Data accuracy and why it matters?

What is data accuracy?

Data accuracy is the degree of alikeness of datasets to the events and real-world entities it’s recorded from. High data accuracy means the presence of reliable and error-free data in a given dataset. 

Imagine that a sales team is doing a reach-out to clients, but they have wrong email addresses and contact details with missing numbers. This could be a serious data accuracy problem and it shows a low accuracy rate. 

What is meant by accurate data?

Accurate data fulfills the following characteristics.

It is correct: free from errors, typos, and inconsistencies. 

It is reliable: represents actual figures and is relevant to the current situation.

It is complete: no missing information needed to complete the task.

It is consistent: the data and its format are the same across all systems.  

Importance of data accuracy

More than half of the survey participants face 25% or more impact on the revenue due to data quality issues. - 2024 data quality survey by Monte Carlo.

The monetary value of data quality mistakes cost $12.9 millions annually for businesses. - Gartner.

The above two statistics show the direct correlation between data accuracy and business revenue. 

But why do you need accurate data? 

Better decision-making - Accurate data leads to informed, growth-driving decisions. Imagine having inaccurate customer behavior or sales data. What happens when you train your forecasting model using that? Inaccurate data leads to inaccurate predictions, which can lead to excess inventory stocking or out-of-stock situations.

In data we trust - An organization with accurate data could gain its trust among stakeholders and ensure they stick with all regulatory requirements. The trust could play a huge role in encouraging innovation and calculated risks by the decision-makers and set up the foundation for data-driven culture.

Spend more time to create. Less time to fix - where there is data and insights, there is accountability, reliability, and high operational efficiency. Most mistakes get avoided, which saves a lump of time and money. Example: an email campaign accurately targeted to the right audience listing products they are most likely to buy.

Things that affect data accuracy

Many things happen during the data lifecycle that affect the quality of data, like typos and manual entry errors, integration issues, duplication, and holding on to outdated information.

Data entry errors

Manual data entry causes errors, especially when there are tons of rows and columns that need to be updated regularly. Some examples of data entry errors could be typos caused while entering categorical data, like customer names or numerical data, like phone numbers.  

Manual entry errors lead to incorrect reports and flawed analysis, which you can’t use to achieve any intended action. 

System errors

Bugs or technical issues could cause systems to lose data and create accuracy errors. Some examples of how system errors could affect data accuracy are loss during data integration, hardware malfunctions, or even outdated software. Such errors are often small and not easily noticeable, but if they aren’t fixed on time, it can lead to data accuracy errors like incomplete or duplicate data, missing information, bias and errors, and other data quality issues. One example of how a system error can lead to data inaccuracies, and thereby create a disaster, is the Knight capital incident. Knight capital is a US-based trading company which lost $440 millions in 45 minutes back in 2012, all because of a basic software error.

Duplicate data

Gartner points out how companies lose their revenue up to $15 million because of duplicate data, which could be more than 20 to 30%. Duplicate data is the presence of similar or the same records in datasets, often with slight variations. This scenario not only increases storage costs but also affects the data quality. An example of duplicate data could be the presence of the same customer information with different spellings. It could also mean the multiple entries of a same transaction. Both such cases could turn disastrous if you use them for analysis. 

Inconsistent data format

Inconsistent data format is another data quality problem that happens due to manual entry and integration issues. These variations in how data is recorded across systems skew metrics and lead to inaccurate calculations.

Examples of data inconsistency could be this: different spellings used in different systems, or inconsistent denominations used in the same field (using a mix of celsius and Fahrenheit in temperature data).

Outdated data

Old data, if not updated periodically, could become obsolete over time. A customer could have changed his contact information, but holding old records would serve no purpose here. This is a major quality issue, and it leads to miscommunication and missed opportunities.

Errors from external data

A company could do nothing and still end up with erroneous data, when they rely on data from third-parties. For example, incorrect market data from partners or research companies. Again, such flawed data sets lead to decision errors, making them unreliable.  

Data accuracy use cases

Data accuracy is wanted everywhere. Here are some places where accuracy is instrumental.

1. Inventory management

Data accuracy is the foremost, if you want to manage accurate inventory data and optimal stock levels. Accuracy ensures that your pick, pack, and load data is precise and reliable, causing no ruckus in day-to-day operations. And, it prevents stock-outs, overstocking, and additional inventory costs. 

2. Fraud analytics

Many financial and BFSI institutions perform fraud detection to prevent illicit transactions. What if the transactional data isn’t accurate in the first place? It could lead to two cases: raising a false alarm and interrupting a legit user or allowing a fraudulent transaction to happen, since the data isn’t precise enough. 

3. Healthcare data

Healthcare institutions must abide by compliance requirements and maintain accurate data. They need to hold accurate patient records to provide proper diagnosis and treatment. To provide improved care and face fewer regulation charges, data accuracy is paramount for hospitals and healthcare companies.

4. Data-driven targeted campaigns

Retail, eCommerce, and SaaS companies run targeted reach-out campaigns. They use both customer demographics and behavioral data to do this. Let’s say they do this to share personalized messages and discounts based on customer preferences. Data accuracy plays a major role in hitting the bullseye and making the campaign a success.

How to measure data accuracy

You could know how your data is accurate in the following ways - using accuracy metrics, data profiling tools, or automated quality checks. Detailed explanations & step-by-step procedure to measure data accuracy below.

1. Set the objective

Start with setting the context of what means accurate data to your organization. 

Define the metrics it must obey - be it duplication, completeness, error%, consistency, and more. 

Error rate - % of erroneous data in the dataset.

Completeness - no missing fields present.

Consistency - same data in same formats present across systems

Validity - how long the data stays relevant. Whether to discard or update after that? 

This is how you should design objectives and metrics to define data accuracy goals.

2. Data profiling

Once you set up the business rules for data accuracy measurement, you can use data profiling tools to check if the datasets meet all the requirements. 

These data profiling tools can identify any data quality issues like

• Missing values 

• Duplicate data 

• Outliers that go beyond expected ranges

Some of the best data profiling tools for growing and large companies are Talend data quality, Apache Griffin, OpenRefine, and Power BI from Microsoft. If its small datasets need profiling, you could use Excel or spreadsheets. 

3. Error detection

Data profiling is one way to do error detection. There are other error detection methods to spot quality issues automatically. Some of them are:

Referential integrity checks - this database technique is used in ETL mainly to check if the relationships between two or multiple tables are valid. For example, if the foreign key’s values match with the primary key values in related tables.

Data type validation - this technique identifies if the field has value that adheres to its defined type. Example: a column supposed to have numeric value, but contains categorical data, which gets flagged by validation tools. Some data type validation tools include Informatica, Apache Griffin, etc.

Range and threshold validation - similar to the above one, range and threshold validation checks minimum and maximum values a field can have. 

Outlier detection - as the name suggests, it detects anomalies and outliers in datasets using mathematical, statistical, or ML-based models.

Completeness check - checks for blank and missing entries in mandatory fields of the transactional data. Example: product ID or order number cannot be empty.  

Custom business rules - every business has certain expectations from data that it needs to comply with. Example: a logistics business needs to ensure that shipping dates cannot be earlier than the order placed date. Such error validations come under custom business rules.

4. Regular audits 

Regular audits help to curb and control quality issues before they escalate into major headaches. Set up an internal and external audit committee who can conduct frequent audits to ensure optimal level of data accuracy. 

This audit committee will 

• Track recurring and new problems, identify quality and accuracy issues, document, and inform relevant team members.

• Ensure that the data management adheres to compliance requirements like CCPA, GDPR, etc., to avoid hefty penalties.

• Look for outdated data and codes that are no longer relevant.

• Check whether data-based use cases and models are functioning correctly and generating accurate insights.

Help the team get access to data profiling and error detection tools. Maintain the logs and audit findings and action-point trackers in a centralized location. Regularize audits for most critical datasets.

5. Monitor and iterate

The key to achieve data accuracy and sustain it for the long-term is doing error detection and audits as long as it takes. Make improvements wherever possible to make it more efficient and diligent. You could also automate the process as your datasets grow in volumes. 

Use tools like Alteryx, FiveTran, Informatica data quality, and Microsoft Power Automate to automate quality checks. These tools streamline data workflows and make accuracy checks a part of the everyday processes.

Final thoughts

With well-planned-out data quality checks and automation tools, you could ensure if your data is accurate. This way, you can make data the cornerstone of your organizational decision-making process and instill trust and confidence among teams in data. 

More accurate data means more accurate financial planning, customer satisfaction, predictive analytics, or any other data use cases you might have. 

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