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Assuring Data Quality: How to Build a Serverless Data Quality Gate on AWS

DataFloq

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.

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6 ‘P’s For A Data Quality Framework

DataFloq

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.

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Why Is Data Quality Always an Afterthought? Strategies to Master Data Quality Management

CIO

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.

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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

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.

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Data-Driven Companies Leverage OCR for Optimal Data Quality

Smart Data Collective

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.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

Datapine Blog

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.

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6 Crucial Steps For Setting Your Data Team KPIs

DataFloq

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.

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Data Lineage is Broken – Here Are 5 Solutions To Fix It

DataFloq

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.

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None Shall Pass! Are Your Database Standards Too Rigid?

The Data Administration Newsletter

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.

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How Metadata Improves Security, Quality, and Transparency

DataFloq

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.

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Data Swamps and The Tragedy of The Commons

DataFloq

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?

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I Don’t Care How Big Your Data Is

DataFloq

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.

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Is “Self-Service” Data’s Biggest Lie?

DataFloq

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.

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I Don’t Care How Big Your Data Is

DataFloq

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.

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7 Data Lineage Tool Tips For Preventing Human Error in Data Processing

Smart Data Collective

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.

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What is a Data Curation and Enrichment Hub, and Why do I Need One?

Grooper

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?

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7 Benefits of Metadata Management

erwin

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.

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What is Data Lineage? Top 5 Benefits of Data Lineage

erwin

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.

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What Tools Do You Need To Manage Unstructured Data?

Smart Data Collective

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.

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Data Governance for Smart Data Distancing

erwin

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.

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Data Governance Stock Check: Using Data Governance to Take Stock of Your Data Assets

erwin

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.

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Providing fine-grained, trusted access to enterprise datasets with Okera and Domino

Domino Data Lab

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.

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Using Strategic Data Governance to Manage GDPR/CCPA Complexity

erwin

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.

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Very Meta … Unlocking Data’s Potential with Metadata Management Solutions

erwin

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.

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Top 5 Data Catalog Benefits: Understanding Your Organization’s Data Lineage

erwin

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.

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Business Process Can Make or Break Data Governance

erwin

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.

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Constructing A Digital Transformation Strategy: Putting the Data in Digital Transformation

erwin

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.

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Building a Data Governance Strategy in 7 Steps

Alation

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?

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Put our Knowledge and Writing Skills to Work for you

Peter James Thomas

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.

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What’s Business Process Modeling Got to Do with It? – Choosing A BPM Tool

erwin

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.

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Data Mapping Tools: What Are the Key Differentiators

erwin

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.

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erwin Automation Framework: Achieving Faster Time-to-Value in Data Preparation, Deployment and Governance

erwin

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.

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Improve Your Data’s Value With Data Governance

Sirius Computer Solutions

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.

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More Definitions in the Data and Analytics Dictionary

Peter James Thomas

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.

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Data Preparation and Data Mapping: The Glue Between Data Management and Data Governance to Accelerate Insights and Reduce Risks

erwin

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.

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The Future of AI: High Quality, Human Powered Data

Smart Data Collective

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.

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Data Governance Best Practices – Where to Start

DataFloq

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?

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Data Catalog First, Master Data Management Second: Here’s Why

Alation

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.

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Machine Learning Project Checklist

DataRobot Blog

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.

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Automating Model Risk Compliance: Model Development

DataRobot Blog

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.

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Unlocking the hidden value of dark data

CIO

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.

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Better Forecasting with AI-Powered Time Series Modeling

DataRobot Blog

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.

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The Data Scientist’s Guide to the Data Catalog

Alation

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.