Automation
Prediction
Optimization 
For a good reason, data has been called the new gold by various people worldwide. Every company that was once offering services relies on data to make improvements and offer new services to keep up. All this can only be possible when data management is done correctly, and that’s something AI can hugely help with.
Don’t get confused by the fact that AI is built upon data, but the one we are discussing today is built specifically for data management. The business possibilities of refined data are endless, with correct permutations and combinations. To make it possible, AI can store the data and process it to create and simulate user scenarios that can help your business.
With a good AI implementation, you can look forward to vital business intelligence, all while mitigating risks. Data based intelligence can propel you further at a much faster rate, and prove to be the most effective strategy towards growth.
Let’s see how AI impacts various data management and analytics requirements.
The Three Basic Goals of Using AI in Data Management and Analytics
AI is used for its three important capabilities: automation, prediction, and optimization.
The data collection in any technology company is huge, and the sets are large enough that any person would fail to comprehend. To manage so much data and refine it for use, AI can assist through automation, prediction, and process optimization. Let’s see how it can do each briefly.
Automation
Both management and analytics can benefit from process automation services implemented using AI. The same can be done for gathering, sorting, storing, etc., while saving major costs and time to do the same. Different processes can be automated to achieve maximum benefit from the data collected by a company.
Prediction
Predicting patterns is the end goal of most data collection, and developing a AI based predictive analytics software can help with it through continuous learning and adaptation. While humans would need to start from scratch every time, AI can store every bit of information generated and predict patterns from thereon.
Optimization
Everything we seek through advanced analytics could be achieved by combining 1000s of database queries as well. But again, no one has that much time to waste, and that’s where AI steps in. Not only does analytics reduce the time to sort data, but AI further optimizes the process to get things done in days instead of months.
Benefits of AI in Data Management
Cost Reduction
The greatest benefit of AI in data management is the cost reduction it offers while offering better management and efficiency. Companies spend a major budget on collecting, filtering, sorting, and managing their raw data, and AI makes it much more efficient and cost-effective. The initial investment may be a bit high, but customer satisfaction that can be improved is vital for any business in the long run.
Proper Data Format
One of the biggest problems in data management is mismatched data formats, leading to errors and discrepancies. AI can automatically convert given formats to suit the entire database in which most such data is. Images can be switched to different formats and compressed, and whatever’s needed can be achieved automatically. AI does this by creating copies and letting the originals remain.
Improved Security and Privacy
Data security and privacy are major concerns, given the data leaks that have occurred in the past few years. AI can help you improve both by continuous monitoring and re-iterations to ensure no data is corrupted or tampered. Security is vital to any business, and AI can help build a secure environment and eliminate the need to display raw data to the human staff.
Multiple Instances
With various sets of possible information from a given set of data, people usually focus on what they need and get it over with. AI can save multiple instances where you can derive what you want with a single click. While not AI systems can achieve the same, the work it would take for every dataset is tremendous. Whereas, with AI, you need to work hard once and enjoy the benefits for as long as you want.
Improved Visibility
System-wide searches, knowledge graphs, representations, and permutations are possible with AI in data management. If you are planning to sort all the data you have from different operations of your business, having AI implementation is a must. It can combine the datasets generated from different entities and combine them as one to derive information that you couldn’t have imagined with generic database queries. Leveraging data management as a service further enhances this process, offering scalable and efficient solutions for integrating and analyzing your data.
Applications of AI in Data Management and Analytics
AI-Based Database
Creating the database is the first step towards analytics; AI integration services can help do that better as well. While normal systems only rely on collection and storage, AI can start the process at the very beginning. A cleaner and more efficiently built database is always better than procrastinating everything toward the end.
Machine learning is something particularly used for this purpose, and it can help with modeling, predicting, scheduling, etc. With time, it can understand the most used queries and optimize the data stored to help process those faster. The achievements possible with AI in data management are endless when it comes to building a smart database, and that’s what makes it the number one use case.
AI-Based Data Catalogs
Prebuilt data catalogs are occupying the majority of the market right now, and likely so is predicted for the coming future. The top companies are the ones that collect the most data, which is then sold to others so they can analyze and use it. The only problem is the catalog is usually disoriented and unsorted, making it impossible for a DBA to make any sense of it.
AI steps in to make things possible, even with missing pieces of information. AI can encircle the whole data catalog to recognize patterns with singular entities, even without missing metadata. To sail through endless data, AI can be a huge help, making it an important part of data management even for wide catalogs.
AI For Data Security and Governance
Alterations, deviations, and leaks all need to be taken care of when dealing with data. Data governance covers everything that includes privacy, protection, security, lineage, etc. But you might be wondering what does AI have to do with any of it? Well, there’s more than what meets the eye.
AI and ML can be used first when it comes to weeding out poor-quality data, which has been the basic goal of governance in the first place. When it comes to privacy and security, AI can create logs for everything that happens in the database. Any intervention can be detected, especially if it alters the information that wasn’t supposed to be.
As we advance further in AI development, one can expect even more toward data protection. While DBAs had access to every bit before, you can now essentially limit their access to modify the data. The heart of any good data management and analytics methodology is securing the raw data and governing it through the process, and AI can help majorly with both.
Data Cleansing
Organizations are spending tremendous amounts of money each year to cleanse their raw data. AI makes it possible to derive patterns and ease the process while increasing the overall output. Raw data is nothing less than an extensive puzzle, which without the help of processing power, would take ages to sort. Artificial intelligence solves this problem both by brute force and smarter processing while accounting for all the data and subsets in the given raw data.
Automating the entire process is the end goal for most organizations, but given the complexities, it’s still far from reach. However, AI does increase the capabilities of multi folds and allows businesses to analyze all kinds of data. If you are planning to implement Ai in your data management, now could be the perfect time to get started.
AI and Knowledge Graphs
Just deriving information doesn’t really make it work for most organizations, and preparing knowledge graphs is essential. Considering most companies aren’t data companies, they need specific knowledge out of the raw data, and AI can help them with it. Not only would it reduce the amount of investment to hire data scientists, but significantly reduce the time to make real and business-ready decisions.
Most AI development services offer this as a primary service, as centralization of data is key to making the process simpler. A dashboard would be prepared for you in case you avail yourself of such services, and it would contain proper knowledge graphs for you to act upon. The choices of what you need vary according to your business specifications and can be set by you at all times.
Conclusion
If you deal with a lot of data, or even if you are simply looking for a way to improve business in the long run, AI in data has a lot to offer. The best part about the implementation is you don’t need to spend a fortune to get on track. You will need to spend money on algorithms based on the type of data you have and the quantity you want to sort.
For small businesses, the initial investment is doable for a lot of reasons, and targeting more customers with more personalization can be key to long-term growth. AI in data management enables just that and much more. If you are planning the same for your business, book a free consultation with us to see the possibilities firsthand.
