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Implementing a Data Quality Framework for a Successful Data Transformation Initiative

Data Quality

Despite the proliferation of data technologies – Big Data applications, automation, and the Internet of Things – organizations either have not understood the importance of data quality or failed to build a sustainable process of continually refining data at an appropriate level. 

Enhanced quality of business data is often attributed to higher returns on investment. However, the reality stands far from that truth when time and time again we see examples such as: 

  • 77% of companies losing revenue to incorrect data (CIO
  • 20-30% of operational expenses are due to poor data quality (PragmaticWorks)  
  • Companies lose $9.7 million dollars every year (Gartner) and more 

This blog will look at why data quality matters, its challenges, and how to implement a data quality framework to ensure data is always relevant for use. 

Why is Data Quality Important? 

What is it? 

Data quality is described as a method of ensuring data stored in a data warehouse and other sources conform to a required threshold level for operational and transactional uses, including business intelligence, analytics, and reporting.  

Firms that enhance the quality of their data can reap the following benefits: 

  • Improve customer profiling and targeting to drive new business 
  • Meet compliance standards to avoid heavy penalties and lawsuits 
  • Increasing returns on investments by basing decisions on accurate data 
  • Improve staff productivity by minimizing time spent verifying bad data 

Why Data Quality is Paramount to Building a Strong Digital Culture 

The dissemination of technologies such as Big Data, cloud, and automation has enabled companies to collect more data than ever before to drive actionable insights such as customer buying insights, spending patterns, most profitable customers, and so on. Research from HubSpot shows that the average company handles nearly 163 Terabytes of data while larger enterprises manage almost 345 Terabytes of data – both figures which are expected to grow in the years to come. 

But all data that is collected is not used. The emergence of ‘dark data’ – stored or processed data that organizations fail to use for analytics, reporting, and other uses – is a rising phenomenon. Splunk highlights that missing or incomplete data is the second biggest reason why companies can’t use dark data. 

data quality

Data quality problems such as incomplete, missing, duplicate data, and other errors can have serious consequences for companies across multiple industries. 

  • Marketing: inaccurate and duplicate contact and account data can result in missed quotas 
  • Retail: inconsistent address details can prevent tracking most lucrative areas for opening stores 
  • Healthcare: incomplete patient history data can affect the accuracy of determining the right diagnosis and treatment 
  • Government: duplicate contact data can make it difficult to identify fraudulent individuals who are seeking health insurance, employment benefits, and more. 

With the right data quality and merge purge software to efficiently identify and fix data errors, companies and enterprises will be better equipped to leverage and harness dark data for operational and transactional uses. 

Challenges of Data Quality 

Updated and relevant data can help companies implement data-driven decision-making to achieve positive outcomes such as improved customer experience, higher transparency and accountability, and better strategic alignment.  

Yet, specific challenges can act as strong barriers to achieving organizational goals. These are:  

  1. Diversity of Data Sources and Structures 

For medium-sized and enterprise businesses, having disparate data sources in the form of on-premises databases, cloud applications, Excel files, and more are common. While the very diversity in structure – unstructured, semi-structured, and structured – of data sources alone can create issues, the problems are compounded even further when the stored data have non-standard formats and validation. 

The duplicity of multiple formats, data structures, and types can require integrating and modifying files according to a standard format, which can be a daunting task for organizations.  

  1. Duplicate or Redundant Data  

The presence of duplicate data is inevitable. Causes such as spelling or punctuation mistakes through manual data entry can result in duplicate data entries in disparate systems or by multiple users. There is also the likelihood of duplication and redundant data when users import or export lists by accidentally copy and pasting it into or from different data systems such as CRM and databases.  

  1. Lack of File Naming Conventions 

Data quality errors can also occur when there are no standard file naming protocols. Multiple users such as sales representatives tasked with recording contact data can have different conventions in many organizations, leading to variations in fields.  

For instance, for the ‘Country’ field, one user may save ‘United States’ as ‘US’ while another may save it as ‘USA’. While a small discrepancy, finding all contact names belonging to ‘US’ may miss out on many that are named otherwise. 

3 Ways to Improve Data Quality at Your Organization 

Enforce Data Validation Rules 

Having a company-wide policy of standard validation and file naming rules can go a long way in minimizing the risk of data quality errors. Rather than taking the responsibility to IT, management should set guidelines as to how each field should be recorded to prevent any discrepancies.  

For example, should contact names be entered as first and last names, middle names, and last names? Or should address details include street name and Zip+4 code too?  

Having these rules as part of a standard protocol that is enforced across the organization can improve data quality. 

Routinely Audit Data 

A routine data health checkup can help verify the accuracy and relevancy of data for business activities. This is particularly important for fields such as title and company, which can quickly become outdated and obsolete, hindering organizational goals and outcomes.  

A routine data audit can ensure all stakeholders are involved and analyze the relevant data sources. User access privileges should also evaluate to ensure all only relevant individuals have access to amend and change data.  

Opt for a Merge Purge Software 

Using a dedicated merge purge software can be effective in removing data errors such as incorrectly formatted and invalid data and identifying and removing duplicate and redundant data. Merge purge tools can cut hours’ worth of effort in finding and eliminating errors using features such as: 

  • Disparate data connectivity: connect data from multiple sources, including on-premise and cloud databases, web applications, Excel files, and more. 
  • Data profiling: inspect data sources for various kinds of errors and anomalies  
  • Data standardization: fix varying field formats  
  • Efficient data matching and deduplication 

More importantly, it can be particularly effective in importing and managing the complexity of different data structures and utilizing sophisticated features to correct data anomalies through prebuilt name and address parsing solutions.


Ensuring high data quality can enable companies to leverage insights better to spearhead their organizational initiatives. However, owing to the challenges involved in disparate data sources, duplicate data, and lack of data governance measures, using a merge purge software can be a suitable solution to handle the complexity of millions of data spread across multiple datasets.  

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