Enhance Data Quality through Machine Learning

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Today, most industries are characterized by a relatively rapid rate of technology adoption and a fast-changing competitive environment. In this context, data become an essential instrument for decision-makers that want to take the prompt measures that the business requires.

In the era of Big Data, companies accumulate a huge amount of information which necessarily need to be processed before being used. Building up an appropriate Data Strategy could be a difficult challenge, but can also bring great growth opportunities for those who embrace it with the right attitude.

Most companies spend too much time in identifying, cleaning and transforming operations on their data, with the information that could be elaborated for over a week before being ready to be used. In this situation, the data that arrives on the management’s desk is already outdated, and can‘t be much helpful for timely measures needed to seize opportunities and respond to crises.

A crucial component in the data elaboration process is represented by Data Quality, which has to be guaranteed through the proper instruments. With data of high quality, the Business Intelligence system is able to produce more precise and effective analyses, improving the overall performance of the company’s Analytics.

According to a study conducted by Gartner[1], the lack of proper Data Quality systems is already damaging companies, with surveyed firms that claim to bear – on average – an additional cost of 15 million dollars per year. Moreover, 60% of companies do not measure the financial effects produced by a poor Data Quality management.

What are the benefits of an efficient Data Quality system?

Decision-making support

With data of high quality, the Business Intelligence system produces more reliable information, risks associated with errors and oversights are significantly reduced and executives avoid taking “gut” decisions.


An efficient Data Quality system facilitates Compliance activities and mitigate the risk of violations and infringements. This is particularly beneficial for companies in industries with a complex normative framework, such as financial services.


Better Data Quality leads to higher productivity. Analysts and ITs will spend less time in activities aimed at identifying and fixing errors in data.

Effective sales and marketing

More precise data allow improvements in customer targeting and clustering activities, enabling the optimization of marketing and price strategies.

How to improve the quality of your Data


Although the benefits of having data of higher quality are clear and established, companies are often cautious in investing in Data Quality systems, as those technologies have been not so effective and overly expensive in the past.

However, with the emergence of Artificial Intelligence, there are many instruments that can be implemented effectively with lower costs, allowing for an automation of Data Quality systems, and -generally speaking – offering more flexible and less onerous solutions.

Here some examples of activities that can be automated by the new generation of Data Quality systems, which employ Machine Learning models.

Automated Data Collection

Data Collection operations can be automatized in order to minimize or even remove human intervention. Through Data Entry tools is possible to collect the desired information and produce structured data, such as customers lists or unstructured data such as images and audio files. In this way is possible to avoid errors caused by distraction and to relocate employees to more valuable activities.

Duplicate identification

A common problem in many companies’ databases is the presence of redundant data, such as duplicates. This phenomenon produces two negative effects: first, it increases the space necessary to store data, inflating the costs of storage, and second, it damages the quality of the analytics, which may be dampened by erroneous information. A consequence of having duplicates may be the provision of flawed reports which employ incorrect metrics and counts that undermine the reliability of the entire Business Intelligence system. In this regard, it is desiderable to have a tool that automatically identifies duplicate records, in order to perform data cleaning operations and eventually improve Data Quality (here at Dataskills we made a couple of tools for these purposes).

Anomaly Detection

Anomaly Detection is a techniques used to identify outliers in the dataset, which are elements markedly different in value from the others of the dataset. Typically, Anomalies are rare events that may indicate an unusual behaviour (such as a fraudulent transaction). Here as well, it is better to have instruments to automatically identify anomalies to proceed with the necessary measures. Automatize those processes could lead to a great saving of time and resources and ultimately improve Data Quality.

Integration with external data

Besides using internal data, it is sometimes useful to retrieve information from external dataset which may be provided by other organizations. AI models can help by identifying kay parameters that need to be integrated with our current datasets, and automatically retrieve data from the appropriate sources, detecting relationship with existing sets of data. By doing this, we can further improve our Data Quality and make available additional information to decision-makers.


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[1] Gartner (2017). Data Quality Market Survey.

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