Cloudera

SEPTEMBER 8, 2020

This is opening up new revenue opportunities, use cases, and even the possibility for different types of business models within the sector, changing the way that CSPs operate. . In many established markets, traditional sources of revenue are either plateauing or declining relatively rapidly. .

IoT 68

IoT Internet of Things B2B Data-driven 68

CIO Business Intelligence

APRIL 24, 2023

Generative AI (Artificial Intelligence) and its underlying foundation models represent a paradigm shift in innovation, significantly impacting enterprises exploring AI applications. These foundation models are trained on vast amounts of data. The model can then predict or classify new, previously unseen data.

Enterprise 52

Enterprise Deep Learning Modeling Machine Learning 52

Ontotext

DECEMBER 30, 2022

We have exciting success stories, including the first and popular mission critical implementation of knowledge graphs – BBC’s website for the FIFA world cup in 2010. Significant engine optimizations made GraphDB more powerful and more efficient across a wider set of workloads.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

Ontotext

DECEMBER 30, 2022

We have exciting success stories, including the first and popular mission critical implementation of knowledge graphs – BBC’s website for the FIFA world cup in 2010. Significant improvements made GraphDB more powerful and more efficient across a wider set of workloads, with key optimizations.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

BizAcuity

FEBRUARY 15, 2019

Sales Analytics in simple terms can be defined as the process used to identify, understand, predict and model sales trends and sales results and in this process of understanding of these trends helps its users in finding improvement points. Image Source: [link].

Sales 67

Sales Analytics B2B ROI 67

DataRobot Blog

AUGUST 23, 2019

We started with the result of every match (and set scores) for ATP and WTA tour matches from 2010 through 2018. Once we had built this prediction model , we could take the draw of any tournament and simulate the results 100,000 times to find out how often each player would win with that particular draw. Andrew received his Ph.D.

Predictive Modeling 52

Predictive Modeling Modeling Marketing Analytics Data Science 52

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40

Ontotext

JULY 29, 2021

One of its pillars are ontologies that represent explicit formal conceptual models, used to describe semantically both unstructured content and databases. And while not all knowledge graphs (see Adoption of Knowledge Graphs, late 2019 ) are built the semantic modelling way , they all have benefited from the Semantic Web.

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. by STEVEN L. Forecasting (e.g.

Forecasting 64

Forecasting Statistics Modeling Software 64

Cloudera

APRIL 21, 2022

(Remember, a pedabyte of data is roughly equivalent to 500 billion pages of standard printed text) A solution was needed to backstop those never-ending streams of data into a single, universally available platform, using advanced analytics powered by machine learning optimized for a cloud service. The core of the EHR system.

Metadata 86

Metadata Informatics Insurance Data Processing 86

Mark Raskino

FEBRUARY 25, 2019

We hear about digital efficiency, digital workplace, and digital optimization. By about 2010 or so – digital meant market facing technology, and “IT” meant back office and the internal efficiency use of technology. Today, everything is “digital”. ERP is now part of a digital platform. Digital equaled customer facing. So far, so good.

IT 49

IT Marketing Technology Interactive 49

Timo Elliott

JANUARY 10, 2022

Most people rent skis rather than buying, because it’s easier and cheaper and more convenient — so why not apply that model to more things? I also loved the episode The Race to Zero: Regenerative Business Models for a Sustainable Future. I’ve been talking about them for a long time. with the CTO of SAP Fieldglass.

Data Processing 121

Data Processing Manufacturing Machine Learning Technology 121

O'Reilly on Data

JULY 25, 2023

I’m a professor who is interested in how we can use LLMs (Large Language Models) to teach programming. Here’s how I worked on it: I subscribed to ChatGPT Plus and used the GPT-4 model in ChatGPT (first the May 12, 2023 version, then the May 24 version) to help me with design and implementation.

Consulting 245

Consulting Interactive Software IT 245

Occam's Razor

SEPTEMBER 15, 2014

In blue is how much time we spent in 2010 and in blue the time spent in 2014. was the dramatic shift between 2010 to 2014 to mobile content consumption. Media-Mix Modeling/Experimentation. If you want to go it alone, get a Red Bull and download this handy-dandy 62 slide Cross Devices Optimization presentation.

Metrics 141

Metrics Reporting Analytics Marketing 141

O'Reilly on Data

JANUARY 25, 2021

Models are neither code nor data, and we don’t have adequate tools for versioning models either (though tools like MLflow are a start). Frequent deployment assumes that the software can be built relatively quickly, but training a model can take days. We don’t yet have adequate tools for versioning data (though DVC is a start).

Machine Learning 362

Machine Learning Software Testing Technology 362

The Unofficial Google Data Science Blog

AUGUST 27, 2019

This problem can be phrased as an optimization problem — given some fixed review capacity how should we sample videos? To obtain a relative error of no more than 20%, we need roughly 100 positive labels, and more often than not, we have zero violation videos in the uniform samples for rarer policies. as estimated from the data.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

The Unofficial Google Data Science Blog

JULY 22, 2020

In practice, one may want to use more complex models to make these estimates. For example, one may want to use a model that can pool the epoch estimates with each other via hierarchical modeling (a.k.a. These MAB algorithms are great at maximizing reward when the models are perfectly specified and probabilities are accurate.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Occam's Razor

JULY 26, 2010

Maybe Google is really good at Volunteers and not optimal for attracting people who donate. Will take you off on a completely different line of inquiry, all from adding June 2009 to look at June 2010. So why is that your mental model? And what to do better. If you focus only on donations you'll devalue Google.

Metrics 96

Metrics Measurement Dashboards Reporting 96

Occam's Razor

AUGUST 28, 2017

The team at Google adopted the strategy of having a robot learn own its own (rather than programming it with pre-configured models). Some of you’ve heard the “hype” before and are deeply skeptical (AI went through a two decade long tundra where it failed to live up to every promise, until say 2010 or so). Professional).

Deep Learning 139

Deep Learning Machine Learning Marketing Strategy 139

Occam's Razor

FEBRUARY 20, 2012

My normal recommendation to address this supremely corrosive issue is to encourage each company to go through the process of creating a Digital Marketing and Measurement Model. It will be a great reflection of how well thought out their acquisition strategy is, or how sub-optimal it is. ~ It is that simple. Aw, come on!

IT 138

IT Measurement Marketing Metrics 138

Occam's Razor

FEBRUARY 22, 2010

In May 2010 (!). The secret to making optimal use of CI data lies in one single realization: You must ensure you understand how the data you are analyzing is collected. The panel methodology is based on the traditional television data capture model. The Optimal Competitive Analysis Process. use to your benefit.

Metrics 123

Metrics Advertising Reporting Software 123

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

We need to know whether the decision we are optimizing is significant enough to justify the time and experiment resources spent optimizing it. Still, if there are too many hypotheses to test with experiments, we should only be testing those that lead to consequential decisions. Be skeptical, intellectually honest. 2] Palese P, Wang TT.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

JUNE 11, 2014

According to eMarketer in 2010 we spent 3:11 (hrs:min) on digital content consumption and 4:24 on TV in the US. From a data perspective this is reflected in our obsession with multi-channel attribution modeling or user based visitor segmentation. No fancy models (though if you miss them they are not too far away as you can see below).

Marketing 150

Marketing Strategy Sales IT 150

Occam's Razor

MARCH 23, 2010

My problem with these mistruths and FUD is that they result in a ton of practitioners and companies making profoundly sub optimal choices, which in turn results in not just much longer slogs but also spectacular career implosions and the entire web analytics industry suffering. This is sad. Even a little frustrating. Aim for this.

Analytics 118

Analytics Consulting Metrics Experimentation 118