2016

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Ad Block Tracking With Google Analytics: Code, Metrics, Reports

Occam's Razor

You don't use an ad blocker, right? Of course not! You would never want to take away the opportunity a content creator has online to monetize their work via ads. I know that at least some of you think I'm being sarcastic. I am not, and this post is all about getting the data to show you that I am indeed not being sarcastic. I am insanely excited that we can track ad blocking behavior in Google Analytics, so easily.

Metrics 103
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Practical advice for analysis of large, complex data sets

The Unofficial Google Data Science Blog

By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. Some people seemed to be naturally good at doing this kind of high quality data analysis.

Metrics 107
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6 ways data is taking over retail

Information Builders

Today is National Book Lover’s Day, and the perfect opportunity to indulge your inner bookworm. According to a Huffington Post blog on the occasion, one of the best ways to celebrate is by giving the gift of reading and passing along a favorite book to others who might enjoy it. With that in mind, we asked some Information Builders colleagues for their best “data reads” and below is a sampling of their suggestions: read more.

IT 75
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Data Looks Better Naked: Maps Edition

Darkhorse

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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Driving Business Impact for PMs

Speaker: Jon Harmer, Product Manager for Google Cloud

Move from feature factory to customer outcomes and drive impact in your business! This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. You will deepen your understanding of your customers and their needs as well as identifying and de-risking the different kinds of hypotheses built into your roadmap.

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Data analytics: on the way to value-based care

ScienceSoft

Now that Meaningful Use transforms into the value-based care delivery model, providers face a major challenge of adapting to the changes in how care delivery is organized, measured and reimbursed. No gilding any pills here, it is the ‘Do or Die’ dilemma. Find out how the healthcare data analytics can solve this dilemma.

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Ask Why! Finding motives, causes, and purpose in data science

Data Science and Beyond

Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. I recently gave a talk where I argued the importance of asking Why , touching on three different topics: stakeholder motives, cause-and-effect relationships, and finding a sense of purpose.

More Trending

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24 Ultimate Data Scientists To Follow in the World Today

DataRobot Blog

Having a hero / heroine helps you navigate through the difficult times. You look up to them and then think that the problems you thought were difficult are actually trivial in nature. If people can solve and deliver at a much larger scale, you can too! Read more. The post 24 Ultimate Data Scientists To Follow in the World Today appeared first on DataRobot AI Cloud.

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The New Data Integration Requirements

In(tegrate) the Clouds

This week SnapLogic posted a presentation of the 10 Modern Data Integration Platform Requirements on the company’s blog. They are: Application integration is done primarily through REST & SOAP services. Large-volume data integration is available to Hadoop-based data lakes or cloud-based data warehouses. Integration has to support the continuum of data velocities starting from batch all the way to continuous streams.

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Installing Packages in SQL Server R Services

Ms SQL Girl

As you may already know In-database Analytics (also known as Advanced Analytics) is available in SQL Server 2016. To simplify, “In-database Advanced Analytics”: you can run powerful statistical / predictive modelling (from R) inside SQL Server. Read the official definition here. Check Out SQL Latest Bits. SQL Server 2016 RC3 : this includes SQL Server R Services that you can install.

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With Business Driving The Bus, IT Should Be Painting The Lines

Bruno Aziza

By the end of 2016, businesses are projected to spend over $73 billion on cybersecurity, yet they’re no more secure for it. Why? We’ve been looking at the problem through the wrong lens; IT too often makes incremental changes instead of planning a forward-looking approach that takes into account both [.

IT 40
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Reimagined: Building Products with Generative AI

“Reimagined: Building Products with Generative AI” is an extensive guide for integrating generative AI into product strategy and careers featuring over 150 real-world examples, 30 case studies, and 20+ frameworks, and endorsed by over 20 leading AI and product executives, inventors, entrepreneurs, and researchers.

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A Great Analyst's Best Friends: Skepticism & Wisdom!

Occam's Razor

Here's something important I've observed in my experience in working with data, and changing organizations with ideas: Great Analysts are always skeptical. Deeply so. This was always true, of course. But, it has become mission critical over the last few years as the depth, breadth, quantity and every other dimension you could apply to data has simply exploded.

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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. In observational studies treatment is assigned by nature, therefore its mechanism is unknown and needs to be estimated.

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What the Algorithmic Economy Means to the Human Decision Maker

Information Builders

Today is National Book Lover’s Day, and the perfect opportunity to indulge your inner bookworm. According to a Huffington Post blog on the occasion, one of the best ways to celebrate is by giving the gift of reading and passing along a favorite book to others who might enjoy it. With that in mind, we asked some Information Builders colleagues for their best “data reads” and below is a sampling of their suggestions: read more.

IT 70
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Things to see while buying a Mutual Fund

MLWhiz

This is a post which deviates from my pattern fo blogs that I have wrote till now but I found that Finance also uses up a lot of Statistics. So it won’t be a far cry to put this on my blog here. I recently started investing in Mutual funds so thought of rersearching the area before going all in. Here is the result of some of my research.

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Entity Resolution Checklist: What to Consider When Evaluating Options

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

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Patient engagement analytics: It’s measurable!

ScienceSoft

No more abstract patient engagement talks. Healthcare data analytics shows the way into transforming the level of patients' involvement in their health into a measurable value.

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Is Data Scientist a useless job title?

Data Science and Beyond

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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Data Quality in Six Verbs

Jim Harris

Once upon a time when asked on Twitter to identify a list of critical topics for data quality practitioners, my pithy (with only 140 characters in a tweet, pithy is as good as it gets) response was, and especially since I prefer emphasizing the need to take action, to propose six critical verbs: Investigate , Communicate , Collaborate , Remediate , Inebriate , and Reiterate.

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Blockchain And IoT: Not Ready For Primetime, But Now’s The Time To Start

Martha Bennett

Few would disagree that for IoT to live up to its promises, devices will sooner or later need to communicate directly, autonomously and securely with each other. Well-architected blockchain-based systems can help deliver those requirements, but they’re not available or even feasible today. In many ways, that’s a good thing, because it opens up great […].

IoT 40
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Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity

Speaker: Nicholas Zeisler, CX Strategist & Fractional CXO

The first step in a successful Customer Experience endeavor (or for that matter, any business proposition) is to find out what’s wrong. If you can’t identify it, you can’t fix it! 💡 That’s where the Voice of the Customer (VoC) comes in. Today, far too many brands do VoC simply because that’s what they think they’re supposed to do; that’s what all their competitors do.

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3 Trends that are Changing the World of Data

In(tegrate) the Clouds

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.

IoT 45
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Social Media Monitoring – Or Is It Surveillance?

Jenny Sussin

Over the last week, there have been a series of articles dumping on one particular social media monitoring application that was allegedly using an unethical value proposition to market its solution: surveillance. As one of Gartner’s social analytics analysts I’ve got to tell you, the tools that each vendor offers to the market could allow the exact same type of use.

IT 40
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How To Protect Your Business From Online Fraud This Holiday Season

Bruno Aziza

Online and mobile shopping are expected to continue growing apace through the rest of the holiday season. Unfortunately, they’re not the only things on the rise: As chip-enabled cards have made brick-and-mortar shopping safer, fraudsters are increasingly targeting online stores.

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Suck Less | A Plea For User-Centric Design: Powered By You!

Occam's Razor

Analysts, honestly, make the world go round when it comes to any successful business – yes, data is that important. As you might expect from any role, they also make a handful of important mistakes. I've written about the biggest mistake web analysts make. Today's post is an adjacent mistake: The cardinal sin of spending too much time with data and in reports!

Testing 86
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The Big Payoff of Application Analytics

Outdated or absent analytics won’t cut it in today’s data-driven applications – not for your end users, your development team, or your business. That’s what drove the five companies in this e-book to change their approach to analytics. Download this e-book to learn about the unique problems each company faced and how they achieved huge returns beyond expectation by embedding analytics into applications.

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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. Because individual observations have so little information, statistical significance remains important to assess.

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Own the Adoption

Darkhorse

How many times has this happened to you: You get to the end of a gruelling analytics project. You’ve tortured the data until it confessed. You’ve made a silk purse out of a sow’s ear. You’ve found the fountain of youth. Against all odds, you solved it and know without a doubt that you have the right answer. You are thrilled. All that effort was worthwhile.

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Pandas For All - Some Basic Pandas Functions

MLWhiz

It has been quite a few days I have been working with Pandas and apparently I feel I have gotten quite good at it. (Quite a Braggard I know) So thought about adding a post about Pandas usage here. I intend to make this post quite practical and since I find the pandas syntax quite self explanatory, I won’t be explaining much of the codes. Just the use cases and the code to achieve them.

IT 40
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Retail trends 2016: bite-size takeaways from Retail Technology Show USA

ScienceSoft

Back from a visit to Retail Technology Show USA 2016, we compiled this year’s retail software development trends in one bite-size guide.

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How to Stay Competitive in the Evolving State of Martech

Marketing technology is essential for B2B marketers to stay competitive in a rapidly changing digital landscape — and with 53% of marketers experiencing legacy technology issues and limitations, they’re researching innovations to expand and refine their technology stacks. To help practitioners keep up with the rapidly evolving martech landscape, this special report will discuss: How practitioners are integrating technologies and systems to encourage information-sharing between departments and pr

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If you don’t pay attention, data can drive you off a cliff

Data Science and Beyond

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.

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Two Acquisitions in Two Weeks!

Rita Sallam

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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Update Your Balanced Scorecard With Business Outcome And Agility Metrics

Martha Bennett

We’ve entered the age of the customer, where powerful customers are disrupting every industry. In response, companies will have to change how they develop, market, sell, and deliver products and services directly to their customers and through their partners. CIOs and their teams are crucial to these strategic responses and will have to track transformation […].

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It’s Time to Rethink Integration

In(tegrate) the Clouds

Last week SnapLogic posted the company’s mission on their blog , which is focused on accelerating how enterprise integration technology is delivered in the enterprise. The company is growing rapidly and hiring. Here is the their mission statement: It’s time to rethink integration. Integrating data and applications with legacy products is slowing down your business.

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Why Modern Data Challenges Require a New Approach to Governance

A healthy data-driven culture minimizes knowledge debt while maximizing analytics productivity. Agile Data Governance is the process of creating and improving data assets by iteratively capturing knowledge as data producers and consumers work together so that everyone can benefit. It adapts the deeply proven best practices of Agile and Open software development to data and analytics.