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Analytics

Understanding and Analyzing the Hidden Structures of a Unstructured Data Set

August 28, 2014 by Kunal Jain

Data set.

The key to using unstructured data set is to identify the hidden structures in the data set. This enables us to convert it to a structured and more usable format.In this article we will talk in more details to understand the data structure and clean unstructured text to make it usable for the modelling exercise.[read more]

Untangling the Retail Supply Chain with Real-Time Analytics

August 26, 2014 by Dale Skeen

Untangling the retail supply chain.

Retail supply chains are longer and more tangled than ever before – the complexity of the data sets and the management of far-flung suppliers coupled with high customer expectations around service and reliability are taxing traditional approaches to supply chain management to their limits.[read more]

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Spark the Flame: The Power Behind Real Time Analytics

August 26, 2014 by Gil Allouche

Real time analytics/Deviantart.net 

Real time data analytics is like a burning fire. Consider this analogy. Once a spark catches, several chemical and physical changes occur. The fuel (data) is processed while heat (or data outcomes) is simultaneously emitted. Once the process of data streaming begins, it is seamless and very efficient. The process will continue to be efficient as long as data is fed into the system.[read more]

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Building Information Technology Liquidity

August 25, 2014 by Paul Barsch

Risky Business column.

In the financial world, liquid assets can allow companies to react and capitalize on market opportunities. Liquidity in IT means that companies have enough extra compute firepower, people resources and are agile enough with IT processes to respond to unplanned events and demand, in whatever shape, form or order they arrive.[read more]

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Data Science: What Companies Need to Know

August 23, 2014 by Kristen Paral

Data science.

Take a look at Google, Uber, Amazon or Airbnb. All of them are utilizing big data and data scientists to derive business insights and making quantum leaps in their respective business models. Many companies don’t understand the full value of what data science can deliver, revolutionizing their big data into actual results.[read more]

5 Tips to Consider When Designing Supply Chain Key Performance Indicators

August 22, 2014 by Ray Major

KPIs.

You can’t predict anything with 100% certainty, and your predictive power wanes the farther out you gaze. The study of KPIs over time is all about finding patterns and signals, then applying intelligence in order to make better decisions and gain wisdom.[read more]

How Big Data Enables Hyper-Local Real-Time Weather Forecasting

August 21, 2014 by Mark van Rijmenam

Weather forecasting.

Accurate weather predictions are very important for economic activities and with more extreme weather conditions happening, knowing what to expect could save a lot of money, and lives. Such hyper-local real-time weather forecasting is becoming more common every day.[read more]

Supply Chain Business Intelligence Is More Than Just Technology

August 20, 2014 by Ray Major

Supply chain BI.

Interestingly the technology implementation requires the most hard dollar costs, and returns the least in terms of ROI. The real value in Holistic BI is in garnering the corporate buy in or, in other words, training the people to make better data-driven business decisions based on the data.[read more]

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How Big Data Is Changing Insurance Forever

August 19, 2014 by Bernard Marr

The Big Data Guru column.

Insurers such as Progressive in the US and Prudential in the UK are now pushing for wider acceptance of “telematics” devices, which feed back real-time data on a driver’s behaviour. So young drivers who can demonstrate that they keep within speed limits and do not brake suddenly too often, can pay less.[read more]

Text Analytics vs. Other Research Methods [VIDEO]

August 19, 2014 by Tom Anderson

Text analytics.

We believe that Text Analytics is important not just because of its implications within market research or any kind of research for that matter, but because of its value across so many other disciplines as well. All of the marketing research techniques represented in the debate are useful of course, and text analytics can be an aid in every single case.[read more]

Comparison of esProc and R Language in Processing Text Files

August 18, 2014 by Jim King

As languages for data computations, both esProc and R language have rich functions to process text files. They have many similarities in basic usage, as well as obvious differences, such as in the aspect of processing files with fixed column width and big text files, reading and writing designated columns, computational performance, etc. The article aims to compare their similarities and differences.[read more]

Got Analytics? Who Will Promote the Industry?

August 17, 2014 by Ted Cuzzillo

Got analytics?

Business people have everything. They’ve got data, and often it’s clean. They’ve got tools, and many are easy to use. They’ve got visualizations, many of which help. They’ve got domain knowledge, at least most do. What some front line observers find they lack is analytical thinking.[read more]

A Pitfall to Avoid When Funding Big Data Analytics

August 15, 2014 by Bill Franks

Funding big data analytics.

If analytics are to continue to become more strategic and grow in impact to organizations, then there must be a mechanism to distinguish between IT costs that are truly overhead and those that can be tied to revenue. The costs of analytic systems must be directly associated not just with IT, but with the business processes and products they support.[read more]

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Big Data: Making an Impact at the Post Office

August 13, 2014 by Gil Allouche

Postal truck.

While the USPS is struggling, big data is exploding. The technology is taking significant steps forward at an extremely rapid pace, making it more and more enticing to numerous different sectors. Big data analytics is becoming especially important because of the speed and accuracy with which the analyses can be performed.[read more]

Similarities and Differences Between Predictive Analytics and Business Intelligence

August 12, 2014 by Dean Abbott

Predictive analytics vs. BI.

BI and PA are important but complementary disciplines. BI is a much larger field and understandably so. PA is more of a specialty, but a specialty that is gaining visibility and recognition as an important skill set to have in any organization. Here’s to further collaboration in the future![read more]