be unbundled and then placed in a location that can be used for monitoring of other processes or ingestion into Content Server. Monarch’s burster node also detects errors and automatically moves faulty files to a special location where they can be corrected before further use. Monarch’s burster node is easy to set up and business users can implement a workflow incorporating burster functions in just a few minutes.

Discover how Altair Monarch automates the transformation of payroll data into usable, structured datasets to streamline reconciliation, reporting and analysis.

Auditors are under significant pressure to keep expenditures down whether they work for an external audit firm or are part of an internal audit team. Achieving cost-effective audits requires organizations to do more with less - while maintaining or increasing audit quality. To succeed auditors not only need the right expertise and process but also the right data analytics tools.

Spreadsheet applications are ubiquitous, flexible, and give users the power to develop complex macro and formula-driven applications. The downside, however, is that spreadsheets are prone to error, difficult to program, produce results that are easy to misinterpret, do not handle data from multiple disparate sources easily, and are extremely difficult to maintain and debug as their size increases. Monarch can vastly reduce mistakes, save time, and support the most complex requirements.

Stop wasting time repeating the same manual data preparation task in spreadsheets. Experience how Altair Monarch is the solution to fast, automated error-free data transformation.

In the time you spend learning complex spreadsheet functions to format data, your entire project could already be done. Experience how Altair Monarch is the solution to transform data in minutes without coding or complicated formula.

Spreadsheets are powerful but not designed for today’s analytics demands. See how Altair Monarch transforms data in minutes.

Machine learning technology leveraging historical and real-time data from sensors mounted to production equipment as well as PLCs, SCADA, and other sources can accurately flag potential failures of whole machines and/or critical components before they can cause downtime.

Most manufacturers must handle large numbers of warranty claims related to a variety of products and components. The volume of claims can easily run to millions per year for consumer goods manufacturers. Patterns within claims data may indicate emerging quality or design problems; therefore, identifying and prioritizing the issues requiring high priority responses is critical to improving quality and reducing the financial impact of claims. This solutions flyer how advanced data analytics tools enable teams to cleanse and organize data from any set of repositories and apply machine learning technology to identify emerging quality or design issues.

Identifying unusual behaviors or patterns in machine components using sensor data can prevent small glitches from creating major operational problems.

Warranty claims contain valuable information about product quality and reliability, customer expectations, and can reveal design or manufacturing defects. The proper utilization of this data enables manufacturers to make improvements to existing products, develop more reliable new products, and drive increased profitability.

Predictive maintenance provides a proactive solution to monitor equipment health and predict failures. It enables organizations to avoid machine failures and unplanned downtime, increase equipment and production line productivity, reduce maintenance costs (including hard component replacement costs and associated personnel time), and improve output quality.

Root Cause Analysis is critically important to the ongoing success of any manufactured product. Detecting design defects, raw material problems, build issues, and quality control shortfalls as early as possible fosters continuous product improvement, increased reliability and performance, and allows the company to maintain strong reputations for product brands.

Despite recent advancements in technology, the education sector has struggled to capitalize on opportunities afforded by data analytics.

Altair Data Analytics enabled government agencies and people of all skill levels to access, generate, and use smart data to make insightful, informed decisions.

Altair Monarch Server Automator provides a comprehensive, enterprise-wide data preparation solution that extends on the work completed using Monarch's desktop solution.

Data Analytics is at the core of driving business value through digital transformation. Transparency in model building and explaining model outcomes leads to confident decision making. Learn how Altair Knowledge Studio fits into an organization’s data-driven approach to solve complex business questions.

Lending organizations consolidate daily debit transactions into auto-generated files (typically Excel format). With thousands of machines and hundreds of thousands of records per day, manual reconciliation to the balance level is laborious and error-prone.

There is tremendous opportunity for telco providers to grow Annual Revenue Per User through increased loyalty

This short video demonstrates machine learning applied to a manufacturing workflow for an organic synthesis process. Altair Knowledge Studio - Altair's solution for advanced machine learning and artificial intelligence - is used in combination with Panopticon - a platform for user-driven monitoring of real-time data.

This is a demonstration of using Monarch to streamline financial reconciliation tasks, ensuring that final transaction records are accurate and correct. When a number of different reports are required to reconcile records, Monarch removes laborious and error-prone manual tasks such as re-keying data from various reports into spreadsheets such as Excel. Monarch automates the process of converting reports into rows and columns for easy reconciliation tasks.

Data overload, dark data and disparate data are three roadblocks that slow down intelligence analysis processes and complicate threat detection. As the amount of data increases, these challenges will only be exacerbated. Self-Service Data Preparation solutions aid the Intelligence Community in making better use of data, facilitating intelligence sharing across organizations and improving threat detection/incident prevention.

Scorecards are used by credit lenders to determine the creditworthiness of borrowers, and by marketers to better understand consumer interest in product or service offerings. To help make decisions about an applicant, data from 3rd-party providers that includes delinquency scores, failure scores, and payment ratings; demographic attributes; and the history of current account activity is often used to create a model that predicts the probability of a loan default.