CIO Business Intelligence

MAY 1, 2024

With millions of records to filter, comprising several terabytes (TB) of data, the IT team was hampered with processes that were manual and repetitive. The poor data quality distorted findings and compromised real-time decisions, as well as created unpredictable scenarios that had to be remedied one at a time.

Digital Transformation 52

Digital Transformation Enterprise Data-driven Software 52

CIO Business Intelligence

MAY 1, 2024

With millions of records to filter, comprising several terabytes (TB) of data, the IT team was hampered with processes that were manual and repetitive. The poor data quality distorted findings and compromised real-time decisions, as well as created unpredictable scenarios that had to be remedied one at a time.

Digital Transformation 52

Digital Transformation Enterprise Data-driven Software 52

Laminar Security

DECEMBER 13, 2023

Machine learning algorithms can be trained to recognize patterns in the data and classify data accordingly. For example, an AI system could be trained to classify emails into categories like “sensitive” or “restricted” based on patterns it has learned from a training dataset. One of the primary challenges is data quality.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

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

O'Reilly on Data

JUNE 18, 2019

As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Smart Data Collective

DECEMBER 21, 2022

This market is growing as more businesses discover the benefits of investing in big data to grow their businesses. One of the biggest issues pertains to data quality. Even the most sophisticated big data tools can’t make up for this problem. Data cleansing and its purpose.

Business Analytics 110

Business Analytics Analytics Data Quality Big Data 110

Alation

JUNE 9, 2023

D&A leaders from around the world gathered to discuss and find ways to overcome the latest challenges through strategies and innovations backed by data, analytics, and data science. The observations comprised a mix of classic (the power of people, data quality ), recent (architectures such as fabric and mesh ), and emerging (AI).

Data Analytics 52

Data Analytics Analytics Data Quality Data-driven 52

Alation

DECEMBER 15, 2022

As I reflected on this topic, it occurred to me that software categories should be no different. How Do You Determine the GOAT for a Software Category? But how do you determine the GOAT for a software category? 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

Rocket-Powered Data Science

FEBRUARY 15, 2023

A business-disruptive ChatGPT implementation definitely fits into this category: focus first on the MVP or MLP. Clean it, annotate it, catalog it, and bring it into the data family (connect the dots and see what happens). When people are encouraged to experiment, where small failures are acceptable (i.e.,

Strategy 289

Strategy Experimentation Uncertainty Machine Learning 289

KDnuggets

OCTOBER 8, 2019

To build an effective learning model, it is must to understand the quality issues exist in data & how to detect and deal with it. In general, data quality issues are categories in four major sets.

Data Quality 63

Data Quality Modeling IT 63

Alation

AUGUST 9, 2022

It is essential to consider data integrity when designing, implementing and using any system that stores, processes, and retrieves data. Many confuse data integrity with data quality. This strategy should also consider data security. Objective: a set of rules that determines what the data should look like.

Data Integration 52

Data Integration Data Quality Measurement Strategy 52

Alation

MAY 24, 2022

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

Data Quality 98

Data Quality Data Governance Metadata Metrics 98

CIO Business Intelligence

NOVEMBER 22, 2022

Data observability becomes business-critical Data observability extends the concept of data quality by closely monitoring data as it flows in and out of the applications. Data observability creates an entirely new solution category, Petrella claims.

Data Strategy 145

Data Strategy Strategy Enterprise Consulting 145

Octopai

APRIL 19, 2021

He is the President of Knowledge Integrity, Inc and an expert in master data management, data quality, and business intelligence. His articles on TDWI deal with advice for analysts, customer data profiling, master data management technology, and machine learning. . TDWI – David Loshin. IRM UK Connects.

Metadata 52

Metadata Management Business Intelligence Data Governance 52

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 68

Data Quality Data Integration Metadata Cost-Benefit 68

Alation

JULY 19, 2021

This includes information on quality, sources, processes used, schemas, and formulas. It’s useful in determining how data can be utilized. High volumes of data and metadata are a growing problem. To manage the sheer volume of metadata, a new category has emerged called active metadata. Metadata Management Best Practices.

Metadata 105

Metadata Management Data Governance Machine Learning 105

IBM Big Data Hub

JUNE 8, 2022

However, a foundational step in evolving into a data-driven organization requires trusted, readily available, and easily accessible data for users within the organization; thus, an effective data governance program is key. Here are a few common data management challenges: Regulatory compliance on data use.

Data-driven 58

Data-driven Enterprise Data Governance Data Lake 58

Cloudera

NOVEMBER 26, 2020

While all our winners are doing phenomenal work, one of the most exciting awards of the night was The Data for Enterprise AI category. The Data Enrichment team within Experian’s B2B business unit (BIS) is responsible for maintaining data quality and reliability.

B2B 103

B2B Machine Learning Big Data Data-driven 103

IBM Big Data Hub

MAY 8, 2023

Risk and limitations of AI The risk associated with the adoption of AI in insurance can be separated broadly into two categories—technological and usage. Technological risk—data confidentiality The chief technological risk is the matter of data confidentiality.

Insurance 74

Insurance Risk Testing Data Quality 74

Timo Elliott

JANUARY 6, 2022

Build a data catalog that systematizes data exploration and makes dark data stand out. Define and enforce cataloging standards (a top-down approach) – but also empower business users to create categories and add to the catalog (a bottom-up approach). Real-time, cloud-based data ingestion and storage.

IT 133

IT Metadata Data-driven Data Governance 133

Smart Data Collective

JULY 15, 2021

It helps you locate and discover data that fit your search criteria. With data catalogs, you won’t have to waste time looking for information you think you have. What Does a Data Catalog Do? Metadata registries organize various data sets according to categories and fields.

Metadata 127

Metadata IT Data-driven Data Quality 127

DataRobot

JULY 16, 2021

We organize the concept of trust in an AI system into three main categories. In each of these three categories, we identify a set of dimensions that help define them more tangibly. They all serve to answer the question, “How well can my model make predictions based on data?”

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

Smart Data Collective

OCTOBER 4, 2021

There is no question that big data is very important for many businesses. Unfortunately, big data is only as useful as it is accurate. Data quality issues can cause serious problems in your big data strategy. It relies on data to drive its AI algorithms. Analyzing the Conversational Data.

Big Data 114

Big Data Data Quality Advertising Interactive 114

AWS Big Data

NOVEMBER 9, 2023

As part of their cloud modernization initiative, they sought to migrate and modernize their legacy data platform. These files, stored in an SFTP location, might contain records, reports, logs, or other kinds of raw data that can be further processed and analyzed. local/bin/dbt test –-select your_package.* /usr/local/airflow/.local/bin/dbt

Data Warehouse 102

Data Warehouse Testing Data Quality Reporting 102

Alation

FEBRUARY 13, 2020

In addition to earning the top ranking in Dresner Advisory Services’ 2018 Wisdom of Crowds® Data Catalog Market Study 2, Alation was also named a winner in the Data Catalog category of Dresner’s 2018 Technology Innovation Awards. Ventana Research: 2018 Digital Innovation Awards for Big Data.

Machine Learning 52

Machine Learning Big Data Data Governance Digital Transformation 52

Data Virtualization

AUGUST 4, 2022

Reading Time: 3 minutes Last month, IDC announced that LeasePlan, a car-as-a-service company, was the winner of IDC’s European Data Strategy and Innovation awards, in the category of Data Management Excellence, for LeasePlan’s logical data fabric. This is a testament to the maturity of.

Data Strategy 52

Data Strategy Data Integration Strategy Management 52

Ontotext

NOVEMBER 9, 2023

While everyone may subscribe to the same design decisions and agree on an ontology, there may be differences in the data quality. In such situations, data must be validated. “Has value” and “in” also fit in this category. Sometimes there is no room for error.

Data Quality 64

Data Quality Testing Reporting Metadata 64

Ontotext

MAY 2, 2024

Use case-driven growth How do we take data and create value? We can talk about three categories, each with different sophistication, but all valuable in and of themselves. Data products – optimization of data assets that drive business, improving data quality and interoperability.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

IBM Big Data Hub

NOVEMBER 24, 2023

Provide a visual representation of the mapping and matching score with BIAN Hierarchical navigation and filters for BIAN levels, API Category and matching score. In addition, teams were able to identify key API categories for building right operational resilience.

Cost-Benefit 100

Cost-Benefit Testing Data-driven Enterprise 100

Alation

MARCH 14, 2023

All critical data elements (CDEs) should be collated and inventoried with relevant metadata, then classified into relevant categories and curated as we further define below. Store Where individual departments have their own databases for metadata management, data will be siloed, meaning it can’t be shared and used business-wide.

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

DataKitchen

MARCH 16, 2023

It lists forty-five metrics to track across their Operational categories: DataOps, Self-Service, ModelOps, and MLOps. For example, Gartner’s DataOps metrics can be categorized into Velocity, Efficiency, and Quality. Under Velocity, the Mean Time to Deliver Data metric measures the time it takes to deliver data.

Metrics 130

Metrics Data Analytics Analytics Measurement 130

Octopai

JANUARY 31, 2021

Specifically, when it comes to data lineage, experts in the field write about case studies and different approaches to this utilizing this tool. Among many topics, they explain how data lineage can help rectify bad data quality and improve data governance. . TDWI – Philip Russom.

Data Governance 57

Data Governance Data Processing Data Quality Metadata 57

Laminar Security

DECEMBER 1, 2023

Of those who experienced a breach, 79% were aware that data had leaked or been exfiltrated versus 58% from the year before. To address these challenges and protect cloud data, organizations need a new category of cloud security solutions. This is known as Data Security Posture Management (DSPM).

Modeling 52

Modeling Cost-Benefit Risk Data-driven 52

CIO Business Intelligence

MARCH 7, 2024

Some organizations are including machine learning in the category, which makes the AI conversation more inclusive. I’m not sure I agree with this because there are plenty of companies not embracing digital transformation and data analytics properly, so they may just take a hit to their bottom line, but extinction is a fear tactic.

Digital Transformation 72

Digital Transformation Machine Learning Risk Testing 72

Cloudera

JANUARY 26, 2024

These will allow the finance team to search for relevant assets as part of their day-to-day activities: Classifications (or “tags”) like “PII”, “SENSITIVE”, “EXPIRES_ON”, “DATA QUALITY” etc. Data Governance,” and “Data Curator.” In this case we will select “type-category” and fill with “ Classifications ” (note 2).

Metadata 72

Metadata Data Governance Management Finance 72

O'Reilly on Data

APRIL 19, 2021

Unfortunately, we don’t know which industries are represented by that category—but it shows that the spread of AI has indeed become broad! The biggest problems in this year’s survey are lack of skilled people and difficulty in hiring (19%) and data quality (18%). Bad data yields bad results at scale. Industries using AI.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

Alation

MARCH 11, 2022

Leaders must also build policies about data into the catalog, from both an internal perspective (“How do we define data quality ?”) to an external, compliance-focused perspective (“How do we ensure analysts use private data legally?”). Again, a data catalog can automate the tedium of compliance at scale.

Metadata 52

Metadata Data Governance ROI Data Quality 52