Data fuels the growth of any modern organization, but what happens when the fuel goes bad? The growth stops. Data-driven enterprises rely heavily on their collected information to make important business decisions, but if this information contains errors, the organization may have to suffer huge losses. In 2021, Gartner reported that organizations incur an average loss of USD 12.9 million due to poor-quality data. Bad data refers to inaccurate data, inconsistencies, duplications, irrelevance, and poor data quality means any data analysis, machine learning models, and growth projections are misleading and would affect decision-making.

Gartner also predicted that by 2022, 70% of organizations would rigorously track data quality levels via metrics, improving it by 60% to reduce operational risks and costs significantly. Improving data quality is difficult, and firms must take immediate steps to eliminate bad data. However, before any steps can be taken, it is vital to identify the sources that poor data is generated. Let’s discuss these factors in detail below.

Table of Contents

  • Factors Affecting the Data Quality

  • Cost of Bad Data

  • Best Practices to Make Data Healthier

  • Conclusion

Factors Affecting the Data Quality

Data quality deteriorates when organizations do not follow the correct data management practices. Problems like these are most prominent in companies operating for over a decade since data was not a big deal then. Data quality issues creep into system databases due to the following factors.

  • Non-Integration of Databases: In large organizations, multiple teams operate as independent entities, each with its data collection pipeline. This operational structure creates data silos within the organization and hinders business processes. With unstructured data pipelines, teams are unaware of each other’s operations, damaging efficiency. This also creates duplication within the databases as different teams might collect the same data without any integration mechanism. Duplicate data takes up additional space and creates problems for data analytics.

  • Unstandardized Data Entry Fields: Much of the garbage gathered in a database is due to incorrect user entries. Many legacy applications lack proper data entry field validations, allowing users to pass wrong inputs. This results in inconsistencies such as

    • Alphabets in numeric fields.

    • Numbers in text fields.

    • Special characters in fields where they are not required.

      All such inputs constitute poor data quality and are time-consuming for data teams that have to put in additional effort to achieve clean data.

  • Data Decay: The responsibility of data governance does not end with collection and storage. Databases need continuous updating for new information and altered fields to eliminate bad data. This is particularly helpful for customer data since clients’ demographics, such as phone numbers and addresses, can change. Healthcare is another important domain where data relevance is critical. Providers should always have updated patient information, which could hinder medical procedures.

  • Lack of Quality Staff: You need skilled workers to construct a strong building. Many organizations lack expert data scientists and engineers familiar with the good practices of data management and ETL pipelines. This skill gap introduces anomalies that result in bad data.

  • Budget: Lack of skill also arises from budget constraints since data science is an expensive field, and many organizations cannot afford senior employees. Tight budgets are also reflected in poor data infrastructure because these also require employee expertise, and most servers and ETL tools are costly.

Unhealthy data is practically useless and yields misleading analytics. Organizations relying on corrupted data will experience more harm than good. But how exactly will bad data impact a business? Let’s discuss this in detail below.

*Exclusive Content By Bill Inman: Avoiding Data Integration*

Cost of Bad Data

We have discussed the importance of data quality, but this article would be incomplete without mentioning the harms of working with unclean data. Poor quality data affects an organization in multiple ways. Some of these are apparent, but others are more indirect, with effects felt in the longer run. Some of these damages are as follows:

  • Additional Effort for Data Experts: Working with unclean data is complex as data experts have to manually pick out the anomalies and clean each dataset field by field. Not only is this time-consuming, but also prone to human errors.

  • Impact on AI: If incorrect data is passed to machine learning algorithms, it will produce unreliable results. Such models can be critical if used in domains such as healthcare or finance.

  • Damaged Reputation: Imagine if a client calls the customer care representative for guidance, but they are unable to help because of incorrect or incomplete data. This will weaken the customer’s trust and eventually drive them away.

  • Revenue Loss: Data analytics are essential to making critical business decisions. If the data is incorrect, any decision will eventually lead the organization in the wrong direction. This can result in monetary losses and high customer churn.

  • Increased Cost: In 2016, IBM estimated that poor data costs US companies $3.1 Trillion annually. is a data warehouse integration solution. It enriches data by cleansing it, enhancing it, and converting it into the correct format for data analysis. Unlike other tools, removes all the jargon associated with data integration, making it a valuable tool for companies that lack coding and data engineering skills. Schedule an intro call now to learn how to enrich your data with ETL. 

Best Practices to Make Data Healthier

Very few modern organizations have proper infrastructures and work with clean data. A 2015 study from Iron Mountain surveyed a total of 1800 senior business leaders in North America and Europe. While 75% of the participants felt that they were utilizing their data well, only 4% followed the proper practices for success. It is not important what the current infrastructure is but instead to identify and eliminate the bad practices.

Organizations need to take initiatives to ensure that their future data sets are not plagued by anomalies and provide correct analysis and insights. Enterprises can take several countermeasures to make data healthier. Let’s discuss some of these in detail.

Identify Risks

Fixing a poor database is time-consuming and costly. A strong motivation is required to initiate this task, and the best way is to identify the harms of bad data on your everyday operations. Poor quality data leads to decision-maker mistakes, damaging the customer experience. Without a satisfied customer base, no business can thrive for long and will eventually have to suffer losses.

Eliminate Silos

Silos within teams prevent them from understanding each other’s operations. This creates a communication gap that affects the quality of tasks and, as a result, impacts the state of collected data. Organizations should take initiatives to improve integration between teams. A common approach for removing silos is introducing the RevOps infrastructure within the enterprise. Under the RevOps infrastructure, multiple teams operate using the same metrics and tools, increasing inter-team collaborations. Integrating databases also helps with big data operations, such as building a data lake.

Data Monitoring

Continuous monitoring of inbound information ensures high-quality data across the organization. Data monitoring protocols include tracking the entire data lineage, i.e., the complete data flow—where this data originated from, the transformation it has undergone at different stages, and why the modifications were required.

Ensuring high-quality data requires the following parameters to be observed.

  • Volume: The size of data should be around its expected value. An extremely large or small dataset means the ETL pipeline needs to be revisited to ensure our procedures are correct.

  • Age of Data: It is important to keep track of the duration since a particular dataset was created. Older datasets should be checked for outdated information and run relevant update operations.

  • Schema: Data from all touchpoints should follow a well-defined schema. Schemas can help optimize space complexity, eliminate errors such as odd characters in specific fields and endure the database design is robust and sustainable.

  • Frontend Validations: Every input field on the application UI should validate the input to ensure no garbage values are passed into the database.

Continuous Data Testing Policy

Data testing policies benefit existing datasets and all data that will be collected in the future. Testing ensures all databases have correct data types and formats and that all values are within range. It also helps identify ambiguities in the data, such as NULL values where they are not expected.

Testing can be done manually by humans, but this is time-consuming and carries the possibility of human error. Opting for automated solutions such as self-service tools for data quality checks is much preferable. Automation solutions accompany a user-friendly interface that anyone can use without much training, and many of these tools can be integrated into ETL pipelines for real-time testing.

Related Reading: What is Data Cleansing and Why Does it Matter?

How Can Help

Data has been a buzzing term for the last few years, and organizations have been exploring methods to use it for business needs. However, very few have given any thought to the state of data quality, and only after drastic setbacks do they realize the impact of bad data. In a survey, Forrester Research reported that out of all the businesses working to improve their CRM processes, only 38% considered the impact of poor-quality data on these processes.

Bad data is more harmful than many realize. It can drive decision-makers to make the wrong choices which may cost the organization. These are also harmful in the long run as it impacts the company’s customer base and damages its reputation. The good news is that data can always be improved, no matter how poor. Organizations have multiple solutions at their disposal which can fix their existing state and create a future-proof data infrastructure. makes it easy for you to make the most of your data, turning your collection of information into meaningful business analytics. Schedule a demo today with to learn how this solution elevates your data's usability and functionality.