There are many ways to improve your data quality with Snowflake. Learn about Table-level rules, Object tagging, and Automation. Then, learn about machine learning and automation in Snowflake. You will be able to identify issues with your data quickly and easily. If you're still stuck, don't give up, just contact a salesperson for a demo! It's easy and free!

Object tagging

Object tagging is a powerful new feature that allows you to identify and classify objects in Snowflake. This new feature applies business context to objects in Snowflake, allowing you to identify and prioritize sensitive data. Objects can be tagged to identify PII, sensitive information, or cost centers. Object tagging extends and expands Snowflake's existing data governance capabilities, making it easier to identify and protect sensitive and PII information. Knowing the location of data is often the first step toward protecting it. Moreover, knowing where your data resides is critical to meeting regulatory compliance requirements.

By using tags, Snowflake can improve the quality of its data. Tags are essential for classifying and managing data. Tags allow you to control access, ensure consistency, and track sensitive data. Tags also help you determine how sensitive or private your data is. For example, you can use object tagging to determine the value of a customer's personal information. If you have a customer database, you can use object tagging to determine the quality of its information.

Table-level rules

A Snowflake query can help you identify the critical tables in your database. Identifying the critical tables will save you valuable time by creating a custom validation check for them. It is also possible to extract data quality metrics, such as completeness and distinctness, from your database. These metrics can be used for reporting and analytics. Moreover, they can help you determine which data is sensitive and needs special attention.

A Snowflake integration such as DataBuck is an excellent way to automate this process. DataBuck automatically scans your data assets and creates a Data Trust Score. It works by using AI/ML algorithms to monitor data quality. Users never have to worry about manual validation again! And DataBuck never needs to move data out of Snowflake! Just configure it in the Snowflake admin panel and you're done!

Automation

The automation of Snowflake data quality is crucial in improving overall business data quality. Many data warehouse projects begin with data ingestion. This process involves moving data from different sources, in different formats, and into Snowflake. However, as data is analyzed, errors and other problems are revealed, lowering the business' confidence in the quality of their Snowflake data. It's estimated that 20 to 30 percent of an analytics project's budget goes towards fixing data issues, and in extreme cases, the project is abandoned.

Moreover, Snowflake's continuous monitoring feature allows organizations to gain access to the dashboard in real time, making it as easy as possible for users to see what's going on in the system. It also provides real-time alerts that pinpoint issues and enable users to take immediate action. Moreover, the latest solution brief for Snowflake shows how ActiveDQ can help improve the management of data quality. By automating the data quality process, you'll be able to enhance ROI and enrich customer experiences.

Machine learning

The importance of machine learning for snowflake data quality cannot be underestimated. The amount of data generated today is so large and diverse that errors are bound to creep in at every step of the way. This is especially true as companies are constantly facing an increase in the amount of data they have to manage, as well as the number of different platforms that they use to process that data. With machine learning, companies can detect bad data, even before their business partners do, and make necessary adjustments before the situation worsens.

Besides being able to predict the quality of Snowflake data, it also helps in the monitoring process. DataBuck is a service that can auto-trigger the Data Trust Score. It can run at scheduled times or can be set to run as part of a data pipeline. To use it, all you need to do is to provide your Snowflake connection information and DataBuck will do the rest. The ML engine will analyze and validate the data for you, without having to write rules or move it out of the service.

Data observability

Observability of Snowflake data quality solutions can accelerate your success with Big Data. Big Data reveals patterns and can be used to automate data quality rules. Observability improves data quality for many business use cases, including Monte Carlo simulations, healthcare, and financial analytics. Observable data is critical to a successful Big Data implementation and can improve operational performance. Its high observability can be useful for many organizations in different industries, and it is highly customizable to your needs.

Observability of Snowflake is useful for data engineers, who can easily discover the causes of downtime and pinpoint the cause to improve performance. Downtime costs a business up to USD 5,600 per minute. By leveraging metadata, data engineers can investigate the source of downtime and fix the problem at the source. Observability of Snowflake enables proactive downtime management, which is especially important for organizations with high volumes of operational data.