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Category: Analytics

Web Reporting for NGS-IQ Tutoring

Posted on September 3, 2019 by Bill Langston

Web reporting is an integrated feature of NGS-IQ. That's not new. But from surveys, site visits, and technical support calls, we know that some customers haven't gotten very far with our Web reporting features. These customers tend to fall into three groups. Read more arrow


Operational Reporting and Analytics:
All-in-One, or Divide and Conquer?

Posted on February 12, 2019 by Bill Langston

In our experience, a single staff member rarely has the knowledge and skill required for both ETL and data visualization. Managerial, technical, and analytical staff usually team up to complete these steps. NGS highly values this need to work together—our software supports each step but also recognizes the optimal way to modularize functions, allowing people with different skills and job duties to work both as a team and independently. Read more arrow


Power from Versatility

Posted on June 26, 2018 by Bill Langston

One of the most interesting aspects of meeting with NGS-IQ customers is seeing all of the different ways people use the software. It’s a testament to the versatility of NGS-IQ that it can help users satisfy such different requirements. Read more arrow


Structured Query Language (SQL) and
Query/Reporting Software—
A Marriage of Convenience

Posted on May 7, 2018 by Bill Langston

Through the years, IBM has steadily enhanced Structure Query Language (SQL) for the IBM i environment and encouraged software developers to use SQL to define and manipulate their Db2 on i database. Read more arrow


Decision Trees vs. Coding on IBM i

Posted on June 20, 2017 by David Gillman

In machine learning, decision trees are a great algorithm family to work with business information. They are not the most precise nor are they considered cutting edge, but they are a first pass algorithm for many data scientists. Maybe in version two of a project, another algorithm family might create a better model for delivering a reliable model, but over most types of transaction or ERP data, decision trees as a class are where most data scientists start. Read more arrow


Missing Data – The “Green” Revolution Continues

Posted on June 13, 2017 by David Gillman

Many machine learning and predictive processes struggle when they encounter missing data; entire records are bypassed if one field value is missing in the algorithm. For example, in a decision tree, if no value exists for the field where the tree splits, that record is useless because the algorithm cannot say what tree branch the record needs to follow. Read more arrow


The “Green” Revolution Rolls On

Posted on June 6, 2017 by David Gillman

Someone messaged me to point out my title of the “Green” Revolution last week might also refer to IBM i and its heritage with green screen terminal interfaces. Read more arrow


Clean Data—A Current “Green” Revolution

Posted on May 30, 2017 by David Gillman

Good quality data is never a bad thing. For fueling analytic processes, it is a must. In order to maximize return on the investment in machine learning and predictive analytics, companies need clean data as a foundation for analysis. (My use of “green” in the title refers to making money for those outside the US.) Read more arrow

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