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

Facebook Causes Continue to Show Little Promise as Fundraising Tools

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

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.

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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.

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.

Intelligent Process Automation: Boosting Bots with AI and Machine Learning

Across all sectors, companies are learning that they can transform their businesses by embracing Intelligent Process Automation, or IPA. With the pairing of AI and RPA, IPA adds a new layer of intelligent decision-making processes to automated RPA tasks. By automating repetitive work, and adding the ability to automate intelligent decision making, intelligent automation frees up your most valuable resources – your employees – to spend more time on higher value and more strategic work. But in order to reap the rewards of Intelligent Process Automation, organizations must first educate themselves and prepare for the adoption of IPA. In our ebook, Intelligent Process Automation: Boosting Bots with AI and Machine Learning.

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

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.

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.

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.

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.

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.

6 Steps to Improving Your Application’s Analytics Experience

No one designs bad dashboards and reports on purpose. So why do so many applications have terrible analytics experiences? Download this ebook for secrets to creating dashboards and reports your users will love.

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.

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.

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.

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.

Rethinking Information Governance In The Age of Unstructured Enterprise Data

Onna is breaking down how the concept of information governance has evolved and ways today’s businesses can develop a holistic framework to keep up with a rapidly accelerating datasphere.

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

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 Best Sales Forecasting Models for Weathering Your Goals

Every sales forecasting model has a different strength and predictability method. It’s recommended to test out which one is best for your team. This way, you’ll be able to further enhance – and optimize – your newly-developed pipeline. Your future sales forecast? Sunny skies (and success) are just ahead!

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

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.

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.

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 […].

How Banks Are Winning with AI and Automated Machine Learning

Banks have always relied on predictions to make their decisions. Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. But times are changing. Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.