January 17, 2020

How Machine Learning can enhance your Data Quality

Alan Tang
machine-learning

How machine learning can enhance your data quality

Data provides an opportunity for organisations to optimise processes and drive growth. But getting the most out of the huge amount of data that’s being collected today can be hard. That is not to mention the additional regulatory requirements that financial institutions must adhere to. 

Modern technologies are here to help. Specifically, machine learning can be used to bridge the gap between the growth of data and the quality of data analysis. 

 

Learning the basics 

Machine learning is a term that’s often misunderstood. But it’s easier to understand than some would have you believe, and it’s certainly not about robots or magic from Silicon Valley. 

Simply put, machine learning uses data to train algorithms based on features of an existing dataset. When compared to human-led methods, it increases the volume and complexity of data that a system can consider, creating a more sophisticated and powerful model. As a result, this means greater accuracy can be achieved when predicting future outcomes or classifying data. 

Machine learning enables an organization to increase the quality of their data consumption. And when assessing high-quality data, organisations gain richer insights that drive positive outcomes.

 

The problem of progress 

As the old adage goes, prevention is better than cure, and data quality is not just about fixing issues as they appear. Modern technologies can address data problems at source. 

It’s fair to say that existing methods of data quality (DQ) management are outdated. This is because the volume of data has grown too large for many organisations to handle effectively. As volume has increased, DQ capabilities have not modernised at the same pace, as the majority of these systems still focus on managing issues locally. This blinkered view is compounding DQ issues. 

Only by investing in new technologies can an organisation tackle future DQ challenges. This is exactly what forward-thinking organisations are doing, gaining an advantage over their peers by adopting modern technologies. Those who refuse to engage are suffering a technical debt as they fall behind. 

Sadly, DQ initiatives are rarely prioritised by organisations and they’re often incorrectly classified as having a low business impact. This results in laborious, ineffective analysis, burdensome monitoring and non-value adding remediation which can harm a business.

 

Data growth winning the race

Fundamentally, the pace of process change has lagged behind data growth. If an organisation applies outdated DQ management techniques to datasets that are exploding in size and value then they will fall further behind every day. 

Adoption of new technologies is the only way to modernise your DQ capabilities, but new technologies can be hard to integrate and it can be difficult to make a business case. At Mudano, we help clients apply data technologies, such as machine learning, to industrialise data quality. 

Here are 3 ways machine learning can improve data quality in your organisation: 

 

 

  • We’re not all the same, and neither is our data

 

Machine learning can be used to identify anomalies in data as algorithms can be trained to differentiate between legitimate differences and anomalies, which highlights DQ issues. This means we can move away from rules which only check superficial matters like completeness or conformity, or are so specific that raise thousands of false positives.

 

 

  • Life isn’t black and white

 

Data is considered to be correct or incorrect which, while technically accurate, isn’t useful for organisations, as it causes them to omit huge areas of correct data, in fear it may be wrong, or process incorrect data. By using models to predict the likelihood data is correct, we can make sensible decisions about its usage, depending on the criticality of the business process. Machine learning allows us to gain deeper insights from swathes of data that would previously have been omitted as useless or uncategorizable. 

 

 

  • Focus on what matters

 

By linking models that focus on data quality, an organisation can analyse cost-benefit models and better understand where they are having a positive impact. Better still, data quality probability models can encourage an organisation’s customers to self-remediate, but only where issues are likely, so you don’t damage customer experience.

 

Rising to the challenge 

Right now, we don’t think that most organisations get the basics right. Our approach is designed to overcome organisational challenges, based upon our belief that automation and advanced decision-making is the answer to processing large volumes of data. 

Machine learning can be adapted by any organisation to a chosen area, prove its value and scale up its application. Even those organisations that are busy fixing current DQ issues can quickly develop a lab that would release resources through focused machine learning prototypes and repurpose their effort towards increased experimentation.

The good news is that the correct integration of machine learning can reap huge benefits in a short space of time, which means that your organisation can begin enjoying the benefits sooner than you might think. And given the current pace of change in data production, collection and analytics, a failure to integrate modern technologies, such as machine learning, are not just slowing organisations down — it’s leaving them lagging behind. 

 

If you’d like to discuss this article or any element of machine learning, we’d love to hear from you. Drop us a line to talk about your organisation’s approach. 

 

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