CIO Business Intelligence

AUGUST 2, 2022

The second is the data quality in our legacy systems. So, as we implement new solutions, we are looking at the data quality component and really thinking through why it matters to collect certain data in some categories and making sure that solutions are part of that new implementation. That’s one.

Data Quality 78

Data Quality Digital Transformation Machine Learning Predictive Analytics 78

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. The post SHACL-ing the Data Quality Dragon I: the Problem and the Tools appeared first on Ontotext. Sometimes there is no room for error.

Data Quality 64

Data Quality Testing Reporting Metadata 64

Anmut

JULY 12, 2021

Unfortunately, traditional approaches to data remediation often focus on technical data quality in isolation from the broader data and business ecosystem. In this blog post, we’ll compare traditional data quality vs data condition — the big picture approach to data improvement we use here at Anmut.

Data Quality 52

Data Quality Measurement Metrics Dashboards 52

Alation

SEPTEMBER 28, 2022

In essence, DataOps is a practice that helps organizations manage and govern data more effectively. However, there is a lot more to know about DataOps, as it has its own definition, principles, benefits, and applications in real-life companies today – which we will cover in this article! Automated testing to ensure data quality.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

DataKitchen

MARCH 12, 2024

Director, Data Analytics Team “We had some data issues. Thanks to Observability, I could diagnose the problem – definitely helped me a lot during the process.” It’s definitive, and that changes the game, especially for senior leadership.” That was amazing for the team.” Databricks was all green.

Metrics 120

Metrics Software Cost-Benefit Testing 120

FineReport

MAY 31, 2023

Finance and accounting teams often deal with data residing in multiple systems, such as accounting software, ERP systems, spreadsheets, and data warehouses. Ensuring seamless data integration and accuracy across these sources can be complex and time-consuming.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52

TDAN

JANUARY 3, 2024

Edwards Deming, the father of statistical quality control, said: “If you can’t describe what you are doing as a process, you don’t know what you’re doing.” When looking at the world of IT and applied to the dichotomy of software and data, Deming’s quote applies to the software part of that pair.

Statistics 52

Statistics Software Data Quality IT 52

Andrew White

AUGUST 26, 2020

I spoke to a client recently who had this question: “I need help to develop a survey to ask our employees around the company how they regard the various subjective metrics tied to data quality.” What was the point of the data quality work? I explained to the client: Stop this bottom up data-focused work.

Data Quality 52

Data Quality Data Governance Metrics Measurement 52

Jim Harris

JANUARY 1, 2016

1 — Investigate Data quality is not exactly a riddle wrapped in a mystery inside an enigma. However, understanding your data is essential to using it effectively and improving its quality. In order for you to make sense of those data elements, you require business context.

Data Quality 61

Data Quality Data Governance Cost-Benefit ROI 61

CIO Business Intelligence

MARCH 24, 2023

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

Data Governance 142

Data Governance Management Metadata Data Quality 142

Peter James Thomas

SEPTEMBER 19, 2018

The peterjamesthomas.com Data and Analytics Dictionary is an active document and I will continue to issue revised versions of it periodically. Here are 20 new definitions, including the first from other contributors (thanks Tenny!): Data Asset. Data Audit. Data Classification. Data Consistency.

Analytics 45

Analytics Testing Data Strategy Data Integration 45

TDAN

SEPTEMBER 7, 2022

Fit for Purpose data has been a foundational concept of Data Governance for as long as I’ve been in the field…so that’s 10-15 years now. Most data quality definitions take Fit-for-Purpose as a given.

Data Governance 59

Data Governance Data Quality Metadata Management 59

DataKitchen

JANUARY 25, 2022

Ensuring that data is available, secure, correct, and fit for purpose is neither simple nor cheap. Companies end up paying outside consultants enormous fees while still having to suffer the effects of poor data quality and lengthy cycle time. . When a job is automated, there is little advantage to outsourcing. .

Consulting 244

Consulting Testing Data Lake Data Quality 244

TDAN

JUNE 18, 2019

The first step to fixing any problem is to understand that problem—this is a significant point of failure when it comes to data. Most organizations agree that they have data issues, categorized as data quality. However, this definition is […].

Data Quality 90

Data Quality Data Architecture IT Data Strategy 90

IBM Big Data Hub

JANUARY 23, 2023

Simply put, data governance is the process of establishing policies, procedures, and standards for managing data within an organization. It involves defining roles and responsibilities, setting standards for data quality, and ensuring that data is being used in a way that is consistent with the organization’s goals and values.

Data Governance 73

Data Governance Data Quality Management IT 73

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)? Implementing a data catalog first will make MDM more successful.

Management 52

Management Data Quality Metadata Testing 52

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

IT 52

IT Data Quality Metadata Data Governance 52

Smart Data Collective

APRIL 5, 2022

Drones Surveyors Are Pioneers in the Data Analytics Field. While this is definitely true, there are a few best practices that users need to learn to use it properly. Drone surveyors must also know how to gather and use data properly. They will need to be aware of the potential that data can bring to entities using drones.

Data Collection 106

Data Collection Data Quality Big Data Data-driven 106

CIO Business Intelligence

MAY 24, 2023

It’s now clear that data governance is most successful when CIOs and CDOs do three things: Involve all key stakeholders in the definition of a data governance framework. You can’t assume data ownership is equivalent to the right to make decisions about the data,” says Thomas.

Data Governance 93

Data Governance Data Quality Data Collection Management 93

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. Data programming.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

CIO Business Intelligence

MAY 2, 2023

Worse is when prioritized initiatives don’t have a documented shared vision, including a definition of the customer, targeted value propositions, and achievable success criteria. But there are common pitfalls , such as selecting the wrong KPIs , monitoring too many metrics, or not addressing poor data quality.

Digital Transformation 134

Digital Transformation IT Data-driven Metrics 134

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

Octopai

DECEMBER 12, 2021

Whenever you save the file, you create metadata about the current status of this data asset: The metadata displayed here includes data asset type, size of the asset, and time of the last change in the asset. Metadata stewardship tools are important for: Data governance Regulatory compliance Evaluating and improving data quality.

Metadata 93

Metadata Management Data Quality Data Governance 93

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

CIO Business Intelligence

JANUARY 24, 2024

“Establishing data governance rules helps organizations comply with these regulations, reducing the risk of legal and financial penalties. Clear governance rules can also help ensure data quality by defining standards for data collection, storage, and formatting, which can improve the accuracy and reliability of your analysis.”

Data Strategy 133

Data Strategy Strategy Unstructured Data Data Governance 133

AWS Big Data

JULY 13, 2023

Data quality for account and customer data – Altron wanted to enable data quality and data governance best practices. Goals – Lay the foundation for a data platform that can be used in the future by internal and external stakeholders.

Optimization 89

Optimization B2B Data Quality Sales 89

erwin

FEBRUARY 28, 2024

What I love in erwin Data Intelligence is that it not only allows you to enter a definition, description, data owner, data steward, data classification, and all the basics you expect – but that it also allows you to completely configure the fields you need to your unique data governance framework.

Metadata 52

Metadata Data Governance Data Quality Technology 52

Smart Data Collective

SEPTEMBER 28, 2022

Big data management increases the reliability of your data. Big data management has many benefits. One of the most important is that it helps to increase the reliability of your data. Data quality issues can arise from a variety of sources, including: Duplicate records Missing records Incorrect data.

Big Data 104

Big Data Management Data Quality Cost-Benefit 104

Ontotext

AUGUST 31, 2023

While there are many other varying definitions that exist, our definition of the knowledge graph places emphasis on defining the semantic relations between entities, which is central to providing humans and machines with context and means for automated reasoning.

Modeling 64

Modeling Optimization Knowledge Discovery Enterprise 64

Octopai

AUGUST 3, 2022

It’s also a critical trait for the data assets of your dreams. What is data with integrity? Data integrity is the extent to which you can rely on a given set of data for use in decision-making. Increased data quality, accessibility, alignment across systems, and context all contribute to increased data integrity.

Data Integration 93

Data Integration Manufacturing Data Quality Data Governance 93

CIO Business Intelligence

MARCH 4, 2024

Harmonizing these data sets with centralized enterprise data faces increasing challenges as shifts to data definitions, schema, and architecture require constant central data team efforts.

Insurance 84

Insurance Strategy Data Governance Data Integration 84

Ontotext

FEBRUARY 19, 2020

Although there is still no single, universally accepted definition, there have been various attempts at it – such as in Towards a Definition of Knowledge Graphs. Clean your data to ensure data quality. Correct any data quality issues to make the data most applicable to your task.

Modeling 90

Modeling Knowledge Discovery Data Quality Technology 90

Cloudera

SEPTEMBER 15, 2022

What is a data fabric? Mark: Gartner states that a data fabric “enables frictionless access and sharing of data in a distributed data environment.” The data fabric provides direct support for the first element of the architecture — linking the distributed source data into the hybrid, multi-cloud environment. .

Data Lake 79

Data Lake Data Architecture Data-driven Data Warehouse 79

Ontotext

JUNE 29, 2023

This standardizes your data processing across different teams and departments, reducing manual effort and improving data quality. Step 1: Definition of Scope [Why You Need A Knowledge Graph] In the first step of the journey, you decide why you need a knowledge graph.

Cost-Benefit 59

Cost-Benefit Data-driven Data Quality Enterprise 59

Anmut

APRIL 6, 2023

How do you do a Data Maturity Assessment? What does a Data Maturity Assessment Measure? What makes a Good Data Maturity Assessment? What is a Data Maturity Assessment? The definition of a data maturity assessment is a measurement of the reliability, effectiveness, and efficiency of an organisation’s data management.

Cost-Benefit 52

Cost-Benefit Data Quality Data Governance Measurement 52

erwin

MARCH 30, 2020

These numerous data types and data sources most definitely weren’t designed to work together. As a result, the data may be compromised, rendering faulty analyses and insights. Data profiling for data assessment, metadata discovery and data validation. Data lineage to support impact analysis.

Metadata 122

Metadata IT Data Governance Data Quality 122

Alation

FEBRUARY 14, 2023

At first, “governance progress” metrics could be utilised, such as: What % of tables have a data steward assigned? What % of tables have a definition? What % of data is or isn’t assigned to a domain? What % of columns have data quality rules implemented? How much time spent locating data is being saved?

Data Governance 52

Data Governance ROI Metrics Cost-Benefit 52