datapine

SEPTEMBER 27, 2022

Our next book is dedicated to anyone who wants to start a career as a data scientist and is looking to get all the knowledge and skills in a way that is accessible and well-structured. Originally published in 2018, the book has a second edition that was released in January of 2022. 4) “SQL Performance Explained” by Markus Winand.

Business Intelligence 292

Business Intelligence Data Warehouse Data Processing Data mining 292

Jen Stirrup

SEPTEMBER 30, 2021

Data Science – Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data Understanding is a crucial aspect of all of these areas, and the process will not proceed properly without it.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

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

Smart Data Collective

SEPTEMBER 25, 2019

However, according to a 2018 North American report published by Shred-It, the majority of business leaders believe data breach risks are higher when people work remotely. If you trust the data, it’s easier to use confidently to make business decisions. Time to Take Action. This article has been updated on Sep 25th, 2019.

Technology 86

Technology Statistics Strategy Insurance 86

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

O'Reilly on Data

JULY 15, 2019

We should clarify that SR 11-7 also covers models that aren’t necessarily based on machine learning: "quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates." Sources of model risk.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

DataKitchen

OCTOBER 11, 2021

Figure 2: Example data pipeline with DataOps automation. In this project, I automated data extraction from SFTP, the public websites, and the email attachments. The automated orchestration published the data to an AWS S3 Data Lake. Historic Balance – compares current data to previous or expected values.

Testing 152

Testing Metadata Dashboards Statistics 152

Smarten

APRIL 19, 2024

The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. Nothing…and I DO mean NOTHING…is more prominent in technology buzz today than Artificial Intelligence (AI). billion, with the market growing by 31.1%

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Peter James Thomas

AUGUST 12, 2018

These normally appear at the end of an article, but it seemed to make sense to start with them in this case: Recently I published Building Momentum – How to begin becoming a Data-driven Organisation. These and other areas are covered in greater detail in an older article, Using BI to drive improvements in data quality.

Metrics 49

Metrics Statistics Data Quality Measurement 49

AWS Big Data

JULY 27, 2023

Then the claims data is ingested into the catalog (so it’s visible to analysts), after enriching it with some relevant details about the corresponding medical providers coming from a separate source. Claim Amount values will likely be used for some calculations, so convert to number, and Claim Data should be converted to date type.

Visualization 83

Visualization Cost-Benefit Data Quality Interactive 83

CIO Business Intelligence

OCTOBER 18, 2023

“IDH holds a potentially severe immediate risk for patients during dialysis and therefore requires immediate attention from staff,” says Hanjie Zhang, director of computational statistics and artificial intelligence at the Renal Research Institute, a joint venture of Fresenius North America and Beth Israel Medical Center. “As

Predictive Analytics 133

Predictive Analytics Analytics Machine Learning Digital Transformation 133

Ontotext

MAY 9, 2023

AIDAVA (short for AI-powered Data Curation & Publishing Virtual Assistant) is a Horizon Europe project, which brings together 14 partners from 9 EU countries. Today delivering this RWD is costly: it requires a lot of manual work often managed by specialized companies.

Technology 52

Technology Cost-Benefit Statistics Publishing 52

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of interesting work on something new that was data management. To some extent, academia still struggles a lot with how to stick data science into some sort of discipline.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

IBM Big Data Hub

JULY 28, 2022

This feature significantly increases the productivity of the data stewards who provide business context to data by ensuring data quality, usefulness and protection for broader consumption. Some of the new capabilities of Watson Knowledge Catalog include: Entirely revisited workflow and user experience.

Metadata 56

Metadata Data Quality Machine Learning Statistics 56

Occam's Razor

JUNE 8, 2006

In the morass of data quality and TV and UV and cookie values and ab test id’s and sessions and shopper_ids we look at massive amounts of data and forget that real people are using our websites. A vast majority of us fail at this, we face bad or incomplete data and we get paralysed. You guys rock!!).

KPI 94

KPI Measurement Statistics Metrics 94

Domino Data Lab

JULY 21, 2019

how modern approaches can be used to obtain better quality data, and at a lower cost, to help support evidence-based policy decisions. unique challenges of sharing confidential government microdata and the importance of access in generating high quality inference. What am I talking about with the new types of data?

Metadata 65

Metadata Statistics Risk Machine Learning 65

The Unofficial Google Data Science Blog

JULY 18, 2023

Editor's note : The relationship between reliability and validity are somewhat analogous to that between the notions of statistical uncertainty and representational uncertainty introduced in an earlier post. We derive our measurement of data quality, ICC, from the variance parameters in the model.$$

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Smarten

AUGUST 9, 2017

If your role in business demands that you stay abreast of changes in business analytics, you are probably familiar with the term Smart Data Discovery. You may also have read the recent Gartner report entitled, ‘Augmented Analytics Is the Future of Data and Analytics’ , Published 27 July 2017, by Rita L.

Analytics 40

Analytics IT Statistics ROI 40

Andrew White

JANUARY 9, 2022

I try to relate as much published research as I can in the time available to draft a response. – In the webinar and Leadership Vision deck for Data and Analytics we called out AI engineering as a big trend. – In the webinar and Leadership Vision deck for Data and Analytics we called out AI engineering as a big trend.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Smarten

SEPTEMBER 6, 2019

SSDP tools allow users to compile and analyze data and test theories and prototypes to support dynamic decisions and planning on their own so the business does not lose time or competitive advantage while waiting for reports or analysis from a central source. Self-Serve Data Prep in Action.

Analytics 51

Analytics IT Business Intelligence Testing 51

datapine

AUGUST 29, 2022

Due to this book being published recently, there are not any written reviews available. 4) Big Data: Principles and Best Practices Of Scalable Real-Time Data Systems by Nathan Marz and James Warren. 12) Big Data at Work: Dispelling the Myths, Uncovering the Opportunities, by Thomas H.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Domino Data Lab

MARCH 3, 2019

This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees. We had big surprises at several turns and have subsequently published a series of reports. Evolving Data Infrastructure: Tools and Best Practices for Advanced Analytics and AI (Jan 2019).

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

AWS Big Data

JANUARY 18, 2024

Easily and securely prepare, share, and query data – This session shows how you can use Lake Formation and the AWS Glue Data Catalog to share data without copying, transform and prepare data without coding, and query data. In 2023, we added support for column-level statistics for tables in the Data Catalog.

Data Lake 90

Data Lake Metadata Data Governance Statistics 90

Andrew White

JANUARY 11, 2021

This was for the Chief Data Officer, or head of data and analytics. Gartner also published the same piece of research for other roles, such as Application and Software Engineering. Try this: Tie Your Data and Analytics Initiatives to Stakeholders and Their Business Goals. where performance and data quality is imperative?

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Jet Global

SEPTEMBER 26, 2023

Data Cleansing Imperative: The same report revealed that organizations recognized the importance of data quality, with 71% expressing concerns about data quality issues. This underscores the need for robust data cleansing solutions.

Data Quality 52

Data Quality Reporting Management Software 52

Jet Global

MAY 9, 2024

ETL pipelines are commonly used in data warehousing and business intelligence environments, where data from multiple sources needs to be integrated, transformed, and stored for analysis and reporting. Data pipelines enable data integration from disparate healthcare systems, transforming and cleansing the data to improve data quality.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52