Why Data Quality is Vital for 21st Century Businesses

IBM says that businesses in the US lose USD 3.1 trillion a year owing to questionable data quality.

There’s an abundance of water on our planet, but only a small fraction is potable.

Similarly, large volumes of seemingly useful data are found across your organization’s machines and the Internet. Nonetheless, data have to be validated and maintained constantly to make business sense.
Machine Learning has changed how you work with data. This technology provides real productivity, cost, and business advantages.
This blog introduces you to 2 applications to show how this revolutionary technology enhances the quality of your data through minimal effort.

Machine Learning is a powerful technique to achieve reliable data quality

How do we become good at something? We obtain theoretical knowledge and then become skilled through experience. Machine learning is a similar concept. It uses an existing, well-structured training dataset to analyze your raw, unverified data and arrives at a similar classification.

Wayamo Open Dataset and Google’s TensorFlow are such datasets meant to teach your computers, through classification algorithms, as to what constitutes a quality dataset.

Algorithms—a technical term for software that perform a sequence of mathematical functions—classify your raw data on the lines of how a training dataset is organized.

Machine learning also uses unsupervised learning in which a training dataset is not required. This learning is based on neural networks. A neural network mimics the structure of the human brain and makes sense of information that’s fed to it.

Using Machine Learning to validate your data

Google’s TensorFlow offers a proven workflow to validate your large datasets using TensorFlow Data Validation (TFDV). At the heart of TFDV is its ability to compute and visualize a set of statistics that represents a training dataset. From these statistics, TFDV helps its users infer a schema of how your data should be organized.

This schema can then serve as a reference to validate your data. Based on this information, TFDV generates actionable details of your data records that do not match the training data. Developers can then fix these anomalies to ensure data validity.

Manually carrying out this task on large datasets could take days or even weeks. Machine Learning can achieve these results in a small fraction of that time.

Machine Learning and duplicates detection

When a single buyer has many associated records in a marketer’s database, marketers see this as a sign of a large buyer or customer. However, duplicate records falsely attribute several records to a customer. Duplicate records can also result in a customer receiving several communications of the same offer.

When there are exact duplicates, common database utilities identify and delete such duplicates. However, if there are only slight variations such as missing information, typos, or small differences in a name, machine learning is used to flag similar records using fuzzy logic to prevent erroneous deletions of valid records.

For instance, fuzzy logic can compare the letters of two names. Consider similar names like John Smith and John F Smith. Machine Learning uses fuzzy logic to compare the names, letter by letter. This information is analyzed statistically to see how strongly the two names correlate.

If similarity is inferred statistically, machine learning compares other attributes such as age, address, profession, etc. to confirm if these two records are duplicates or not.

This multi-step process can be carried out across millions of records within a few hours using the power of several desktop computers working in tandem. This drastically brings down the cost of executing this important procedure on heaps of data.

Your customers feel the quality of your data

Today, organizations are incurring huge capital expenditure on sophisticated software. However, without reliable data, such software will produce unreliable information that your team members could pass on to your customers.

This way, your customer feels poor data quality through poor service.
Using machine learning, you achieve two objectives of staying business in the 21st century—speed and accuracy through quality data.

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