How is AI Transforming Data Management?
Effective data management is the key to a healthy IT infrastructure but also to the success of your business. Thus, improving data management processes is a critical aim for any organization. Artificial intelligence is playing an increasingly significant role in this – since its emergence, it has been used to significantly transform data management. In this article, we focus on this transformation and the changes that AI is still bringing to data management.
Table of contents
The Role of AI in Data Management
Whenever you read a text about artificial intelligence, you’re likely to stumble across something along the lines of this phrase: AI can quickly process vast amounts of data, which a human cannot. And it’s true, the capabilities of AI regarding information are truly breathtaking.
From aiding the creation of information architecture to eliminating low-quality data and breaking down data silos, the role of AI in data management is significant.
In fact, in the 451 Voice of the Enterprise: Data Platforms and Analytics survey in 2019, 66% of respondents agreed or mostly agreed with the statement that AI and ML are important components of their data platform and analytics initiatives. AI serves as a tool for major improvements, giving organizations that use it a true edge over their competitors in the form of enhanced decision-making and boosted productivity – key goals for any business.
How Is AI Improving Data Management?
AI and ML (machine learning) improve data management in a number of critical areas:
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Building a Scalable Data Architecture
The first aspect in which AI transforms and enhances data management is building a data architecture. This step is extremely important for two main reasons: accessibility and the possibility of introducing AI for other data-related purposes.
With artificial intelligence, it is possible to label or categorize data automatically, storing it in the optimum location within your architecture. This, in turn, means that it’s easier to retrieve and use that data.
Integrating Data and Breaking Down Silos
One of the most burning issues in many organizations is data silos – information stored in several places, sometimes duplicated, at other times unique. Usually, this is a result of different teams within the same company using different platforms, and teams working within one silo have difficulty accessing data stored in another. This results in several problems:
- inconsistent messages to clients,
- more time required to complete tasks,
- increased costs of data management,
- technical debt,
- inefficient decision-making.
While it is possible to integrate data manually, this option is out of reach for many organizations due to the need for a skilled team and significant resources. AI provides an alternative.
An AI-driven data integration platform will outperform manual solutions as it is simpler, scalable, and often does not require much coding to set up or manage. This completely changes the landscape for smaller organizations, letting them catch up with the giants in terms of data management and improving their performance as a whole.
Cleaning out Low-Quality Data
Outdated, inaccurate, incomplete, or inconsistent data can lead to poor or incorrect decisions and can be hard to eliminate. AI makes it easier.
Previously, most data cleaning required manual work, and extensive knowledge of the subject matter, and had to be carried out regularly. In the past, maintaining high data quality could be extremely expensive.
AI, or in this case mostly machine learning, can address the problems of bad data by scanning your database and removing any duplicated, inaccurate, or anomalous entries. You still need a data scientist to build the AI model for the cleansing process, but they won’t need to spend hours identifying and addressing low-quality or bad data; leaving them more time for value-adding tasks.
What’s more, manual data cleansing becomes more difficult as the dataset grows. This is reversed with AI and ML data management systems – the more data there is, the better the system performs. The nature of machine learning means that with every anomaly detected, the system gains knowledge, enabling it to become more efficient.
Improving Data Security
Finally, one more crucial data management aspect that may be enhanced with the use of AI is data security. Artificial intelligence is capable of introducing numerous innovative measures to protect your datasets from threats.
- Encryption – AI makes decisions quickly and is constantly on alert. It is also capable of adjusting or impacting your whole database efficiently. Hence, AI is often used to set and change encryption methods live. The result is that it becomes increasingly difficult for outsiders to access your data – they would need to change their attack methods constantly to successfully penetrate your security.
- Identifying threats – With AI, traffic can be under constant analysis, ensuring that any suspicious activity is detected promptly.
- Automation – Data security is a constant priority — there is no downtime. To ensure data remains safe, regular checks are needed, and AI enables you to do that automatically.
AI and Data Management: Key Takeaways
How is AI transforming data management? It has improved numerous aspects of it: from building a scalable infrastructure, through optimizing it, to ensuring its security. What is most important is that, in many cases, AI gives access to improvements that were previously possible but cost too much in resources for most companies to consider introducing them. This leads to the main conclusion: AI levels the playing field for smaller businesses while improving the overall quality of data management.
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