O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 332

Management Machine Learning Experimentation Metrics 332

Jedox

NOVEMBER 7, 2019

Automating consolidation processes such as data preparation and consolidation measures can significantly reduce manual intervention and shorten process times. From data collection to reporting. The result is efficient data collection and validation.

Software 41

Software Data Collection Reporting Data Quality 41

O'Reilly on Data

OCTOBER 22, 2020

Taking the time to work this out is like building a mathematical model: if you understand what a company truly does, you don’t just get a better understanding of the present, but you can also predict the future. Since I work in the AI space, people sometimes have a preconceived notion that I’ll only talk about data and models.

Consulting 296

Consulting Risk Cost-Benefit Modeling 296

CIO Business Intelligence

SEPTEMBER 2, 2022

Back-end software engineers are responsible for maintaining the structure of server-side information by optimizing servers, implementing security measures, and developing data storage solutions. Back-end software engineer.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

AUGUST 31, 2022

Back-end software engineers are responsible for maintaining the structure of server-side information by optimizing servers, implementing security measures, and developing data storage solutions. Back-end software engineer.

Finance 98

Finance IT Software Reporting 98

FineReport

MAY 31, 2023

Return on assets measures the net profit generated per unit of asset, while return on equity (ROE) signifies the return on shareholders’ equity, indicating the efficiency of the company’s own capital. The Dupont analysis model comprises three key components: profit margin, asset turnover, and equity multiplier.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52

Sisense

SEPTEMBER 3, 2020

Data Scientist. Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Data analysts in one organization might be called data scientists or statisticians in another.

Statistics 97

Statistics Metrics Visualization Finance 97

Sisense

OCTOBER 15, 2020

Every data professional knows that ensuring data quality is vital to producing usable query results. Streaming data can be extra challenging in this regard, as it tends to be “dirty,” with new fields that are added without warning and frequent mistakes in the data collection process.

Dashboards 98

Dashboards IoT Optimization Internet of Things 98

Ontotext

AUGUST 23, 2021

The Common Information Model – the Mother of All Electrical Standards. One of the most important standards developed by the electrical power industry is CIM (the Common Information Model). From a data interoperability perspective, though, what the standard lacks is “a little semantics.”

Marketing 52

Marketing Data Quality Modeling Management 52

The Unofficial Google Data Science Blog

JULY 18, 2023

E ven after we account for disagreement, human ratings may not measure exactly what we want to measure. How do we think about the quality of human ratings, and how do we quantify our understanding is the subject of this post. While human-labeled data is critical to many important applications, it also brings many challenges.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Sisense

OCTOBER 29, 2020

“Because AVs collect data in public where there is little ‘reasonable expectation of privacy’, they are not subject to many of the privacy laws in the U.S. The data collected by AVs in the U.S. will likely be owned by the collector of the data, not the data subject. and abroad,” she explained. Advertising?

Machine Learning 90

Machine Learning Data Collection Cost-Benefit Advertising 90

Occam's Razor

AUGUST 13, 2012

The questions reveal a bunch of things we used to worry about, and continue to, like data quality and creating data driven cultures. Yehoshua Coren: Best ways to measure user behavior in a multi-touch, multi-device digital world. What's possible to measure. What's not possible to measure.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. This is aligned to the five pillars we discuss in this post.

Data Strategy 66

Data Strategy Strategy Data Warehouse Prescriptive Analytics 66

Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

Andrew White

JANUARY 9, 2022

Data mesh seems to mean a network of systems and hubs connected to help cope with silos. Perhaps a data mesh is what a pure architecture might design. First, how we measure emissions and carbon footprint is about data design and policy. In other words, D&A plays a key role in the foundational measuring angle.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Domino Data Lab

JUNE 23, 2019

These measurement-obsessed companies have an advantage when it comes to AI. Google, Facebook, other leaders, they really have set up a culture of extreme measurement where every part of their product experience is instrumented to optimize clicks and drive user engagement. There’s no substitute for that experience.

Management 110

Management Machine Learning Experimentation Metrics 110

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! Digital Marketing & Measurement Model.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Occam's Razor

APRIL 21, 2014

I am having issues prioritizing 1) recommending fixing on site issues affecting real traffic levels versus 2) correcting significant configuration issues in Analytics measuring current site traffic. Even the worst analytics configuration in the world will most likely allow you to measure cart and checkout abandonment rate.

Marketing Analytics 131

Marketing Analytics Marketing Analytics Measurement 131

Domino Data Lab

FEBRUARY 4, 2019

Lately there’s been lots of dialog about bias in machine learning models, though considering the noise part of the equation is an important corollary. We’d love to hear your insights and perspectives about running data teams. The ability to measure results (risk-reducing evidence). Lack of data, or data quality issues (silos).

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

datapine

AUGUST 29, 2022

Most people are aware that companies collect our GPS locale, text messages, credit card purchases, social media posts, Google search history, etc., and this book will give you an insight into their data collecting procedures and the reasons behind them. The subsequent chapters focus on predictive and descriptive analysis.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

DataKitchen

JUNE 24, 2021

Chapin also mentioned that measuring cycle time and benchmarking metrics upfront was absolutely critical. “It It struck me that DataOps has the potential to be a transformative capability, and not just from a technology perspective, but from the lens it brings to how we approach these complex data environments. Take a show-me approach.

ROI 211

ROI Metrics Measurement Cost-Benefit 211

datapine

JANUARY 31, 2022

Because things are changing and becoming more competitive in every sector of business, the benefits of business intelligence and proper use of data analytics are key to outperforming the competition. All this data is then used to set pricing fees, meet demand, and ensure an excellent service for both their drivers and clients.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Alation

DECEMBER 15, 2021

Modern business is built on a foundation of trusted data. Yet high-volume collection makes keeping that foundation sound a challenge, as the amount of data collected by businesses is greater than ever before. An effective data governance strategy is critical for unlocking the full benefits of this information.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

Alation

MARCH 5, 2021

How Alation Activates Data Governance. Why is Data Governance Important? As data collection and storage grow, so too does the need for data governance. Where data governance once focused primarily on compliance, the age of big data has broadened its applications. Data Governance Roles.

Data Governance 59

Data Governance Data Quality Dashboards Risk 59

Alation

FEBRUARY 17, 2023

For this reason, your data program will benefit from bringing data governance and data management together. Our organization is struggling with the guardrails between the data governance and management concepts. What frameworks and operating models have you seen work well? Here’s an example.

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

DataKitchen

JANUARY 23, 2023

It allows organizations to see how data is being used, where it is coming from, its quality, and how it is being transformed. DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data lineage does not directly improve data quality.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Alation

AUGUST 26, 2021

Data intelligence first emerged to support search & discovery, largely in service of analyst productivity. For years, analysts in enterprises had struggled to find the data they needed to build reports. This problem was only exacerbated by explosive growth in data collection and volume. Data lineage features.

Metadata 64

Metadata Data Governance Dashboards Software 64