2019

How Do You Define Unfair Bias in AI?

DataRobot

Art is subjective and everyone has their own opinion about it. When I saw the expressionist painting Blue Poles , by Jackson Pollock, I was reminded of the famous quote by Rudyard Kipling, “It’s clever, but is it Art?” Pollock’s piece looks like paint messily spilled onto a drop sheet protecting the floor. The debate of what constitutes art has a long history that will probably never be settled, there is no definitive definition of art.

IT 84

Why Data Driven Decision Making is Your Path To Business Success

datapine

We read about it everywhere. The term ‘big data’ alone has become something of a buzzword in recent times – and for good reason. By leveraging the wealth of digital insights available at your fingertips and embracing the power of business intelligence , it’s possible to make more informed decisions that will lead to commercial growth, evolution, and an increased bottom line.

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The quest for high-quality data

O'Reilly on Data

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners.

Tukey, Design Thinking, and Better Questions

Simply Statistics

Roughly once a year, I read John Tukey’s paper “The Future of Data Analysis” , originally published in 1962 in the Annals of Mathematical Statistics. I’ve been doing this for the past 17 years, each time hoping to really understand what it was he was talking about. Thankfully, each time I read it I seem to get something new out of it. For example, in 2017 I wrote a whole talk around some of the basic ideas. Well, it’s that time of year again, and I’ve been doing some reading.

Facebook Causes Continue to Show Little Promise as Fundraising Tools

How to Perfect Your Data Culture Recipe

Corinium

One of the first questions new clients generally ask us is: “How do we maximize the value from our data?”. Data Strategy

Waterfall to Agile: A Necessary Mindset Shift For Business Analysts

BA Learnings

I tend to describe the agile approach as a way of working; A targeted way of working that allows us to make changes, respond to customers’ needs and manage uncertainty with minimal delays, and without needing to wade through “red tape”.

More Trending

Strategy is Unleashing the Potential of Enterprise Data

Corinium

Enterprises across the globe are waking up to the fact that data is an asset that requires its own strategy. Those that treat it as such are now seeing substantial returns on their investments. Data Strategy

Analytics and Business Intelligence for a Data-Driven World

David Menninger's Analyst Perspectives

Ventana Research provides unique insight into the analytics and business intelligence (BI) industry. This is important, as its processes and technology play an instrumental role in enabling an organization’s business units and IT to utilize its data in both tactical and strategic ways to perform optimally. To accomplish this, organizations must provide technology that can access the data, generate and apply insights from analytics, communicate the results and support collaboration as needed.

Research quality data and research quality databases

Simply Statistics

When you are doing data science, you are doing research. You want to use data to answer a question, identify a new pattern, improve a current product, or come up with a new product. The common factor underlying each of these tasks is that you want to use the data to answer a question that you haven’t answered before. The most effective process we have come up for getting those answers is the scientific research process. That is why the key word in data science is not data, it is science.

Automating ethics

O'Reilly on Data

Machines will need to make ethical decisions, and we will be responsible for those decisions. We are surrounded by systems that make ethical decisions: systems approving loans, trading stocks, forwarding news articles, recommending jail sentences, and much more. They act for us or against us, but almost always without our consent or even our knowledge. In recent articles, I've suggested the ethics of artificial intelligence itself needs to be automated.

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.

An Exhaustive Guide to Detecting and Fighting Neural Fake News using NLP

Analytics Vidhya

Overview Neural fake news (fake news generated by AI) can be a huge issue for our society This article discusses different Natural Language Processing. The post An Exhaustive Guide to Detecting and Fighting Neural Fake News using NLP appeared first on Analytics Vidhya.

Take Advantage Of The Top 16 Sales Graphs And Charts To Boost Your Business

datapine

Billionaire Tilman Fertitta walks into the room. You can’t believe this heavyweight, the CEO and sole owner of multiple restaurant franchises, has given you the time of day. Tilman sits down, settles himself, and glances at the clock.

Sales 236

Stock Coverage: days cover calculation and other stock metrics

Phocas

Anyone who manages inventory is familiar with stock coverage. The question is, how do you measure 'days cover calculation' and other useful inventory metrics. Industry - Wholesale Distribution Job Role - Inventory/Operations

Ten Trends of Blockchain in 2020

DataFloq

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.

Glossaries of Data Science Terminology

Rocket-Powered Data Science

Here is a compilation of glossaries of terminology used in data science, big data analytics, machine learning, AI, and related fields: Glossary of common Machine Learning, Statistics and Data Science terms. Data Science Glossary on DataScienceCentral. Data Science Glossary. Machine Learning Glossary at Google. Glossary of Artificial Intelligence Terms (From A to Z). Big Glossary of Artificial Intelligence on Wikipedia. 28 Artificial Intelligence Terms You Need to Know.

Laying the Foundations for AI Success

Corinium

AI is now well into its ‘early adoption’ phase, with businesses throughout the Middle East and Africa clamoring to launch new initiatives. AI & Machine Learning

5 Weird and Hilarious Uses of Data Science

Analytics Vidhya

Introduction “Ripley’s Believe or Not” features some of the weirdest and most bizarre facts from around the world. How about creating our own Ripley’s. The post 5 Weird and Hilarious Uses of Data Science appeared first on Analytics Vidhya.

5 Key Reasons Why Data Scientists Are Quitting their Jobs

Analytics Vidhya

Introduction The stock of a data scientist is at an all-time high right now. There aren’t too many professions out there that can rival. The post 5 Key Reasons Why Data Scientists Are Quitting their Jobs appeared first on Analytics Vidhya.

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.

Data Management on Display at Informatica World 2019

David Menninger's Analyst Perspectives

This year, I attended Informatica World 2019, Informatica's annual user conference. The main focus this year was on the cloud with a heavy does of AI. Under that focus, Informatica's conference emphasized capabilities across six areas (all strong areas for Informatica): data integration, data management, data quality & governance, Master Data Management (MDM), data cataloging, and data security.

10 things R can do that might surprise you

Simply Statistics

Over the last few weeks I’ve had a couple of interactions with folks from the computer science world who were pretty disparaging of the R programming language. A lot of the critism focused on perceived limitations of R to statistical analysis. It’s true, R does have a hugely comprehensive list of analysis packages on CRAN , Bioconductor , Neuroconductor , and ROpenSci as well as great package management.

Artificial intelligence and machine learning adoption in European enterprise

O'Reilly on Data

How companies in Europe are preparing for and adopting AI and ML technologies. In a recent survey , we explored how companies were adjusting to the growing importance of machine learning and analytics, while also preparing for the explosion in the number of data sources.

How to Challenge Business Assumptions with Data

Corinium

The notion that businesses should be making data-driven decisions may seem obvious to a CDAO. But the fact is, people in other business functions don’t always see things that way. CDAO UK 2020

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.

Top 10 IT & Technology Buzzwords You Won’t Be Able To Avoid In 2020

datapine

The modern world is changing more and more quickly with each passing year. If you don’t pay attention to new changes or keep up the pace, it’s easy to fall behind the times (and the market) while other companies beat you to the punch. The solution? To keep abreast of current changes – at least at a level of basic understanding. Adding to that, if you can’t understand the buzzwords others are using in conversation, it’s much harder to look smart while participating in that conversation.

[eBook] The top 7 key performance indicators for mid-market CEOs and executives

Phocas

Data and analytics have never been more important for mid-market CEOs and executives to set the course for their businesses. Data is key to transformation in a business. The true value of your data is how your people use data insights and how it impacts decision-making.

5 Ways AI and Big Data Are Changing the Customer Experience

DataFloq

AI-driven technologies (e.g., natural language processing and machine learning) are now used in many customer service applications to help brands deliver an outstanding customer experience that will drive sales and increase customer retention.

How Data Democratization Will Transform Banking

Corinium

Data culture has become a huge focus for data leaders in recent years. It’s clear that data leaders will only be able to maximize the value of their companies’ data assets if they can get frontline staff to adopt new, data-driven ways of thinking and working.

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!

6 Challenging Open Source Data Science Projects to Make you a Better Data Scientist

Analytics Vidhya

Overview Here are 6 challenging open-source data science projects to level up your data scientist skillset There are some intriguing data science projects, including. The post 6 Challenging Open Source Data Science Projects to Make you a Better Data Scientist appeared first on Analytics Vidhya.

Game (Theory) for AI? An Illustrated Guide for Everyone

Analytics Vidhya

Overview What is Game Theory? And how does it apply to artificial intelligence (AI)? Game theory for AI is a fascinating concept that we. The post Game (Theory) for AI? An Illustrated Guide for Everyone appeared first on Analytics Vidhya. Technique Artificial Intelligence Game Theory game theory for AI

Mathematics behind Machine Learning – The Core Concepts you Need to Know

Analytics Vidhya

Overview Here’s an intuitive and beginner friendly guide to the mathematics behind machine learning Learn the various math concepts required for machine learning, including. The post Mathematics behind Machine Learning – The Core Concepts you Need to Know appeared first on Analytics Vidhya. Machine Learning

Historical Databases: Becoming a Thing of the Past?

David Menninger's Analyst Perspectives

Organizations’ use of data and information is evolving as the amount of data and the frequency with which that data is collected increase. Data now streams into organizations from myriad sources, among them social media feeds and internet-of-things devices. These seemingly ever-increasing volumes of devices and data streams offer both challenges and opportunities to capture information about a business and improve its operations.

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.