Data Quality

5 Reasons Why Data Quality Initiatives Fail

While good data quality leads to better insights, workflows, and customer experience, common misconceptions about managing data can have a detrimental impact.


Data quality and enforcing data management standards have become a primary focus for executives. But while 90% of C-level executives across the Fortune 2000 cite data as a critical business success factor, only 5% of them trust the data they have. When it comes to maximizing the potential of their data, many organizations are still struggling to maintain quality, action-ready data.

While good quality data obviously leads to better insights, workflows, customer experience, and more, common misconceptions about how to manage that data can have a detrimental impact. Despite repeated efforts to improve and standardize data, 43% of data projects lead to failure. And while every data quality project is different, the reasons for failure are often rooted in the same misconceptions. Take a look at some of these common assumptions below; what valuable action items could your organization be missing out on?

The Top Reasons Why Data Quality Initiatives Fail

  1. Tolerating low-quality data as the norm

Research from the Harvard Business Review (HBR) reveals that only 3% of a company’s data quality scores are rated as acceptable, meaning that 97% of data falls just below favorable. According to HBR, these results indicate an “unhealthy organizational tolerance of bad data and underscore the magnitude of improvement organizations need to make in order to be truly effective in the knowledge economy.”

That’s not to say that most enterprises believe low-quality data is a good thing. They may be “nose blind” to their dirty data or – more often – feel like 99+% data quality is simply impossible. Accepting defeat, these companies aren’t embracing new advancements in data quality that can deliver accurate data in near-real-time.

  1. Mistaking expensive analytics or data warehouses for data quality platforms

Stakeholders often mistake business analytics platforms, CRMs, and data warehouses with solutions that truly manage and maintain quality data. While valuable, sophisticated CRM or ERP solutions aren’t developed with this business need in mind. Even in the case of large-scale data warehouses, the solution is built for the purpose of housing already clean and validated data – something that ideally needs to happen outside of the solution.

While pilling on investments in data management may seem redundant, those expensive platforms simply aren’t capable of delivering the return-on-investment executives are looking for. The complexity and scale at which we now ingest data requires purpose-built software to manage the constant flow of data throughout the enterprise. By leveraging embedded best practices and superior technology, it's now faster and easier than ever before to turn disparate data into a competitive advantage.

  1. Not dedicating the right resources to data quality

According to a recent HFS report, CXOs claim a high level of data quality is even more important than management acumen to an enterprise's success. In fact, nearly half of those same respondents agree that “they are significantly under-utilizing their data resources due to a lack of an effective data management strategy.”

Managing that strategy for large-scale, global enterprises may seem daunting. Developing a data governance plan can empower users to think beyond their day-to-day tasks. Backing data quality initiatives with data governance creates a collaborative framework for managing and defining policies, business rules, and assets to provide the necessary level of data quality control.

  1. Maintaining buy-in and interest in data quality over time

It's clear with any priority initiative that leveraging buy-in from stakeholders early-on is key to driving that initiative forward across the business. But how to align the greater business with things like data quality, data governance, cataloging, or metadata management is not always so clear.

Companies big and small need purposeful planning in order to move a data quality strategy from that initial buy-in to a long-term program tied to valuable metrics. In a recent interview, the Head of Enterprise Data Governance at the London Stock Exchange Group (LSEG), Diane Schmidt, explains how to get everyone from primary stakeholders to end-users engaged with a new data management program. According to Schmidt, it really comes down to one thing: an executable strategy.

“When you’re able to go back to the strategy and how that strategy supports the business and the business benefits, the dialogue starts to change,” says Schmidt.  

Read more about Schmidt’s approach to data management here.

By fine-tuning your data quality program to the greater goals of the business, downstream data management protocols stay aligned with the greater business needs, making it easier to demonstrate real value and return on investment.

  1. Postponing data quality initiatives until it’s too late

Postponing on properly cleaning and updating your data can cost more in time, money, and resources in the long run. While 30% of revenue is lost by U.S. businesses because of bad data, that number exponentially increases as the scale of the organization grows. Additionally, Gartner has found that the average cost of poor data quality on businesses amounts to anywhere between $9.7 million and $14.2 million annually.

Even if most leaders today recognize the importance of data quality, it's easy to mistake other data management projects such as migrations, archiving, or cataloging as the answer to the problem. In reality, data quality checks should be integrated as part of these additional workflows to alleviate risk, latency, and costly downstream integration errors. Establishing data quality procedures in the organization culture early on can prevent revenue loss and optimize the value of the data you already own.

Need more convincing? Check out the Impact of Data Quality on Sales & Marketing infographic, here.

Jumpstart Your Data Quality Initiatives

Syniti delivers a platform for data quality and governance that serves as the necessary foundation to managing enterprise-scale data. With expert-led solutions like Data Jumpstart, Syniti enables organizations to give context to their data sets at the metadata level and maintain control over their data. A fast, cloud-based data quality analysis designed to help organizations understand the impact of data quality, Syniti’s Data Jumpstart drives growth, reduces risk, and maximizes the value of major initiatives such as M&A and digital transformations.

  • Continuously improve trust and confidence in your enterprise data and empower users across the business
  • Predict and prevent business disruptions by initiating the right workflows with the right data owners
  • Accelerate business initiatives with trusted integration, replication, and migration projects that impact data quality
  • Support global regulations & analytics demands in a scalable, trusted manner
  • Jumpstart future transformations and data initiatives by de-risking advanced data migration projects with clean, ready data

Curious how Syniti can help? Contact us now to learn more about this 3-week program.

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