datapine

MAY 2, 2019

Modern dashboard software makes it simpler than ever to merge and visualize data in a way that’s as inspiring as it is accessible. But while doing so is easy, a great dashboard still requires a certain amount of strategic planning and design thinking. Knowing who your audience is will help you to determine what data you need. Knowing what story you want to tell (analyzing the data) tells you which data visualization type to use.

Visualization 173

Visualization Dashboards Sales KPI 173

David Menninger's Analyst Perspectives

MAY 1, 2019

About 10 years ago, social media tools like Facebook, Twitter and LinkedIn introduced a wave of collaborative analytics and BI capabilities. We saw chat streams associated with specific analyses that users could like or endorse. The number of contributions a user made to the community was part of his or her profile so others could accordingly weigh the importance of the input.

Analytics 154

Analytics Business Intelligence Technology 154

Peter James Thomas

MAY 3, 2019

Larger PDF version (opens in a new tab). Introduction. As part of my consulting business , I end up thinking about Data Capability Frameworks quite a bit. Sometimes this is when I am assessing current Data Capabilities, sometimes it is when I am thinking about how to transition to future Data Capabilities. Regular readers will also recall my tripartite series on The Anatomy of a Data Function , which really focussed more on capabilities than purely organisation structure [1].

Strategy 55

Strategy Data Architecture Data Quality Data Strategy 55

TDAN

APRIL 30, 2019

AI is a new buzzword in the corporate environment, and although 98% of companies are aiming to become data-driven, less than one third succeeded in 2018. There are multiple reasons behind this inability to adopt AI and data-driven approaches. For most organizations, there are significant obstacles to using AI daily, which have to do with […].

Data-driven 53

Data-driven Data Architecture Data Strategy Big Data 53

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Experimentation

Analytics Vidhya

APRIL 28, 2019

Introduction Have you ever been stuck at work while a pulsating cricket match was going on? You need to meet a deadline but you. The post Learn how to Build and Deploy a Chatbot in Minutes using Rasa (IPL Case Study!) appeared first on Analytics Vidhya.

Analytics 302

Analytics 302

O'Reilly on Data

MAY 1, 2019

Watch highlights from expert talks covering machine learning, predictive analytics, data regulation, and more. People from across the data world are coming together in London for the Strata Data Conference. Below you'll find links to highlights from the event. Making the future. James Burke asks if we can use data and predictive analytics to take the guesswork out of prediction.

Advertising 187

Advertising Machine Learning Predictive Analytics Data-driven 187

David Menninger's Analyst Perspectives

MAY 3, 2019

Domopalooza 2019 marked the first annual user conference after Domo went public, but the energy, excitement and new feature announcements have not slowed. With thousands in attendance and growing fast, this year's conference focused on five key areas: digitization, real time connectivity, driving insight based actions, applying AI & machine learning, and building applications.

IoT 124

IoT Machine Learning Data Integration Business Intelligence 124

Analytics Vidhya

MAY 2, 2019

Introduction What is one of the most important and core concepts of statistics that enables us to do predictive modeling, and yet it often. The post Statistics 101: Introduction to the Central Limit Theorem (with implementation in R) appeared first on Analytics Vidhya.

Statistics 300

Statistics Predictive Modeling Modeling Analytics 300

O'Reilly on Data

MAY 1, 2019

Drawing insights from recent surveys, Ben Lorica analyzes important trends in machine learning. Continue reading Sustaining machine learning in the enterprise.

Machine Learning 197

Machine Learning Enterprise 197

Corinium

APRIL 30, 2019

Very often, there is a confusion on the difference between Data Scientists and Data Engineers. Most organizations use the terms interchangeably thus causing further confusion between the exact roles and responsibilities for each profile.

150

150

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.

Management

Smart Data Collective

MAY 2, 2019

Large companies around the world are investing in big data. Big data has been especially important for optimizing their marketing campaigns. However, a number of small businesses are getting in on the action too. Local marketing agencies have discovered that SEO is more dependent on big data than ever. They are developing more data driven solutions to offer better search marketing strategies.

Big Data 111

Big Data Optimization Modeling Deep Learning 111

Analytics Vidhya

APRIL 30, 2019

Introduction Data science is an ever-evolving field. As data scientists, we need to have our finger on the pulse of the latest algorithms and. The post Don’t Miss these 5 Data Science GitHub Projects and Reddit Discussions (April Edition) appeared first on Analytics Vidhya.

Data Science 299

Data Science Analytics Deep Learning Machine Learning 299

O'Reilly on Data

MAY 2, 2019

Sandra Wachter argues that a right to reasonable inferences could protect against new forms of discrimination. Continue reading Privacy, identity, and autonomy in the age of big data and AI.

Big Data 134

Big Data 134

Corinium

APRIL 30, 2019

Ok guys , heres my first blog and I hope it’s a good start. Happy to get your feedback whether be it positive or constructive, however my objective here is to share my ideas and communicate with you regardless of where you are located. The topic is about the analytical and data story telling.

Analytics 150

Analytics IT 150

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

Testing

Martha Bennett

MAY 1, 2019

More than six months have passed since I wrote Forrester’s predictions 2019 report for distributed ledger technology (DLT, AKA blockchain). In the blockchain world, that’s ages ago. As I keep being asked how those predictions are shaping up, and having just attended two excellent events in New York, now’s a good time to take stock. […].

Reporting 110

Reporting Technology 110

Analytics Vidhya

APRIL 29, 2019

Introduction It is not the strongest or the most intelligent who will survive but those who can best manage change. Evolution is the only. The post 8 Awesome Data Science Capstone Projects from Praxis Business School appeared first on Analytics Vidhya.

Data Science 272

Data Science Management Analytics IT 272

O'Reilly on Data

MAY 1, 2019

Mick Hollison describes why hybrid and multi-cloud is the future for organizations that want to capitalize on machine learning and AI. Continue reading The enterprise data cloud.

Enterprise 116

Enterprise Machine Learning 116

Corinium

APRIL 29, 2019

Are you extracting maximum insights from your data? You know crude oil is more valuable when it’s processed. Data is the same. It’s much more valuable when you can use it to drive your business forward. Conventional data warehouses can’t handle the volume, complexity, and variety of today’s data, and they can’t empower all your teams to access and analyze that data in real time.

Data Warehouse 150

Data Warehouse Data Quality Data-driven IT 150

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IT

CIO Business Intelligence

MAY 1, 2019

With business generating more data than ever before, organizations are rapidly investing in business intelligence (BI) capabilities to help them generate insights from that data to drive better business decisions and identify new opportunities.

Analytics 101

Analytics Business Intelligence 101

Analytics Vidhya

APRIL 30, 2019

Introduction In the last 6 years – we have helped millions of users in learning data science, we helped hundreds of companies across the. The post Last Chance to Register on our Flagship Course – AI & ML BlackBelt appeared first on Analytics Vidhya.

Data Science 195

Data Science Analytics Machine Learning 195

O'Reilly on Data

MAY 2, 2019

Chris Taggart explains the benefits of “white box data” and outlines the structural shifts that are moving the data world toward this model. Continue reading The unstoppable rise of white box data.

Modeling 113

Modeling 113

Corinium

MAY 1, 2019

AI will allow us to meet a cause that was defined 15 years ago but it has its challenges, says Faraz Shafiq, SVP and CAIO of Cambia Health Solutions.

IT 150

IT 150

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.

Customer Analytics

BizAcuity

APRIL 28, 2019

According to a Federal Bank report, more than $600 billion of household debt in the U.S. is delinquent as of June 30th, 2017. Out of which, $400 billion is delinquent for more than 90 days. This shows the need to rectify this issue before it becomes a crisis. A crisis that not only poses a serious threat to consumers, as debt accumulates interest, but may also cause trouble for businesses, as there is a chance that it may eat into their revenues.

Predictive Analytics 98

Predictive Analytics Analytics Big Data Data mining 98

Analytics Vidhya

APRIL 29, 2019

Introduction 90-95% of the times we find ourselves working with tabular data in machine learning. That number jumps up even more in machine learning. The post Winning Solutions and Approaches from the Machine Learning Hikeathon: Feature Engineering Special! appeared first on Analytics Vidhya.

Machine Learning 188

Machine Learning Analytics Visualization Data Science 188

O'Reilly on Data

MAY 2, 2019

Mike Tidmarsh looks at how data and AI are radically reshaping the world of marketing communications. Continue reading Rise of the (advertising) machines.

Advertising 117

Advertising Marketing 117

Teradata

MAY 1, 2019

Teradata CMO Martyn Etherington discusses how brands can build, measure, and maintain brand equity. He also explains why customer experience is critical to a brand's success.

Measurement 97

Measurement 97

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.

Risk

DataRobot

MAY 2, 2019

So, you have successfully trained a machine learning model after choosing the best algorithm and high-quality training data. Time to celebrate, right? Not quite!

Machine Learning 101

Machine Learning Modeling 101

Bruno Aziza

APRIL 28, 2019

Career changes are difficult. I’ve had the opportunity to work at very small, very large as well as midsize corporations. And, no matter the size, I’ve found that the skill set that has helped me the most is what I call the “startup mindset.

96

96

O'Reilly on Data

MAY 1, 2019

Cait O’Riordan discusses the North Star metric the Financial Times uses across the organization to drive subscriber growth. Continue reading Finding your North Star.

Metrics 110

Metrics 110

Data Science 101

MAY 3, 2019

Occasionally a product in Microsoft Azure will go down. Luckily, Azure has a status page to tell you which servers and services are down. Here is a quick video to help you find that status page.

Data Science 95

Data Science 95

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Strategy