Ontotext

NOVEMBER 2, 2023

Achieving this advantage is dependent on their ability to capture, connect, integrate, and convert data into insight for business decisions and processes. This is the goal of a “data-driven” organization. We call this the “ Bad Data Tax ”. This is partly because integrating and moving data is not the only problem.

IT 69

IT Cost-Benefit Data-driven Technology 69

CIO Business Intelligence

JUNE 28, 2023

For all of generative AI’s allure, large enterprises are taking their time, many outright banning tools like ChatGPT over concerns of accuracy, data protection, and the risk of regulatory backlash. Experimentation with a use case driven approach. Looking forward.

Enterprise 52

Enterprise Experimentation Risk Data-driven 52

Ontotext

JULY 6, 2023

In today’s fast changing environment, enterprises that have transitioned from being focused on applications to becoming data-driven gain a significant competitive edge. There are four groups of data that are naturally siloed: Structured data (e.g., Transaction and pricing data (e.g.,

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

Octopai

OCTOBER 26, 2022

Data-driven organizations are a bad idea. Using data to drive your organization is wonderful. But data, at best, can only be a powerful vehicle, or a reliable GPS system. Except sometimes we call organizations “data-driven” when really the data is driving them up the wall. And it should be.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

DataRobot Blog

AUGUST 10, 2022

Change is happening fast across the NHS with the focus squarely on harnessing the huge amount of data the NHS generates — to drive forward the transformation programmes needed to address the backlog for elective care and growing demands for services. Resetting urgent care performance and delivery.

Forecasting 52

Forecasting Experimentation Data-driven Risk 52

DataRobot

APRIL 19, 2021

Data Scientists Are AI Experts. But for more complex business decisions, including those that use less structured data, we have artificial intelligence systems. Data scientists train the algorithms using datasets that contain curated learning examples. They need data assembly, data science, integration, and visualization skills.

Machine Learning 52

Machine Learning Visualization Data-driven Data Science 52

AWS Big Data

OCTOBER 4, 2023

Amazon DataZone enables customers to discover, access, share, and govern data at scale across organizational boundaries, reducing the undifferentiated heavy lifting of making data and analytics tools accessible to everyone in the organization. This is challenging because access to data is managed differently by each of the tools.

Metadata 103

Metadata Data Lake Publishing Data Governance 103

Rocket-Powered Data Science

JULY 6, 2021

In at least one way, it was not different, and that was in the continued development of innovations that are inspired by data. This steady march of data-driven innovation has been a consistent characteristic of each year for at least the past decade.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

DataRobot Blog

MARCH 16, 2023

Data scientists run experiments. To work effectively, data scientists need agility in the form of access to enterprise data, streamlined tooling, and infrastructure that just works. We’ve tightened the loop between ML data prep , experimentation and testing all the way through to putting models into production.

Modeling 52

Modeling Experimentation Machine Learning Data-driven 52

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science. Introduction. This is not that.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Occam's Razor

OCTOBER 23, 2006

The title of my presentation at the Washington DC Emetrics summit was: Creating a Data Driven Web Decision Making Culture – Lessons, Tips, Insights from a Practitioner. Seven Steps to Creating a Data Driven Decision Making Culture…… Slide 1: Decision Making Landscape. 2 Solve for the Trinity. #

Data-driven 117

Data-driven Metrics KPI Reporting 117

datapine

FEBRUARY 4, 2020

Today’s digital data has given the power to an average Internet user a massive amount of information that helps him or her to choose between brands, products or offers, making the market a highly competitive arena for the best ones to survive. First things first – organizing and prioritizing your marketing data.

Reporting 280

Reporting Marketing Advertising Metrics 280

Domino Data Lab

JULY 28, 2021

The data science lifecycle (DLSC) has been defined as an iterative process that leads from problem formulation to exploration, algorithmic analysis and data cleaning to obtaining a verifiable solution that can be used for decision making. The data science process in a business environment begins with the Manage stage.

Data Science 59

Data Science Enterprise Modeling Experimentation 59

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. Experimentation & Testing (A/B, Multivariate, you name it). Benchmarking (exactly how you can do it), impactful actionable executive dashboards (what they should contain), creating a data driven organization.

Analytics 60

Analytics Metrics Marketing Dashboards 60

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. I was asked a few weeks back: " What companies should we proactively help with analytics, for free, so that they can make smarter data-influenced decisions ?" Data quality plays a role into this.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Sisense

MAY 18, 2021

If marketing were an apple pie, data would be the apples — without data supporting your marketing program, it might look good from the outside, but inside it’s hollow. In a recent survey from Villanova University, 100% of marketers said data analytics has an essential role in marketing’s future.

Marketing 52

Marketing Data Analytics Analytics Metrics 52

Cloudera

NOVEMBER 25, 2020

The challenges Matthew and his team are facing are mainly about access to a multitude of data sets, of various types and sources, with ease and ad-hoc, and their ability to deliver data-driven and confident outcomes. . Most of their research data is unstructured and has a lot of variety. Challenges Ahead.

Data Warehouse 57

Data Warehouse Unstructured Data Analytics Visualization 57

Domino Data Lab

MAY 8, 2019

For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way. The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. ML model interpretability and data visualization. back to the structure of the dataset.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

MAY 27, 2015

A data-first strategy is a winning formula. The content of the letter could be customized to Stephanie's data/behavior. even if you've never visited the site) has access to tons of intent signals from you right now, tons of third-party cookies that litter your browser right now, and immense Big Data and algorithms.

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

Occam's Razor

JANUARY 9, 2012

Now here's another surprise: These rules/insights/mind shifts are not about data! Here's important context (before we get into the rules for revolutionaries)… The Fundamental Web Analytics Problem Is Not Data! Most companies are astonishingly blasé about data and possibilities of measurement. We expect more.

Marketing 140

Marketing B2B Measurement Risk 140

Occam's Razor

JULY 26, 2010

Suggestions that might increase the probability that you'll bump into things that might be insightful, and communicate data more effectively. The data is easily available in the web analytics tool so why not. Get the data out of the crm / erp / "backend" system. Here we go. #1: 1: Go as deep as you can.

Metrics 96

Metrics Measurement Dashboards Reporting 96

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. The term “AI,” meanwhile, is No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

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. The first two are from editions of my newsletter, The Marketing – Analytics Intersect (it goes out weekly, and is now my primary publishing channel, sign up!).

Marketing 136

Marketing Analytics Machine Learning Strategy 136

O'Reilly on Data

MAY 11, 2020

Infinite Jest was published in 1996, just as the modern Web was coming into being. Television only lacked the immediate feedback that comes with clicks, tracking cookies, tracking pixels, online experimentation, machine learning, and “agile” product cycles. That loop isn’t new, of course; it was well-known to TV network executives.

Modeling 190

Modeling Advertising Recreation/Entertainment Metrics 190