CIO Business Intelligence

MARCH 21, 2022

As such, a data scientist must have enough business domain expertise to translate company or departmental goals into data-based deliverables such as prediction engines, pattern detection analysis, optimization algorithms, and the like. Get the latest insights by signing up for our newsletters. ]

Unstructured Data 135

Unstructured Data Data Analytics Analytics Structured Data 135

Insight

MARCH 27, 2019

DeepAugment is an AutoML tool focusing on data augmentation. It utilizes Bayesian optimization for discovering data augmentation strategies tailored to your image dataset. To address this problem, Google published AutoAugment last year, which discovers optimized augmentations for the given dataset using reinforcement learning.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

Cloudera

FEBRUARY 16, 2022

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. So, we have workspaces, projects and sessions in that order.

Machine Learning 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

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. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing. Online, offline or nonline.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

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

Experimentation & Testing (A/B, Multivariate, you name it). What's the optimal organization structure (and who should own web analytics!)? Benchmarking (exactly how you can do it), impactful actionable executive dashboards (what they should contain), creating a data driven organization. Qualitative and quantitative.

Analytics 60

Analytics Metrics Marketing Dashboards 60

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. Data collected from this system reflects the way the world works when we just observe it.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

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

AWS Big Data

JULY 3, 2023

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance. Sell 1 (PVH, PVH) 2022-09-06 18321.729571 55.15

Snapshot 82

Snapshot Data Lake Testing Strategy 82

Cloudera

JUNE 6, 2019

This strategy works well for managing internal chargebacks, limiting the impact of less sophisticated users on more experienced users, and overall encouraging individuals to think about and optimize their jobs and queries now that they have a smaller (but dedicated) cluster. 2) By workload type. 3) By workload priority.

Metadata 86

Metadata Data Lake Optimization Strategy 86

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. The biggest time sink is often around data collection, labeling and cleaning.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

DECEMBER 12, 2016

You got me, I am ignoring all the data layer and custom stuff! But, at the end of the day presence of a Tag Manager communicates to me that the company is serious about data collection and data quality. Even if you follow the 10/90 rule, it is important to focus our time and resources optimally. All that is great.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

SEPTEMBER 15, 2014

Company UX leaders are happy to stink less by taking the sub-optimal path of responsive design, rather than create a mobile-unique experience (your customers tend to do different things on your desktop site than your mobile site!). In this post we will look mobile sites first, both data collection and analysis, and then mobile applications.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

JULY 18, 2011

To not have it as an active part of your marketing portfolio is sub-optimal. Optimal Acquisition Email Metrics. Most email programs now have preview panes that typically block images and scripts (Outlook, Thunderbird, Gmail, everyone), and default settings prevent data collection due to concerns about viruses.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

Occam's Razor

OCTOBER 19, 2010

Move from a data collection obsession and develop a crush on data analysys. A huge vast majority of clicks coming from search engines continue to be organic clicks (which is why I love and adore search engine optimization). Experimentation and Testing Tools [The "Why" – Part 1]. Google Website Optimizer.

Analytics 135

Analytics Testing Measurement Optimization 135

Occam's Razor

AUGUST 13, 2012

It is hard, it is time consuming, but it also allows you to test your hypotheses on possible optimal allocations, test them in the real world, find the best answers and be brilliant with your marketing spend mix. I can use that to hypothesize what an optimal budget allocation might look like.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

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. Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Occam's Razor

MAY 27, 2015

Here are the digital myths that are leading us down a profoundly sub-optimal path: 1. A data-first strategy is a winning formula. Per our friends at Wikipedia, Programmatic encompasses an array of technologies that automate the buying, placement and optimization of media inventory. Programmatic platforms are a panacea.

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

datapine

JANUARY 31, 2022

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.

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. Be skeptical, intellectually honest.

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. That's it.

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. By lowering your bounce rate all you managed to accomplish is get your ads created and targeted properly and optimize the landing pages.

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. Optimal Starting SCOTUS Starting Points. If you would like to pursue my personal strategy, here are a collection of cases to use as starting points.

Marketing 136

Marketing Analytics Machine Learning Strategy 136