Practical Ways to Identify and Resolve Common Data Quality Issues

In today's data-driven landscape, the stakes for maintaining high data quality are higher than ever. Despite the reliance on analytics, over half of senior executives report dissatisfaction due to prevalent data quality issues, including inaccurate data, NULL data values, and data duplication.

These challenges compromise data reliability and undermine operational decisions, posing significant risks to business health. Drawing on extensive discussions with world-class analysts and our own expertise, this article will navigate through the common pitfalls in data quality management.

We'll explore the workflow stages for diagnosing and addressing data quality issues, highlighting effective strategies to enhance data reliability and operational efficiency, and ensuring your decisions are based on robust and accurate information.

Note: This post was written in December 2021 and has been completely updated based on the recent updates in October 2024.

In a nutshell (and in terms of marketing data), quality data is relevant, up-to-date data without errors and discrepancies. If we look up data quality on Wikipedia, we’ll see more than 10 (!) definitions. Furthermore, Wikipedia cites the latest research by DAMA NL into definitions of dimensions of data quality using ISO 9001 as a frame of reference for data scientists.

Data quality refers to the accuracy, completeness, reliability, and relevance of data, ensuring it is suitable for its intended use, particularly in decision-making and analytics. High-quality data must be free from errors, up-to-date, and consistent across different sources.

The concept encompasses various dimensions, including validity, timeliness, and consistency, highlighting the critical role of maintaining rigorous standards to support effective business processes, strategic decision-making, and ensuring data-driven initiatives are based on sound, reliable information.

Nearly 40% of all business initiatives fail due to poor data quality. Data quality issues occur when a dataset contains defects that undermine the reliability and trustworthiness of the data to a degree.

When data is spread across various sources, it’s almost inevitable that data quality issues will arise. These can stem from multiple factors, including human mistakes, inaccurate data entries, outdated information, incomplete data, or a general lack of expertise in data management within the organization.

Since data underpins crucial business operations, such issues can lead to significant risks and harm. The importance of utilizing high-quality data in all business operations is evident. Consequently, leaders are investing in creating data quality teams, aiming to assign specific responsibilities for achieving and maintaining high data standards.

In addition, intricate data quality frameworks are being developed, and cutting-edge technology is being utilized to ensure rapid and precise data through data quality management tools.

To identify data quality issues, follow these steps:

• Accuracy: Ensure data reflects real-world values accurately by comparing it against trusted sources and applying validation rules.
• Completeness: Check for missing or null values in essential fields to ensure no critical data is omitted.
• Consistency: Maintain uniform data formats and aligned values across datasets to avoid discrepancies.
• Timeliness: Verify that the data is current and relevant, ensuring it meets the needs of the analysis or application.
• Uniqueness: Identify and eliminate duplicate records to maintain the data set's integrity and reliability.

Whether you are a marketer, digital analyst, or any decision-maker, you can't rely on analytics based on the data you don't trust. Identifying data quality issues is a fundamental step in safeguarding the integrity of an organization's data ecosystem.

Here are key reasons why recognizing and addressing data quality issues is essential:

• Accuracy in Decision-Making: High-quality data ensures decisions are based on accurate and reliable information, reducing the risk of costly mistakes.
• Operational Efficiency: Clean, consistent data streamlines business processes, eliminating inefficiencies and redundancies caused by inaccurate data and duplicate data.
• Customer Satisfaction: Accurate data improves customer interactions and services, enhancing overall satisfaction and loyalty.
• Compliance and Risk Management: Identifying data quality issues helps comply with data regulations and minimize risks associated with data breaches or inaccuracies.
• Strategic Planning: For effective forecasting and planning, businesses rely on data. Quality issues can skew insights and lead to misguided strategies.
• Competitive Advantage: In a market where data is a crucial asset, having reliable data can provide insights that differentiate a business from its competitors.

By addressing data quality issues, organizations protect their operational integrity and position themselves for future growth and innovation.

In digital transformation, poor data quality is the primary obstacle to leveraging the full potential of machine learning technologies. Prioritizing data quality is essential to harnessing the power of machine learning effectively.

Let's look at the common issues below:

In an era where customer data influxes from various sources like cloud storage, local databases, and real-time streams, duplicate data is inevitable. This results in redundant customer details impacting their experience and the efficiency of marketing efforts, along with skewed analytics and machine learning models.

For sectors under tight regulatory scrutiny, such as healthcare, data accuracy is paramount. Inaccurate data skews reality, hindering effective planning and execution. This issue stems from human errors, outdated data, data decay, and drift, or orphaned data with a notable decay rate.

In the complexities of large-scale data management systems, the risk of introducing errors such as ambiguous identifiers, inconsistent formatting, or typographical mistakes remains high. These issues can significantly distort analytics outcomes.

Frequently, organizations fail to fully leverage their data assets, leading to valuable insights remaining untapped within isolated or neglected data repositories. This underutilization stems from data being siloed across different departments or lacking a unified data-sharing strategy.

In an era where data is sourced from various channels, inconsistencies, be it in data formatting, spelling variations, or unit measurements, are inevitable. Such discrepancies can erode the trustworthiness and utility of data.

As digital footprints expand, businesses need to work on managing more data and an overwhelming influx of information. This deluge can obscure the discovery and preparation of data pertinent to specific analytical endeavors, compounding existing data quality issues.

Data downtime can critically affect an organization's operational efficiency, particularly during significant mergers or system migrations. This period of non-availability or unreliability of data can lead to operational disruptions, impacting decision-making and customer satisfaction.

In dealing with the vast amount of data that marketers and analysts use daily, it’s a challenge to eliminate errors and discrepancies. Only 3% of businesses' data meets basic quality standards. It’s extremely difficult to provide quality data to an end-user immediately. However, data errors can be actively fought and proactively found.

Firstly, let’s look at the process of working with data and lay down the steps where you can identify data quality problems and issues and fix them:

Let’s see in more detail what data quality issues can arise at these data processing steps and how to solve them.

Even though there are no errors in data at this step, we cannot completely omit it. The devil is in the details, and collecting data for analysis begins with detailed planning. We recommend always starting with an express analysis and carefully planning the collection of all the marketing data you need.

Skipping this step leads to an unstructured approach and insufficient data for new tasks or projects. The goal is to collect fragmented data from all sources. Decisions and actions are flawed from the beginning without all the data.

Let’s see what data you should collect before starting new projects:

• User behavior data from your website and/or application
• Cost data from advertising platforms
• Call tracking, chatbot, and email data
• Actual sales data from your CRM/ERP systems, etc.

Once you’ve created your measurement plan, let’s proceed to the primary data collection step.

During this step, among all the other challenges you must overcome, you must consider controlling access to your data (it’s all about data security) and preparing in advance for the creation of your data storage or data warehouse.

We recommend using single storage with automated data import if you want to gain complete control over your raw data without modifying it. For marketing needs, Google BigQuery remains the best option because of the Google ecosystem.

What data quality difficulties you can come across at this step:

Advertising platforms and services collect vast amounts of valuable user behavior data, and the problem occurs when you try to get all of this information in full from these data sources without damaging its completeness.

⚠️ Problem

Advertising services collect and update user action data, which may change after the transfer, leading to potential data delivery issues and quality degradation. Analysts, unaware of these updates or new accounts, might utilize incomplete or incorrect data for business analytics.

Common challenges stem from uncollected data due to new advertising services not accounted for by analysts.

✅ Solution

With the complexities of data collection via APIs, organizations can solve challenges by assigning specific data collection responsibilities to different team members for improved oversight.

Additionally, it is crucial to embrace automated data import tools that adjust to API changes and detect data gaps. These tools can retrospectively fill in missing data, ensuring continuous and comprehensive data collection.

We know how much we spend on advertising by analyzing data from advertising services. While from website user behavior data, we get information about how much we earn. Since business questions usually sound like "Which advertising pays off, and which does not?" it’s essential to know the income/expense ratio.

⚠️ Problem

The disparity between website user behavior data and cost data from advertising services is a significant issue, primarily because user behavior data is directly captured by website owners and often exceeds the volume of cost data. If not correctly managed, this can lead to challenges in data analysis and decision-making.

The root causes of these discrepancies and potential data losses include the absence of Google Tag Manager (GTM) containers on all website pages, which is essential for capturing comprehensive data on user interactions and advertising campaign results. Without GTM, there's a risk of missing crucial data points.

Additionally, lapses in maintaining underlying infrastructure, such as untimely payments for Google Cloud services, can halt data collection processes altogether. Another common issue is the lack of validation for user-provided information through website forms, which can lead to inaccuracies in the captured data.

✅ Solution

As with collecting data from an API, the solutions for website data collection include: using automated data import tools; these tools not only facilitate the seamless integration of data from various sources but also play a pivotal role in identifying and alerting users to potential gaps or inaccuracies in data collection.

Aggregated and sampled data is generalized data that appears in cases when not all data is processed and used for analysis and reporting. This happens when services like Google Analytics analyze only part of the data to reduce the load on servers and balance the speed and accuracy of data processing.

Since sampling results in generalization, it leads to a lack of trust in the obtained results.

⚠️Problem

Sampled reports distort performance data, and that can cost you a fortune when it comes to money-related metrics such as goals, conversions, and revenue. To create reports as soon as possible and save resources, systems apply sampling, aggregation, and filtering instead of processing massive data arrays.

Because of this, you risk not noticing a profitable advertising campaign and may turn it off due to distorted data in a report, or vice versa — you may spend all your money on inefficient campaigns.

✅ Solution

The only thing you can do to avoid data sampling is to collect raw data and constantly check data completeness throughout all your reports. This process monitoring is preferably done automatically as a way to elude human factors. For example, you can apply automatic testing of correct metrics collection on your website, as our client did with the help of OWOX BI.

After collecting all the necessary data, it’s time to normalize it. At this step, analysts turn available information into the form required by the business. For example, we must get phone numbers into a single format.

Data normalization is a manual and routine "monkey job" that usually keeps analysts from more exciting tasks, such as extracting useful data insights. Not to mention that normalization difficulties usually take up a lot of an analyst’s work time overall.

Data quality difficulties one can come across at this stage:

The phenomena of insertion, updating, and deletion dependencies represent significant challenges in data management, particularly during the normalization of unstructured data. These dependencies can introduce a range of issues that complicate the integrity and consistency of the data being processed.

⚠️ Problem

The common outcome of these data dependencies is that reporting systems discard such incorrect data while analyzing it. As a result, we end up with inaccurate reports that aren’t based on full data.

For example, we can have a session object and an advertisements object. In sessions, we have data for days 10 to 20, and in advertisements, there is data from days 10 to 15 (for some reason, there is no cost data for days 16 to 20).

Undesirable side effects appear when an advertising service API is changed, unavailable, or returns incorrect data. Accordingly, either we lose data from advertisements for days 16 to 20, or data from sessions will only be available for days 10 to 15.

✅ Solution

In the same way you check for data collection errors, you should always verify the data you work with. Moreover, if the user doesn't know the specifics of data merging, mistakes will occur while normalizing the data.

In practice, the best decision at this step is to develop a data quality monitoring system that alerts the person responsible for triggers and anomalies. You can use services like OWOX BI, which has embedded data monitoring functionality.

The diversity in data formats, structures, and levels of detail presented by various advertising platforms necessitates a comprehensive approach to data management. This integration challenge is compounded by the need to reconcile differing units of measurement, time zones, and data categorizations.

⚠️Problem

Creating cohesive reports from disparate data sets is akin to constructing a triangular fortress with only round and oval pieces – it's a challenging endeavor that requires significant effort to standardize and unify the data beforehand. This challenge arises from various data schemas employed across advertising platforms and services.

For example, what one platform might label as "Product Name," another could refer to as "Product Category." Additionally, the issue of differing currencies across platforms, such as dollars used for Twitter Ads and pounds for Facebook Ads – further complicates data unification.

Without data normalization, the accuracy and utility of reports are significantly compromised, leading to potential misinterpretations and flawed decision-making based on inconsistent data insights.

✅ Solution

Before analyzing data, it must be converted to a single format; otherwise, nothing good will come from your analysis.

For example, you should merge user session data with advertising cost data to measure the impact of each particular traffic source or marketing channel and to see which advertising campaigns bring you more revenue. Of course, this can be done manually using scripts and SQL, but applying automated solutions is a better choice.

Business-ready data is a cleaned final dataset in the structure corresponding to the business model. In other words, if you have completed all the steps in working with data and completed everything, you should get the final dataset. Its ready-made data can be sent to any data visualization service such as Power BI, Tableau, Looker Studio (formerly Google Data Studio) or Google Sheets.

However, you shouldn’t confuse it with raw data, which you can try to build a report on. It leads to recurring issues, lengthy error detection, and duplicated business logic in SQL queries. Managing updates and changes becomes challenging, causing problems like updating cost data history after adjustments by advertising services or handling repurchased transactions.

What data quality difficulties can appear during this step:

It’s challenging to control changes in transformation logic due to inconsistent or absent definitions of the data types required throughout data processing. This lack of standardized definitions for data types used throughout the data processing lifecycle can lead to discrepancies and errors.

Without a shared understanding and clear guidelines on how data should be interpreted and handled, teams may apply assumptions or inconsistent methodologies, resulting in varied outcomes. This inconsistency hampers the reliability of data analytics, leading to confusion and potential misalignment in strategic decision-making.

⚠️ Problem

When a business has not clearly defined its core data and data model, then users aren’t on the same page about data use: they aren’t sure which table or column to query, which filter to use, or who to ask for information about data objects. Therefore, the logic for merging data is incomprehensible.

Besides, it takes too long to navigate through and understand all data objects from raw data, including their attributes, their place in the data model, and their relevance to each other.

✅ Solution

Foremost, don’t apply business logic to each report or dataset but use data modeling at the company level. Within the company, there should be a transparent business data model and control of the data lifecycle. This means that all definitions used must be clear. For example, end users should know what conversion and website visitor metrics represent.

Additionally, as it’s challenging to prepare and maintain up-to-date simulated data, the answer lies in applying automated solutions (e.g. OWOX BI Transformation) that can clean, normalize, blend, and attribute your data so it’s business-ready and prepared for reporting.

Visually presenting key metrics is the last step to making data work, so your data presentation should be informative and user-friendly. Automated and properly configured visualizations can significantly reduce the time to find a problem; you can perform more iterations with less effort over the same period to improve data quality.

Also, it’s important to remember data visualization services like the popular Looker Studio cannot merge or transform data. Suppose you need reports based on many data sources. In that case, we recommend collecting all the data you need beforehand and putting it into a single data storage to avoid any difficulties.

Common Data quality issues you can come across at this step:

These emerge when inaccuracies infiltrate the data handling process, starting from initial collection to normalization, ultimately manifesting in the reports produced by data visualization tools. These inaccuracies can significantly distort the insights derived from data analytics, leading to misguided decision-making. The complexity of these errors is compounded by the many stages involved in data processing, each carrying its own risk of introducing inaccuracies.

⚠️Problem

Creating reports riddled with factual data errors can significantly drain resources, leading to futile efforts that do not enhance business strategy or reveal actionable insights. This scenario resembles chasing an elusive goal, where the desired outcome remains out of reach despite considerable investment.

The root cause of this issue lies in the irrelevance of the visualized data, which stems from errors or gaps present in the underlying data itself.

✅ Solution

The only way to solve this problem is to address this challenge, which necessitates a rigorous approach to data preparation and quality assurance before reporting generation. Organizations can significantly mitigate the risk of incorporating errors into their reports by establishing stringent protocols for data verification and continuous monitoring of data integrity.

Data requirements are constantly changing, and SQL queries also change. This ever-changing environment can strain the reporting infrastructure, making it susceptible to errors and breakdowns.

As the complexity of these systems escalates, the likelihood of encountering issues such as broken queries or errors in reports rises, posing challenges in maintaining the system's integrity and reliability.

⚠️Problem

There’s nothing wrong with changes unless there are so many; remembering what changes were made, where, and when is impossible. Eventually, all carefully built reporting systems can disappear since the SQL queries aren’t working and there’s no correct data to visualize.

It’s quite a challenge to remember every small thing, so the typical mistake is to forget to apply edits on all datasets where they’re needed.

✅ Solution

The solution centers on simplifying the report generation process to minimize dependence on complex SQL queries and frequent modifications. Streamlining this process involves implementing more intuitive and flexible reporting tools that allow for easy adjustments without extensive coding.

Establishing a centralized documentation system for tracking changes and deploying version control mechanisms can also enhance the manageability of SQL queries and reports.

One of the most common problems is misunderstanding data (and, therefore, misusing it). This happens when a particular metric or parameter can be interpreted differently.

For example, say there’s a conversion metric in a report, and different users use this report. One user thinks a conversion means a website visit, while another means placing an order. However, a third person also thinks this conversion metric refers to delivered and purchased orders.

As you can see, there are many potential interpretations, so you must clarify what information is presented in the report.

⚠️Problem

If there’s no clear understanding of what data is used in reports and dashboards, there’s no guarantee that your decisions will be based on the facts. An unclear explanation of metrics and parameters used in reports or an inappropriate type of data visualization can lead to poor decisions. This ambiguity and inappropriate data visualizations can distort the perception of performance and trends.

✅ Solution

Data verification doesn’t end when you ensure your input data is correct and relevant. It's equally crucial to present this data in a manner that is both comprehensive and understandable to end users. This data can still be misused.

To avoid this problem, end users have to have access to complete, up-to-date, business-ready data with clear and precise explanations of what information is presented in the reports.

Maintaining high-quality data is crucial for any organization aiming to make informed decisions and achieve competitive advantages. Data quality directly impacts operational efficiency, customer satisfaction, and analytics accuracy, making it essential to establish robust data management practices.

• Define Data Quality Standards: Establish criteria for data accuracy, completeness, and reliability. These benchmarks guide ensuring data entering the system meets predefined quality levels.
• Implement Data Governance: Set clear roles for data stewards, create a governance framework, and document all data management processes.
• Engage in Data Profiling: Conduct thorough examinations of datasets to detect irregularities and anomalies. This proactive analysis aids in identifying potential issues early, ensuring data cleanliness.
• Automate Data Validation: Utilize software to check data against established rules during input automatically. This approach not only streamlines the validation process but also significantly reduces the likelihood of human error.
• Master Data Management (MDM): Develop a unified source of critical data for the organization. MDM streamlines data sharing among departments and ensures all stakeholders access the same accurate information.
• Prioritize Data Integration: Integrate data from diverse sources into a coherent framework. Effective integration processes reconcile differing data formats and structures, providing a consolidated view.
• Utilize Data Quality Tools: Implement tools to identify and correct data issues. These technologies automate processes such as data cleansing, deduplication, and correction.
• Conduct Regular Audits: Review your data for accuracy, completeness, and relevance. Regular audits help catch and rectify issues promptly.

The OWOX BI team knows more than anyone how severe the data problem is, since each client encounters it. We have made a product that allows analysts to automate the routine, deliver business value from data, and ensure data quality.

OWOX BI is a unified platform that empowers you to collect, prepare, and analyze all your marketing data. It automates data delivery from siloed sources to your analytics destination, ensuring data is always accurate and up to date.

Applying OWOX BI lets you get business-ready data according to your business model with transparent monitoring and an easy-to-use report builder for unlocking insights without SQL or code.

Let’s look at how OWOX BI can help you with all the steps mentioned above.

Create a measurement plan for your business or develop a system of metrics especially for your business needs with the help of our specialists.

OWOX BI collects raw data from advertising services, on-site analytics, offline stores, call tracking systems, and CRM systems in your data storage. The platform works smoothly with large ad accounts and uploads all data regardless of the number of campaigns.

No more connectors and custom integrations.

When using OWOX BI, you don’t need to manually clean, structure, and process data. You’ll receive ready datasets in the most transparent and most convenient structure. Moreover, you can get a visual report on the relevance of data from advertising services uploaded to Google Analytics.

With OWOX BI, you have trusted business-ready data at your fingertips. There’s no longer any need to create a new dataset for every new report, as you get prebuilt final datasets prepared according to your business data model. With up-to-date and unified data ready for further data segmentation, you can gain insights into your business's speed and increase the value of your data.

The OWOX BI platform lets you analyze and visualize your data wherever you want. Once your marketing data is ready, you can send it to the BI or visualization tool of your choice with a few clicks.

Book a free demo to see how OWOX BI guarantees quality data and how you can benefit from fully automated data management today!