The Importance of Data Quality in Analytics and Business Intelligence

Businesses make data standards a top priority because the key to making informed decisions is good data quality. In a competitive market, where every brand is focused on outdoing its competitor, you can’t afford to be impulsive when it comes to decision-making.

Accurate and good-quality data gives you a much-needed edge to meet customer expectations and provide them with an altogether good experience. Analytics and business intelligence offer the right infrastructure, tools and applications to make informed decisions.

A business using poor-quality data will potentially harm itself with inaccurate insights and flawed decision making which will damage its reputation and lead to losses. This is why data quality is vital for business growth and success. Good quality data can be your greatest asset which will foster customer satisfaction, enhance reputation, mitigate risks and enable innovation. This makes it highly important for businesses to invest in quality data and get the right tools and technologies needed for data-backed decision making.

What is data quality?

Data quality is focused on using data that is suited to the business’ intended use. It should be accurate, valid, complete, consistent and up-to-date and only then will it be able to give relevant information that the business is looking for.

To achieve data quality, organizations need to apply quality management techniques to their data. Data is only considered high-quality when it meets specific requirements and is fit for its purpose.

According to Heavy AI, some good examples of data quality problems include duplicate data, missing information, disorganized data and weak data security.

Why is data quality important?

Quality data, along with analytics and business intelligence, is an excellent combination that can help businesses make accurate and informed decisions. Accurate and reliable data is crucial to get relevant real-time information that is needed to make business decisions for various departments.

For example, the marketing team cannot create a good strategy unless it has accurate data on current market trends and customer preferences. The team can only create a strategy with reliable insights until they align with their audience’s needs so they can make the right impact.

Working with flawed data will lead to increased operational costs and incorrect conclusions which will result in an all-around failure of business processes. Ensuring your data is of high quality is vital for avoiding any pitfalls and fostering success.

According to a list compiled by Alation, quality characteristics of data should include the following:

• Accuracy
• Completeness
• Consistency
• Integrity
• Reasonability
• Timeliness
• Uniqueness/Deduplication
• Validity
• Accessibility

What is Good Data Quality?

Data quality measures how quantitative and qualitative data sets meet standards for accuracy, completeness, validity, consistency, and uniqueness.

What exactly is good data quality? Good quality data is information that can be used to make assessments and important decisions within businesses and improve optimization and development strategies. Poor quality data increases the risk of poor decisions and losing revenue, so good quality is vital for every organization.

To avoid the problems associated with low quality data, there are six factors that you need to look at when assessing the data sets that you possess.

Accuracy:

The data reflects what it is supposed to represent, and this is confirmed by reputable sources.

Consistency:

The data is uniform across all data sets and groups and does not contain any conflicts or differences.

Validity:

The data is structured correctly and contains the correct values.

Completeness:

The data incorporates all metadata that should accompany the data sets.

Timeliness:

The data is current and up to date at the time of its posting and has all essential information.

Uniqueness:

The data does not contain duplicate records within the set, with each individual quality being distinct.

Data Quality Challenges

There are several challenges that you will encounter when trying to find the best quality data in your endeavours. Here are some of the data quality challenges that are most prevalent across the industry.

Human Error

Even though we have advanced technology and AI, human errors still happen. Typographical mistakes, mixed-up data sets, and mistakes migrating the data to other platforms are all common errors that can be made. To avoid these kinds of errors, make use of data quality validation tools.

Inaccurate and Missing Data

Inaccurate or missing data is another common data quality challenge. These also often come from data entry errors where the wrong values have been inputted or forgotten entirely. It can also occur after multiple different data collection methods have been used, as things can easily become confused.

Data Duplication

Data duplication occurs when multiple sets of the same data are entered, creating duplicate records and skewed results. To help eliminate this issue, use Data Duplication Tools to track your entries, standardize and validate that data.

How to Determine Data Quality Standards and Dimensions?

There are six main data quality standards and dimensions that you should follow in order to maintain good data quality.

Completeness:

The data is considered complete, covers all necessary attributes that it needs to and includes all metadata required for each data set.

Accuracy:

The data correctly represents an event, location, or fact and is verified by reliable sources.

Consistency:

The data is uniform and consistent across all data sets and fulfills all obligations of cohesiveness.

Validity:

The data and information conform to business rules, follows the correct format, and all the information is entered correctly.

Uniqueness:

All data that appears in the database only appears once and is specific, there are no duplicates, and everything can be identified.

Integrity:

Integrity in data means that all the data has been collected and stored in diverse systems and that the integral data is made available for clients whenever they need it.

How to improve data quality in analytics and business intelligence?

Improving data quality is crucial for businesses looking to grow and make full use of data-driven insights for impeccable decision making. Here are some useful ways you can get the most out of your data:

Set clear guidelines

Given that high-quality data is crucial for any business, it is important to set clear guidelines for data entry. Set defined rules and standards for how data should be entered and formatted and double-check entered values to ensure that your data is accurate.

While accuracy is integral, the relevancy of your data is equally important. This is why it's essential to establish clear and consistent guidelines and best practices for data entry and formatting to ensure that the data you collect aligns with your customer’s requirements.

Ensure your data is up to date

Keeping relevant data that is up to date is vital for improving data quality. Regardless of the industry, accurate and current data is essential for making informed decisions that benefit your company.

Outdated or inaccurate data hinders your ability to make the right choices for your business. That's why it's crucial to ensure you have the latest data. By doing so, you give yourself the best chance to make beneficial decisions which will drive the success of your company.

Conduct regular audits

Errors can arise at any stage of data collection and storage within a company, but regular audits will improve your data quality immensely. Detecting these errors can be challenging and expensive but with the right tools, it's achievable. Conducting regular data quality assessments, like audits, allows you to spot errors early on and ensure your data remains accurate and reliable.

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Importance of Data Quality FAQs

What is data quality?

Data quality is defined as a measure of the accuracy or completeness of your data set.

Why is data quality important?

It helps improve efficiency, productivity and decision making.

What is the difference between data quality and data integrity?

Data quality oversight is one aspect of data integrity. Data integrity refers to the process of optimizing data, including data integration, data quality, location intelligence and data enrichment.

What is Data Quality Assurance?

Data quality assurance is the process of ensuring that data is good quality through profiling, cleansing and control. Examples where this is important include healthcare, public health, financial services, and manufacturing.

Sources:

Heavy AI , Alation , The ECM Consultant