How AI Is Improving Data Management

Data management is necessary to ensure data is useful throughout an organisation. When data management is done right, it mitigates any issues that result from bad data, like inadequate predictions, general inaccessibility, or data friction, preferably before these issues happen. 

With global data creation projected to grow to 180 zettabytes by 2025, organisations are generating and handling more data than ever before. 

Managing this information efficiently is critical for success, but data management tasks (like removing, cleaning, incorporating, and cataloguing data) require lots of work, which can regularly irritate employees and data analysts within a company. 

Artificial intelligence is a great aid that can help with data management, as it improves how organisations manage data through several important areas. 

What Is AI-Based Data Management?

AI data management involves using artificial intelligence to make using, collecting, and storing data easier. AI can help optimise data management cost-effectively and efficiently, helping organisations make important short-term decisions and choices that affect the future.

Adding AI into data management helps automate data gathering, lower financial costs, increase accuracy, and enables team members to concentrate on the more beneficial areas of data management. 

It’s important to note that AI alone is not a substitute for established data management processes, like creating data supply chains and taking measures to improve data quality. 

However, AI is a worthwhile investment which can greatly improve productivity and the worth organisations receive from their data. 

How AI Is Improving Data Management

AI’s abilities can empower businesses to improve accuracy, streamline business operations, and gain deeper insights into important data. 

Here are five key areas where AI Is improving data management. 

1. AI Data Analytics

AI data analytics involves using artificial intelligence to analyse vast amounts of data, reveal insights, and streamline trends. An advantage of AI in data management is that AI can analyse great amounts of data rapidly and efficiently, which would be impossible for humans to handle at the same rate. 

AI tools can help with data cleaning, collecting data from several sources, and organising data for analysis. 

For instance, in finance, AI can detect credit card fraud detection and improve customer service through personalised recommendations. In healthcare, AI can assess patient data to personalise treatment plans and predict disease outbreaks. 

AI models can help predict future trends based on data analytics findings, helping organisational leaders make better, data-enhanced decisions. 

2. Data Cataloguing

An AI data catalogue is an index that gives organisations a centralised data store, making it easier to search for, discover, and analyse data. An AI data catalogue uses machine learning to improve these processes by automating practices used to search, manage, and organise data. 

Artificial intelligence in data cataloguing helps suggest relevant datasets and anticipate user needs, creating a more data-driven, productive workplace. This enhanced searchability is particularly valuable for organisations which manage greater datasets, where data procurement needs to be accurate. 

AI data catalogues can also track data and implement access controls to check that data usage meets an organisation’s internal policies. This keeps sensitive information secure and can improve stakeholder trust in data quality. 

3. Improving Data Quality

Artificial Intelligence can revolutionise data management by improving data quality throughout a variety of domains. 

AI uses natural language processing (NLP) and machine learning algorithms to identify and fix errors, like typos, missing values, inconsistencies, and duplicates. By recognising data patterns, AI can detect anomalies, like outliers or irregularities, to secure data integrity. 

AI can standardise information from several sources, tackling discrepancies in units and structures. Using reinforcement learning, where AI uses feedback to learn through trial and error, AI constantly improves its data cleansing abilities, increasing its efficiency and accuracy as time passes. 

AI can also improve present data by using information from external sources, adding context to make data more relevant. These advanced techniques can deliver a higher standard of data quality and reduce the need for manual data intervention, allowing businesses to make more informed and confident decisions. 

4. Protecting Data Security

The Government’s 2024 Cyber Security Breaches Survey found that 70% of medium businesses and 74% of large businesses experienced a form of cyber security breach in the last year. With the threat of cyber attacks increasing, it’s become even more critical to protect sensitive information. 

AI can improve data security by monitoring large amounts of data in real-time. This helps identify patterns and anomalies which can indicate possible threats, like unusual data access patterns or suspicious login times. 

After a threat is detected, AI systems can automatically respond by isolating affected systems, addressing weaknesses, or blocking malicious IP addresses, which can minimise damage and prevent data breaches. 

AI can also improve data security with applications like data loss prevention (DLP), intrusion detection systems (IDS) and phishing detection.

AI-powered DLP solutions monitor data transfers to prevent sensitive information from being mishandled or leaked. Advanced IDS can detect sophisticated attacks which traditional systems might miss, while AI-driven phishing detection analyses emails and user behaviour to identify and block phishing attempts. 

Integrating AI into these areas can help organisations improve their data security measures and defend themselves against a range of cyber threats. 

5. Data Integration

Data integration is the process of combining data from various sources into a single format, which can be used for operational, decision-making, and analytical practices. AI can improve the data integration process by streamlining key stages. 

In the data extraction phases, AI algorithms can assess the content of diverse data sources and extract relevant information automatically. Through machine learning and recognising patterns, AI can adapt to fresh information sources, streamlining the extraction process from complex formats, like images and text. 

Natural Language Processing (NLP) also allows AI to detect and extract information, making it easier to find meaningful insights from documents, financial reports, and even medical images, like X-rays. 

AI also improves data mapping by using machine learning to automatically match data elements from different sources. 

Through analysing patterns and historical information, AI can suggest intelligent mapping solutions, even when dealing with dissimilar data formats. Along with saving time and resources, this ability improves the accuracy of the data integration process. 

How We Can Help

This is only the start of AI’s applications in data management. Businesses can improve their success rate and gain a competitive advantage through leveraging AI today. 

As the volume of data continues to grow, embracing AI is crucial for organisations looking to make the most of their data assets. 

At Configur, our unified data platform includes AI-driven analytics tools that can help future-proof your organisation. 

With advanced machine learning, interactive dashboards, and natural language processing, we make it easy for you to streamline operations and enhance the decision-making process. 

Give us a call on 01633649263 to discuss your needs in more detail, or find out more about our Artificial Intelligence solutions on our services page.