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Common data quality problems that hold your organisation back

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The continuous expansion of data volumes presents endless opportunities for using them as a resource. However, with 85% of companies embracing big data, only 37% have successfully translated it into actionable insights that resonate with their consumers, products, and services.

While big data promises numerous advantages, this dynamic sector continues to grow, often leaving organisations grappling with issues such as inadequate technological expertise, concerns over data privacy, and insufficient analytical capabilities. Moreover, the need for more skilled professionals versed in big data technologies has grown exponentially.

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Did you know?

  • Automated data management systems can process data 100 times faster than manual systems.
  • Data management tools yield a return on investment of 570% over five years.
  • Mismanaged data is responsible for 81% of data breaches.
  • Up to 80% of the time spent on data projects is dedicated to cleaning and preparing data.

Data issues and their solutions

We all understand that poor-quality data leads to reduced operational efficiency. But how exactly do data quality issues impact businesses? There are various data problems, each causing different adverse effects. Here are some common consequences that data teams often face due to mismanagement, resulting in data degradation.

Duplicate data

Duplicate data refers to instances where a system or database stores multiple versions of the same data record or information. Common causes include multiple data re-imports, improper decoupling in data integration processes, acquiring data from various sources, and data silos. For example, if an auction item is listed twice on a website, it can negatively impact both potential buyers and the website’s credibility. Duplicate records also waste storage space and increase the likelihood of skewed analytical results.


  • Establish data governance frameworks that include guidelines for data entry and data storage.
  • Use unique identifiers for data elements such as customers, items, and products.
  • Employ data duplication software to identify and eliminate quality issues from different systems.
  • Engage in manual data cleaning processes.

Irrelevant data

Many organisations assume that capturing and storing extensive customer data will automatically prove beneficial. However, this isn’t always true. Due to the sheer volume of data collected, businesses often need help with issues related to irrelevant data quality. Over time, irrelevant data becomes outdated and loses its value, placing a strain on IT infrastructure and requiring significant management attention from data teams.

For example, details like job titles may not offer meaningful insights into a company’s product sales trends, thereby distracting from the analysis of more critical data elements.


  • Define data requirements, including necessary elements and sources for each project.
  • Employ filters to exclude irrelevant data from large data sets.
  • Choose and use appropriate data resources relevant to the project.
  • Employ data visualisation techniques to emphasise relevant patterns and insights.

Unstructured data

Unstructured data poses numerous challenges for maintaining data quality due to its varied nature, including text, audio, images, and beyond, without adhering to a specific structure or model. Similar to other types of raw data, unstructured data comes from multiple sources and may contain duplicates, irrelevant information, or errors. Extracting meaningful insights from unstructured data requires specialised tools and integration processes, highlighting the need for expertise and skilled data analysts.


  • Use advanced technologies such as artificial intelligence, machine learning, and natural language processing.
  • Recruit and train talents skilled in data management and analysis.
  • Implement reliable data governance policies to standardise data practices throughout the organisation.

Data downtime

Data downtime comes when data is unavailable or inaccessible, disrupting organisations’ and customers’ access to critical information. This disruption leads to subpar analytical results and customer dissatisfaction. Various factors contribute to data downtime, including issues with management systems such as schema changes, migration challenges, technical glitches, and server failures. Data engineers have to invest their time and effort in updating and ensuring the reliability of data pipelines since prolonged downtime may increase operational costs and undermine customer trust.

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  • Implement redundancy and failover mechanisms like backup servers and load balancing to ensure continuous availability of critical data.
  • Conduct regular maintenance and updates to prevent instances of data downtime.
  • Monitor the performance of data pipelines and network bandwidth to detect and address potential issues.
  • Automate data management processes.

Inconsistent data

Since the data comes from different places, it’s normal to find differences in the same information. These discrepancies stem from human errors in manual data entry and inefficient data management practices. For example, consider date representation: May 18, 2024, can be expressed in formats like 18/05/2024 or 05-18-2024, depending on the source’s format requirements. While all of these formats are correct, such variations significantly impact data quality. Regardless of the cause or specific format, inconsistent data undermines integrity and diminishes data values, disrupting business operations.


  • Implement data governance policies to ensure data consistency across all data streams.
  • Use technologies such as artificial intelligence, machine learning, and natural language processing to automate the detection and improvement of inconsistent data.
  • Conduct regular verification and cleaning of data systems to maintain data integrity.
  • Automate data entry processes using drop-down menus or data picklists to reduce manual errors.

Hidden data

Enterprises extract and analyse data to improve operational efficiency. However, due to the vast data available today, most organisations use only a fraction. The unused or inaccessible data, stored in data silos or embedded within files and documents, is referred to as hidden data. This data includes valuable but untapped information, such as invisible metadata.

Hidden data presents a dilemma—it should either be used effectively or removed altogether. Ignoring this data quality issue wastes resources and poses risks such as potential privacy breaches and non-compliance with regulatory standards, mainly if sensitive data is inadvertently included in data sets


  • Invest in data catalog solutions.
  • Use data masking to replace sensitive data with fictitious data while retaining the original data format.
  • Use machine learning algorithms to identify hidden data.
  • Limit access to specific data types based on employee roles and responsibilities.

Outdated data

Today, the collected data can quickly become outdated, leading to what is known as data degradation. Outdated data refers to information that is no longer accurate or relevant in the current context. For example, customer data like names and contact information require frequent updates to ensure timely engagement regarding the company’s services and promotions.

The issue of outdated data goes beyond just accuracy—it also highlights enterprises’ delays and insufficient investment in database management systems. The repercussions of outdated data include incorrect insights, impaired decision-making, and misleading outcomes.


  • Conduct regular reviews and updates to ensure data accuracy and data security.
  • Establish a reliable data governance strategy to systematically manage data across the organisation.
  • Consider outsourcing data management services or data stewards if managing data internally is challenging.
  • Implement machine learning algorithms to detect and flag outdated data automatically. 

What to pay attention to?

Managing data and ensuring its quality implies emerging data quality challenges. Paying attention to the following aspects is important to maintain high data standards and make informed decisions.

Source system

The optimal approach to identify data quality issues is at the source of the data itself, focusing on the systems and processes used during data collection. Addressing issues at this level is complex due to the extensive involvement required at the business process layer. Additionally, entrusting data to third parties over whom you have limited control can lead to further data challenges, including the risk of inaccurate data.

Extract, transform, load process

To ensure data quality in your data warehouse through the ETL (extract, transform, load) process, consider these important steps:

  • Data profiling: It involves analysing data sources’ structure, content, and metadata to generate statistics and summaries that define their characteristics and quality. This step identifies issues such as inaccurate or inconsistent data types, incompatible formats, completeness, correctness, and integrity.
  • Data cleaning: It is the process of rectifying, deleting, or replacing incorrect, incomplete, or inconsistent data using rules, functions, or algorithms to meet quality standards.
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  • Data validation: It ensures that data loaded into the data warehouse accurately reflects the source data and meets the expected output. It involves comparing, testing, and confirming data to detect and resolve errors or inconsistencies.
  • Monitoring: Establish a continuous data quality assessment framework, enabling ongoing tracking, measurement, and evaluation of data integrity and relevance.

Meta-data layer

 When you have limited control over the ETL process and must analyse a data set “as is,” a viable approach is to use rules and logic within a metadata layer to manage data quality issues. This involves applying rules and logic similar to those used in ETL processes but without altering the underlying data. Instead, these rules are dynamically applied during query execution, allowing on-the-fly corrections to address immediate issues such as inaccuracies in the data.

Data problem-solving steps throughout your organisation

Step 1: Evaluate your current data quality

All company stakeholders, including business departments, IT, and the Chief Data Officer, must clearly understand the current state of data within your ecosystem. The data quality management team should conduct thorough checks on the database for errors, duplicates, and missing records.

Step 2: Create a data quality plan

Develop a data quality plan outlining strategies and procedures for improving and sustaining data integrity. It should delineate specific data use cases, associated quality requirements, and data collection, storage, and processing methods.

Include the list of appropriate data quality management tools, ranging from internally developed scripts to advanced data quality solutions. These tools will be tailored to meet the diverse needs identified within each data use case.

A critical aspect of the plan is to proactively identify and resolve potential data quality challenges or inconsistencies that may emerge in the future. So, it would be wise to establish protocols for data validation, error handling, and continuous monitoring to ensure ongoing data quality.

Step 3: Perform preliminary data cleanup

During this stage, the focus is on cleaning, preparing, and rectifying any identified data quality issues. This includes addressing duplicate entries, filling in missing data points, and correcting discrepancies across data sets. The goal is to initiate the data quality management process, ensuring the data is accurate, complete, and consistent. By undertaking these cleansing efforts, the organisation aims to enhance the reliability and usability of its data assets for subsequent analysis and data driven decisions.

Step 4: Implementation of your data quality plan

Now, the focus shifts to implementing your data plan and data quality strategy to improve data management organisation-wide. You have to effectively integrate data quality norms and standards into daily business operations.

The process entails educating employees on new data quality procedures and modifying existing processes to incorporate data quality checks. The ultimate goal is to establish data quality management as a self-correcting, continuous process.

Step 5: Monitor data quality

As you know, effective data quality management is an ongoing process. Organisations must regularly track and analyse data quality to ensure continuous compliance with requirements. So, conducting regular audits, generating reports, and evaluating dashboards helps to gain insights into data quality consistency over time.


Before proceeding with testing and remediation, establish data quality metrics. This sets the stage for systematically identifying and assessing data quality issues. Common data quality checks include:

  • Detecting duplicates or overlaps to ensure data uniqueness.
  • Verifying data completeness by checking for necessary fields, null values, and missing values.
  • Enforcing formatting rules to maintain uniformity.
  • Validating data validity by assessing value ranges.
  • Ensuring data freshness by reviewing the recency of updates.
  • Performing row, column, conformance, and value validation tests to uphold data integrity.

Like any complex system, data ecosystems are susceptible to quality issues such as erroneous, redundant, and duplicated data. Poor data quality can have substantial financial ramifications, costing companies an average of $15 million annually. Overlooking the importance of data quality can lead to destructive outcomes for decision-making processes and erode a company’s market position.

At Altamira, we prioritise data quality, which is fundamental to the successful adoption of artificial intelligence and machine learning. Our solutions are designed to help you regain control over your data and turn it into a valuable asset.

Data quality management problems we resolve

  • Data quality, accessibility, and fragmentation issues: We address inconsistencies, improve data accessibility, and eliminate fragmentation to ensure your data is reliable and ready for analysis.
  • Underutilised data: We help you unlock new opportunities by using your data for internal insights or monetisation.
  • Data validation, cleansing, and augmentation: We prepare your data sets for advanced AI/ML applications by ensuring they are accurate, complete, and enriched with the necessary information.
  • Integration of data management processes: We streamline data management to maintain high quality data for future use.


As your business grows, your data infrastructure needs to scale accordingly. We help you plan for future scalability, preventing data handling from becoming a bottleneck and enabling the smooth implementation of advanced AI/ML solutions.

Get in touch to get a free expert consultation! 

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