Darkhorse

AUGUST 19, 2016

We've explored improving our bar charts , data tables , and pie charts – all with the maxim: remove to improve. In this new installment of our Data Looks Better Naked series, we take on maps. More specifically, the choropleth map. There are libraries with entire floors devoted to the art and science of cartography so, clearly, this animation is general advice for one specific type of map.

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Reporting IT Visualization 73

Data Science and Beyond

AUGUST 4, 2016

Data science can be defined as either the intersection or union of software engineering and statistics. In recent years, the field seems to be gravitating towards the broader unifying definition, where everyone who touches data in some way can call themselves a data scientist. Hence, while many people whose job title is Data Scientist do very useful work, the title itself has become fairly useless as an indication of what the title holder actually does.

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Statistics Data Science Software 59

In(tegrate) the Clouds

AUGUST 4, 2016

In my last post, I wrote about the new data integration requirements. In this post I wanted to share a few points made recently in a TDWI institute interview with SnapLogic founder and CEO Gaurav Dhillon when he was asked: What are some of the most interesting trends you’re seeing in the BI, analytics, and data warehousing space? There are three trends that I believe are fundamentally changing the world of data.

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Jenny Sussin

AUGUST 30, 2016

Over the last week or so, there have been a lot of news articles flying around about WhatsApp being open for businesses and businesses are losing their collective minds with excitement. Before we get into WHY this is the reaction, let me explain what the status quo was before this announcement. PRIOR TO this update to their terms of service , WhatsApp told businesses that they were not allowed to use their application for commercial purposes: advertising, commerce, customer service…This w

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Advertisement

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Visualization

Rita Sallam

AUGUST 17, 2016

It’s been an exciting two weeks in the world of BI! Just yesterday, Saleforce announced its intention to buy smart data discovery vendor, BeyondCore, a new Visionary entrant to this year’s BI and Analytics Magic Quadrant ! At the end of July, Workday announced its intention to acquire Platfora , one of the original Hadoop-based data discovery vendors and new to the Niche quadrant of the MQ.

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Nutanix

AUGUST 29, 2016

IT organizations face significant challenges in providing applications on-demand, while ensuring high uptimes and responsiveness. Legacy IT infrastructure can no longer cater to evolving business needs, especially in cases where IT acts as a cost center rather than an enabler.

Technology 20

Technology Enterprise IT 20

Data Science and Beyond

AUGUST 21, 2016

You’re a hotshot manager. You love your dashboards and you keep your finger on the beating pulse of the business. You take pride in using data to drive your decisions rather than shooting from the hip like one of those old-school 1950s bosses. This is the 21st century, and data is king. You even hired a sexy statistician or data scientist, though you don’t really understand what they do.

Dashboards 52

Dashboards Data-driven Management 52

Occam's Razor

AUGUST 9, 2016

Being book smart is good. The outcome of book smart is rarely better for analytics practitioners then folks trying to learn how to fly an airplane from how-to books. Hence, I have been obsessed with encouraging you to get actual data to learn from. This is all the way from Aug 2009: Web Analytics Career Advice: Play In The Real World! Or a subsequent post about how to build a successful career: Web Analytics Career Guide: From Zero To Hero In Five Steps.

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The Unofficial Google Data Science Blog

AUGUST 31, 2016

By DAVID ADAMS Since inception, this blog has defined “data science” as inference derived from data too big to fit on a single computer. Thus the ability to manipulate big data is essential to our notion of data science. While MapReduce remains a fundamental tool, many interesting analyses require more than it can offer. For instance, the well-known Mantel-Haenszel estimator cannot be implemented in a single MapReduce.

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Nutanix

AUGUST 29, 2016

Cisco® recently removed Nutanix from of its Solution Partner Program (SPP). Cisco® Partner Certification is done through this program. The termination surprisingly followed the successful conclusion of working with very cooperative, and enthusiastic Cisco® teams (Engineering, Product Management, Technical Marketing, Sales) to certify Nutanix’s software on UCS®.

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Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

Management

Nutanix

AUGUST 4, 2016

It’s that time of the year again! VMworld US 2016 is around the corner and there is nothing (other than our own.NEXT!) like the energy of over 20,000 people in Las Vegas talking technology. As always, Nutanix has a lot going on both on the show floor and off.

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Technology 20

Nutanix

AUGUST 22, 2016

Traditionally, the main datacenter has been the center of gravity for most business IT organizations.

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IT 20

Darkhorse

AUGUST 5, 2016

This is the third part of a series of articles about effective analytics implementation. The first part “ The Five Faces of Analytics ” explores the roles you need on your team. The second “ Pluck the Low-hanging Fruit ” helps you discover and prioritize projects. An analytics project is a house of cards. Each project is really three: a change management project stacked on top of a software development project stacked on top of a research project.

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