Have you ever heard from a client that they are already using the service you are promoting? Or maybe you wanted to contact the client and heard there is no such number? Perhaps you advertised the service and product in a place where it was already available?
If even one of these situations happened to you, it means that your data wasn’t of high quality. Let’s take a moment to discuss data quality, an extremely important topic in business analytics. Find out how data engineering services can help your company!
Table of Contents
Six characteristics of data quality
Data quality is the assessment of the suitability of data to meet a specific purpose.
VALIDITY
The validity of the database is determined by how closely it complies with a defined format. Online forms are a great example. In such a form, the data:
- Must be compatible with the data type
- Should be within the specified range
- Fits into a specific pattern
Moreover, there may be mandatory data that must be completed. Otherwise, you will not be able to proceed.
ACCURACY
The accuracy of the data defines whether the data is valid and conveys true information. There is an important feature because incorrect data hurt the analysis and subsequent decisions.
COMPLETENESS
In turn, data completeness determines how comprehensive it is. The information contains the right amount of data to process the information and turn it into specific knowledge.
CONSISTENCY
We talk about data consistency when the individual pieces of data work together, the form matches the content, and the updated data is in line with our goals. When different sources measure the same thing but record different results, that indicates poor data quality.
UNIFORMITY
Data uniformity refers to data that is homogeneous for different units, e.g., measure, color, etc. So, it determines whether the data is presented in the same format.
RELEVANCE
The relevance of data measures how useful it is for the purpose for which it has been collected.
What are the benefits of using high-quality data?
When talking about the importance of data quality in data engineering, it is impossible not to mention the benefits of using high-quality data.
Every domain needs accurate, reliable, and timely data. High-quality data is important because, with it, you can:
- Improve the ability to make decisions
- Increase efficiency
- Reduce the risk
- Increase sales
- Improve customer experience
- Help in the development of a product or service
- Enable and maintain full compliance
What can happen when you don’t pay attention to data quality
As already mentioned, working with substandard data can negatively affect your organization. Here’s what can happen if you work with low-quality data.
The low-quality data can reduce the effectiveness of your business processes and therefore increase the likelihood of your organization slowing down. It can also negatively affect your brand image. Currently, anyone can post their opinion on the web. For example, if you’re releasing a product, we’ll assume it’s a language learning app. If it turns out that the data published there are incorrect or incomplete, your product will receive a negative opinion. As a result, potential customers will likely choose not to purchase your application.
Competitors could benefit from data insights about the customer more efficiently than you. If the competition has an advantage over you (they will have new ideas for services, products, or additional functions), you can lose this chance to attract customers.
All these factors mentioned above lead to loss of income. Unreliable, incomplete data, in a word, poor-quality data has a negative impact on the financial result of your company and on the decisions you make.
How to improve data quality
DATA CLEANSING
It is best to prevent undesirable situations. However, if the problem of incorrect data has already appeared, then one of the ways to improve their quality is the so-called data cleaning.
PARSING
Parsing allows one complex field to be broken down into multiple fields based on the meaning of the data and the context (for example, full name, code, city, etc.).
STANDARDIZATION
It allows you to replace many different instances of the same variable value with one value. For example, the values ”Krakow”, “Krakow”, “Krakow”, and “Krakow” will be standardized and replaced with one value – “Krakow”.
DEDUPLICATION
It allows you to detect duplicate records and consolidate them. Deduplication isn’t always an easy task. Sometimes it is necessary to use sophisticated algorithms to determine the probability that two records are duplicates.
PREVENTION
Preventing data errors is much more effective and cheaper than cleaning them.
Conclusion: Data engineering and data quality
In this article, we tried to describe as precisely as possible what data quality is and why it is so important for every business. We also looked at the benefits that high-quality data can bring to your business. We also described what can happen if you worked with low-quality data and showed how you could improve the data quality.
