2018

Deep automation in machine learning

O'Reilly on Data

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline.

Five Strategies for Slaying the Data Puking Dragon.

Occam's Razor

If you bring sharp focus, you increase chances of attention being diverted to the right places. That in turn will drive smarter questions, which will elicit thoughtful answers from available data. The result will be data-influenced actions that result in a long-term strategic advantage.

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Divergent and Convergent Phases of Data Analysis

Simply Statistics

There are often discussions within the data science community about which tools are best for doing data science. The most recent iteration of this discussion is the so-called “First Notebook War” , which is well-summarized by Yihui Xie in his blog post (it is a great read).

What Business Analysts Can Learn From Swiss Cheese

BA Learnings

Swiss cheese has holes in various places on different slices of cheese when you cut it up. Let’s imagine these holes reflect weaknesses in the system where mistakes can pass through, afterall no system is perfect.

How to Solve 4 Common Challenges of Legacy Information Management

Speaker: Chris McLaughlin, Chief Marketing Officer and Chief Product Officer, Nuxeo

After 20 years of Enterprise Content Management (ECM), businesses still face many of the same challenges with finding and managing information. Join Chris McLaughlin, CMO and CPO of Nuxeo, as he examines four common business challenges that these legacy ECM systems pose and how they can be addressed with a more modern approach.

Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters.

News and Announcements from Tableau and TC18

David Menninger's Analyst Perspectives

Once again I attended Tableau's Users Conference, along with 17,000 other attendees, affectionately self-referred to as "data nerds".

More Trending

Predictions 2019: Steady Evolution In Blockchain Will Continue, Unless Disillusionment Causes A “Winter”

Martha Bennett

“The visionaries will forge ahead; those hoping for immediate industry and process transformation will give up.” This was the opening sentence of my blog post accompanying Forrester’s DLT/blockchain predictions for 2018.

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IRM Is Essential for Digital Transformation Success

John Wheeler

Last week, I had the distinct privilege to join my Gartner colleagues from our Risk Management Leadership Council in presenting the Q4 2018 Emerging Risk Report. We hosted more than 500 risk leaders across the globe in our exploration of the most critical risks.

How AI is Lowering the Barrier to Entry for BI and Analytics

Birst BI

According to Gartner, more than 3,000 CIOs ranked Business Intelligence (BI) and Analytics as the top differentiating technology for their organizations. If BI and Analytics is such a game-changer, then why is the average adoption rate in organizations only 32%?

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In 2018, Data Will Put the Human Back into Human Experience – Part 1

Kirk Borne

In this article, Part 1 of the latest in his series exclusive to Data Makes Possible , Dr. Kirk Borne, Principal Data Scientist for Booz Allen Hamilton, explains the importance and value proposition of improving the human experience in the digital enterprise, and why the year of experience must include customers, end-users, employees, and any other stakeholders. The Message is in the Madness. A few years ago, I heard someone describe their data product in this way: “analytics at the speed of your business.” Well, no disrespect intended, but I think they got the message backwards. Why? Because business is no longer able to keep up with the flood of data that is coming in, from forces and sources everywhere: social, mobile, internet, intranet, images, video, audio, and documents. Consequently, what you really need is business at the speed of your data! Building and maintaining that can actually be quite hard. But you can do something about it. The first “something” that you must do is to realize that your stakeholders are also moving at the same rapid pace. This means that even small moves can have huge impacts: the behavioral economics folks call that a “ nudge ”. In fact, big moves are scarier when you’re moving fast. Small moves (nudges) are preferred and are also more effective! The second thing that you can do is to tap into the digital data streams that are emanating from your stakeholders to learn how to make the small impactful moves that will make their life experience more engaging, more efficient, and more effective – at that place, in the moment and in their current context! Experiences Good, Bad & Great . Very few people need to buy newspapers or encyclopedias anymore. You can create similarly generic non-targeted information products and services even in digital format, but that won’t guarantee that users will come to your site. People prefer, and often demand, targeted, personalized information and products that meet their specific needs, at a specific time and location, and in the right context. That’s the dream experience for your stakeholders! Good experience will attract customers, users, and employees to your products, services, and business. Bad experience will repel them. That’s basic physics! Great experience will make them loyal fans and your advocates in the marketplace. Not only will users come, but they will bring others with them. We frequently hear phrases like User Experience (UX) and Customer Experience (CX) in conversations around digital disruption and business transformation. We should also be including Employee Experience (EX) in those discussions. The (user, customer, employee) journey is also discussed in these same contexts. The new data analytics discipline of Journey Sciences , pioneered by ClickFox , has emerged to make the corresponding process and its application more scientific. In addition, the healthcare industry is referring more and more to the importance of patient journey and patient experience. The ABC’s of the Year of Experience. The focus on Experience seems to be everywhere now. We might even call 2018 the Year of the Experience Economy, or simply the Year of Experience. Personalized experiences are shaping business interactions with stakeholders by changing the expectations of those stakeholders and customer communities. To retain, delight, and build advocacy within your stakeholder audiences, the focus on Experience is critical. We will highlight a few examples in this article. Each of these examples will be specific to one of these categories (UX, EX, or CX), but each example can be generalized and applied more broadly to other digital stakeholders across your enterprise. Consequently, we may not actually be documenting the ABC’s of Experience, but rather the UEC’s of Experience (UX, EX, and CX)! In any case, we will see how data makes possible more helpful, engaging, and delightful experiences for all your stakeholders. (1) UX and the Empathy Equation : Knowing and understanding how someone feels, that’s empathy. Detecting, measuring, and responding to your stakeholders’ sentiment is empathetic. Empathy elicits positive sentiment and positive experience. Empathy is amplified through human-centered design. Design thinking therefore must be a cornerstone of your digital experience development process – it is human-centric, data-informed, and friction-reducing for your digital users. Find the slow spots (the moments of “bad experience”) by exploring and analyzing the user data trails. Remember another law of physics – the law of inertia: that which is in motion will stay in motion, and that which is at rest will stay at rest. Therefore, aim to keep your user moving forward. If they reach a stopping point or a point of increased friction, they may leave altogether. Data makes possible a better UX: data is the input, analytics is the lever, and positive UX should be the outcome. Always remember this aphorism: the wheels of progress are not turned by cranks! Negative UX puts the brakes (real friction) on your business progress. (2) EX and the New AI : The new A.I. is augmented (or assisted) intelligence. For example, customer call center personnel are so much happier when solving the interesting hard problems, rather than answering frequently asked questions. Conversational bots are one of the hottest technologies in the AI marketplace right now. Surveys show that over 50% of consumers prefer to talk to a customer service bot for simple questions and requests. That makes sense for the consumer and also for the employee – the consumer doesn’t want to wait “on hold” for 10, 20, or 30 minutes just to get a simple question answered (even a specific question about their personal account), and the customer service representative doesn’t need (or want) to answer that kind of non-challenging question anyway. EX is not just for customer service folks, but for any employee in your organization. Creating engaging employee experiences may include online discussion groups, training programs, customized and personalized company newsletters, and more. Data makes possible a better EX: by exploring and analyzing the data sources that tell which employees are opening newsletters, reading specific emails, performing certain tasks, or visiting company portals, you will discover where, when, and how to personalize the content and improve the job experience for each employee. (3) CX and the Knowledge Graph : Knowing what a customer is searching for, which choices are most relevant to that individual, how to spice it up with unexpected results, and how it relates to other things that the customer already knows is pure gold. Just look at the major Internet search companies today: they don’t sell their services to their primary consumers, yet they are some of the largest revenue-generating firms in the known universe. Sure, there is some really interesting mathematics (actually, linear algebra) behind the curtain, but their success stories are primarily about delivering personalized knowledge and human-centered meaning. Call it semantics, or the knowledge graph, or linked data, or whatever – that is the engine, but it’s not the reason. And now, we are seeing the birth of hyper-personalization as a hot new trend in CX. But, that will be a useless exercise if the experience is not helpful, relevant, surprising, and engaging. The semantic connections between persons, places, and things in the knowledge graph reveal the “who, what, when, where, and how” of reaching your customer in engaging ways that go beyond “same product” recommendations: “people who bought this refrigerator also bought this refrigerator.” Really? Data makes possible a better CX: by exploring and exploiting the knowledge graph of products, services, and content within your business domain, you will help enrich the experience of each customer (amplifying their own productivity and effectiveness) in such a way that they should return again and again! In the second part of The Year of Experience , Dr. Borne will dive into mobile device experience, explore the culture of experimentation, and discuss his “5 E’s of Experience.” Meanwhile, take the conversation to Twitter! Agree or disagree with Kirk’s thoughts on user, customer, or employee experience? Want to tell us your story? Tweet @KirkDBorne using the hashtag #datamakespossible right now! Note: There is a poll embedded within this post, please visit the site to participate in this post's poll. Forward-Looking Statements. Certain blog and other posts on this website may contain forward-looking statements, including statements relating to expectations the market for our products and applications of our products. These forward-looking statements are subject to risks and uncertainties that could cause actual results to differ materially from those expressed in the forward-looking statements, including development challenges or delays, changes in markets, demand, global economic conditions and other risks and uncertainties listed in Western Digital Corporation’s most recent quarterly and annual reports filed with the Securities and Exchange Commission, to which your attention is directed. Readers are cautioned not to place undue reliance on these forward-looking statements and we undertake no obligation to update these forward-looking statements to reflect subsequent events or circumstances. The post In 2018, Data Will Put the Human Back into Human Experience – Part 1 appeared first on Data Makes Possible. PERSPECTIVES big data analysis business intelligence Data Minds data science Kirk Borne

The Magic of Intent: Start Knowing The Goals of Your Users

Speaker: Terhi Hanninen, Senior Product Manager, Zalando, and Dr. Franziska Roth, Senior User Researcher, Zalando

It's important to know your users - what are their preferences, pain points, ultimate goals? With user research and usage data, you can get a great idea of how your users act. The tricky part is, very few users reliably act the same way every time they use your product. Join Terhi Hanninen, Senior Product Manager, and Dr. Franziska Roth, Senior User Researcher at Zalando, as they explain how they were able to reach a new level of user understanding - by taking their user research and segmenting their users by point-in-time intent. You'll leave with a strategy to change how your product team, and organization at large, understands your users.

Building tools for enterprise data science

O'Reilly on Data

The O’Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science. In this episode of the Data Show , I spoke with Vitaly Gordon , VP of data science and engineering at Salesforce.

Unsexy Fundamentals Focus: User Experiences That Print Money

Occam's Razor

Like me, I'm sure you are working on complex challenges when it comes to data. Multi-petabyte data warehouses. Multi-touch, cross-channel attribution analysis. Media mix modeling. Predictive analytics. Human-centric analysis.

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The economic consequences of MOOCs

Simply Statistics

tl;dr check out our new paper on the relationship between MOOC completion and economic outcomes! Last Monday we launched our Chromebook Data Science Program so that anyone with an internet connection, a web browser, and the ability to read and follow instructions could become a data scientist.

Confirmation Bias: What BAs Can Learn From Data Scientists

BA Learnings

When we have a strong belief about something or a bias towards a particular opinion, we consciously or unconsciously seek out evidence that validates what we already believe. When we come across contrary evidence, our default behaviour is to ignore it, diminish it or in some cases, conclude that it’s wrong prematurely without exploring its merits. This behaviour is due to a cognitive bias known as confirmation bias.

Encouraging Innovation in an Established Product Culture

Speaker: Richard Cardran, Chief Creative Officer and VP Strategy, HIA Technologies

Innovation is both a process and an outcome. The best way to begin innovating your products is by innovating your internal process. We'll explore the challenges, solutions, and hands-on techniques for becoming a successful "agent of change" within a well-established product culture. We'll examine the importance of UX and user-centric feature analysis, the adaptation of Agile Methodologies to the creative process, as well as a way to drive successful culture change for setting expectations and winning approvals with cross-functional stakeholders. Innovation and Leadership go hand in hand. Join Richard Cardran, Chief Creative Officer and VP Strategy, HIA Technologies, as we assess some case studies to see how to lead with a clear strategy well-defined tactics, and an unbiased understanding of the fundamental question: "why are you innovating?"

Recent top-selling books in AI and Machine Learning

Rocket-Powered Data Science

Below are the individual links to these Data Science, Artificial Intelligence and Machine Learning books, all of which are top sellers on Amazon… “The Book of Why: The New Science of Cause and Effect” “Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems” “Deep Learning (Adaptive Computation and Machine Learning)” “Applied Artificial Intelligence: A Handbook For Business Leaders” “Machine Learning For Absolute Beginners: A Plain English Introduction” “Life 3.0: Being Human in the Age of Artificial Intelligence” “An Introduction to Statistical Learning: with Applications in R” (7th printing; 2017 edition). “Grokking Algorithms: An illustrated guide for programmers and other curious people” “Prediction Machines: The Simple Economics of Artificial Intelligence” “Deep Learning with Python” “Python Machine Learning” (2nd edition). “Advances in Financial Machine Learning” “The Future of Leadership: Rise of Automation, Robotics and Artificial Intelligence” “Deep Learning With Python: Step By Step Guide With Keras and Pytorch” “Deep Reinforcement Learning Hands-On” “Pattern Recognition and Machine Learning” Disclosure statement: As an Amazon Associate I earn from qualifying purchases made here. These earnings offset the costs of hosting this website. Artificial Intelligence Data Science Machine Learning

The Market of Data at Strata

David Menninger's Analyst Perspectives

In 2017 Strata + Hadoop World was changed to the Strata Data Conference. As I pointed out in my coverage of last year’s event , the focus was largely on machine learning and artificial intelligence (AI).

Will Digital Healthcare Technology Disrupt Independent Physicians

Perficient Data & Analytics

Why fear change? Change is good and has developed the world into what it is today. Change partners with adaptation, to promote a new way of doing things. However, is change in the healthcare industry putting independent physicians at risk? With the increased usage of digital healthcare technology, will the independent physician still be able to maintain the walk-in base of customers?

Top 10 Data Governance Predictions for 2019

erwin

This past year witnessed a data governance awakening – or as the Wall Street Journal called it, a “global data governance reckoning.” There was tremendous data drama and resulting trauma – from Facebook to Equifax and from Yahoo to Marriott. The list goes on and on.

Embedding Operational Reports: Everything Product Managers Should Know

Speaker: Dean Yao, Sr. Director of Product Marketing, Logi Analytics

Businesses are run with analytics - but companies continue to struggle with interpreting, analyzing, and distributing data. Operational reports help get information to the people who need it most, in formats they understand, and in a timeframe that matters. Join the webinar to learn how embedding operational reports can give your users a precisely formatted, ready-to-analyze view of their operational activities. World-class software teams are embedding operational reports to empower end users with interactive data visualizations, detailed information, and highly precise formats that can be shared via email, PDF, print, or online.

A Comprehensive Guide to Real-Time Big Data Analytics

ScienceSoft

Our big data consultants have come up with an easy guide to real-time big data analytics. We explain the term and describe a typical architecture, as well as share our thoughts about whether real-time analytics can be a competitive advantage

Inside the Mind and Methodology of a Data Scientist

Birst BI

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. And it doesn’t help reduce the confusion when every tech vendor rebrands their products as AI.

Big Data And Analytics Has Been Around Forever! Why Is It Still Important?

Timo Elliott

These are some quick answers to some common questions I get about Business Intelligence, Big Data, and Analytics: Big Data. The term has been around for quite some time. Why is it still important for innovative businesses? It’s clear that data is one of the most important assets of the future. Organizations want to optimize their end-to-end customer experience, to improve productivity, and to engage the workforce in new ways. All of these things require data and analytics.

Managing risk in machine learning

O'Reilly on Data

Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September.

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Your 2-Part Metrics Audit for High-Value Products

Speaker: Sam McAfee, Product Development Consultant, Startup Patterns

You know what they say: what's measured improves. As product managers we're in a golden age of being able to get all sorts of metrics and run all sorts of experiments. But what are your measurements and analytics focused on? Are they really truly objective? Do they contribute to the ultimate vision of your product? And is everybody clear on that vision? Join Sam McAfee, Product Development Consultant, as he takes you through a two-part measurement audit. First, you'll learn how to make sure your measurements actually align with your product strategy. And second, you'll learn how to evaluate your culture of using measurements, so future experiments will more consistently provide high-value results.

Six Nudges: Creating A Sense Of Urgency For Higher Conversion Rates!

Occam's Razor

By every indicator available, ecommerce is continuing to grow at an insane speed. Although it may seem impossible to imagine with ecommerce already totaling up to 5% of overall commerce, there’s astronomical growth still to come.

The role of academia in data science education

Simply Statistics

I was recently asked to moderate an academic panel on the role of universities in training the data science workforce. I preceded each question with opinionated introductions which I have fused into this blog post. These are weakly held opinions so please consider commenting if you disagree with anything. To discuss data science education we first need to clearly state what it means.

5 Simple Ways BAs Can Avoid Repeating Mistakes From Past Projects

BA Learnings

Once you embark on a new business analyst job or project, chances are that you will try to avoid past mistakes and look for ways in which you can deliver better results. A lot can be said for this motivation. The beginning of a project is usually an opportunity for a fresh start.

Data Scientist’s Dilemma – The Cold Start Problem

Rocket-Powered Data Science

The ancient philosopher Confucius has been credited with saying “study your past to know your future.” This wisdom applies not only to life but to machine learning also.

Assessing and Fostering a Culture of Innovation

Speaker: Magnus Penker, CEO & Founder, Innovation360 Group

Welcome to an interactive empowering session on how to sharpen your future through innovation management, which can help guide your company’s goals. During this webinar, Magnus Penker, international thought leader and author, will dive into how to assess and foster culture and capabilities for innovation.

From Analytics to Action Requires Collaboration

David Menninger's Analyst Perspectives

All too often, software vendors view analytics as the end rather than the beginning of a process.

Creating a Holistic View: Data Consolidation and Integration

Perficient Data & Analytics

The consolidation of data and integration of systems is essential to providing a holistic 360-degree view of patients and members. This view can enable a variety of activities to enhance and drive efficiency in business and clinical activities, such as increasing patient safety and the quality of care healthcare delivery organizations provide to patients. One organization that understands the challenges associated with bringing data together across a large number of hospitals is Mayo Clinic.

Cloudera + Hortonworks, from the Edge to AI

Cloudera

We’ve just announced that Cloudera and Hortonworks have agreed to merge to form a single company. I want to explain the thinking behind the deal and the combination. Rob Bearden from Hortonworks has written up a post sharing his thoughts, as well. First, remember the history of Apache Hadoop.

To all Data Scientists - The one Graph Algorithm you need to know

MLWhiz

Graphs provide us with a very useful data structure. They can help us to find structure within our data. With the advent of Machine learning and big data we need to get as much information as possible about our data. Learning a little bit of graph theory can certainly help us with that.

Measure the Immeasurable: Beyond Vanity Metrics

Speaker: Sari Harrison, Product Management Instructor, Product School

As a product manager, it's your job to realize your product’s vision by executing your chosen strategy. It’s also your job to deliver value to the business. Ultimately, these two outcomes are aligned so the temptation is to focus primarily on business metrics. Doing this can cause you to lose focus on the real value you are trying to achieve, in favor of moving the vanity metrics such as launches and time spent. Join Sari Harrison, Product Management Instructor at Product School, as she explains how to use immeasurable success criteria along with your more standard KPIs to deliver products that don't just get used a lot, but deliver real value.