Data is filtering into your business faster than ever before, with exponential growth expected over the next decade. Organizations looking to take advantage of their asset are finding it challenging to ascertain what data they handle and its incumbent quality.
To ensure they can move forward with data projects, enterprises must conduct a regular data quality analysis. Without an efficient data quality analysis, you can quickly become overrun by dirty data, leading to unnecessary cost and incorrect decision making.
What is dirty data?
Several attributes contribute to bad data.
- Inaccurate data such as misspellings, missing information, or blank fields
- Non-compliant data that does not meet regulatory standards
- Data that is contaminated due to a lack of continuous monitoring
- Unsecured data that is susceptible to cybercrime
- Data that has never been updated, thus making it obsolete
- Dormant data that is taking up storage space
According to a study from Havard Business Review, only 3% of data meets the standards for essential data quality in organizations. With that being the case, a business that can perform a data quality analysis not only improves its data hygiene but also holds a competitive advantage.
Performing a data quality analysis
It is essehtial to be proactive in conducting a data quality analysis. The checks that you put in place should be able to validate and verify your data before it enters the core business systems. Typically, this means having ways of monitoring information internally, via the web and through cloud services.
An effective data quality analysis will require you to integrate these disparate sources, usually with data integration tools and cleansing software. The objective will be to detect the root cause of any data quality issues, putting the right controls in place.
The terms “Big Data,” “machibe learning,” and “AI,” generate a lot of hype, but they are vital in data quality analysis to automate your checks. Businesses should not underestimate the power of such solutions which will quickly help them to scale up.
Data quality analysis steps
A data quality analysis will typically follow a six step process.
The first, and potentially most crucial step, is defining the business goals for improving the quality of your data. For example, the objective could be to ensure that you have a database of unique customer records. For every goal, there should be a set of rules in place that show how the quality is measurable.
The data goes through assessment against existing data to see the current state of affairs, before analyzing the results and visualizing the gap between where you are and the business goals. When you fully understand the analysis and the actions required, the organization can takes steps to improve the data quality. Any plans will look at the timeframe, resources and costs involved.
The final two stages of a data quality analysis are implementation and control. The solutions that you discover during the improvement phase can be planned via existing change management frameworks. Following that, it must be periodically measured to guararntee the data quality measures are keeping under control.
Businesses need to make data quality a priority if they are to make best use of their assets. A data quality analysis helps get to the root of any issues, analyses, and improves them. There are several vendors offering data quality automation tools to enrich the practice, working with both new and legacy systems. It has become imperative that anyone using data looks after their data hygiene.