datapine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. 4) How can you ensure data quality?

IT 317

IT Statistics KPI Data-driven 317

CIO Business Intelligence

APRIL 8, 2024

Digital Athlete draws data from players’ radio frequency identification (RFID) tags, 38 5K optical tracking cameras placed around the field capturing 60 frames per second, and other data such as weather, equipment, and play type. million video frames and documents about 100 million locations and positions of players on the field.

Analytics 98

Analytics Broadcasting Predictive Analytics Metrics 98

Octopai

MAY 15, 2023

Data lineage helps identify redundant or inefficient data integration steps, allowing organizations to streamline their processes and reduce development, maintenance, and operational costs associated with ETL. Data quality costs: Poor data quality can result in significant costs for organizations.

Data Quality 52

Data Quality Data Governance Data Integration Risk 52

erwin

AUGUST 7, 2019

A data catalog benefits organizations in a myriad of ways. With the right data catalog tool, organizations can automate enterprise metadata management – including data cataloging, data mapping, data quality and code generation for faster time to value and greater accuracy for data movement and/or deployment projects.

Metadata 90

Metadata Data Governance Data Quality Data Warehouse 90

Alation

DECEMBER 21, 2022

Master Data Management (MDM) and data catalog growth are accelerating because organizations must integrate more systems, comply with privacy regulations, and address data quality concerns. What Is Master Data Management (MDM)? Data Catalog and Master Data Management. Assess Data Quality.

Management 52

Management Data Quality Metadata Testing 52

Octopai

JANUARY 19, 2020

BCBS 239 is a document published by that committee entitled, Principles for Effective Risk Data Aggregation and Risk Reporting. The document, first published in 2013, outlines best practices for global and domestic banks to identify, manage, and report risks, including credit, market, liquidity, and operational risks.

Metadata 61

Metadata Risk Management Business Intelligence Data Governance 61

Juice Analytics

APRIL 6, 2021

Habit 2: Create a shared vocabulary for your data What is an “active user”? These are terms that need to be carefully defined and documented so we can move on to how we are going to improve them. Val Logan of The Data Lodge is one of the premier thinkers on how organizations can build shared skills in using data.

Metrics 119

Metrics Dashboards Data-driven Data Quality 119

Alation

FEBRUARY 7, 2023

Back then, visualizing impact analysis seemed futuristic with great promise. The business analysts were dealing with a problem that may sound familiar to folks in the data management space. They sought documentation to help them locate the source of the data from the warehouse. What’s the right lineage level? It depends!

Metadata 52

Metadata Data Warehouse Visualization Data Quality 52

AWS Big Data

JUNE 6, 2023

Data consumers lose trust in data if it isn’t accurate and recent, making data quality essential for undertaking optimal and correct decisions. Evaluation of the accuracy and freshness of data is a common task for engineers. Currently, various tools are available to evaluate data quality.

Data Quality 75

Data Quality Data Lake Data-driven Metrics 75

FineReport

APRIL 26, 2023

Having visually appealing graphics can also increase user adoption. Advanced analytics capabilities : The tool should be able to analyze data and identify patterns, as well as forecast future events with complex forecasting algorithms, going beyond simple mathematical calculations. It supports master-slave replication for data backup.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

DataKitchen

FEBRUARY 21, 2023

Errors have caused the company’s leaders to lose confidence in the data products their team produces. His team often delivers late and manages 70 disjointed independent weekly jobs to ingest, transform, visualize, and deliver results to his customers. Summary: 10x your data engineering game.

Data-driven 130

Data-driven Testing Risk Cost-Benefit 130

DataRobot

JULY 16, 2021

They all serve to answer the question, “How well can my model make predictions based on data?” In performance, the trust dimensions are the following: Data quality — the performance of any machine learning model is intimately tied to the data it was trained on and validated against.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

Ontotext

JUNE 22, 2023

All derived facts can be further put into context with structured data, which improves data quality and presents researchers with clear evidence and provenance for all insights Then, Ontotext’s Target Discovery provides deeper insights into the data stored in this highly-interlinked knowledge graph, where long sequences of relations can be mined.

Metrics 52

Metrics Statistics Visualization Metadata 52

Alation

JUNE 2, 2022

Data management is not yet a solved problem, but modern data management is leagues ahead of prior approaches. These include tracking, documenting, monitoring, versioning, and controlling access to AI/ML models. How Data Lineage Is a Use Case in ML. Data lineage allows companies to troubleshoot errors in data processes.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

Ontotext

MAY 2, 2024

Data products – optimization of data assets that drive business, improving data quality and interoperability. In this way, people around the world could visualize the glossary and the policy library tied together with workflows, best practices, and so on.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

Alation

JULY 19, 2021

Data intelligence integrates intelligence derived from active metadata into categories like data quality, governance, and profiling. This document describes the market and provides deep details on the strengths and cautions of nearly 20 data catalog and metadata management solution vendors. Gartner Magic Quadrant.

Metadata 105

Metadata Management Data Governance Machine Learning 105

IBM Big Data Hub

JANUARY 8, 2024

 It includes a built-in schema registry to validate event data from applications as expected, improving data quality and reducing errors. Event Endpoint Management produces valid AsyncAPI documents based on event schemas or sample messages.

Data-driven 73

Data-driven Management Advertising Enterprise 73

O'Reilly on Data

DECEMBER 12, 2019

Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8].

Machine Learning 198

Machine Learning Modeling Testing Risk Management 198

Octopai

JUNE 24, 2019

Document classification and lifecycle management will help you deal with oversight of unstructured data. – Data management : As part of maintaining the integrity of your data, it will be necessary to track activities. This maintains a high priority in your data governance strategy.

Data Governance 42

Data Governance Metadata Unstructured Data Software 42

AWS Big Data

MARCH 7, 2023

ETL (extract, transform, and load) technologies, streaming services, APIs, and data exchange interfaces are the core components of this pillar. Unlike ingestion processes, data can be transformed as per business rules before loading. You can apply technical or business data quality rules and load raw data as well.

Analytics 72

Analytics Data Warehouse Data Lake Metadata 72

Sisense

FEBRUARY 10, 2021

Utilities employ skilled professionals as knowledge workers, but creating a simple, visual way to analyze their data is a hard skillset to find in abundance. It started with implementing data validation rules, including basics like only accepting numbers for numerical inputs, etc.

Dashboards 52

Dashboards Enterprise Risk Software 52

Ontotext

MARCH 8, 2023

However, this information is typically stored in disparate locations, often hidden within departmental documents or applications. Yet, the biggest challenge for risk analysis continues to suffer from lack of a scalable way of understanding how data is interrelated.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

datapine

APRIL 15, 2020

It’s necessary to say that these processes are recurrent and require continuous evolution of reports, online data visualization , dashboards, and new functionalities to adapt current processes and develop new ones. Working software over comprehensive documentation. Finalize documentation, where necessary.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

Occam's Razor

AUGUST 22, 2011

Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love! Web Data Quality: A 6 Step Process To Evolve Your Mental Model. Convert Data Skeptics: Document, Educate & Pick Your Poison. Data Mining And Predictive Analytics On Web Data Works? Six Data Visualizations That Rock!

KPI 124

KPI Metrics Measurement ROI 124

datapine

FEBRUARY 17, 2023

That said, data and analytics are only valuable if you know how to use them to your advantage. Poor-quality data or the mishandling of data can leave businesses at risk of monumental failure. In fact, poor data quality management currently costs businesses a combined total of $9.7 million per year.

Analytics 191

Analytics Cost-Benefit Dashboards Business Intelligence 191

Smart Data Collective

SEPTEMBER 26, 2021

Everyone has to manage raw data at one point or another; yet, not everybody stores it in a way that’s useful for further analysis or comparison to other data sets. As data sets become bigger, it becomes harder to visualize information. Draw a chart highlighting each endpoint in your data. Speaking of which.

Metadata 98

Metadata Visualization Unstructured Data Data mining 98

Alation

JULY 19, 2022

The traditional data science workflow , as defined by Joe Blitzstein and Hanspeter Pfister of Harvard University, contains 5 key steps: Ask a question. Get the data. Explore the data. Model the data. Communicate and visualize the results. A data catalog can assist directly with every step, but model development.

Metadata 59

Metadata Data Quality Statistics Data Science 59

datapine

JANUARY 21, 2020

The data-driven world doesn’t have to be overwhelming, and with the right BI tools , the entire process can be easily managed with a few clicks. One additional element to consider is visualizing data. This kind of report will become visual, easily accessed, and steadfast in gathering insights. Enhanced data quality.

Business Intelligence 259

Business Intelligence Reporting Cost-Benefit Dashboards 259

DataRobot Blog

JULY 21, 2022

If there is no forward-looking predictive component to the use case, it can probably be addressed with analytics and visualizations applied to historical data. Inquire whether there is sufficient data to support machine learning. Document assumptions and risks to develop a risk management strategy. Define project scope.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

CIO Business Intelligence

FEBRUARY 20, 2023

A candidate who earns the certification will have proved their ability to build bundle configurations, pricing, output documents, and renewals and amendments. The consultant provides implementation, customization, and integration expertise in these technologies, as well as training and support.

Consulting 60

Consulting B2B Cost-Benefit Sales 60

erwin

OCTOBER 29, 2020

Earlier this year, erwin conducted a research project in partnership with Dataversity, the 2020 State of Data Governance and Automation. We asked participants to “talk to us about data value chain bottlenecks.” Data quality and accuracy are recurring themes as well.

Data Governance 105

Data Governance Metadata ROI Digital Transformation 105

erwin

MAY 27, 2020

And it exists across these hybrid architectures in different formats: big and unstructured and traditional structured business data may physically sit in different places. What’s desperately needed is a way to understand the relationships and interconnections between so many entities in data sets in detail.

Modeling 98

Modeling Metadata Data Governance Visualization 98

AWS Big Data

NOVEMBER 16, 2023

Through Amazon Redshift in-memory result set caching and compilation caching, workloads ranging from dashboarding to visualization to business intelligence (BI) that run repeat queries experience a significant performance boost. Caching Amazon Redshift uses multiple layers of caching to deliver subsecond response times for repeat queries.

Enterprise 84

Enterprise Data Warehouse Snapshot Cost-Benefit 84

InsightOut

MAY 20, 2022

In listening to them describe their experience – what went well and not so well – and reviewing their existing approach and documentation, I have observed a number of patterns. With these projects, there are clear technical challenges that can also lead to problems, such as data quality and accessibility.

Analytics 52

Analytics Dashboards Optimization Technology 52

Jet Global

MARCH 13, 2020

In a Deloitte survey, 67 percent of the 1,048 executives who participated said they were uncomfortable accessing data using the tools they have available. They prefer to ask an accountant or someone from IT to retrieve data for them. If your financial intelligence needs an IQ upgrade, partner with insightsoftware.

Finance 64

Finance Snapshot Reporting Advertising 64

IBM Big Data Hub

OCTOBER 12, 2023

Documentation can also be generated and maintained with information such as a model’s data origins, training methods and behaviors. Checkpoints can be created throughout the AI lifecycle to prevent or mitigate bias and drift.

Cost-Benefit 76

Cost-Benefit Metadata Data Governance Modeling 76

erwin

MARCH 21, 2019

Data governance also relies on business process modeling and analysis to drive improvement , including identifying business practices susceptible to security, compliance or other risks and adding controls to mitigate exposures. Organizations also can use a BPM tool to identify the staff who function as “unofficial data repositories.”

Modeling 75

Modeling Metadata IT Data Governance 75