O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 279

Machine Learning Software Metadata Testing 279

Rocket-Powered Data Science

FEBRUARY 15, 2023

These rules are not necessarily “Rocket Science” (despite the name of this blog site), but they are common business sense for most business-disruptive technology implementations in enterprises. Love thy data: data are never perfect, but all the data may produce value, though not immediately.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

O'Reilly on Data

MARCH 18, 2020

By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models. This is consistent with the results of our data quality survey.

Enterprise 350

Enterprise Deep Learning Data Governance Risk 350

Octopai

MARCH 23, 2022

If you’re a conscientious data scientist, you’re going to clean up your data before using it to make models, predictions and recommendations. In the past, it’s been estimated that data scientists spend somewhere between 30% and 80% of their time just prepping and cleaning data. Data Supervision. Not really.

Data Quality 98

Data Quality Reporting Modeling Interactive 98

CIO Business Intelligence

MAY 24, 2022

In order to help maintain data privacy while validating and standardizing data for use, the IDMC platform offers a Data Quality Accelerator for Crisis Response. Cloud Computing, Data Management, Financial Services Industry, Healthcare Industry

Finance 140

Finance Management Metadata Machine Learning 140

IBM Big Data Hub

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. Data quality Data quality is essentially the measure of data integrity.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time. The program must introduce and support standardization of enterprise data.

Data Governance 142

Data Governance Management Metadata Data Quality 142

Timo Elliott

JANUARY 6, 2022

The data you’ve collected and saved over the years isn’t free. If storage costs are escalating in a particular area, you may have found a good source of dark data. Analyze your metadata. If you’ve yet to implement data governance, this is another great reason to get moving quickly.

IT 135

IT Metadata Data-driven Data Governance 135

IBM Big Data Hub

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

Data Architecture 52

Data Architecture Data Quality Strategy Data Lake 52

CIO Business Intelligence

NOVEMBER 22, 2022

Data fabric is an architecture that enables the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems. The fabric, especially at the active metadata level, is important, Saibene notes.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

CIO Business Intelligence

MARCH 9, 2022

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. Each ETL step risks introducing failures or bugs that reduce data quality. .

Data Lake 119

Data Lake Unstructured Data Data Warehouse Big Data 119

AWS Big Data

MAY 7, 2024

Figure 1: Flow of actions for self-service analytics around data assets stored in relational databases First, the data producer needs to capture and catalog the technical metadata of the data asset. The producer also needs to manage and publish the data asset so it’s discoverable throughout the organization.

Metadata 109

Metadata Data Lake Data Processing Data-driven 109

Andrew White

JANUARY 12, 2021

By 2023, ERP data will be the basis for 30% of AI-generated predictive analyses and forecasts. Through 2023, up to 10% of AI training data will be poisoned by benign or malicious actors. By 2024, 75% of organizations will have deployed multiple data hubs to drive mission-critical data and analytics sharing and governance.

Analytics 144

Analytics Metadata Enterprise Data-driven 144

Cloudera

SEPTEMBER 9, 2022

Cloudera has been supporting data lakehouse use cases for many years now, using open source engines on open data and table formats, allowing for easy use of data engineering, data science, data warehousing, and machine learning on the same data, on premises, or in any cloud.

Metadata 102

Metadata Machine Learning Unstructured Data Data Lake 102

Cloudera

DECEMBER 15, 2022

With in-place table migration, you can rapidly convert to Iceberg tables since there is no need to regenerate data files. Only metadata will be regenerated. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback.

Metadata 80

Metadata Data Warehouse Snapshot Machine Learning 80

AWS Big Data

MARCH 7, 2023

Sources Data can be loaded from multiple sources, such as systems of record, data generated from applications, operational data stores, enterprise-wide reference data and metadata, data from vendors and partners, machine-generated data, social sources, and web sources.

Analytics 97

Analytics Data Warehouse Data Lake Metadata 97

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. Then, you transform this data into a concise format.

Data Strategy 103

Data Strategy Strategy Data Warehouse Prescriptive Analytics 103

Andrew White

JUNE 11, 2019

A couple of years ago we postulated that an organizations data and analytics platform, that sits at the heart of their digital business , comprises three core platforms or layers: Analytics/BI, data science/ML and AI. Data Management. Data and Analytics Governance. Some aspects of Data Quality.

IT 82

IT Data Quality Data Integration Business Objectives 82

IBM Big Data Hub

APRIL 27, 2022

Gartner defines a data fabric as “a design concept that serves as an integrated layer of data and connecting processes. The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale.

Management 79

Management Metadata Data Architecture Data Lake 79

AWS Big Data

DECEMBER 19, 2024

Data has become an invaluable asset for businesses, offering critical insights to drive strategic decision-making and operational optimization. The business end-users were given a tool to discover data assets produced within the mesh and seamlessly self-serve on their data sharing needs.

Data Governance 52

Data Governance Publishing Data-driven Metadata 52

Smarten

JANUARY 30, 2024

Business users cannot even hope to prepare data for analytics – at least not without the right tools. Gartner predicts that, ‘data preparation will be utilized in more than 70% of new data integration projects for analytics and data science.’ So, why is there so much attention paid to the task of data preparation?

Analytics 67

Analytics Visualization Data Quality Metadata 67

Cloudera

AUGUST 30, 2022

For state and local agencies, data silos create compounding problems: Inaccessible or hard-to-access data creates barriers to data-driven decision making. Legacy data sharing involves proliferating copies of data, creating data management, and security challenges. Towards Data Science ).

Data Architecture 79

Data Architecture Data Lake Data Warehouse Metadata 79

Alation

JULY 19, 2022

As they attempt to put machine learning models into production, data science teams encounter many of the same hurdles that plagued data analytics teams in years past: Finding trusted, valuable data is time-consuming. Obstacles, such as user roles, permissions, and approval request prevent speedy data access.

Metadata 59

Metadata Data Quality Statistics Data Science 59

Cloudera

APRIL 1, 2024

Cloudera, a leader in big data analytics, provides a unified Data Platform for data management, AI, and analytics. Our customers run some of the world’s most innovative, largest, and most demanding data science, data engineering, analytics, and AI use cases, including PB-size generative AI workloads.

Unstructured Data 105

Unstructured Data Cost-Benefit Metadata Machine Learning 105

Domino Data Lab

OCTOBER 1, 2020

This data supports all kinds of use cases within organizations, from helping production analysts understand how production is progressing, to allowing research scientists to look at the results of a set of treatments across different trials and cross-sections of the population.

Enterprise 105

Enterprise Metadata Cost-Benefit Data Science 105

Alation

AUGUST 30, 2022

Centralization of metadata. A decade ago, metadata was everywhere. Consequently, useful metadata was unfindable and unusable. We had data but no data intelligence and, as a result, insights remained hidden or hard to come by. This universe of metadata represents a treasure trove of connected information.

Metadata 52

Metadata Data Quality Data Governance Machine Learning 52

datapine

JANUARY 31, 2022

As Dan Jeavons Data Science Manager at Shell stated: “what we try to do is to think about minimal viable products that are going to have a significant business impact immediately and use that to inform the KPIs that really matter to the business”.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

AWS Big Data

JUNE 14, 2023

This dashboard helps our operations team and end customers improve the data quality of key attribution and reduce manual intervention. This framework can be described as follows: Findable – Metadata and data should be easy to find for both humans and computers. Outside of work, Ameya is a professional pilot.

Dashboards 105

Dashboards KPI Scorecard Metadata 105

CIO Business Intelligence

AUGUST 11, 2023

In this model, the centralized hub — Enterprise D&A — will provide the platform, security, and best practices, along with tracking and governance of data products. The spokes — Dow’s businesses and functions — will perform much of their own analytics and data science.

Data Analytics 98

Data Analytics Analytics Data-driven Business Driver 98

AWS Big Data

APRIL 19, 2023

Background The success of a data-driven organization recognizes data as a key enabler to increase and sustain innovation. The goal of a data product is to solve the long-standing issue of data silos and data quality. The data science algorithm Valentine is an effective tool for this.

Technology 105

Technology Data-driven Machine Learning Sales 105

Timo Elliott

JANUARY 5, 2022

The first one is: companies should invest more in improving their data quality before doing anything else. You must master your metadata and make sure that everything is lined up. To make a big step forward with data science, you first need to do that painful work.

Digital Transformation 115

Digital Transformation Machine Learning Enterprise Data Science 115

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Consideration of both data & metadata in the migration.

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

IBM Big Data Hub

JANUARY 17, 2023

These new technologies and approaches, along with the desire to reduce data duplication and complex ETL pipelines, have resulted in a new architectural data platform approach known as the data lakehouse – offering the flexibility of a data lake with the performance and structure of a data warehouse.

Data Lake 52

Data Lake Metadata Data Warehouse Data Governance 52

Octopai

JANUARY 19, 2021

Octopai can fully map the BI landscape and trace metadata movement in a mixed environment including complex multi-vendor landscapes. Octopai’s cloud-based offerings hasten data delivery and allow full automation to dramatically accelerate the entire BI data lifecycle.

Metadata 52

Metadata ROI Machine Learning Data Quality 52

Octopai

MAY 31, 2022

The ultimate data organizational tool, letting any data user in your organization easily see what data assets you have and all the pertinent details about them, is a data catalog. Bonus tool: active metadata management. An active metadata management tool adds a maître d’ to self-serve analytics.

Management 52

Management Metadata Business Intelligence ROI 52

Alation

DECEMBER 15, 2022

March 2015: Alation emerges from stealth mode to launch the first official data catalog to empower people in enterprises to easily find, understand, govern and use data for informed decision making that supports the business. May 2016: Alation named a Gartner Cool Vendor in their Data Integration and Data Quality, 2016 report.

Metadata 52

Metadata Data Governance Data Quality Marketing 52

IBM Big Data Hub

MAY 9, 2022

Data privacy laws such as the GDPR in the EU, CCPA in California and PIPEDA in Canada have been enacted at the same time businesses are revitalizing efforts to establish data quality, not just data volume. Poor data quality costs organizations an average of $12.9 million each year [1] and $1.2 28, 2021. [2].

Management 56

Management Data Quality Metadata Data Integration 56