Data management in Machine learning : challenges, techniques, and systems

With all the attention that machine learning models, such as ChatGPT, have garnered in the past few years, it has become increasingly important to grasp how to develop a functional, unbiased model. Datasets, structured collections of data, are a central cog in any machine learning model. This is because any model is only as good as the data used to train it.

This article, then, will go through the central importance of data in machine learning models, explaining the challenges that are associated with every step of data management, from data acquisition to data protection. 

Data acquisition and preparation

Challenges of acquiring and preparing data for machine learning

One of the most challenging, yet important, steps in handling data for machine learning is cleaning data.

Data cleaning refers to the removal of data that is incorrect or irrelevant, which includes duplicates and redundancies in your dataset. This step is done before any data analysis takes place, to have quality data and decision-making power. 

Data quality is determined by five factors:

• Validity: the easiest factor to control, validity is checking whether the information collected is correct. 
• Accuracy: this factor determines whether the information collected is accurate and feasible.
• Completeness: this factor seeks to avoid missing values. Incomplete information is hard to use, and impossible to fix, meaning that data can be dropped from the dataset. 
• Consistency: this checks whether the information collected from a person is consistent with information from other sources. 
• Uniformity: uniformity determines whether the same units of measure are used across the dataset.

Missing information remains one of the biggest problems when collecting data. As said above, it is impossible to perfectly fix missing data.

Data formatting, also known as data transformation, is the transformation of data into a format that makes data processing easier for computers or people.

Formatting includes normalisation (organising data to eliminate unstructured data and redundancies) and standardisation (converting data into a format that is easier for a computer to use).

Tools to improve data quality

Data cleaning can be achieved by improving data quality:

It is possible to improve data accuracy with assumptions. Since data has to be feasible, some data can be easily removed. For example, a person cannot measure 100m, nor can they weigh 1000 Kg.

Cross-checks can be performed across platforms to ensure consistency of an individual’s information.

You can remove incomplete data from your dataset to help clean your data. Removing data but this will result in the loss of information, however, which could impair your final results.

You can, on the other hand, fill in missing values. This risk is, however, that your data isn’t as trustworthy, since you are inputting data based on your assumptions.

Another option open to you is data augmentation, which consists in adding slightly altered versions of pre-existing data to your dataset. This will help increase diversity in your data. 

The alterations to your data have to be random but realistic for data augmentation to be effective. 

Data storage and organisation

With today’s world becoming so data-intensive, data storage and organisation has become a top priority for businesses.

Although a pressing need, storing data comes with its set of challenges.

Challenges of storing data

Storing data requires infrastructure, both digital and physical. This means either investing in physical storage space for servers (in an office, for example) or digital storage with cloud hosting. 

Data storage is also an expensive undertaking, as a data centre requires continual investment. Costs include setup, maintenance, and paying people involved in maintenance. 

An issue that remains central to data storage is security. You have the ability to encrypt your data, yet no data centre remains entirely secure. Bear in mind that the more developed your security system, the higher your costs will be.

There is also the question of having data that is accessible to all, not just data analysts. More accessible data, however, means time spent processing and analysing it.

Data lakes and data warehouses

It is important for companies to be able to store large amounts of data in a way that is both secure and accessible.

It is impossible, however, for a company to properly analyse and document all its data, as this would be much too time-consuming. 

Companies have thus developed two main ways of storing their data: data lakes and data warehouses.

Data lakes store data in an unstructured form, to be used for present or future use. The data within data lakes is analysed by the projects looking to use said data. Data lakes are thus mostly used by data analysts looking to work with raw data.

Data lakes have relatively low storage costs and are less time consuming than data warehouses, which makes them an appealing option for storing huge amounts of data. 

In order to navigate data lakes, companies develop data catalogs, which provide tools to analysts to find the data they are looking for. Data catalogs include metadata, data management, and search tools.

Data catalogs are what differentiates a data lake from a data swamp, a huge collection of data that is unmanageable, and thus has little value to users.

Data warehouses, on the other hand, store only structured data that has been cleaned and processed. 

Processing all that data is expensive and time-consuming, making data warehouses a more costly, yet more accessible, way of storing data.

Data warehouses thus offer data that is accessible to business-end users, who use the data for business and strategic purposes.  

Data governance and privacy

The centrality of data in today’s economy has given rise to questions regarding the ethics and practices of managing data.

The question of data governance within an institution has become fundamental. Data governance refers to the level of control a business has over its data. 

Managing data in compliance with regulations and ethical principles

Data governance focuses on the authority and control over given data, and the transparency with which data is handled.

Indeed, progress in data governance has sought to develop traceability within an organisation when handling data. The aim is, among others, to reduce potential risks, implement compliance standards, and, ultimately, increase the value of data. 

Increasing the value of data starts with ensuring data privacy. Data privacy focuses on how data is used, shared, and collected by various organisations. 

There are some regulations in place regarding data privacy, and enterprise companies are made to abide by them. 

There is also some pressure from customers, who see privacy as a human right and expect a certain level of transparency and ethical practices from business. A clear violation of ethics will break trust and drive people away from businesses. 

Data security/protection is also vital when handling data. It refers to how secure private data is once it is being handled by a business. Customers expect that their data will remain protected from third parties. 

Data breaches due to hacking will reduce customers’ trust. 

The goal, then, is to have data that is both protected and usable by the company.

Protecting private data

There are multiple ways to protect data from third parties, whilst it is still accessible to business employees.

Data masking (also known as data obfuscation) consists in altering private data so that it is unusable by third-party users and usable by business personnel.

Data anonymization removes any information that is identifiable in private data. Users can thus remain totally anonymous.

Whereas both processes anonymise sensitive information, data masking is still usable by authorised personnel. 

Data access control adds a level of security within a business by regulating employee access to files. Based on the principle of least privilege (POLP), those highest-ranking in the organisation will have the most access.

Such a process makes sure that fewer people have access to sensitive information, limiting the risks of data breaches.

Conclusion

Data comes with its issues from the first stages of acquisition and organisation to storage and protection. Data will only be usable if it has been cleaned before being processed and analysed. Clean data can be obtained by removing incomplete data or adding realistic data to the dataset. Data storage can be costly and time-consuming, so developing data lakes and data warehouses is recommended. Whereas the former stores unstructured data at a lower cost, the latter stores accessible and organised data at a higher price. Once data has been stored, security becomes a vital priority in respecting the rights and privacy of customers. 

Data management remains central when developing a machine learning solution to a problem. Data scientists usually spend more time on preparing their data than on machine learning. This is for good reason: a machine learning model is only as good as the data used to train it.

The essential remains to build a large, clean, diverse, and unbiased dataset when looking to develop a successful machine learning model.
If you have any questions regarding data management and machine learning models, don’t hesitate to contact us.