CIO Business Intelligence

AUGUST 16, 2023

In order to do that, a digital transformation was required, and when it comes to information provision, there wasn’t much, so we put in place basic platforms to handle data, and developed a cloud architecture for infrastructure and applications.” It’s important to get access to data in order to digitize an entire company,” she says.

Risk 98

Risk Cost-Benefit Digital Transformation Internet of Things 98

O'Reilly on Data

MARCH 31, 2020

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. These companies eventually moved beyond using data to inform product design decisions.

Management 332

Management Machine Learning Experimentation Metrics 332

Rocket-Powered Data Science

JULY 7, 2019

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: (1) Retail. (2) 2) Disaster Response. (3) 3) Machine maintenance. (4)

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Smart Data Collective

JUNE 21, 2023

This frees up time for experimentation and achieving superior results. Alongside capturing precious memories, Snappic’s software doubles as a data collection tool, providing valuable insights about your guests through features like surveys and competitions.

Software 76

Software Technology Experimentation Data-driven 76

Insight

MARCH 27, 2019

Ways to get better data Efforts to improve the quality of data often have a higher return on investment than efforts to enhance models. There are three main ways to improve data: collecting more data, synthesizing new data, or augmenting existing data.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

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

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. Fortunately, Circle of Friends had plenty of user data to mine. Circle of Friends had huge volumes of information on its users and how they used the product.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Insight

MARCH 12, 2020

In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. The latter is any type of external data that has been attached to a book? Instead, I built the AIgent. In other words, if 0.1%

Modeling 59

Modeling Metadata Publishing Sales 59

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. Get on board with data literacy! will look like).

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

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

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. There wasn’t much more information on the profile. Yet, this challenge is not insurmountable.

Management 110

Management Machine Learning Experimentation Metrics 110

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. Of course, some questions in business cannot be answered with historical data.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

AWS Big Data

JULY 3, 2023

Look – ahead bias – This is a common challenge in backtesting, which occurs when future information is inadvertently included in historical data used to test a trading strategy, leading to overly optimistic results. Sell 1 (PVH, PVH) 2022-09-06 18321.729571 55.15 Sell 2 (INVH, INVH) 2022-09-06 27419.797094 38.20

Snapshot 85

Snapshot Data Lake Testing Strategy 85

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. Information can get quite distorted after being abstracted that many times.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

DataRobot Blog

OCTOBER 4, 2022

This automation drastically reduces model building, testing, evaluation and deployment time, promotes creativity, and enables rapid experimentation for time-sensitive use cases. The dataset consist of sales data collected for multiple retail stores across North America. Improved Productivity.

Forecasting 59

Forecasting Sales Data-driven Modeling 59

Cloudera

JUNE 6, 2019

While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ data lake. Now the admins need to synchronize multiple copies of the data and metadata and ensure that users across the many clusters are not viewing stale information.

Metadata 85

Metadata Data Lake Optimization Strategy 85

Occam's Razor

SEPTEMBER 15, 2014

Let's get the information we need to get our CEOs, CMOs and CUXOs to unleash a tsunami of creativity and awesomeness when it comes to mobile. In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation. Framing the Opportunity.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

MARCH 23, 2010

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. If you don't have a robust experimentation program in your company you are going to die. Oh and when I say Experimentation I don't mean testing button sizes (BOO!). Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Occam's Razor

AUGUST 13, 2012

Ensure you don't have to wait for "Web Authors" (or HiPPOs) to give you the information you need. Do the work that will make your baby-boomer c-levels care about the site and data (assuming you discover the site's of value) and. Help you focus on providing analysis rather than simply puking data out.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

datapine

JANUARY 31, 2022

The main use of business intelligence is to help business units, managers, top executives, and other operational workers make better-informed decisions backed up with accurate data. BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions. 1) Informed strategic decisions.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

JANUARY 7, 2014

Remember none of these jobs will do any data collection/IT work, even in medium-sized companies.) But if their primary output is just data, and not actions to take expressed in English or verbally in weekly senior staff meeting, then they are simply Reporting Squirrels. Most companies hire a Web Analyst, Sr.

Data-driven 140

Data-driven Consulting Reporting Marketing 140

Occam's Razor

APRIL 29, 2013

PS: The phrase "real-time data analysis" is an oxymoron. Real-time data is super valuable if zero human beings are involved from data collection to action being taken. Eight Silly Data Things Marketing People Believe That Get Them Fired. 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. They are entertaining, engaging and deeply informative. There is never a boring moment, there is never time when you can’t do something faster or smarter.

Marketing 136

Marketing Analytics Machine Learning Strategy 136