DataFloq

OCTOBER 24, 2022

Data is a vital element in business decision-making. Modern technologies and algorithms allow for processing and storage of huge amounts of data, converting it into useful predictions and insights. But they also require high-quality data to ensure prediction accuracy and insight value. In today's world, the importance of data quality validation is hard to overestimate. Great Expectations – A Data QA Tool of Choice.

Data Quality 191

Data Quality Testing Reporting Dashboards 191

DataFloq

MAY 2, 2022

Why do you need data quality ? The same goes for data. If we are going to generate revenue from our data, reduce risk, nurture trust for people to reuse the data so they dont create their own Shadow IT with their multiple spreadsheet copies of data.

Data Quality 233

Data Quality Measurement Risk Sales 233

CIO

NOVEMBER 8, 2022

For probably the umpteenth time, we use the term “garbage in, garbage out” when summarizing problems with data quality. Various industry studies have uncovered the high cost of bad data, and it’s estimated that poor data quality costs organizations an average of $12 million yearly.

Data Quality 280

Data Quality Strategy Business Intelligence Management 280

Alation

MAY 24, 2022

Additionally, a set of key features will accelerate data governance and simplify the security of sensitive metadata. Our Data Quality Journey. The perennial problem of using data to make decisions, as coined by George Fuechsel, is garbage in, garbage out.

Data Quality 82

Data Quality Data Governance Metadata Metrics 82

Smart Data Collective

SEPTEMBER 29, 2022

OCR is the latest new technology that data-driven companies are leveraging to extract data more effectively. OCR and Other Data Extraction Tools Have Promising ROIs for Brands. Big data is changing the state of modern business. Each data point is linked to its reference.

Data-driven 106

Data-driven Data Quality Optimization Business Intelligence 106

Datapine Blog

SEPTEMBER 29, 2022

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality.

Data Quality 76

Data Quality Metrics ROI Measurement 76

DataFloq

AUGUST 4, 2022

As analytics professionals, we deal in data: serving ad-hoc reports on a minute's notice, pulling queries for executives, and generally forecasting company performance across a variety of metrics. The same goes for data leaders and KPIs.

Data Quality 215

Data Quality Metrics KPI Measurement 215

DataFloq

OCTOBER 20, 2022

Data lineage isn't new, but automation has finally made it accessible and scalable-to a certain extent. This traditional method of documenting lineage was time-intensive and nearly impossible to maintain. What is data lineage? Focus on quality over quantity through lineage.

IT 238

IT Metadata Big Data Data Quality 238

The Data Administration Newsletter

AUGUST 3, 2021

Database standards are common practices and procedures that are documented and […]. Rigidly adhering to a standard, any standard, without being reasonable and using your ability to think through changing situations and circumstances is itself a bad standard.

Data Quality 94

Data Quality Data Architecture Modeling Management 94

DataFloq

MARCH 29, 2022

One word: data. The core data here is in the music – the basic components of songs like the title, artist, and duration. More directly, metadata provides context about the data, more than what you see in the rows and columns. Metadata management in a data catalog platform.

Metadata 207

Metadata Snapshot Data Quality Dashboards 207

DataFloq

OCTOBER 30, 2022

A silent alarm rings in my head whenever I hear someone utter the phrase, “data is everyone's responsibility.” ” You can just as easily translate this to mean that “data is no one's responsibility,” too. So, how can we fix this data tragedy of the commons?

Testing 148

Testing Big Data Data Quality Metrics 148

DataFloq

JULY 25, 2022

At some point in the last two decades, the size of our data became inextricably linked to our ego. We watched enviously as FAANG companies talked about optimizing hundreds of petabyes in their data lakes or data warehouses. However, big data has always been defined beyond volume.

Scorecard 130

Scorecard Machine Learning Big Data Snapshot 130

DataFloq

AUGUST 30, 2022

Data self-service, the ability for stakeholders in the organization to answer their own business questions with data, is a top initiative for nearly every data leader I've spoken to this year. Data engineers have a love / hate relationship with being a data wrangler.

Data Warehouse 148

Data Warehouse Big Data Data Quality Metadata 148

DataFloq

JULY 19, 2022

At some point in the last two decades, the size of our data became inextricably linked to our ego. We watched enviously as FAANG companies talked about optimizing hundreds of petabyes in their data lakes or data warehouses. However, big data has always been defined beyond volume.

Scorecard 130

Scorecard Machine Learning Big Data Snapshot 130

Smart Data Collective

APRIL 20, 2022

Errors in data entry might have serious effects if they are not discovered quickly. Human mistake is the most common cause of data entry errors. The company’s daily data entry needs must be met by a sufficient number of people. Make Data Profiling Available.

Data Processing 106

Data Processing Data Quality Optimization Technology 106

Grooper

APRIL 9, 2020

The heart of digital transformation is data, and organizations without a clear strategy for data curation and enrichment from documents are at risk of failed transformation and missing out on massive revenue opportunities. And what is a data curation and enrichment hub?

Digital Transformation 60

Digital Transformation Data Quality Enterprise Risk 60

erwin

FEBRUARY 19, 2021

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives.

Metadata 90

Metadata Management Data Governance Digital Transformation 90

erwin

APRIL 29, 2020

Data lineage is the journey data takes from its creation through its transformations over time. It describes a certain dataset’s origin, movement, characteristics and quality. Tracing the source of data is an arduous task. Five Benefits of Data Lineage.

Metadata 91

Metadata Key Performance Indicator KPI Reporting 91

Smart Data Collective

SEPTEMBER 22, 2021

Unstructured data represents one of today’s most significant business challenges. Unlike defined data – the sort of information you’d find in spreadsheets or clearly broken down survey responses – unstructured data may be textual, video, or audio, and its production is on the rise.

Unstructured Data 89

Unstructured Data Management Finance Machine Learning 89

erwin

APRIL 15, 2020

During this coronavirus emergency, we are all being deluged by data from politicians, government agencies, news outlets, social media and websites, including valid facts but also opinions and rumors. Yay, data geeks! In business, data is the food that feeds the body or enterprise.

Data Governance 107

Data Governance Metadata Testing Measurement 107

erwin

MARCH 8, 2019

GDPR) and to ensure peak business performance, organizations often bring consultants on board to help take stock of their data assets. This sort of data governance “stock check” is important but can be arduous without the right approach and technology. That’s where data governance comes in …. While most companies hold the lion’s share of operational data within relational databases, it also can live in many other places and various other formats.

Data Governance 56

Data Governance Consulting Risk Metrics 56

Domino Data Lab

OCTOBER 1, 2020

Domino and Okera – Provide data scientists access to trusted datasets within reproducible and instantly provisioned computational environments. They can work with data without worrying about setting up Apache Spark clusters or getting the right version of libraries.

Enterprise 87

Enterprise Metadata Data Processing Data Quality 87

erwin

JULY 12, 2019

In light of recent, high-profile data breaches, it’s past-time we re-examined strategic data governance and its role in managing regulatory requirements. for alleged violations of the European Union’s General Data Protection Regulation (GDPR). With all the advance notice and significant chatter for GDPR/CCPA, why aren’t organizations more prepared to deal with data regulations? Strengthen data security.

Data Governance 87

Data Governance Management Risk Management Enterprise 87

erwin

OCTOBER 24, 2019

Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata , or the data about the data. However, most organizations don’t use all the data they’re flooded with to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or make other strategic decisions.

Metadata 83

Metadata Management Data-driven Risk Management 83

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. Data cataloging helps curate internal and external datasets for a range of content authors.

Data Warehouse 73

Data Warehouse Visualization Enterprise Testing 73

erwin

SEPTEMBER 19, 2019

Data governance isn’t a one-off project with a defined endpoint. Data governance, today, comes back to the ability to understand critical enterprise data within a business context, track its physical existence and lineage, and maximize its value while ensuring quality and security. Click here to download our latest, best practice guide for Data Modeling for free. Data governance success hinges on business process modeling and enterprise architecture.

Data Governance 85

Data Governance Risk Data Architecture Software 85

erwin

JULY 17, 2019

Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there. Constructing A Digital Transformation Strategy: Data Enablement. Many organizations prioritize data collection as part of their digital transformation strategy. However, few organizations truly understand their data or know how to consistently maximize its value. Digital Transformation Strategy: Smarter Data.

Digital Transformation 79

Digital Transformation Strategy Data Enablement Contextual Data 79

Alation

DECEMBER 15, 2021

Modern business is built on a foundation of trusted data. Yet high-volume collection makes keeping that foundation sound a challenge, as the amount of data collected by businesses is greater than ever before. Data governance requires a system. What is a Data Governance Strategy?

Data Governance 52

Data Governance Metadata Strategy Data Strategy 52

Peter James Thomas

JANUARY 8, 2020

The recently launched Data Strategy Review Service is just one example. White Papers can be based on themes arising from articles published here, they can feature findings from de novo research commissioned in the data arena, or they can be on a topic specifically requested by the client.

Consulting 58

Consulting Data-driven Publishing Data Strategy 58

erwin

MARCH 21, 2019

With business process modeling (BPM) being a key component of data governance , choosing a BPM tool is part of a dilemma many businesses either have or will soon face. Historically, BPM didn’t necessarily have to be tied to an organization’s data governance initiative. With insights from the BPM tool, you can clarify roles and responsibilities – which in turn should influence an organization’s policies about data ownership and make data lineage easier to manage.

Modeling 63

Modeling IT Metadata Manufacturing 63

erwin

MARCH 14, 2019

The need for data mapping tools in light of increasing volumes and varieties of data – as well as the velocity at which it must be processed – is growing. Data mapping tools have always been a key asset for any organization looking to leverage data for insights. Isolated units of data are essentially meaningless. By linking data and enabling its categorization in relation to other data units, data mapping provides the context vital for actionable information.

Digital Transformation 67

Digital Transformation Insurance Data-driven Reporting 67

erwin

JANUARY 17, 2019

Data governance is more important to the enterprise than ever before. It ensures everyone in the organization can discover and analyze high-quality data to quickly deliver business value. It assists in successfully meeting increasingly strict compliance requirements, such as those in the General Data Protection Regulation (GDPR). A mature and sustainable data governance initiative must include data integration.

Metadata 55

Metadata Enterprise Management Modeling 55

Sirius Computer Solutions

AUGUST 12, 2020

What is your organization doing to protect the value of your data? A strong data governance strategy helps ensure that your data is usable, accessible and protected, guaranteeing trust in the quality and consistency of the data.

Data Governance 52

Data Governance Dashboards Data-driven Management 52

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. Data Asset. Data Audit. Data Classification. Data Consistency. Data Controls. Data Curation (contributor: Tenny Thomas Soman ). Data Democratisation. Data Dictionary. Data Engineering. Data Ethics. Data Integrity. Data Lineage. Data Platform.

Testing 43

Testing Data Integration Data Strategy Strategy 43

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. But the attempts to standardize data across the entire enterprise haven’t produced the desired results. The problem usually starts by relying on manual integration methods for data preparation and mapping. Data Quality Obstacles.

Data Governance 40

Data Governance Risk Data Warehouse Data Lake 40

Smart Data Collective

AUGUST 11, 2022

Human labeling and data labeling are however important aspects of the AI function as they help to identify and convert raw data into a more meaningful form for AI and machine learning to learn. Artificial Intelligence, in turn, needs to process data to make conclusions.

Data Collection 100

Data Collection Business Intelligence Data Quality Machine Learning 100

DataFloq

OCTOBER 12, 2022

Long recognized as a must in the data-driven world, data governance has never been easy for big and tiny organizations alike. Today, the complexities associated with adopting data governance best practices are greater than ever. What is data governance?

Data Governance 226

Data Governance Metadata Big Data Data Quality 226

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)? Assess Data Quality.

Management 52

Management Data Quality Metadata Modeling 52

DataRobot Blog

JULY 21, 2022

Machine learning and AI empower organizations to analyze data, discover insights, and drive decision making from troves of data. Inquire whether there is sufficient data to support machine learning. Document assumptions and risks to develop a risk management strategy.

Machine Learning 83

Machine Learning Modeling Metrics Testing 83

DataRobot Blog

MAY 10, 2022

The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States. Figure 1: AI Catalog within DataRobot provides critical capabilities for data management, version tracking, as well as profiling.

Risk 60

Risk Modeling Machine Learning Data Quality 60

CIO

AUGUST 11, 2022

IT leaders seeking to derive business value from the data their companies collect face myriad challenges. Perhaps the least understood is the lost opportunity of not making good on data that is created, and often stored, but seldom otherwise interacted with.

Business Intelligence 260

Business Intelligence Data Lake Unstructured Data Metadata 260

DataRobot Blog

DECEMBER 15, 2022

By simplifying Time Series Forecasting models and accelerating the AI lifecycle, DataRobot can centralize collaboration across the business—especially data science and IT teams—and maximize ROI. Let’s run through the process and see exactly how you can go from data to predictions.

Forecasting 71

Forecasting ROI Modeling Sales 71

Alation

JULY 19, 2022

These days, data scientists are in high demand. Across the country, data scientists have an unemployment rate of 2% and command an average salary of nearly $100,000. Obstacles, such as user roles, permissions, and approval request prevent speedy data access. Get the data.

Metadata 56

Metadata Statistics Data Quality Data Science 56