How to Implement a Data Quality Framework


According to IDC, 30-50% of businesses experience gaps between their data expectations and reality. They have the data they need, but due to the presence of intolerable defects, they cannot use it as needed.

Data Quality Dimensions Are Crucial for AI


As organizations digitize customer journeys, the implications of low-quality data are multiplied manyfold. Since the data from such processes is growing, data controls may not be strong enough to ensure the data is qualitative.


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How to Architect Data Quality on Snowflake


Without effective and comprehensive validation, a data warehouse becomes a data swamp. With the accelerating adoption of Snowflake as the cloud data warehouse of choice, the need for autonomously validating data has become critical.

Data Trustability: The Bridge Between Data Quality and Data Observability


If data is the new oil, then high-quality data is the new black gold. Just like with oil, if you don’t have good data quality, you will not get very far. So, what can you do to ensure your data is up to par and […].

Why Data Quality Problems Plague Most Organizations (and What to Do About It)


For business leaders to make informed decisions, they need high-quality data. Unfortunately, most organizations – across all industries – have Data Quality problems that are directly impacting their company’s performance.

When It Comes to Data Quality, Businesses Get Out What They Put In


The post When It Comes to Data Quality, Businesses Get Out What They Put In appeared first on DATAVERSITY. Imagine you’ve invited your boss over for a dinner party to try to show off your culinary skills (and perhaps get a promotion).

What to Expect in 2022: Data Privacy, Data Quality, and More


Three big shifts came this year, namely in the realms of consumer data privacy, the use of third-party cookies vs. first-party data, and the regulations and expectations […]. The post What to Expect in 2022: Data Privacy, Data Quality, and More appeared first on DATAVERSITY.

4 Key Takeaways for Your Data Quality Journey


The road to better Data Quality is a path most data-driven organizations are already on. The path becomes bumpy for organizations when stakeholders are constantly dealing with data that is either incomplete or inaccurate.

Why Synthetic Data Still Has a Data Quality Problem


According to Gartner, 85% of Data Science projects fail (and are predicted to do so through 2022). I suspect the failure rates are even higher, as more and more organizations today are trying to utilize the power of data to improve their services or create new revenue streams.

Thoughts on Data Literacy & Data Quality

The Data Administration Newsletter

Last week, we presented a webinar in our Data Governance — Best Practices series on data quality.

What I Learned from Executing Data Quality Projects

The Data Administration Newsletter

Getting to great data quality need not be a blood sport! This article aims to provide some practical insights gained from enterprise master data quality projects undertaken within the past […].

Being Data-Driven Means Embracing Data Quality and Consistency Through Data Governance


Organizations are constantly coming to us wanting help in becoming more “data-driven.” The post Being Data-Driven Means Embracing Data Quality and Consistency Through Data Governance appeared first on DATAVERSITY. Click to learn more about author Terence Siganakis.

Data Quality Problems Everywhere You Look

The Data Administration Newsletter

Data is everywhere! But can you find the data you need? What can be done to ensure the quality of the data? How can you show the value of investing in data? Can you trust it when you get it?

Data Quality Is In the Eye of the Beholder

The Data Administration Newsletter

1 In this article, I will apply it to the topic of data quality. I will do so by comparing two butterflies, each that represent a common use of data quality: firstly and most commonly in situ for existing systems, and secondly for use […].

Start Small and Scale Up with Data Profiling, Data Quality, and Data Governance


Organizations today are rallying business users around using data to make better business decisions. Business users want to know where that data lives, understand if people are accessing the right data at the right time, and be assured that the data is of high quality.

Why Data Quality Programs Fail to Deliver Results


Fortune 1000 organizations spend approximately $5 billion in total each year to improve the trustworthiness of data. Yet, only 42% of the executives trust their data.

Good AI in 2021 Starts with Great Data Quality


The post Good AI in 2021 Starts with Great Data Quality appeared first on DATAVERSITY. Click here to learn more about Heine Krog Iversen. More and more companies want to use artificial intelligence (AI) in their organization to improve operations and performance.

Why Data Quality Matters

The Data Administration Newsletter

Quality is never an accident. ” John Ruskin, prominent Victorian era social thinker Data-driven decision-making is fast becoming a critical business strategy for organizations in every sector. It is always the result of intelligent effort.”

Data is Risky Business: Data Quality Discussion about Data Ethics

The Data Administration Newsletter

The Data Ethics Conundrum The recent DAMA EMEA conference was a valiant effort to connect the DAMA membership in the EMEA region through an innovative virtual conference format. One of these polls asked, “Are Data Ethics Principles Universal?”

Data Management’s Next Frontier is Machine Learning-Based Data Quality

The Data Administration Newsletter

Regardless of how accurate a data system is, it yields poor results if the quality of data is bad. As part of their data strategy, a number of companies have begun to deploy machine learning solutions.

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.

What Is Data Quality?


Data is often the most valuable asset for a company because it’s possible to use it in so many ways. You can use data to improve processes, gather insights, or predict trends through data analysis. What Is Data Quality? Why Does Data Quality Matter?

Expanding Role of Data Governance, Metadata Management, and Data Quality

The Data Administration Newsletter

Ensuring data quality is an important aspect of data management and these days, DBAs are increasingly being called upon to deal with the quality of the data in their database systems more than ever before. The importance of quality data cannot be overstated.

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.

Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC

Smart Data Collective

Companies that utilize data analytics to make the most of their business model will have an easier time succeeding with Amazon. One of the best ways to create a profitable business model with Amazon involves using data analytics to optimize your PPC marketing strategy.

Data Integrity: The Last Mile Problem of Data Observability


Data quality issues have been a long-standing challenge for data-driven organizations. Even with significant investments, the trustworthiness of data in most organizations is questionable at best.

The Importance of Data Quality in Financial Reporting

Insight Software

Data quality has always been at the heart of financial reporting , but with rampant growth in data volumes, more complex reporting requirements and increasingly diverse data sources, there is a palpable sense that some data, may be eluding everyday data governance and control.

What is Data Quality - Definition, Dimensions, Characteristics, and How to Improve It | Simplilearn


Data saturates the modern world. Data is information, information is knowledge, and knowledge is power, so data has become a form of contemporary currency, a valued commodity exchanged between participating parties.

Data Agility and ‘Popularity’? vs. Data Quality in Self-Serve BI and Analytics!

ElegantJ BI

One of the most valuable aspects of self-serve business intelligence is the opportunity it provides for data and analytical sharing among business users within the organization. So, there is definitely a need to provide both approaches in data analysis.

Data Quality Management: Growing Role in Big Data Initiatives [Updated] | Simplilearn


Data is at the heart of everything important for businesses these days. Whether it’s customer data, sales forecasts, supply chain scheduling, or any other critical process, data drives most business operations.

The Rise of Data-Centric AI


Data-centric AI is gaining momentum among engineers. With a model’s performance so dependent on the quality of […]. The post The Rise of Data-Centric AI appeared first on DATAVERSITY.

Dear Laura: Data Governance Budget Woes


As I’ve been working to challenge the status quo on Data Governance – I get a lot of questions about how it will “really” work. In 2019, I wrote the book “Disrupting Data Governance” because I firmly believe […]. Welcome to the Dear Laura blog series!

Data Observability and Its Impact on the Data Operations Lifecycle


The quality of the data you use in daily operations plays a significant role in how well you will generate valuable insights for your enterprise. The post Data Observability and Its Impact on the Data Operations Lifecycle appeared first on DATAVERSITY.

Five Tips for Telling a Compelling Data Story


Running a business is impossible without data. Data clarifies the facts, revealing insights that help everyone from top executives to front-line employees make better decisions. Nonetheless, it is as much an art as a science to make sense of data and use it to maximum effect.

Balance Data Quality with Data Agility!

Elegant BI

Data Quality vs. Data Agility – A Balanced Approach! When it comes to analytical quality versus analytical agility, we might see the issue in the same light. Original Post: Data Quality and Data Agility are Both Important to Success!