How Artificial Intelligence is Disrupting Data Management

Data management and artificial intelligence have always had a symbiotic and reciprocal relationship. When integrated into the former’s tools, the latter can automate, simplify, and optimize processes that are related to data governance and quality, main enterprise, and metadata management analytics. On the opposite side, proper data management remains critical to adopting enterprise AI. Without a robust data management strategy and infrastructure in place, there’s a good chance of failure in your artificial intelligence development efforts. With that said, let’s look into how AI is disrupting data management.

Data fabric

The race in the implementation of applied artificial intelligence is on. Unfortunately, establishing the capabilities of enterprise AI typically requires high-performance and expansive data architecture. And for the vast majority of companies, creating an ecosystem similar to big tech organizations like Google or Facebook is a castle in the air, considering the complexity of legacy systems and budgetary limitations. This is when data fabric can help. The term essentially refers to a distributed platform for data management that ensures the connection of essential tools and services with all the data.

To put it another way, it serves the purpose of being a unifying layer that allows for the seamless access and process of data in what would otherwise be a siloed environment for their storage. Some benefits it offers are sizable storage for various data types, simple integration with centralized access to data that’s multi-sourced, and superior risk management tools. Additionally, the consolidation of all applications and data sources into a unified, single-distributed environment of data fabric can accelerate the adoption of artificial intelligence.

Data cleansing

Bad data can cost brands and companies money. Not only is the process of data cleansing labor-intensive and time-consuming, but poor-quality data will lead to poor business decisions. In fact, IBM found businesses to lose over a trillion dollars yearly because of bad-quality data alone. Because of this, organizations are increasingly leveraging AI and a subset of the technology, ML or machine learning, to accelerate and automate data cleansing. In doing so, they’re able to optimize the process much quicker and with minimal human error.

Remember that data annotation is necessary to improve precision, fast-track model training, create labeled datasets easily, and streamline the user experience for the AI model. As a result, artificial intelligence models can function more efficiently with these solutions and help businesses thrive and grow.

Actionable Insights

AI analyzes data and information at scale, providing actionable insights sourced from unstructured and structured data. It works quicker than people and requires no breaks, resulting in faster forecasts and results. And data management platforms integrated with artificial intelligence-powered tools can get the most out of their collected data than those that don’t.

Conclusion

With the business ecosystems’ rapid digitization, companies must deal with the changing and growing data on stakeholders, employees, suppliers, products, services, and consumers. The capacity to handle these volumes of data and transform them into actionable information is vital to success, and the right artificial intelligence models and data management platforms can make it happen. For this reason, more and more businesses are adopting these technologies which give them opportunities to get ahead of their competition.