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Blogs
How to improve data quality: Define, design, and deliver
Today, data is no doubt one of the biggest assets of an organization. It is used everywhere – from a company’s day-to-day operations to its
Guide to pattern matching: What it means and how to do it?
Finding patterns is easy in any kind of data-rich environment; that’s what mediocre gamblers do. The key is in determining whether the patterns represent signal
Data standardization guide: Types, benefits, and process
When you pull data from various applications fitted across the enterprise, you expect to receive a consistent definition and format of the same information. But
Data integration explained: Definition, types, process, and tools
Leaders often underestimate the time and effort required to enable business intelligence across an organization. They believe it to be as easy as pulling data
How poor data quality impacts a recession survival plan
“You get recessions, you have stock market declines. If you don’t understand that’s going to happen, then you’re not ready. You won’t do well in
8 principles of data management
An average enterprise – with 200-500 employees – uses about 123 SaaS applications to digitize their business processes. With large amounts of data being generated
Data quality management: What, why, how, and best practices
Quality is never an accident; it is always the result of high intention, sincere effort, intelligent direction, and skillful execution; It represents the wise choice
How to improve data quality in financial services
24 percent of insurers say that they are ‘not very confident’ about the data they use to assess and price risk. Corinium Intelligence The economic
Data quality in healthcare – Benefits, challenges, and steps for improvement
38 percent of U.S. healthcare providers have incurred an adverse event within the last two years due to a patient matching issue. Survey from eHI
Common data quality issues in the retail industry and how to fix them
In the previous blog The role of data quality in the world of retail, we discussed the role clean data plays in the retail industry
The role of data quality in the world of retail
According to an Accenture survey, over 75% of consumers are more likely to purchase from retailers who know their name and buying preferences, and about
12 most common data quality issues and where do they come from
According to O’Reilly’s report on The state of data quality 2020, 56% of organizations face at least four different types of data quality issues, while
Building a case for data quality: What is it and why is it important
According to an IDC study, 30-50% of organizations encounter a gap between their data expectations and reality. A deeper look at this statistic shows that:
Data quality API: Functions, architecture, and benefits
While surveying 1900 data teams, more than 60% cited too many data sources and inconsistent data as the biggest data quality challenge they encounter. But
Batch processing versus real-time data quality validation
A recent survey shows that 24% of data teams use tools to find data quality issues, but they are typically left unresolved. This means that
The impact of poor data quality: Risks, challenges, and solutions
It is challenging to correlate a data problem to business risk or impact. Poor data quality can have devastating risks on your business. Most organizational
Designing a Framework for Data Quality Management
According to O’Reilly’s report on The state of data quality 2020, 56% of organizations face at least four different types of data quality issues, while
5 data quality processes to know before designing a DQM framework
Most companies trying to become data-driven cite poor data quality as one of the top 5 challenges. Invesp published a report where they discovered that