[Infographic] The Analytics Big Bang

Originally published on Data Floq, this infographic outlines the progression of big data and analytics over the last century, including the move from descriptive to prescriptive analytics.

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8 Data Requirements for Advanced Logistics Planning

As I began to write about advanced logistics planning, the first thing that popped into my head, literally, was “damn motorcycles.” I’m currently at 35,000 feet, flying home after a couple of weeks with my wife visiting relatives and friends in Thailand. My mother-in-law’s home in central Bangkok used to be on a quiet, dead-end street (“soi” in Thai). At least, that was until about 10 years ago, when a major Asian parcel delivery company determined (somehow) that the best location for their Thai head office was further in our soi!

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The Failure of Traditional Business Intelligence & the Path Forward

Gartner estimates that the majority of all Business Intelligence (BI) projects fail to meet their desired goals. In today’s data centric world, this is terrifying the majority of businesses. Ask any executive if they think BI is important, and nearly all will agree it is. However, most businesses still fail to unlock the potential gains from the insights in their information, even after investing loads of resources into BI solutions.

Terrified is the correct reaction to this, as the deciding factor between successful companies and ones that fail in the next several decades will, to a large degree, be determined by their ability to utilize analytics to optimize their business flows. Companies unable to do this simply won't be able to compete with the ones who are.

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User Review: IBM's CPLEX Optimization Studio Falls Short

After completing a program in predictive analytics and choosing IBM’s SPSS Modeler as the tool of choice, I attended my first IBM conference (IBM Insight) in October 2014. My first priority was to learn more about the SPSS modeling tool.

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A Brief History of Optimization Modeling - Part 2

In our first blog on the history of opimization modeling, we discussed early software development and shifting ideologies from the 1800s to the 1980s. In this article, you'll learn how modeling software evolved over the course of the past 25 years, bringing us to where we are today.

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Peter Bull: Why I Joined River Logic

Many people in the technology industry have the ambition to work on products that are “game changers,” in terms of the impact on people using the products and the market in general. I’ve been fortunate in my career to work for a number of companies who have been genuine game changers, including SAS Institute and Microsoft. Recently, when I came to think about a new career move, signing up for something that was going to make a big impact and be a real game changer was my top priority.

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Prescriptive Analytics - Closing the Loop

Data becomes action when supported by analytics, and when it is organized into stages that provide business insights using embedded models and forward-looking technology. Exponential investment return is achieved every time a business steps forward in the analytics ladder, toward closing the loop in business integration.

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A Brief History of Optimization Modeling - Part 1

Ever since algebra was invented by a Persian named Muhammad ibn Musa al-Khwarizm in 820 AD, our species has used it for practical and increasingly complex purposes  to the point where our modern civilization could not function without algebra. 

What most people don’t realize is that our civilization would also not function as efficiently without optimization modeling (yes, it’s that important!).

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How Prescriptive Analytics Delivers on the Promise of Big Data

Can you imagine the possibility of making a strategic business decision based on recommended actions derived from past performance, current conditions, and desired outcomes? Advanced analytics have matured to make all that possible and more.

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Avoiding Analysis Paralysis in Prescriptive Analytics

In considering modern business analytics, I was asked recently how connected and dependent all the pieces were. Specific to prescriptive analytics, is it necessary to have predictive analytics in place to find success in prescriptive analytics? The answer is a resounding no.

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