CIO Business Intelligence

MAY 16, 2023

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data.

Advertising 98

Advertising Recreation/Entertainment Experimentation Enterprise 98

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. This thought was in my mind as I was reading Lean Analytics a new book by my friend Alistair Croll and his collaborator Benjamin Yoskovitz.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Rocket-Powered Data Science

JULY 7, 2019

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). Edge Computing (and Edge Analytics): Industry 4.0: Algorithm: A set of rules to follow to solve a problem or to decide on a particular action (e.g., See [link].

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

O'Reilly on Data

MAY 14, 2020

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

Management 363

Management Experimentation B2B Machine Learning 363

Occam's Razor

JUNE 6, 2007

I am thrilled to say that my book Web Analytics: An Hour A Day has been published and is now widely available. Five Reasons Why You Should Buy Web Analytics: An Hour A Day: Web Analytics: An Hour A Day for the first time provides a Practitioner's in the trenches perspective on web analytics. There I said it.

Analytics 60

Analytics Metrics Marketing Dashboards 60

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. This is critical in our massively data-sharing world and enterprises.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

Occam's Razor

DECEMBER 12, 2016

There is, almost literally, an unlimited number of things you could focus on to create a high impact data-influenced organization. And, as if unlimited is not enough, nearly every month your analytics vendors release new features, you discover new analytics solutions, and as your business is more successful (hurray!)

Analytics 141

Analytics Data-driven Strategy Consulting 141

Occam's Razor

SEPTEMBER 15, 2014

Remember, when someone says mobile analytics, first ask the clarifying question: Do you mean mobile application or mobile website ? Then approach each separately (even though there are tools like Google Analytics that will do both). Media-Mix Modeling/Experimentation. It will save you hours and hours of time, effort and focus.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

MAY 27, 2015

It is an investment in numerous report writers or data (puking) automation or hiring a small army in India or Philippines to do that, before investing in any smart Analyst. It is being hyper-conservative when it comes to creativity and experimentation because of quant-issues. Definitely buy my data books! :). < /A > ].

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

Ontotext

MAY 18, 2020

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Occam's Razor

MARCH 23, 2010

There are more mistruths and F U D about Web analytics out there than I think is reasonable. Web Analytics, this beautiful child, was born just the other day in the midst of tumultuous times, quite literally, when everything changes every day. Here are ten web analytics ground truths… 1. Part of it fueled by Vendors.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

O'Reilly on Data

MARCH 18, 2020

It seems as if the experimental AI projects of 2019 have borne fruit. However, organizations need to address important data governance and data conditioning to expand and scale their AI practices. [1] This year, about 15% of respondent organizations are not doing anything with AI, down ~20% from our 2019 survey. But what kind?

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Companies that succeed at machine learning tend to build on those existing analytics use cases.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

OCTOBER 19, 2010

What is the first thing you want when you think about web analytics? recommending tools for the complete web analytics 2.0 Disclosure:] I am the co-Founder of Market Motive Inc and the Analytics Evangelist for Google. Web Analytics 2.0. This blog post is about web analytics 2.0. Of course tools. Don't be.

Analytics 135

Analytics Testing Measurement Optimization 135

Cloudera

JUNE 6, 2019

At the other end of the spectrum, the admin may instantiate a number of low-priority dev clusters – these clusters may often run at capacity, not require performance guarantees, but also provide more agility and flexibility for experimentation. We look forward to sharing more information about these new capabilities in the near future.

Metadata 87

Metadata Data Lake Optimization Strategy 87

Occam's Razor

AUGUST 13, 2012

A couple weeks back I'd requested the nice folks following me on Google+ and Facebook to submit their most important digital marketing and analytics questions. The questions reveal a bunch of things we used to worry about, and continue to, like data quality and creating data driven cultures.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

datapine

JANUARY 31, 2022

Using business intelligence and analytics effectively is the crucial difference between companies that succeed and companies that fail in the modern environment. Your Chance: Want to try a professional BI analytics software? This methodology of “test, look at the data, adjust” is at the heart and soul of business intelligence.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Occam's Razor

JULY 18, 2011

A secondary thing would be to ensure, or your mom will really be mad, that *every* link in your email is tagged with campaign tracking parameters your web analytics solution needs (without this you can’t do the B and O analysis). Most good email providers will do this automatically for whatever web analytics tool you use. That is okay.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

by ERIC HOLLINGSWORTH A large part of the data we data scientists are asked to analyze was not collected with any specific analysis in mind, or perhaps any particular purpose at all. This post describes the analytical issues which arise in such a setting, and what the data scientist can do about them.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

JANUARY 7, 2014

Every indicator we have is that companies are investing more in every facet of analytics. There is an analytics ladder of awesomeness each company needs to climb, and it just takes time. First… it is important to realize that big data's big imperative is driving big action. You know what your Return on Analytics is!

Data-driven 140

Data-driven Consulting Reporting Marketing 140

Occam's Razor

APRIL 29, 2013

Here's why… Real-time data is very expensive. It is expensive from a systems/platforms/data processing/data reporting perspective. You end up paying substantial amounts of money to your analytics/big data vendor – for questionable value. PPS: I've mentioned one exception in the past.

Marketing 166

Marketing Metrics Advertising Measurement 166

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. Like this blog, it will be particularly relevant for those who are in digital analytics and digital marketing. Years and years of practice with R or "Big Data."

Marketing 136

Marketing Analytics Machine Learning Strategy 136