Data Quality

What is Data Quality? FAQs and Use Cases

Learn about the top methods, use cases, and benefits of maintaining good enterprise data quality.

Be it operational, customer, transactional, or production related – the quality of an enterprise’s data can make or break even the strongest organization. To achieve a high level of data quality, it’s important to understand what data quality is and how others are benefiting from optimizing their data quality management.

What is Data Quality?

Data quality can essentially be defined as how accurate or complete your data is. Enterprises with good quality data typically employ a reliable data management strategy that includes regular data hygiene, data migration, consolidation, and much more in order to transform and improve data to maintain trust.

Data quality management includes the planning, implementation, and control of data management systems and processes in order to ensure that data is readily and accurately available for the needs of the business and consumers. The level of quality of data is typically measured by its accuracy, completeness, consistency, timeliness, validity, and uniqueness.

Why do you need a Data Quality strategy?

Data quality is a critical component of successful data management. Increased data quality quickly translates to improved profit margins, innovation, and operational efficiency.

In many ways, data quality has a direct impact on the health of the business. Touching on nearly every aspect of organizations, from inventory management to customer experience, and the health of an organization’s data. Poor data quality leads to delays, inventory issues, and increased costs. In the US economy alone, according to Harvard Business Review, bad data is estimated to cost organizations, an incredible $3.1 trillion* annually (about 6% of the total economy).

Organizations that don’t invest in a data quality strategy are missing opportunities to remove legacy systems, improve data quality, ensure proper business process execution, and plan for business-as-usual data operations post-migration, causing your new environment to atrophy more quickly, increasing data management costs in the long run.

The Top Challenges of Data Quality

According to the IDC, 54% of enterprises identify data management optimization programs as a high-priority technology investment in the year ahead. And it’s no wonder why organizations are being forced to keep up: adopting new platforms and systems today often requires already-clean data in order to truly maximize on the investment. While 36% of tools offer some kind of automated data quality and cleansing capabilities, organizations aren’t able to fully benefit from these kinds of features due to dirty data. More sophisticated users and citizen data scientists today also equates to an increased demand for more advanced, embedded analytics and reporting features that depend on high-quality data to function. Some of the top challenges of data quality include:

  1. Volume and variety of siloed data and systems management across the organization
  2. Inaccurate, outdated, and duplicate data can be the result of a poor data management strategy but are also issues that need to be remediated in a long-term strategy.
  3. Privacy protection laws and regulations like GDPR and CCPA hold organizations accountable for maintaining accurate and consistent data on individuals.

Key Benefits of Data Quality

As data management technologies grow more advanced and customer expectations continue to demand faster, more personalized experiences, the value of high-quality data increasingly has a direct impact on business performance. While data-driven cultures have long used data to inform decision-making when it comes to marketing, product development, or even hiring, new advantages and technologies in data quality quickly leave late adopters behind. Competitors who not only keep pace with changing regulations and requirements for data management but instead look to it as a strategic investment will be able to take advantage of new opportunities when it comes to migrating or maintaining their infrastructure.

  1. Rapid return-on-investment (ROI)

Improving your organization's level of data quality could be the most effective, sure-fire way to increase revenue and ROI. Trustworthy and consistent data can help identify potential cost savings, remediate cash flow issues, P&L, or process improvements, as well as inform a strategic roadmap for future data initiatives.

  1. Improved analytics and data intelligence

Boost confidence by delivering the trusted data your analytics team needs to enable faster, more accurate decision-making. New technologies are also increasingly dependent on high-quality data to support artificial intelligence (AI), machine learning, and other automated workflows.

  1. Faster downstream data projects
    Accelerate future data quality, master data management, data migration, and data governance projects. Remediating data quality issues before a project begins can decrease risk and keep initiatives on budget and on time while increasing transparency into how data moves throughout the organization.

  2. Customer experience

Whether it be verifying addresses and customer account information right at checkout or consolidating databases to reduce duplicate records, establishing data quality best practices creates a more seamless customer experience.

  1. Trusted business decisions and improved business performance

Good quality data drives essential business decisions and delivers real results. Increased confidence in the accuracy and validity of your data means faster time-to-answers and lower operational costs.

Listen to the full discussion: Connecting Data Quality to the Business Bottom Line

Methods of Data Quality Management

Maintaining the quality of data in a way that effectively manages the volume and scale of your enterprise’s data should follow a data management strategy. A combination of technology and methodology focused on quality is necessary to keep data’s value consistent and continuous. These approaches to maintaining quality data don’t just focus on the end use or byproduct of analytics and automation; best practices include continuous monitoring and cleansing of data and source systems.

Data quality management remedies used to prevent data quality issues and eventual data cleansing include:

  1. Data Governance: Easily create, manage and enforce data terms, rules, policies, best practices, and more in a silo-free platform that manages your data governance and strategic data initiatives.
  2. Data Catalog & Profiling: Comprehensive data quality platforms automatically profile datasets, so users are alerted to the quality issues whenever the data changes.
  3. Master Data Management (MDM): Master Data Management ignites business growth by optimizing enterprise data management
  4. Data consolidation and transformation: When managing multiple on-prem, hybrid, or cloud-based systems and databases, successful Data migration is required to identify missing records, values, and broken relationships across tables or systems. According to the IDC, data integrity issues are the #1 challenge faced when migrating to new systems.
  5. Data cleansing: Data hygiene best practices like data harmonization, matching, and deduplication maintains consistent and accurate data to keep customer records up-to-date across systems.

Use Cases of Data Quality

No matter the industry, every organization benefits from prioritizing data quality management in every data initiative. Whether navigating acquisitions or divestitures, managing exploding volumes and timelines, or synchronizing data across siloed systems, the right data quality strategy will yield higher returns across the organization.

  1. Maple Leaf Foods – Food & Packaging

Following a divestiture with company Canada Bread, Maple Leaf Foods needed a seamless solution for extracting large volumes of data from its databases, applications, ERP, and SAP systems without sacrificing data integrity. With accurate data quality solutions and methodology, the company was able to remove 57% of data in its product environment, translating to 88 terabytes of space saved and over a million dollars in cost savings due to storage and infrastructure. Having optimized the quality and volume of data needed for day-to-day operations, Maple Leaf Foods increased process improvements and prepare for upcoming deployments to SAP S/4HANA.

“There were so many barriers and data rules in place within our SAP systems that manually deleting the Canada Bread data in our SAP systems would have been extremely time-consuming, costly and difficult without the help of Syniti.”

- Vicky Pasqualini, Director of ERP Integration and Release Management, Maple Leaf Foods

Read the full case study

  1. Geberit – Manufacturing & Supply, Sanitary Products

A quickly growing organization, the complexity and volume of Geberit’s data was ballooning, and they needed a solution to ensure high data quality in their ERP (Enterprise Resource Planning) and PIM (Product Information Management) systems. By implementing a comprehensive data management solution, Geberit was able to automate their data quality management process with capabilities like data staging, rule execution, failed data reporting, scheduling, and notifications. By establishing an integrated, passive data governance solution, Geberit can centrally manage and monitor data of multiple domains across heterogeneous operational systems, pinpoint data quality issues, notify stakeholders, and quickly remediate errors.

“The flexibility of Syniti’s Knowledge Platform made it a perfect fit for comparing data from different systems from our Sanitec merger, as well as for extending our data quality management over broader data domains.”

- Jonas Arheit, Master Data Manager, Geberit

Read the full case study

  1. Banco BICSA – Banking & Financial Services

Banco BICSA implemented a comprehensive data management solution to migrate and synchronize data between their various systems in a fast, reliable manner. Due to the sensitive nature of the data, the bank required a high level of accuracy when it came to customer records and visibility into data flow. As a result of implementing a flexible automated data management solution with an intuitive graphical interface meant little training was required, and users of nearly any skillset could make changes to datasets and applications.

“Syniti helps us move our data efficiently and reliably across multiple corporate databases, which allows our business intelligence, analytical, and data storage systems to be 100% integrated in an easy, clean, and reliable way. We would recommend this product to anyone who wants a fast and easy data integration solution.”

- Yivan Enrique Alveo Castillo, Business Intelligence Officer, Senior SQL DBA, Banco BICSA

Read the full case study

It’s obvious that the quality of data directly impacts business outcomes, efficiency, and agility well beyond the customer experience. Organizations that maintain a high level of quality data are able to act smarter, faster, and with greater returns.

Read the free resource, The Top 5 Reasons to Implement a Data Quality Solution, to learn more about how a unified data quality tool optimizes growth across the business. Ready to get started? Contact Syniti's team of experts today.

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