Impact of Data Quality in Machine Learning

“Impact of Data Quality in Machine Learning”

From movie streaming services to ChatBots, to helping inform how supermarkets arrange their shelves and guiding us through major transport hubs, ML influences our lives in ways that were unimaginable a decade ago. But what happens if the algorithm is set to work on the foundation of poor data quality? The risks in the future could be far more severe than being served a film you don’t like. In an era of these automated and Self-Service Business Analytics, Data Quality has assumed even more importance as average business users do not often have prior knowledge or skills to differentiate between bad and good data, but they are suddenly equipped with Advanced Analytics tools for extracting competitive and actionable intelligence from piles of these complex data.

What is data quality?

Data quality is an assessment or a perception of data’s fitness to fulfill its purpose. Simply put, data is said to be high quality if it satisfies the requirements of its intended purpose. The quality of data can be measured by six dimensions:

Completeness:

Data completeness is the expected comprehensiveness. Data is considered complete if it meets the expected expectations.

Consistency:

Data is said to be consistent if all the systems across the enterprise reflect the same information.

Accuracy:

Data accuracy is defined as the degree with which data correctly reflects the event in question or the ‘real world’ object.

Timelessness:

It references whether data is available when required.

Validity: 

Data is valid if it conforms to type, format and range of its definition.

Uniqueness:
Every data entry is one of its kind

Thus in today’s competitive world, we requires a well-designed and sustainable Data Strategy to combat the complexities of multi-source, multi-type, and very high volumes. It takes a lot of manual effort to clean and run that data and add some business intelligence on top of it. Also the consequences of “poor quality data” are wasted IT investments, loss of trust on enterprise data, and ineffective business decisions etc. Although the global IT community has partially mitigated the absence of qualified Data Scientists by designing AI or Machine Learning (ML) powered, semi- or fully-automated Analytics Systems, the fundamental problem of Data Quality still remains. End users cannot and will not trust insights that are acquired by processing corrupt, duplicate, inconsistent, missing, broken, or incomplete data.

ML algorithm as a self-teaching entity that learns from available data and those sets of rules and calculations that help solve defined problems — can either support the improvement of data quality or be thrown off by inaccurate data should the possibility of poor data not be considered in their construction.

How can we ensure Data Quality ?

Every organization values the importance of data and its contribution to its success. The case is even worse in this era of big data, cloud computing and AI. The relevance of data goes beyond its volume or how it is used. If a company has terrible data quality, actionable analytics in the world will make no difference. How Artificial Intelligence, Machine Learning and Master Data Management can work together is a hot topic right now in the MDM realm. MDM platforms are incorporating AI and Machine Learning capabilities to improve accuracy, consistency, manageability among others. AI has managed to improve the quality of data through the following ways.

1.Automatic data capture

According to research, AI helps in improving data quality by automating the process of data entry through implementing intelligent capture. This ensures all the necessary information is captured, and there are no gaps in the system. AI can grab data without the intervention of manual activities. If the most critical details are automatically captured, workers can forget about admin work and put more emphasis on the customer.

2.Identify duplicate records

Duplicate entries of data can lead to outdated records that result in bad data quality. AI can be used to eliminate duplicate records in an organisation’s database and keep precise golden keys in the database. It is difficult to identify and remove recurring entries in a big company’s repository without the implementation of sophisticated mechanisms. An organisation can combat this by having intelligent systems that can detect and remove duplicate keys. An example of AI implementation is in SalesForce CRM. It has an intelligent functionality that is powered on by default to ensure contacts, leads and business accounts are clean and free from duplicate entries.

3.Detect anomalies

A small human error can drastically affect the utility and the quality of data in a CRM. An AI-enabled system can remove defects in a system.Data quality can also be improved through the implementation of machine learning-based anomaly.

4.Third-party data inclusion

Third-party organisations and governmental units can significantly add value to the quality of a management system and MDM platforms by presenting better and more complete data, which contributes to precise decision making. AI makes the suggestions on what to fetch from a particular set of data and the building connections in the data.