Resources
Blogs
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
The definitive buyer’s guide to data quality tools
A recent survey reported that the top KPI for data teams in 2021 was data quality and reliability. But the majority of the respondents said
Building a data quality team: Roles and responsibilities to consider
According to Seagate’s Rethink Data Report 2020, 44% of enterprise data is lost, and only 56% is made available for use. Moreover, out of the
8 best practices to ensure data quality at enterprise-level
In February 2020, Facebook handed over an anonymized dataset to Social Science One – for the purpose of gaining insights on social media communications and
Understanding data quality and master data management: Choosing between the two approaches (part 3 of 3)
Note: this blog is part 3 in a series of 3. If you want, do check out the previous blogs where we discussed the need
Understanding data quality and master data management: Is an MDM solution the answer to your data woes? (Part 2 of 3)
Note: this blog is part 2 in a series of 3. If you want, do check out the previous blog where we talked about the
Understanding data quality and master data management: The need for systematic, centralized data (part 1 of 3)
Biggest data challenge faced by most companies Having delivered data solutions to Fortune 500 clients for over a decade, we have encountered various types of
Data quality dimensions – 10 metrics you should be measuring
“84% of CEOs are concerned about the quality of the data they’re basing their decisions on.” 2016 Global CEO Outlook, Forbes Insight & KPMG Almost
Data quality measurement: When should you worry?
In March 2017, Rescue 116 crashed into a 282ft obstacle – the Blackrock Island off the County Mayo coast. Further investigations revealed that the CHC
What is data profiling: Scope, techniques, and challenges
Today, enterprises highly depend on data for growing their businesses and scaling their goals and expectations. Huge efforts are being invested in devising the perfect
Why duplicates exist and how to get rid of them?
According to Natik Ameen, Marketing Expert at Canz Marketing, duplicate data in the company’s CRM happens due to a range of reasons: “from a human error
Merging Data from Multiple Sources – Challenges and Solutions
Merging data from multiple sources Data merging is a process where data is unified from multiple sources to represent a single point of reference or a single point
Using Record Linkage to Resolve Patient Matching Errors
“Accurate identification of patients is one of the most difficult operational issues during a public health emergency, and the nationwide response to the pandemic, including