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5 Reasons Why Bad Data Can Ruin Your Business

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What is Bad Data?

In simple terms, bad data refers to unstructured information that suffers from quality issues such as inaccuracies, incompleteness, inconsistency, and duplication. It is an inherent characteristic of raw data, which needs to be processed before it can be used for analysis or business intelligence purposes. Social media data, for example, is often unstructured and requires cleaning and structuring before meaningful insights can be derived.

Some common problems associated with bad data include:

  • Misspelled names and incorrect address information
  • Fake or invalidated addresses
  • Missing phone numbers
  • Data inconsistencies due to lack of standardized formats
  • Unintended use of punctuation or bullet icons in fields

Although these issues may seem insignificant, they can become major obstacles when migrating data to a business intelligence platform or when using data for analysis.

Data cleansing to get rid of bad data

5 Ways Bad Data Can Harm Your Business

  1. It generates flawed insights: Duplicate data is one of the leading causes of inaccurate insights. For instance, a company may assume it has 100 active customers, but due to duplicate data across multiple sources, it may actually have only 63 active customers, with the remaining 37 being duplicates. Scaling this example to a larger dataset can lead to significantly skewed conclusions, affecting decision-making based on faulty information.

  2. It causes failed migration projects: When transitioning from one platform to another, data governance and standardization rules often differ. This can pose challenges when migrating and mapping data accurately. Preparing the data to remove inconsistencies and duplicates becomes necessary before initiating a migration process.

  3. It impacts organizational efficiency: In today’s data-driven era, poor data quality directly affects organizational performance. Flawed or unreliable data can lead to costly mistakes, such as sending emails to the wrong audience. Data is the lifeline of every organization, and when its quality is compromised, it can have serious consequences on processes, people, and overall goals.

  4. It becomes a bottleneck in digital transformation: Poor data quality hampers digital transformation initiatives. When data quality issues arise, transformation projects are often put on hold to address these problems. This delay can significantly impede progress and hinder companies from achieving their digital transformation objectives.

  5. It results in costly expenses: According to Gartner’s 2017 Data Quality Market Survey, organizations were losing up to $15 million on average due to poor data quality. With the increasing focus on data collection and analysis in recent years, this figure is likely to have risen significantly.

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In addition to these major concerns, bad data quality can give rise to numerous other minor issues that are often overlooked until they become significant bottlenecks for businesses to handle.

How to Manage Bad Data Effectively

When companies encounter bad data, they often resort to hastily hiring more data analysts in the hope that they can magically fix the errors. However, this approach is not practical or efficient.

Data analysts are primarily responsible for deriving insights from data, not cleaning it. Furthermore, even if they were to engage in data cleaning, it would be a time-consuming process to rectify millions of rows of faulty data across multiple sources. In-house solutions can also prove costly due to the expenses associated with recruitment, testing, and the time required to sort through the data.

Fortunately, there are commercial solutions like Data Ladder that excel in data cleaning and matching accuracy at a fraction of the time and cost it would take an in-house team to achieve the same results. These solutions offer the following benefits:

  • Data Cleaning: Automated solutions make it easy to clean data across multiple datasets. This process involves rectifying typos, spelling errors, character issues, punctuation problems, and other minor details that may be missed by human operators.

  • Data Deduplication: The root cause of bad data is often duplication. When companies have multiple systems and applications, data duplication becomes inevitable. Data deduplication software enables the identification and removal of duplicate data across all datasets, providing a consolidated overview that avoids corrupt insights.

  • Data Standardization: These solutions also facilitate the implementation of uniform standards across data sources. For example, converting lowercase letters to capital letters in name fields can be a time-consuming task. With a data cleaning solution, this conversion becomes as simple as a click, saving valuable time for data analysts.

  • Data Governance: Implementing a commercial tool allows for the creation of data governance rules within the organization. By understanding the common issues affecting data quality and the solutions available, companies can prevent the repetition of these issues through a well-defined data governance strategy.

  • Data Quality Framework: A data quality framework ensures that data is cleansed and ready for real-time use. Implementing such a framework involves setting quality benchmarks at different stages of the data cleaning process, ensuring data integrity throughout.

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Managing bad data is no longer an issue that companies can afford to ignore. To thrive in the data-driven era, implementing a robust data quality framework is crucial. By doing so, businesses can avoid the detrimental consequences associated with bad data.

Data governance helps with bad data

In summary, bad data can have severe implications for businesses. As companies invest heavily in data capture and analysis, it’s crucial to recognize the importance of data quality. Flawed data can lead to inaccurate insights, migration challenges, operational inefficiencies, digital transformation bottlenecks, and significant expenses. By adopting solutions that address data cleansing, deduplication, standardization, governance, and quality, organizations can better position themselves for success in the data era.

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