What is data quality and what are its uses?

Based on factors like accuracy, completeness, consistency, reliability, and up-to-datedness, data quality is a measure of its condition. Organizations can find data errors that need to be fixed and determine whether the data in their IT systems is suitable for its intended use by measuring the quality of the data.

As data processing has become increasingly intertwined with business operations and organizations increasingly employ data analytics to guide business decisions, enterprise systems have placed a greater emphasis on data quality. Data governance programs, which aim to ensure that data is formatted and used consistently throughout an organization, are frequently closely linked to efforts to improve data quality. Data quality management is a key component of the overall process of managing data.

How does FutureAnalytica’s data engineering improve and verify the data quality?

Your data might not always be sufficient to create the appropriate Artificial Intelligence models. By utilizing data from a variety of sources, users of FutureAnalytica data enrichment applications can enrich data for the construction of high-end AI models and uncover deep data insights. Data management is a time-consuming IT task. End users can manage data from various sources and integrate it into the platform with the help of FutureAnalytica data management apps, allowing for seamless collaboration and the creation of new AI models.

Why data quality is important: Companies can lose a lot of money if they use bad data. Incorrect analytics, erroneous business strategies, and operational hiccups are frequently attributed to bad data. Problems with data quality can have a negative impact on the economy in a number of ways, including increased costs when products are shipped to the wrong customer addresses, missed sales opportunities as a result of inaccurate or incomplete customer records, and penalties for reporting financial or regulatory compliance errors.

There Are Three Kinds of Quality

1. Basic fact-checking Because the data we collect is derived from the world around us, some of its properties can be verified by comparing them to previously established records, such as:

· Is this an actual address?

· Is this a working website?

· Do we offer this product for sale?

· Are the values in the price column positive or negative?

· Is it not null for a field that is required?

· The min and max are known because the values come from a specific range.

In most cases, obtaining the values needed for validation necessitates querying a different data set that consistently provides the answer. This set of data may be contained within the company itself, such as employee records in HR systems. As well as sources outside of the company, like OpenStreetMap, a global database of street, city, and country registrations.

The test itself is merely a compare/contain query after the validating values have been obtained, and its accuracy is limited to that of the external data set.

The data itself, not its metadata, are validated by this test. To avoid issues with accuracy, it is best to carry out such validations as close to the data collection as possible. For instance, if the data is gathered by filling out a form, the digital form may only provide valid choices. Validation of the values at the ingest stage is advised because this is not always possible.

2. Fact-checking is testing a value within a single record with set-level sanity. We must test the attributes of the set we have when dealing with big data. A model, an ETL process’s output, or data from a specific operational system can all be included in the set. We would like to confirm a set of characteristics that it possesses, regardless of its origin. These are statistical characteristics like:

A certain distribution is expected to produce the data.

It is highly likely that the average, variance, or median value will fall within a specified range.

You must still know what to expect for statistical tests, but your expectation now takes a different form:

Is there a sufficient likelihood that this data comes from this distribution?

With a 95% probability, this column’s average should fall within this range.

Take a look at a poker table that holds the hands dealt to the players (yes, gaming websites also have BI). The anticipated hand distribution can be pre-calculated in this instance.

3. Similar to statistical accuracy tests, we examine the characteristics of a set of records for history-based set level sanity. Only in this instance do we not have a reliable source from the real world. The data set’s history is available to us: the identical data set as it has changed over time.

We can test to see if the new data set of today is consistent with the baseline by using this historical data to establish a baseline for the characteristics of the data.

In addition to improving the test’s outcome, learning the baseline also improves the baseline values’ validity. We carry out the same statistical test as in type 2, but because the baseline to which we compare is only correct with a certain probability because it was statistically deduced from historical data, we run an additional risk to its accuracy.

Conclusion

Data quality analysts carry out data quality assessments. They evaluate and interpret each individual data quality metric, compile a score for the data’s overall quality, and offer businesses a percentage to represent the accuracy of their data. A scorecard with a low data quality indicates that the data are of poor quality, of low value, can be misleading, and may result in poor decision-making that could harm the organization. In contrast, data of lower quality frequently does not track all of the influencing variables or is highly error-prone.

FutureAnalytica.com offers a next-generation technology known as “no-code AI,” which enables individuals with no prior knowledge of data science or coding to create cutting-edge Artificial Intelligence/Machine Learning solutions. Send us an email at info@futureanalytica.com for any queries or scheduling a demo.