2017

A Business Analyst's Guide To Managing Change Requests

BA Learnings

It’s common knowledge that people tend to resist change as much as they can. However, without change, there can’t be progress and BAs would certainly not have that much to do. When working on large projects, change requests from stakeholders are to be expected. Successful project managers and analysts know how to manage them without bringing the project to a standstill.

Artificial Intelligence: Implications On Marketing, Analytics, And You

Occam's Razor

A rare post today. It looks a little further out into the future than I normally tend to. It attempts to simplify a topic that has more than it’s share of coolness, confusion and complexity. While the phrase Artificial Intelligence has been around since the first human wondered if she could go further if she had access to entities with inorganic intelligence, it truly jumped the shark in 2016.

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Virtually Celebrate New Year’s Eve in Every Timezone with the ‘VRChat’ Community

IBM Big Data Hub

Ever wished you could jet set around the world fast enough to ring in the New Year at a party in every timezone? Well that might not be quite possible, but VRChat is offering something close. VRChat , a social VR experience supporting major PC VR headsets via Steam , is celebrating New Year’s Eve virtually in every timezone, every hour, on the hour. Fireworks included. Image courtesy VRChat.

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Big Data: Examples, Sources and Technologies explained

ScienceSoft

While defining big data, we share multi-industry examples of its practical application, list its internal and external sources, as well as name most popular big data technologies

100 Pipeline Plays: The Modern Sales Playbook

For the first time, we’re sharing the winning plays that took us from scrappy startup to a publicly traded company. Use our proven data-driven plays to grow your pipeline and crush your revenue targets.

Predictions 2018: The Blockchain Revolution Will Have To Wait A Little Longer

Martha Bennett

The visionaries will forge ahead, those hoping for immediate industry and process transformation will give up. This is the answer I usually give when asked for a one-sentence summary of how I see 2018 shaping up in the blockchain technology arena. Following blockchain technology feels a little like living in two parallel universes: One is […]. blockchain prediction

Predictions 2018: AI Hard Fact – Treat It Like A Plug-And-Play Panacea and Fail

Boris Evelson

Look right, look left, you’ll see a fellow CIO contemplating their AI move. Failing to act is not an option in most organizations. However, as enterprises are kicking off their AI pilots or seeing early results, the honeymoon is over as enterprises that naively celebrated the cure-all promises of artificial intelligence (AI) technologies is over. Enterprises needed better data foundations. They underestimated the level of business expertise […].

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My 10-step path to becoming a remote data scientist with Automattic

Data Science and Beyond

About two years ago, I read the book The Year without Pants , which describes the author’s experience leading a team at Automattic (the company behind WordPress.com, among other products). Automattic is a fully-distributed company, which means that all of its employees work remotely (hence pants are optional). While the book discusses some of the challenges of working remotely, the author’s general experience was very positive.

Spotfire Tips & Tricks: Hierarchical Cluster Analysis

TIBCO

Hierarchical cluster analysis or HCA is a widely used method of data analysis, which seeks to identify clusters often without prior information about data structure or number of clusters. Strategies for hierarchical clustering generally fall into two types: Agglomerative and divisive. Agglomerative is a bottom up approach where each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.

Just Buying Into Modern BI and Analytics? Get Ready for Augmented Analytics, the Next Wave of Market Disruption

Rita Sallam

Machine learning automation is affecting all of enterprise software, but will completely transform how we build, analyze, and consume data and analytics. Over the past 10 years or more, visual-based data discovery tools (e.g. Tableau, Qlik, Tibco Spotfire) have disrupted the traditional BI market (e.g. IBM Cognos, SAP BusinessObjects). Yet, as transformative as these tools have been, analytics is once again at a critical inflection point.

Smart Data Visualization Walks You Through to Success

Smarten

Take the Guesswork Out of Analytics with Smart Data Visualization! Smart data visualization takes the guesswork out of data analysis. Why ask your business users to use cumbersome, difficult tools to analyze data or expect them to wait for professional analysts or IT staff to satisfy their analytical needs. They have a job to do and you hold them accountable for results but if they don’t have the right data visualization tools, they can’t get the most out of data.

Optimize the Performance of Your Serverless Functions

Run mission-critical applications on serverless without sacrificing visibility.

A List of Business Process Management Certifications

BA Learnings

While there’s nothing like having some experience under your belt, Business Process Management (BPM) certifications can aid analysts in some key ways: they serve as concrete evidence of business process management knowledge; offer the opportunity to learn new concepts /best practices; and benefit from the experience of trainers/mentors. While some of these certifications are offered by professional organizations, others have been put together by software vendors.

It's Not The Ink, It's The Think: 6 Effective Data Visualization Strategies

Occam's Razor

Ten years, and the 944,357 words, are proof that I love purposeful data, collecting it, pouring smart strategies into analyzing it, and using the insights identified to transform organizations. In the quest for that last important bit, I am insanely obsessive about 1. simplification and 2. pressing the right emotional buttons.

Learning Machine Learning? Six articles you don’t want to miss

IBM Big Data Hub

Digital disruption has revolutionized the way we live and do business — and machine learning is the latest wave of that revolution

4 Types of Data Analytics to Improve Decision-Making

ScienceSoft

Learn about different types of data analytics and find out which one suits your business needs best: descriptive, diagnostic, predictive or prescriptive

The Big Payoff of Application Analytics

Outdated or absent analytics won’t cut it in today's data-driven applications. And they won’t cut it for your end users, your development team, or your business. Learn how 5 companies used embedded analytics to achieve huge returns and greater value than anticipated.

Whose cloud? The business strategy question every CEO should consider.

Mark Raskino

Before we get into this important issue I have to declare a disinterest. I’m not a cloud analyst at Gartner. I don’t cover the vendors or their offerings. I can’t tell you which one is best under different circumstances. I have many colleagues who can help you with those decisions. What I do know is this – deciding which cloud(s) your company will become reliant on is a strategy question that cannot be left to technical thinkers alone.

Everything you wanted to know about SAP Leonardo but were afraid to ask

Boris Evelson

Large enterprise software vendors seem to be enamoured with using the names of historical figures or literary characters as brand names. Unfortunately, this is really confusing for the buyers, since the vendors apply these branding names differently. For example (in an increasing order of branding approach complexity): OpenText Magellan is a collection of business intelligence […].

Causality in machine learning

The Unofficial Google Data Science Blog

By OMKAR MURALIDHARAN, NIALL CARDIN, TODD PHILLIPS, AMIR NAJMI Given recent advances and interest in machine learning, those of us with traditional statistical training have had occasion to ponder the similarities and differences between the fields. Many of the distinctions are due to culture and tooling, but there are also differences in thinking which run deeper. Take, for instance, how each field views the provenance of the training data when building predictive models.

Advice for aspiring data scientists and other FAQs

Data Science and Beyond

Aspiring data scientists and other visitors to this site often repeat the same questions. This post is the definitive collection of my answers to such questions (which may evolve over time). How do I become a data scientist? It depends on your situation. Before we get into it, have you thought about why you want to become a data scientist? Hmm… Not really. Why should I become a data scientist? I can’t answer this for you, but it’s great to see you asking why.

Your Guide to Using Conversational Marketing to Drive Demand Generation

What is conversational marketing really about? This guide will examine the market forces at play, shifting buyer trends, how to leverage conversation marketing, and the tactics involved in adopting it for a B2B demand generation strategy.

Watson Use Cases in Customer Service

Perficient Data & Analytics

According to a recent Forrester report, usage of chat bots and automated customer interaction tools is growing, but the success rate is dramatically low. While more than half of global organizations are using these tools, or planning to use the solutions soon, failure rates are often reported around 70%. One factor contributing to this failure rate is the current capabilities of solutions that rely solely on linear steps, driven only by keyword identification.

Open Source: A primer for Big Data

DSI Analytics

It is nearly impossible to talk about Big Data without making frequent reference to a broad ecosystem of computer code that has been made available for use and modification by the general public at no charge. History of open source In the early days of computing, computer code could be considered an idea or method, […]. The post Open Source: A primer for Big Data appeared first on DSI Analytics. Technology apache hadoop Big Data open source

Smart Data Visualization: Simpler, Better, Clearer, Faster

Smarten

Smart Visualization Tools: Analysis and Data Displays Made Simple (and Clear). Smart Data Visualization! This concept seems alien to some people. Is it data that can read your mind and automatically display itself in a way that will help you understand? Is it a method you use to see data in a clear way; a technique you learn in a class? The answer is yes…and no.

Business Analysts & The Anchor Effect

BA Learnings

I’ve always been intrigued by the concept of cognitive bias for the simple reason that it exposes the flaws in one’s thinking and ability to make sound decisions. Every analyst, and in fact everyone, stands to benefit from understanding what cognitive biases are so they can be kept to a minimum or spotted when interacting with others.

Monitoring AWS Container Environments at Scale

In this eBook, learn how to monitor AWS container environments at scale with Datadog and which key metrics to monitor when leveraging two container orchestration systems (ECS and EKS).

The Very Best Digital Metrics For 15 Different Companies!

Occam's Razor

The very best analysts distill, rather than dilute. The very best analysts focus, when most will tend to gather. The very best analysts are display critical thinking, rather than giving into what’s asked. The very best analysts are comfortable operating with ambiguity and incompleteness, while all others chase perfection in implementation / processing / reports.

Influencers assess 2017 and make predictions for 2018

IBM Big Data Hub

As the year winds down, questions tend to arise about what the big trends of the past year have been and what the year to come may hold

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Spark vs. Hadoop MapReduce: Which big data framework to choose

ScienceSoft

Hadoop MapReduce or Apache Spark? We explore two leading big data frameworks to understand the business needs each of them can satisfy

A story chart of the corporate information age

Mark Raskino

It was nineteen-eighty-something. “ Information technology, is a societal, epochal technology ” said my university lecturer, quoting a translated Japanese author (that made the insight seem even more wise and visionary). Like … wow man.

The Hitchhiker’s Guide to Embedded Analytics – 4 Mission-Critical Steps to Take on Your Analytics Journey

The right analytics capabilities will turn data into valuable insights for your end users. This research-based guide, derived from insights of industry professionals, will allow you to create an optimal strategy for acquiring those capabilities.

Hyperopt - A bayesian Parameter Tuning Framework

MLWhiz

Recently I was working on a in-class competition from the “How to win a data science competition” Coursera course. You can start for free with the 7-day Free Trial. Learned a lot of new things from that about using XGBoost for time series prediction tasks. The one thing that I tried out in this competition was the Hyperopt package - A bayesian Parameter Tuning Framework. And I was literally amazed

Attributing a deep network’s prediction to its input features

The Unofficial Google Data Science Blog

By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs.

The Five Faces of the Analytics Dream Team

Darkhorse

The chasm between Business and IT is well documented and has existed since the first punch-card mainframe dimmed the lights of MIT to solve the ballistic trajectory of WWII munitions. Analytics and now Data Science are trapped in the middle. Everyone hopes they'll deliver the productivity gains, but the jury is still out. Some studies suggest that analytics projects have an 80% failure rate. A recent HBR article put it at 100% for data science projects. That’s abysmal.

Growth and Change for Telecom Companies Isn’t Slowing Down

TIBCO

Just a few short years, ago phone booths were scattered around the cities. We were accustomed to keeping spare change simply for the sake of placing a call. Then, suddenly everything changed. The introduction of prepaid phone cards took over and the need for coins slowly diminished but still, the pay phones remained with the same simple functionality—talking. The few major telephone companies were able to bill in advance thanks to the phone card credit.

Risk 46

LinkedIn + ZoomInfo Recruiter: Better Data for Better Candidates

Check out our latest ebook for a guide to the in-depth, wide-ranging candidate and company data offered by ZoomInfo Recruiter — and make your next round of candidate searches faster, more efficient, and ultimately more successful.