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Analytics

ATM Replenishment: Forecasting and Optimization

January 25, 2015 by Michael Gilliland

ATM.

ATM replenishment is a perfect example of combining two areas of advanced analytics, forecasting and optimization. For DBS Bank, the first step was to understand withdrawal activity. Withdrawal rate is impacted by many factors, such as location, day of week, day of month, and time of day, and can be dramatically impacted by holidays or other special events.[read more]

Business Analytics Error: Learn from Uber’s Mistake During the Sydney Terror Attack

January 24, 2015 by RK Paleru
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Uber.

Recently, as a sad day of terror ended in Sydney, a bad case of Uber’s analytical approach to pricing came to light - an “algorithm based price surge.” Uber’s algorithm driven price surge started overcharging people fleeing the Central Business District (CBD) of Sydney following the terror attack.[read more]

Analytics Capability Landscape: The Importance of Decisions

January 23, 2015 by James Taylor

Analytics.

As part of my recently completed research on the analytic capability landscape, we did an interesting straw poll. We asked those attending a webinar on the topic which of these was the business goal for their analytic efforts today and how did they see that changing in the next 12-24 months.[read more]

Free Data Sources to Upgrade Your Business Decision-Making

January 19, 2015 by Eran Levy

Data.

We know by now that data is essential to the modern business. Everyone collects it, and most have some sort of analysis plan or software in place. But what a lot of companies still haven’t gotten the hang of is using outside data sources to boost their strategic decision-making. This requires a BI software that can combine your internal data with third-party sources, to analyze it and gain new insights.[read more]

Aligning Big Data

January 18, 2015 by Martyn Jones

Aligning big data.

This is an overview of the realignment and placement of Big Data into a more generalized architectural framework, an architecture that integrates data warehousing (DW 2.0), business intelligence and statistical analysis.[read more]

Interview with Jonathon Karelse, Co-Founder of NorthFind Partners

January 17, 2015 by Michael Gilliland

Jonathon Karelse.

In December the Institute of Business Forecasting published the first of a new blog series on Forecast Value Added. Each month I will be interviewing an industry forecasting practitioner (or consultant/vendor) about their use of FVA analysis. The December interview featured Jonathon Karelse, co-founder of NorthFind Partners.[read more]

2015: The Year of IoT Pioneers, Analytics and Data Privacy

January 16, 2015 by Puneet Pandit

2015.

This past year welcomed widespread Internet of Things (IoT) adoption and hype, big data implementation, and growing concerns around data privacy and cloud deployment. As 2014 draws to a close, we look ahead with much anticipation for what promises to be a signature year for machine learning, predictive analytics, new IoT pioneers and a full overhaul in the distribution of the IoT.[read more]

VC Investment Analytics on 20 Years of Investment Data

January 15, 2015 by Nilesh Jethwa

VC investment analytics.

In this article, we will perform straight analytics on a huge dataset available from https://www.pwcmoneytree.com/. PWC Money Tree provides a data dump of all the venture capital investments from 1995 onwards. Having data that goes that far back should give us what we need to extract interesting and helpful findings.[read more]

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How Your Small Business Should be Taking Advantage of Big Data

January 14, 2015 by Philip Cohen

Big data and small business.

Big data has been a term widely related to big business. However, more small businesses are taking advantage of the insights that it can offer. If you own a small business and you're not sure how you should take advantage of big data, keep reading for more helpful tips.[read more]

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The 12+ Unmissable Big Data Stories of the Past Year

January 13, 2015 by Bernard Marr

The Big Data Guru column.

While 2013 brought an explosion of awareness around big data, 2014 showed the market for big data services beginning to mature. We also saw how “datafication” is starting to impact the lives of more and more people around the world.[read more]

Customer Engagement Analytics Getting Simpler with Verint

January 13, 2015 by Richard Snow

Reasons for improving engagement.

Verint is a well-established vendor of workforce optimization systems. It recently acquired KANA Software, as I discussed, which enabled Verint to move further into the customer engagement market. Now Verint has combined the two companies’ range of analytics products to create Verint Engagement Analytics.[read more]

Peer into the Analytics Crystal Ball for 2015

January 9, 2015 by Bill Franks

Analytics crystal ball.

Ensemble methods have been around for some time. Recently, however, their popularity has been increasing even further. In my opinion, this isn’t just because ensemble methods work, but also is the result of a few other trends coming together.[read more]

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Big Data: The Secret Snacking Ingredient

January 9, 2015 by Gil Allouche

Big data and snacks.

Companies are looking to use the vast amounts of information on consumer behavior to figure out which products to make, and where to focus their efforts. The right data and analytical tools can help manufacturers understand taste preferences and which retailers cater to which consumers.[read more]

Predictive Analytics Interview Series: Pasha Roberts at Talent Analytics

January 8, 2015 by Eric Siegel

Pasha Roberts.

We interviewed Pasha Roberts, Co-Founder and Chief Scientist at Talent Analytics Corporation. View the Q-and-A below to see how Pasha has incorporated predictive analytics into the workforce of Talent Analytics Corporation. Also, glimpse what’s in store for the new PAW Workforce conference.[read more]

Customer Data Quality: What Is the Value-at-Risk?

January 7, 2015 by Gayle Nixon
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Value-at-Risk.

If you work in financial services or, more specifically, Capital Markets, then you are likely to be familiar with the concept of VaR or “value at risk.” VaR is a statistical calculation used in finance to incorporate a quantifiable measure of the financial risk that an asset (or a portfolio of assets) will decline in value.[read more]