AI-Driven Data Management for Enterprises Explained
Each day, data plays an increasingly significant role in enterprise operations. Teams require data to understand their customers better, improve workflows, and increase employee productivity. The ability of an enterprise to collect, manage, and analyze data determines its competitiveness, growth, and profits — data-driven enterprises are 58% more likely to achieve their revenue goals than their non-data-driven competitors.
For these reasons, business leaders started to invest in data management software and data services, along with expanding their corporate data analytics departments. However, the desire to collect as much data as possible and a dramatic increase in the volume and variety of business data had a flipside as data management is becoming more complex and labor-consuming than ever.
At Itransition, we believe that adopting emerging technologies such as AI may be the most effective and future-proof way to streamline and increase the ROI of enterprise data management. In this article, we'll discuss how enterprises can incorporate artificial intelligence into their data management workflows and explain what benefits it can bring.
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Augmented data management
In short, augmented data management combines traditional data management and artificial intelligence. Empowered with AI, the software can detect anomalies in large datasets, find sources of issues and conflicts, and suggest particular actions. Coupled with machine learning (a branch of AI), data management systems can also continuously learn to become even more intelligent and efficient.
Using such AI-based software, teams can significantly improve the quality of their data, gain more accurate insights, discover valuable business opportunities, and make more intelligent business decisions.
Simultaneously, AI enables teams to automate their data management workflows. Sophisticated AI models can quickly perform complex tasks, relieving analysts from repetitive activities. For instance, AI-powered data management tools can automatically collect, organize, catalog, and combine both business and technical metadata and then conduct automatic data quality checks. This way, enterprises can help their employees focus on more strategic activities and reduce risks related to human factors.
With the help of AI, enterprise managers can automate security and performance audits, updates, backups, and many other routine tasks that administrators are typically responsible for; in other words, AI-powered databases can administrate themselves without any external help.
Thus, enterprises can optimize the work of their technical departments as automation enables managers to allocate IT, specialists to more strategically important activities. With AI, teams can also mitigate risks related to cyber security and data loss — intelligent software can automatically monitor corporate databases, enabling the rapid detection and elimination of any threats and issues.
By adopting augmented analytics, enterprises can utilize technologies like AI and ML to transform the creation, use, and sharing of analytics. In particular, AI enables team members to implement innovative approaches to analytics that were not possible with legacy data management software.
For instance, teams can adopt automated analytics to analyze data in the background continuously. This way, corporate digital systems can quickly and accurately detect certain anomalies or changes across large data sets. At the same time, teams can implement augmented data preparation to accelerate the process of gathering and preparing data and streamline the ETL (extract, transfer, load) processes
Coupled with AI, data management solutions can provide teams with capabilities related to natural language generation (NLG) and natural language processing (NLP). With the help of these technologies, team members can interact with the data in more accessible ways.
For example, users can communicate with a data management solution, ask questions in natural language, and receive data insights without any IT assistance; in particular, a user can request such a digital system to provide information related to a specific customer segment or data over a particular period of time. As a result, enterprise managers can significantly increase the productivity of their non-technical employees (such as sales, marketing, and customer support) since they can easily access relevant data.
These were just a few examples of how AI can be applied to enterprise data management. Depending on their unique business cases and requirements, enterprises can get more options to speed up decision-making, boost data quality, and increase the ROI of their data management systems.
Efficient data management is essential for any enterprise looking to succeed and grow. However, while data provides multiple business opportunities, it also poses specific challenges. Today, enterprises process so much data (and its amount is constantly growing) that employees have to spend more and more time on data management; this distracts them from more essential and strategic activities and increases the chance of data interpretation errors.
Fortunately, enterprises can adopt AI to solve these and other data management challenges. In particular, teams can implement augmented data management, autonomous databases, and augmented analytics to improve data quality, automate routine tasks, and continuously gain valuable business insights.
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