The importance and role of AI in data management

Data governance refers to the people, practices, and programs involved in adequately managing that asset within an organization. Although a significant part of data governance is focused on data privacy and compliance, profitable business is supported by effective data governance responsible for storage, data architecture, data quality, data integration. Good data management technology ensures that the information available is consistent, trustworthy, and optimal for AI applications.
Big Data ExchangeArtificial intelligence tools continue to evolve, providing ever-greater opportunities for complex calculations and the use of growing amounts of data – the jatapp.com breakthrough is a prime example of this. The role of data management in enabling the future of artificial intelligence is no longer science fiction but part of everyday life with constant recourse to GPS, smart texts, assistants, and more. Discussing the importance of AI in data management, The Data Administration Newsletter found that most enterprises view the impact of AI as the most significant of all technologies, as its inherent nature allows for the systematic consumption and creation of valuable data. Let’s look at four important opportunities that AI provides in data management.

Quick Sort Data

Most business giants accumulate a huge amount of dark information, leaving the importance of data management behind. However, AI combined with analytics can use machine learning to get information faster and easier. Together, these systems can engage various algorithms to sort documents, emails, images, videos, audio files, etc. You will store all data on individual servers. All that remains to be done is to enable the expert to analyze the recommendations for data classification in an automated process and, if necessary, customize it and implement it into business strategies. A significant part of this process is also related to the problem of data storage. Analytics and AI are helping to develop a set of recommendations for removing data from files.

Identification of Stale Data

Analytics, AI, and machine learning can identify data rarely or never used objectively. However, these technologies are not as exact as the company’s employees are. For example, these processes allow you to identify which records or data have not been available in the last five years. Thus, the system will enable you to delete data that may technically be out of date. How does automated data management help the company? First, it saves employees time and keeps them busy searching for potentially outdated data. Second, they can rely on an automated process, fulfilling their primary missions. But the final decision should come from employees: whether to store the revealed data or not.

Effective Data Grouping

Computer system analysts are often responsible for determining what data they should collect for queries. However, they tend to create a repository for this type of application during this process. Then they put various types of data from different sources into the repository, thereby creating a so-called pool of analytical systems. But before they can complete this step, they need to develop integration strategies to gain access to the various sources they draw data from. Although it is worth noting that this procedure is still largely manual now, AI in data management can improve the efficiency of the process by automatically developing “mappings” between the application data repository and data sources. This leads to a significant reduction in integration and aggregation time.

Organizing Data Storage

Over the past five years, many storage service providers have made significant progress in automating storage management. All of this has been made possible by the advancement and widespread use of AI. Thanks to this, IT departments no longer have to think twice about using some smart data storage mechanism. This technology is very effective because it uses machine learning to understand frequently accessed data. The automation process is handy here. It can be used to automatically store data in a slow or fast fashion, depending on the business requirements set by the AI algorithms. This level of automation is very beneficial for employees, as it helps them speed up their workflow and move away from manually optimizing storage.

Business Intelligence Tools Powered by AI

We have described the four main opportunities provided by AI in data management. Now using AI for data analysis may seem more promising than before. Therefore, we have compiled three of the most successful AI-based programs for business intelligence:

 

  • The platform can use AI to create pivot tables to improve performance based on end-user behavior. This is how AtScale ensures the speed of mental queries on billions of rows of data. AI provides real-time access to data, giving businesses the most up-to-date information to help predict and make decisions.
  • This self-service data visualization and exploration application allow you to conduct deep data analysis quickly, oversee manage data, instantly identify relationships and visualize the analysis results in an understandable form. Smart visualization tools, combined with Qlik’s patented indexing engine, push the boundaries of analysis beyond the capabilities of traditional hierarchical query-based data models.
  • The AI-based tool provides personalized information for the business. Developers can use the low-code platform to build interactive data applications. Non-technical people can use ThoughtSpot to answer queries on their own.

With these tools, you can more accurately control artificial intelligence data management. However, you can only appreciate the fullness of the power of these tools after using them!

Conclusion

Artificial Intelligence analyzes huge amounts of data to provide you with the most helpful information to improve your business. The tool does not need interruptions. It works faster than humans, providing more accurate results and predictions. If you want to get the most out of your business, it’s time to start thinking about implementing AI.