Quality data can go a long way in ensuring that a company successfully reaches its goals. In fact, almost all industries rely on accurate data insights to aid in their decision-making. With 85% of organizations saying that poor-quality data negatively impacts their processes, it’s never been more crucial for professionals to understand exactly what makes data usable. So, what defines quality data, and how does it benefit businesses? We break down the answers in this guide.
What is quality data?
The six elements of quality data are accuracy, completeness, uniqueness, consistency, timeliness and validity. This means that for data to be considered high quality, it should be consistent across multiple sources, reflect reality, and be a full representation of the truth. It should also be stored in a usable format, and be available when users need it – each piece of data should only occur once in a database, and not be duplicated.
How can quality data be used?
Predict trends accurately
It’s almost impossible to predict the future trends of any market without data-backed information to refer to. This is essentially how predictions are created – to accurately predict upcoming influences, we need to have a solid understanding of the patterns that have occurred in the past.
So, the quality of your data can drastically impact your ability to analyze trends effectively. If it doesn't adhere to the six quality requirements, it can completely skew your predictions, resulting in time and money that can potentially be wasted in the wrong places.
Make objective decisions
Most big decisions in business are made off the back of predicted trends, so if your trends aren’t accurate, you’re not going to be able to make decisions that are reliable enough to propel you towards your goals.
If you want to exercise objectivity in decision-making, quality data is key. Objective decisions are based on facts, most of which will be presented in data form – so the data needs to be proven accurate and valid before being used to drive any business changes.
Identify issues and challenges
Quality data makes for easier interpretation, where any anomalies can be identified swiftly and with confidence. This can help you to flag issues and inconsistencies as and when they crop up in your data. Without full, accurate data sets, many of your issues will essentially become invisible – or, you may find that you misread problems altogether, as they can become hard to flag if there are multiple inconsistencies in your data set.
Protect personal information
Large amounts of sensitive information can be stored in company databases, from employer and employee details to company plans, processes and finances. Quality data requires a certain level of protection – it needs to be accessible to users who need it, and protected from those who don’t. Data that is poor in this way can result in disastrous ethical consequences for employers and employees, as well as their clients and customers.
How can you improve data quality?
Poor data quality can have far-reaching consequences. It can result in loss of funds and resources, cause ethical issues, and even damage your company’s reputation. So, it’s important to continuously look for ways to improve, by assessing and cleansing your data to remove any errors or inconsistencies. You may want to work on creating a more standardized format for your data, integrate it with other data sets, or enrich it with added information. No matter where you start, making your data more familiar can help to discern where any changes or improvements are needed.
