Machine learning makes improving Data Quality easier. Data Quality refers to the accuracy of the data: High-quality data is more accurate, while low-quality data is less accurate. Accurate data/information supports good decision-making. Inaccurate data/information results in bad decision-making. 

So, intelligent decision-making can be supported by supplying accurate information through the use of machine learning. 

Machine learning (ML) is a subdivision of artificial intelligence (AI). However, during the late 1970s to early ’80s, AI researchers lost much of their research funding – by way of exaggerated and broken promises. The small machine learning community that had developed had the option of going out of business, or adapting machine learning to accomplish small, specific tasks for the business world. They chose the second option. 

While the term “artificial intelligence” is often used in promoting machine learning, machine learning can also be treated as a separate industry.

A variety of individual, successful machine learning algorithms have been used to perform several different tasks. These tasks can be broken down into three basic functions: descriptive, predictive, and prescriptive. A descriptive machine learning algorithm is used to explain what happened. A predictive ML algorithm uses data to forecast what will happen. A prescriptive ML algorithm will use data to suggest what actions should be taken.

Automation vs. Machine Learning

The automation used for modern computer systems can be described as a form of software that follows pre-programmed rules. It means that machines are replicating the behavior of humans to accomplish a task. For instance, invoices can be sent out using an automated process, producing them in minutes and eliminating human error. 

Automation is the use of technology to perform tasks historically performed by humans. 

Aside from being a component of artificial intelligence, machine learning can also be considered an evolutionary step in automation. At a very basic level, machine learning can be treated as a form of automation that can learn from its mistakes and adjust its responses to new situations. 

The ML software is exposed to sets of data and draws certain conclusions from that data. It then applies those conclusions to similar situations. 

How Machine Learning Works

Machine learning uses algorithms. At its most basic level, an algorithm is a series of step-by-step instructions, similar to a baking recipe. The recipe is called a “procedure,” and the ingredients are called “inputs.” Machine learning algorithms have instructions that allow for alternative responses, while using previous experiences to select the most probable appropriate response. 

A large number of machine learning algorithms are available for a variety of circumstances.  

Machine learning starts training with data – text, photos, or numbers – such as business records, pictures of baked goods, data from manufacturing sensors, or repair records. Data is collected and prepared for use as training data. And the more training data, the better the resulting program.

After selecting and collecting the training data, programmers select an appropriate ML model, provide the data, and then allow the machine learning model to train itself to find patterns in the data and make predictions. As time passes, a human programmer can tweak the model, changing its parameters to help achieve more accurate results. 

Some data is deliberately withheld from the training process and is used later in testing and evaluating the accuracy of the ML training program. This training and testing process produces a machine learning model that can be used for specific tasks requiring flexible responses. 

While machine learning can be remarkably useful, it is not perfect, and when it makes a mistake, it can be quite surprising. 

Applying Machine Learning to Data Quality

Machine learning algorithms can detect anomalies and suggest ways to improve error detection. Generally speaking, this is ideal for improving Data Quality. Listed below are some examples of the tasks machine learning algorithms perform to improve Data Quality:

• Reconciliation: The process of comparing data from trusted sources to ensure the completeness and accuracy of migrating data. By examining user actions and historical data about how reconciliation issues were resolved previously, machine learning algorithms can use these examples for learning and, by using fuzzy logic, make the reconciliation process more efficient.

• Missing data: ML regression models are used primarily in predictive analytics to predict trends and forecast outcomes, but can also be used to improve Data Quality by estimating the missing data within an organization’s system. ML models can identify missing records and assess missing data. These models constantly improve their accuracy as they work with more data. 

• Data Quality rules: Machine learning can translate unstructured data into a usable format. Machine learning can examine incoming data and automatically generate rules that can proactively communicate quality concerns about that data in real time. Manual or automated rules work for known issues, however, the unknowns in data are rising with the increasing complexity of data. With more data, the ML algorithms can predict and detect the unknowns more accurately.

• Filling in data gaps: Machine learning algorithms can fill in the small amounts of missing data when there is a relationship between the data and other recorded features, or when there is historical information available. ML can correct missing data issues by predicting the values needed to replace those missing values. Feedback from humans can, over time, help the algorithms learn the probable corrections.

• In-house data cleansing: Manual data entry often includes incomplete addresses, incorrect spellings, etc. Machine learning algorithms can correct many common errors (which spellcheck would not correct, because this involves names and addresses) and help in standardizing the data. ML algorithms can learn to continuously use reference data to improve the data’s accuracy. (If there is no reference data, it’s possible to use recorded links to the data for backtracking purposes.)

• Improving regulatory reporting: During regulatory reporting, incorrect records may accidentally be turned over to the regulators. Machine learning algorithms can identify and remove these records before they are sent. 

• Creating business rules: Machine learning algorithms – such as decision tree algorithms – can use an existing business rules engine and information taken from the data warehouse to create new business rules, or improve existing business rules.

The Risks of Poor-Quality Data

The use of poor-quality data can damage a business and result in unnecessary expenses. Decisions based on inaccurate data can result in severe consequences. Fortunately, machine learning algorithms can catch some of these issues before they cause damage. For example, financial institutions can use machine learning to identify forged transactions. 

Many businesses are already using machine learning as a part of their evolving Data Management strategy. The availability of off-the-shelf ML software has made access to machine learning much easier.

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