David Menninger's Analyst Perspectives

JANUARY 29, 2019

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.

Business Intelligence 261

Business Intelligence Data-driven Analytics Optimization 261

O'Reilly on Data

JANUARY 3, 2019

The O’Reilly Data Show Podcast: Haoyuan Li on accelerating analytic workloads, and innovation in data and AI in China. In this episode of the Data Show , I spoke with Haoyuan Li, CEO and founder of Alluxio , a startup commercializing the open source project with the same name (full disclosure: I’m an advisor to Alluxio). Our discussion focuses on the state of Alluxio (the open source project that has roots in UC Berkeley’s AMPLab ), specifically emerging use cases here and in China.

Technology 212

Technology Analytics 212

BA Learnings

JANUARY 11, 2019

In order to keep your business competitive, drive growth, ensure continuous improvement and enhance profits, a Business Intelligence (BI) strategy is essential. But a BI strategy is not just about technology or choosing the right platform. Like any part of your business, BI requires strategy, planning, buy-in, execution and continuous review. In this blog post, we will look at the key steps to implementing a business intelligence strategy.

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Business Over Broadway

JANUARY 13, 2019

The practice of data science requires the use of analytics tools, technologies and programming languages to help data professionals extract insights and value from data. A recent survey of nearly 24,000 data professionals by Kaggle revealed that Python, SQL and R are the most popular programming languages. The most popular, by far, was Python (83% used).

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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

Business Intelligence

Occam's Razor

JANUARY 7, 2019

Some moments in time are perfect to reflect on where you are, what your priorities are, and then consider what you should start-stop-continue. In those moments, you are not thinking of delivering incremental change… You are driven by a desire to deliver a step change (a large or sudden discontinuous change, especially one that makes things better – I’m borrowing the concept from mathematics and technology, from “step function”).

Marketing Analytics 89

Marketing Analytics Marketing Analytics Strategy 89

Timo Elliott

JANUARY 22, 2019

2019 is the year that analytics technology starts delivering what users have been dreaming about for over forty years — easy, natural access to reliable business information. 1. Machine learning everywhere. We’ve reached the third great wave of analytics, after semantic-layer business intelligence platforms in the 90s and data discovery in the 2000s.

Analytics 96

Analytics Machine Learning Unstructured Data Digital Transformation 96

O'Reilly on Data

JANUARY 29, 2019

A recent survey investigated how companies are approaching their AI and ML practices, and measured the sophistication of their efforts. In 2017, we published “ How Companies Are Putting AI to Work Through Deep Learning ,” a report based on a survey we ran aiming to help leaders better understand how organizations are applying AI through deep learning.

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BA Learnings

JANUARY 5, 2019

You meet a client to discuss a problem. You present the facts and suggest solutions. They refuse to acknowledge your ideas. Worse still, they even refuse to accept the facts or shift their perspective. What can a Business Analyst do in such circumstances? Here are five ways to minimize confrontation, manage denial and find ways forward on business projects. 1.

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Management Risk IT 100

DataRobot Blog

JANUARY 27, 2019

by Jen Underwood. In my previous articles Predictive Model Data Prep: An Art and Science and Data Prep Essentials for Automated Machine Learning, I shared foundational data preparation tips to help you successfully. Read More.

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Machine Learning Modeling Predictive Modeling Predictive Analytics 92

Business Over Broadway

JANUARY 27, 2019

A recent survey revealed that 84% of data pros have used at least one ML framework in the last 5 years while 51% of data pros have used at least one ML product in the last 5 years. The most popular ML frameworks include Scikit-Learn, Tensorflow and Keras. The most popular ML products include SAS, Cloudera and Azure. Figure 1. Machine Learning Frameworks used in last 5 years.

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Machine Learning Data Science Technology Analytics 89

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Data-driven

Timo Elliott

JANUARY 23, 2019

I was honored yesterday to be one of the panelists in one of a series of SAP Radio 2019 prediction shows, hosted by Bonnie D. Graham. You can catch a recording of the session Voice America site or below. . [link]. I’m on at 21′ in — here’s a rough transcript of what I talked about: “My prediction for 2019 is that we’re at the start of a new golden age for human intelligence. .

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Data Processing Predictive Analytics Machine Learning Dashboards 94

Domino Data Lab

JANUARY 14, 2019

This blog post provides insights on how to use the SHAP and LIME Python libraries in practice and how to interpret their output, helping readers prepare to produce model explanations in their own work. Introduction. Part 1 of this blog post provides a brief technical introduction to the SHAP and LIME Python libraries, including code and output to highlight a few pros and cons of each library.

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Testing Deep Learning Modeling Data Science 89

O'Reilly on Data

JANUARY 8, 2019

From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data. Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead. Increasing focus on building data culture, organization, and training. In a recent O’Reilly survey , we found that the skills gap remains one of the key challenges holding back the adoption of machine learning.

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IoT Machine Learning Internet of Things Data Science 207

IBM Big Data Hub

JANUARY 22, 2019

The Forrester Wave has named IBM a leader across five AI-related categories. Most recently, IBM Cloud Private for Data earned its place among vendors offering enterprise insights platforms.

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Enterprise IT 82

Speaker: Mike Rizzo, Founder & CEO, MarketingOps.com and Darrell Alfonso, Director of Marketing Strategy and Operations, Indeed.com

Though rarely in the spotlight, marketing operations are the backbone of the efficiency, scalability, and alignment that define top-performing marketing teams. In this exclusive webinar led by industry visionaries Mike Rizzo and Darrell Alfonso, we’re giving marketing operations the recognition they deserve! We will dive into the 7 P Model —a powerful framework designed to assess and optimize your marketing operations function.

Marketing

MLWhiz

JANUARY 16, 2019

Recently, I started up with an NLP competition on Kaggle called Quora Question insincerity challenge. It is an NLP Challenge on text classification and as the problem has become more clear after working through the competition as well as by going through the invaluable kernels put up by the kaggle experts, I thought of sharing the knowledge. Since we have a large amount of material to cover, I am splitting this post into a series of posts.

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Deep Learning IT 81

Business Over Broadway

JANUARY 20, 2019

Analysis of usage patterns of 16 data science programming languages by over 18,000 data professionals showed that programming languages can be grouped into a smaller set (specifically, 5 groupings). That is, some programming languages tend to be used together apart from other programming languages. A few of the different groupings of languages reflect specific types of applications or specific roles that data professionals could support, including analytics, general-purpose, and front-end effort

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Machine Learning Data Science Data-driven Statistics 79

Birst BI

JANUARY 29, 2019

How do you deliver more insights out to more people? Operationalizing BI and analytics – that is, putting the power of data in the hands of everyone across the enterprise, not just analysts and data scientists – has always been the mantra for Birst co-founder Brad Peters. According to research from Eckerson Group, when an organization deploys a BI and analytics system, roughly 10% of employees have the skills needed to produce insights from corporate data and deliver them to decision makers.

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Analytics KPI Machine Learning Visualization 78

Data Science and Beyond

JANUARY 7, 2019

Bootstrap sampling techniques are very appealing, as they don’t require knowing much about statistics and opaque formulas. Instead, all one needs to do is resample the given data many times, and calculate the desired statistics. Therefore, bootstrapping has been promoted as an easy way of modelling uncertainty to hackers who don’t have much statistical knowledge.

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Statistics Uncertainty Testing Modeling 77

Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

Business Intelligence

O'Reilly on Data

JANUARY 17, 2019

The O’Reilly Data Show Podcast: Andrew Burt on the need to modernize data protection tools and strategies. In this episode of the Data Show , I spoke with Andrew Burt , chief privacy officer and legal engineer at Immuta , a company building data management tools tuned for data science. Burt and cybersecurity pioneer Daniel Geer recently released a must-read white paper (“Flat Light”) that provides a great framework for how to think about information security in the age of big data and AI.

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Machine Learning Big Data Data Science Risk 207

IBM Big Data Hub

JANUARY 23, 2019

If you’re a data scientist or leading a team, Think 2019 is where you’ll want to be in February to hear success stories from clients using IBM’s data science portfolio of solutions.

Data Science 79

Data Science 79

Cloudera

JANUARY 30, 2019

It’s official – Cloudera and Hortonworks have merged , and today I’m excited to announce the availability of Cloudera Data Science Workbench (CDSW) for Hortonworks Data Platform (HDP). Trusted by large data science teams across hundreds of enterprises —. Western Union and IQVIA to name just a couple — CDSW is now also ready to help Hortonworks customers accelerate the delivery of new data products through secure, collaborative data science at scale.

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Data Science Machine Learning Experimentation Visualization 75

Paul DeBeasi

JANUARY 25, 2019

Developers with no data science experience are now able to integrate Machine Learning (ML) with IoT. As the number of IoT endpoints proliferate, the need for organizations to understand how to architect machine learning with IoT will grow rapidly. However, for this to occur, IoT architects and data scientists must overcome the challenge of having two very different disciplines collaborate closely to design an ML-powered IoT system.

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IoT Machine Learning Deep Learning Data Science 75

Speaker: Donna Laquidara-Carr, PhD, LEED AP, Industry Insights Research Director at Dodge Construction Network

In today’s construction market, owners, construction managers, and contractors must navigate increasing challenges, from cost management to project delays. Fortunately, digital tools now offer valuable insights to help mitigate these risks. However, the sheer volume of tools and the complexity of leveraging their data effectively can be daunting. That’s where data-driven construction comes in.

Data-driven

BizAcuity

JANUARY 25, 2019

Today, there is a vast ocean of data that is being amassed every minute, every day. The biggest challenge is what we make of that data and how fast. Real-time analytics is a practice that analyzes this data as and when it comes into the system. Analysts are continuously sifting through and studying this data in order to identify a pattern or identify important insights that help can help businesses make informed decisions.

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MLWhiz

JANUARY 5, 2019

Recently I started up with a competition on kaggle on text classification, and as a part of the competition, I had to somehow move to Pytorch to get deterministic results. Now I have always worked with Keras in the past and it has given me pretty good results, but somehow I got to know that the CuDNNGRU/CuDNNLSTM layers in keras are not deterministic, even after setting the seeds.

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IT 75

O'Reilly on Data

JANUARY 31, 2019

The O’Reilly Data Show Podcast: Maryam Jahanshahi on building tools to help improve efficiency and fairness in how companies recruit. In this episode of the Data Show , I spoke with Maryam Jahanshahi , research scientist at TapRecruit, a startup that uses machine learning and analytics to help companies recruit more effectively. In an upcoming survey, we found that a “skills gap” or “lack of skilled people” was one of the main bottlenecks holding back adoption of AI technologies.

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Machine Learning Analytics Statistics Reporting 183

IBM Big Data Hub

JANUARY 18, 2019

It’s that time of the year to step back, evaluate what worked, what did not, and what to do differently to make things better personally and professionally. The same applies to businesses.

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Speaker: Christophe Louvion, Chief Product & Technology Officer of NRC Health and Tony Karrer, CTO at Aggregage

Christophe Louvion, Chief Product & Technology Officer of NRC Health, is here to take us through how he guided his company's recent experience of getting from concept to launch and sales of products within 90 days. In this exclusive webinar, Christophe will cover key aspects of his journey, including: LLM Development & Quick Wins 🤖 Understand how LLMs differ from traditional software, identifying opportunities for rapid development and deployment.

Sales

Jet Global

JANUARY 17, 2019

In today’s data-driven world, business intelligence (BI) and analytics play a huge role in better understanding your customers, improving your operations, and making actionable business decisions. While there’s no doubt about the value of implementing a BI solution, many Dynamics ERP customers face the same data challenges with the quality and credibility of their data before a project even begins.

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Business Intelligence Management Data Warehouse Data Governance 75

Tim Mitchell

JANUARY 1, 2019

I have been in a serious relationship for more than 12 years. My partner in this relationship has brought me joy through the years, but lately, I feel like I’m giving to this relationship far more than I’m getting out of it. The relationship no longer brings me the joy that it once did, and has suffered from several breaches. The post Why I’m Breaking Up with Facebook appeared first on Tim Mitchell.

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IT 75

BizAcuity

JANUARY 21, 2019

Technology is the pursuit for more, isn’t it? Today, across all spectrums of business, technology is being used to empower smarter and efficient solutions. At BizAcuity, we’ve identified one such domain of business that we felt could use a technological makeover. And that is what today’s blog is about – Customer Behaviour Analysis.

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Recreation/Entertainment Data Collection Modeling Technology 75

Cloudera

JANUARY 3, 2019

A new year is always an opportunity for change. This year, we’re making a big one. On January 3, we closed the merger of Cloudera and Hortonworks — the two leading companies in the big data space — creating a single new company that is the leader in our category. We are well positioned to deliver even more innovation and success than we have independently over the last decade.

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IoT Machine Learning Data Warehouse Enterprise 74

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