Smart Data Collective

MAY 4, 2023

Big data has been discussed by business leaders since the 1990s. It refers to datasets too large for normal statistical methods. Professionals have found ways to use big data to transform businesses. Furthermore, many websites have implemented anti-scraping measures to prevent bots from collecting data.

Data mining 79

Data mining Big Data Data-driven Measurement 79

CIO Business Intelligence

FEBRUARY 13, 2023

The name references the Greek letter sigma, which is a statistical symbol that represents a standard deviation. The process aims to bring data and statistics into the mesh to help objectively identify errors and defects that will impact quality. Six Sigma was trademarked by Motorola in 1993.

Management 97

Management Statistics Manufacturing Measurement 97

BizAcuity

JULY 7, 2020

With limited seating and slot machine on the floor, casinos are ensuring that the risk of the spread of the virus is contained. With respect to developments or changes in inbound markets, gaming data, player statistics, economic recovery speed, and more, adjust and reiterate core strategies. This too shall pass.

Revenue Optimization 98

Revenue Optimization Measurement Interactive Marketing 98

AWS Big Data

MAY 16, 2023

In this first post of the series, we show you how data collected from smart sensors is used for building automated dashboards using QuickSight to help distribution network engineers manage, maintain and troubleshoot smart sensors and perform advanced analytics to support business decision making.

Dashboards 78

Dashboards Statistics Data Collection Business Intelligence 78

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before. Machine learning adds uncertainty.

Management 339

Management Machine Learning Experimentation Metrics 339

Sisense

SEPTEMBER 3, 2020

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. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization.

Statistics 97

Statistics Metrics Visualization Finance 97

The Unofficial Google Data Science Blog

OCTOBER 31, 2016

By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. On the flip side, you sometimes have a small volume of data.

Metrics 107

Metrics Measurement Statistics Data Collection 107

IBM Big Data Hub

AUGUST 7, 2023

In an ideal scenario, they would be able to, with relative and consistent accuracy, predict performance of clinical trial sites that are at risk of not meeting their recruitment expectations. A mitigation plan facilitates trial continuity by providing contingency measures and alternative strategies.

Optimization 55

Optimization Forecasting Data-driven Strategy 55

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

The Unofficial Google Data Science Blog

APRIL 17, 2017

First, the system may not be understood, and even if it was understood it may be extremely difficult to measure the relationships that are assumed to govern its behavior. They can arise from data collection errors or other unlikely-to-repeat causes such as an outage somewhere on the Internet.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

erwin

APRIL 21, 2020

By combining physical system catalogs, critical data elements, and key performance measures with clearly defined product and sales goals, you can manage the effectiveness of your business and ensure you understand what critical systems are for business continuity and measuring corporate performance.

Measurement 97

Measurement Sales Data Governance Metadata 97

erwin

APRIL 21, 2020

One of the biggest lessons we’re learning from the global COVID-19 pandemic is the importance of data, specifically using a data catalog to comply, collaborate and innovate to crisis-proof our businesses. So one of the biggest lessons we’re learning from COVID-19 is the need for data collection, management and governance.

Measurement 96

Measurement Sales Data Governance Metadata 96

Smart Data Collective

AUGUST 4, 2022

According to statistics, an astonishing 62% of managers are reluctant to talk to their employees about anything, while one in five business leaders feel uncomfortable when it comes to recognizing employees’ achievements. The authors state that data analytics saves managers time and reduces the risk of inadvertent bias.

Data-driven 129

Data-driven Metrics Software Data Collection 129

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. . – back to the structure of the dataset. Let’s look through some antidotes. Ergo, less interpretable.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Jet Global

MAY 15, 2020

A financial Key Performance Indicator (KPI) or metric is a quantifiable measure that a company uses to gauge its financial performance over time. Under modern day reporting standards, companies are formally obligated to present their financial data in the following statements: balance sheet, income statement, and cash flow statement.

Metrics 98

Metrics Finance Reporting KPI 98

Andrew White

JANUARY 9, 2022

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. This was not statistic and we have not really explored this in any greater detail since. I suspect we should.

Analytics 64

Analytics Measurement Modeling Data-driven 64

datapine

AUGUST 29, 2022

The book has three main ideas: The biggest risk your company faces is investing a lot of time and resources into building something that the market doesn’t want. Most people are aware that companies collect our GPS locale, text messages, credit card purchases, social media posts, Google search history, etc.,

Big Data 263

Big Data Data Analytics Analytics Data mining 263

O'Reilly on Data

OCTOBER 18, 2022

Data can be wrong. When we use AI in security applications, the risks become even more direct. As AI technologies are adopted more broadly in security and other high-risk applications, we’ll all need to know more about AI audit and risk management. Predictions can be wrong. System designs can be wrong.

Risk Management 297

Risk Management Risk Testing Measurement 297

Domino Data Lab

JUNE 23, 2019

All you need to know, for now, is that machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn based on data by being trained on past examples. These measurement-obsessed companies have an advantage when it comes to AI.

Management 110

Management Machine Learning Experimentation Metrics 110

datapine

DECEMBER 28, 2021

1) What Is A Misleading Statistic? 2) Are Statistics Reliable? 3) Misleading Statistics Examples In Real Life. 4) How Can Statistics Be Misleading. 5) How To Avoid & Identify The Misuse Of Statistics? If all this is true, what is the problem with statistics? What Is A Misleading Statistic?

Statistics 363

Statistics Advertising Visualization Data mining 363

Occam's Razor

MARCH 23, 2010

Part of it is fueled by a vocal minority genuinely upset that 10 years on we are still not a statistically powered bunch doing complicated analysis that is shifting paradigms. Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. Usually for free.

Analytics 118

Analytics Consulting Metrics Experimentation 118

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Data was expensive to gather, and therefore decisions to collect data were generally well-considered. 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

Jet Global

MAY 1, 2023

Manage compliance through up-to-the-minute performance measures, workflow automation, and essential regulatory reports. CFO Priorities Manage expenses and cash flow Enable profitable growth Contain risk Plan for the future Connect the Dots Do the math. Let’s just give our customers access to the data.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

O'Reilly on Data

JUNE 16, 2020

We know, statistically, that doubling down on an 11 is a good (and common) strategy in blackjack. The quality of the decision is based on known information and an informed risk assessment, while chance involves hidden information and the stochasticity of the world. Risk, Probability, Impact, and Decisions.

Uncertainty 282

Uncertainty Testing Risk Reporting 282