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

Are Your Embedded Analytics DevOps-Friendly?

Does your analytics solution work with your current tech stack and DevOps practices? If not, any update to the analytics could increase deployment complexity and become difficult to maintain. Learn the 5 elements of a DevOps-friendly embedded analytics solution.

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

Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

by ERIC TASSONE, FARZAN ROHANI We were part of a team of data scientists in Search Infrastructure at Google that took on the task of developing robust and automatic large-scale time series forecasting for our organization. In this post, we recount how we approached the task, describing initial stakeholder needs, the business and engineering contexts in which the challenge arose, and theoretical and pragmatic choices we made to implement our solution.

More Trending

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.

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 […].

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.

Digital Trends Report 2020

As part of our goal to continue helping our community during these times, we wanted to share with you this critical data on the state of digital products across industries and provide context on how businesses are responding to the changing winds.

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

5 Things a Data Scientist Can Do to Stay Current

DataRobot together with Snowflake – a leading cloud data platform provider — is helping data scientists stay current with the latest technology and data science best practices so that they can excel in an increasingly AI-driven workplace. Five Things a Data Scientist Can Do to Stay Current offers data scientists guidance for thriving in AI-driven enterprises.

Deep Learning in Cloudera

Cloudera

Deep learning is in the news. It’s changing the game. It’s changing your life. It’s changing everything. It will change the world. It’s good to see people excited about technology. But deep learning is a tool that enterprises use to solve practical problems. Nothing more, and nothing less. In this blog, we provide a few examples that show how organizations put deep learning to work.

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.

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.

Should I Eat This Fish - A Redesign

Darkhorse

A few years ago we worked with the Alberta Government on a tool that would make Fish Consumption Advisories more accessible to the general public. And after working its way through the government’s approvals process that tool is finally here. One of the problems with eating fish, besides the smell it can leave in the office microwave, is that mercury accumulates in their bodies over time, and we humans don’t mix well with mercury. So you want to stay away from fish that have prodigious amounts.

Testing at Every Stage of Development

Up to 80% of new products fail. The reality is harsh and the reasons why are endless. Perhaps the new product couldn’t oust a customer favorite. Maybe it looked great but was too hard to use. Or, despite being a superior product, the go-to-market strategy failed. There’s always a risk when building a new product, but you can hedge your bets by understanding exactly what your customers' expectations truly are at every step of the development process.

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.

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.

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.

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.

How Embedding AI-Powered Analytics Can Give You a Competitive Advantage

Embedding dashboards and reports aren’t enough. Futureproof your application by offering instant, actionable insights that will give you and your customers a competitive advantage.

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

Apache Hadoop 3.0.0 is Generally Available!

Cloudera

The Apache Hadoop community recently released version 3.0.0 GA , the third major release in Hadoop’s 10-year history at the Apache Software Foundation. We covered earlier releases like 3.0.0-alpha1 and 3.0.0-alpha2 on the Cloudera Engineering blog, and 3.0.0 GA is bigger and better than ever. General availability (GA) marks a point of quality and stability for the release series that indicates it’s ready for broader use.

Building Like Amazon

Speaker: Leo Zhadanovsky, Principal Solutions Architect, Amazon Web Services

Amazon's journey to its current modern architecture and processes provides insights for all software development leaders. To get there, Amazon focused on decomposing for agility, making critical cultural and operational changes, and creating tools for software delivery. The result was enabling developers to rapidly release and iterate software while maintaining industry-leading standards on security, reliability, and performance. Whether you're developing for a small startup or a large corporation, learning the tools for CI/CD will make your good DevOps team great. We are excited to be joined by Leo Zhadanovsky, a Principal Solutions Architect at Amazon Web Services.

Fitting Bayesian structural time series with the bsts R package

The Unofficial Google Data Science Blog

by STEVEN L. SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. Introduction Time series data appear in a surprising number of applications, ranging from business, to the physical and social sciences, to health, medicine, and engineering. Forecasting (e.g.

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.

The Value is in the Data (Wrangling)

Darkhorse

TL;DR: Gather data from inside and outside the firewall Understand (and document) your sources and their limitations Clean up the duplicates, blanks, and other simple errors Join all your data into a single table Create new data by calculating new fields and recategorizing Visualize the data to remove outliers and illogical results Share your findings continuously If you aspire to be a data scientist, you’re really aspiring to be a data wrangler.

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

Rethinking Information Governance In The Age of Unstructured Enterprise Data

Today’s organizations are faced with the overwhelming challenge of managing, finding, and leveraging their information. This eBook discusses a newly discovered information discipline and is filled to the brim with helpful information.