CIO Business Intelligence

NOVEMBER 13, 2023

Inadequate data management and governance Data is at the heart of digital transformation, and companies that don’t have adequate data management processes in place are likely to struggle. Ensuring data quality, privacy, and security is essential.

Digital Transformation 143

Digital Transformation Cost-Benefit Experimentation Measurement 143

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

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. Another pattern that I’ve seen in good PMs is that they’re very metric-driven.

Management 110

Management Machine Learning Experimentation Metrics 110

Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. SPC tests can do the same thing for the data flowing through your pipelines.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

AWS Big Data

JULY 20, 2023

Prior to the creation of the data lake, Orca’s data was distributed among various data silos, each owned by a different team with its own data pipelines and technology stack. Moreover, running advanced analytics and ML on disparate data sources proved challenging.

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

Occam's Razor

NOVEMBER 13, 2009

Chapter 3 The Awesome World of Clickstream Analysis: Metrics. The second half shows exactly how to pick the best metrics for your org and, my absolute favorite (Page 64), how to diagnose the root cause of a metrics performance. Chapter 7 Failing Faster: Unleashing the Power of Testing and Experimentation. A good thing.

Analytics 71

Analytics Metrics Measurement Testing 71

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 364

Marketing Experimentation Metrics Testing 364

O'Reilly on Data

APRIL 19, 2021

The biggest problems in this year’s survey are lack of skilled people and difficulty in hiring (19%) and data quality (18%). The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%). Bad data yields bad results at scale.

Enterprise 364

Enterprise Risk Modeling Manufacturing 364

Occam's Razor

AUGUST 22, 2011

" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love! Web Data Quality: A 6 Step Process To Evolve Your Mental Model. "Engagement" Is Not A Metric, It's An Excuse. How to focus?"

KPI 125

KPI Metrics Measurement ROI 125

Occam's Razor

DECEMBER 12, 2016

Data quality plays a role into this. And, most of the time, regardless of the size of the size of the company, you only know your code is not working post-launch when data is flowing in (not!). You got me, I am ignoring all the data layer and custom stuff! All that is great. For most of us, you plus the CMO/equivalent.].

Analytics 142

Analytics Data-driven Strategy Consulting 142

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 Machine Learning Data-driven 66

CIO Business Intelligence

FEBRUARY 5, 2025

By focusing on domains where data quality is sufficient and success metrics are clear such as increased conversion rates, reduced downtime, or improved operational efficiency companies can more easily quantify the value AI brings. Break the project into manageable, experimental phases to learn and adapt quickly.

Cost-Benefit 117

Cost-Benefit ROI Metrics Measurement 117