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Elder Research is a recognized leader in the science, practice, and technology of advanced analytics. Topics on our blog cover analytics tips, analytical modeling, data and text mining tools, data visualization, analytics best practices, case studies, etc. to provide business leaders with actionable information on real world analytics problems.
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Do We Have the Right Data?In our experience the mistake of “waiting for perfect data” probably kills more projects than any other. Here’s a typical scenario: The project starts out well. The management team defines the goals, calculates the potential return on investment, develops a project plan, gets a budget approved, assembles the team, and launches the project. The trouble starts with a desire to make sure that the data is in “good” condition. ...
In our experience the mistake of “waiting for perfect data” probably kills more projects than any other. Here’s a typical scenario:
The project starts out well. The management team defines the goals, calculates the potential return on investment, develops a project plan, gets a budget approved, assembles the team, and launches the project. The trouble starts with a desire to make sure that the data is in “good” condition.
Transaction Classification Aids Credit Risk Assessment
A significant transformation is currently underway in the lending market. Banks are competing to provide lending decisions in a single day, with a vastly simplified customer experience as their primary way of growing market share. The key technology allowing this transformation is being able to accurately automate the credit decision; that is, to use advanced analytics to estimate a customer’s probability of default, affordability, and financial position to make the credit decision quickly....
A significant transformation is currently underway in the lending market. Banks are competing to provide lending decisions in a single day, with a vastly simplified customer experience as their primary way of growing market share. The key technology allowing this transformation is being able to accurately automate the credit decision; that is, to use advanced analytics to estimate a customer’s probability of default, affordability, and financial position to make the credit decision quickly.
RADR: A Powerful Visual Risk Analytics Tool
The Risk Assessment Data Repository (RADR) is a powerful risk analytics platform used to enhance productivity in the investigation of fraud, waste and abuse. This server-based, data analytics product fuses data from multiple sources, with sophisticated predictive and machine learning risk modeling, and an intuitive visual interface. RADR enables proactive identification of risk—namely fraud, waste, and abuse behaviors—and simplifies the investigative process. RADR provides...
The Risk Assessment Data Repository (RADR) is a powerful risk analytics platform used to enhance productivity in the investigation of fraud, waste and abuse. This server-based, data analytics product fuses data from multiple sources, with sophisticated predictive and machine learning risk modeling, and an intuitive visual interface. RADR enables proactive identification of risk—namely fraud, waste, and abuse behaviors—and simplifies the investigative process. RADR provides visualizations for risk propensity and their related data so that managers, auditors, investigators, and analysts can easily access data on high-risk items and focus on the highest ROI cases.
Data Engineering with Discipline
As a data science consultancy, we frequently run into difficult data infrastructure challenges at our clients across multiple industries. To solve a business problem or get decision-making insights from data, we often must start by helping to clean up and organize the data architecture so we can build data science and machine learning (ML) models. This process of getting the data ready for the application of the science is called data engineering....
As a data science consultancy, we frequently run into difficult data infrastructure challenges at our clients across multiple industries. To solve a business problem or get decision-making insights from data, we often must start by helping to clean up and organize the data architecture so we can build data science and machine learning (ML) models. This process of getting the data ready for the application of the science is called data engineering.
5 Key Reasons Why Analytics Projects Fail
With the news full of so many successes in the fields of analytics, machine learning and artificial intelligence, it is easy to lose sight of the high failure rate of analytics projects. McKinsey just came out with a report that only 8% of big companies (revenue > $ 1 billion) have successfully scaled and integrated analytics throughout the organization. In some ways, the very notable successes of analytics and data science contribute to the high failure rate, as...
With the news full of so many successes in the fields of analytics, machine learning and artificial intelligence, it is easy to lose sight of the high failure rate of analytics projects. McKinsey just came out with a report that only 8% of big companies (revenue > $ 1 billion) have successfully scaled and integrated analytics throughout the organization. In some ways, the very notable successes of analytics and data science contribute to the high failure rate, as ill-prepared organizations flock to implement projects. There are various reasons for failure, and all are instructive.
The Problem with Random Stratified Partitioning
When an organization invests in data science, they need to have confidence that the predictive models will be robust; that is, actually work when applied to future cases. However, this critical requirement is too often poorly addressed. Schoolbook answers are partly to blame. Consider this innocuous quote from Investopedia:...
When an organization invests in data science, they need to have confidence that the predictive models will be robust; that is, actually work when applied to future cases. However, this critical requirement is too often poorly addressed. Schoolbook answers are partly to blame. Consider this innocuous quote from Investopedia:
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