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:
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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This article is written by Nandita Kaushal and edited by Candice McDowell.