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.

ABCs of Data Normalization for B2B Marketers

Data normalization. It’s not a far stretch to suggest that the topic isn’t exactly what gets marketers excited in their day-to-day workflow. However, if lead generation, reporting, and measuring ROI is important to your marketing team, then data normalization matters - a lot. In this eBook, we’ll break down the ins and outs of data normalization and review why it’s so critical for your marketing strategies and goals!

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.

Risk 103

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%?

KPI 94

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

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.

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.

Sales 119

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.

The 2019 Technographic Data Report for B2B Sales Organizations

In this report, ZoomInfo substantiates the assertion that technographic data is a vital resource for sales teams. In fact, the majority of respondents agree—with 72.3% reporting that technographic data is either somewhat important or very important to their organization. The reason for this is simple—sales teams value technographic data because it makes essential selling activities easier and more efficient.

Recent top-selling books in AI and Machine Learning

Rocket-Powered Data Science

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).

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.

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

Kirk Borne

The Time-Saving Power of Intent Data for Sales

By using the power of intent data, capturing buyer interest has become more feasible for sales. Not only that, but using it will save immense time during your workflow; a win-win on all fronts.

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.

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.

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.

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.

Risk 265

How ZoomInfo Enhances Your Database Management Strategy

Forward-thinking marketing organizations have continuously invested in a database strategy for enabling marketing processes. Download this ebook to learn how to maintain a strategy that includes refreshed information, database cleanses, and an accurate analysis at the same time.

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.

How ZoomInfo Enhances Your ABM Strategy

For marketing teams to develop a successful account-based marketing strategy, they need to ensure good data is housed within its Customer Relationship Management (CRM) software. More specifically, updated data can help organizations outline key accounts for their campaigns. And to begin the targeting process, marketing teams must develop an Ideal Customer Profile (ICP) with appropriate firmographic and behavioral data to ensure they’re going after the correct audience.Download this eBook to learn how to start improving your marketing team's data!

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.

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.

Data Science Challenges – It’s Deja Vu all over again!

Peter James Thomas

The rather famous tautology, “It’s déjà vu all over again”, has of course been ascribed to that darling of malapropisms, baseball catcher Yogi Berra [1]. The phrase came to mind for me today when coming across the following exhibit: © Business Over Broadway (2018).

Design Thinking for Product Teams: Leverage Human Insight Throughout Development

Product teams must increase their exposure hours with customers—seeing and hearing them. Human insights and the design thinking framework can be applied to your development cycle to help you build better products and experiences for your customers.