The craze of artificial intelligence is obvious. Its advent has revolutionized typical business practices by introducing some automatic ways of enhancing efficiency and accuracy. Moreover, catching up insights into business performance, sales, operations, accounts, or anything is no more a challenging task. Simply put, AI applications or devices are like spinning machines that bring out intelligence as you put data into them. This is the reason its market size is likely to surpass nearly USD 2,575.15 billion value by 2032.
In essence, you can make all stages of data management seamless, be it related to handling, processing, securing, or analysing datasets. This post will help you how deep is AI into data management practices for data & business transformation.
Artificial Intelligence (AI) Transforming Data Management
AI is actually changing the way various companies manage data. Here is how:
1. Data Integration and ETL (Extract, Transform, Load)
Data integration is concerned with bringing all sources of data together to stream in crucial details at a place. It can be a server or any database. A decade ago, it used to done manually. Simply put, a team of data specialists is established to streamline this dataflow from multiple sources. Each experts used to do it with his or her own hands. With the inception of AI, things have completely changed. This integration process is completely transformed, which is done through ETL tools. ETL expands for extract, transform, and load. Talend, for example, can better illustrate it. The tool like this involves machine learning algorithms to measure datasets and thereby, ensure the whole processing of accurate and error-free data records in a short span.
2. Data Quality Management
Quality is the foremost concern for those who are into data management or other complementary practices. Despite being complicated, artificial intelligence (AI) tools, like informatica, have put these quality measurements on automation. It means that the software will automatically identify and fix errors, inconsistencies or imperfections in your database. This kind of tools uses AI for data profiling and cleansing of the data while preventing flaws. In simpler words, AI is indeed reliable because it has proven data-driven algorithms that adapt changes and get better over time. It ensures excellent quality data to manage for informed decisions.
3. Master Data Management (MDM)
Master data represent a master database where critical business details, such as customer profiles, products descriptions, and employees’ sensitive records, are stored. Just imagine how challenging and lengthy process it used to be before AI stormed into our lives. Reltio, for instance, is a fantastic master data management platform, which is evolved to make master data management a cakewalk. Like aforementioned examples, it also has machine intelligence that you can trust, rely, and use to view your master records. Additionally, this type of tools are able to refine relationships and hierarchies of various records while adapting to transforming business requirements.
4. Data Cataloging and Metadata Management
This is particularly related to cataloging contents, which is also a practice of managing voluminous datasets. Unlike traditional methods, Collibra like data intelligence platforms ensure that meta data would be discovered, streamlines, and documented effortlessly. Artificial intelligence directs its algorithms to automate tagging and improve search ability. This happens through ML models, which understand complex relationships within content with a laser-fast speed.
5. Data Storage and Retrieval
The size of big data is continuously increasing, which generates urgent requirement for scalable data storage. With this capacity, backups and retrievals can be easier in such big data environments. Apache Hadoop, for example, has AI algorithms working in the backend to consistently enhance its data accessibility. Moreover, it won’t be challenging for it to optimize data storage in accordance with usage.
6. Data Security and Governance
Security is a major concern for those who effectively manage records & documents. Unlike a human being, artificial intelligence barely takes a few seconds to detect anomalies and potential threats. Its data-driven models automate this process and turn its response mechanism lightning-fast. Let’s take an example of Darktrace platform that is capable of understanding natural behaviour of datasets and accordingly, determines breaching signs.
7. Data Analysis and Business Intelligence
Managing a massive amount of data is not easy, especially when it is done to simplify data-driven intelligence process. Tableau, let’s say, has removed all time-consuming barriers by automating this process. Also, it is capable of anticipate future trends, risks, barriers, or anything using its natural language processing.
8. Data Exploration and Visualization
With artificial intelligence (AI) tools, exploring and visualizing data insights is like walkover. Undoubtedly, it helps technical experts and analysts to access records in visuals and draw decision is a shot span. Microsoft’s Power BI tool is one of the AI tools that won’t need much human intervention to visualize and then, understand what data express. Once understood, its management gaps and upsides can be discovered easily.
9. Cognitive Search
Cognitive search integrates information from multiple sources to deliver a comprehensive result. With AI-powered cognitive search tools, such as Elasticsearch, retrieving any piece of information that resonates with the user intent and matches his content is easier. This is how cataloging or tagging during management of content are automatically done.
10. Data Privacy and Compliance
Organizations hire IT experts and engineers to ensure robust data security and privacy, which is indeed a pricey deal. But with the introduction of tools like OneTrust, these tasks are no more restrained to humans. This AI intervention streamlines compliance efforts and data subject requests as per data protection regulations. And this happens automatically while ensuring privacy.
Conclusion
Artificial intelligence is typically introducing transformation in the domain of data management. It’s actually automating all such tasks that are complementary to managing data. It can automate data cleansing, processing, and drawing insights into what they state. Simply put, AI-powered tools are helping organizations to leverage its outstanding capacities to convert data into actionable intelligence, deriving innovation, and hence, adding a cutting edge. It’s also notable that this machine learning powered intelligence is progressive in nature, which makes data management an effortless and seamless practice. Also, companies or organizations come in a position where harnessing and leveraging data intelligence requires minimal efforts.