Five major trends in semantic AI and data management

1. Graph databases and knowledge graphs will become the dominant force in 2022

##Many people predict that graph databases will become the secret weapon in 2022. Carl Olofson, research vice president at IDC, expects graph database usage to grow 600% over the next 10 years, starting this year. In an article written by analyst Dave Vellante, a summary of how to take advantage of a typical relational database’s uses and limitations: “With a relational database, you can [find relationships, see how many levels there are in the chain], but this It requires a lot of programming. In fact, you can do almost any of the above using a relational database, but the problem is, you have to program it. Whenever you have to program it, it means you can't track it, you can't define it. As far as functionality You can't publish it, and it's really hard to maintain it over time."

In a graph database, users can overcome the common problems of relational databases Limitations because its design intent is to provide rich relationship analysis and context mapping. Since they are actually a visual network of various types of data, they can be used to trace connections in the data so that companies can get a holistic overview of all data, documents, etc.

Although knowledge graphs are popular to adapt to the data management trends of 2022, knowledge graphs are often a bit complicated to describe, which can sometimes make ordinary users unhappy. Data scientists are calling for more and more people to be taught what knowledge graphs are and how they work so that more companies can adopt them and benefit from them. What are knowledge graphs? What are its benefits? For beginners, they provide a very smart way to create rich connections between data points; define the concepts of data objects and their properties to easily Search them; merge siled data structures to make data accessible in one place; interpret unstructured text through natural language processing (NLP) to make it actionable.

Although the knowledge graph looks complicated, it actually talks about the data that makes up it. Through the knowledge graph, information can be stored in the way people naturally think and ask questions. For example: Lily is a person, she is very interested in Leonardo da Vinci, Leonardo da Vinci painted the Mona Lisa, Mona Lisa is in the Louvre in Paris, James lives there, James is Lily's friend. We come full circle and it's easy to understand it because we follow the direction of the data points and thus the relationships of the plot. The same is true for tracking company data such as a customer’s purchasing history, supply chain operations, HR staff structure, and more.

Knowledge graph helps enrich unstructured data, and data managers will continue to prioritize unstructured data as an asset , which is a good thing. In the past, companies ignored their unstructured data because it was too cumbersome to process and derive insights from, now people see it as an opportunity to analyze different aspects of the data.

Semantic AI helps us better interpret unstructured data because it combines machine learning and NLP technology with knowledge graphs, enabling algorithms to not only process words, but also understand underlying concepts and their context to better analyze the text. In other words, Semantic AI will tell the computer that a car purchasing market document is about the luxury car brand Jaguar, not about the jungle animal Jaguar.

Unstructured data is everywhere, so using a software that can extract relevant terms from hundreds of pages and derive useful information from them will meet the user's maximum Benefit. ​

Another data management trend in 2022 is to put content management at the forefront The cutting edge of data strategy. If people start caring about their unstructured data, then naturally they will also care about how a content management system (CMS) works.

Besides the typical problems posed by text-based content (such as language ambiguity mentioned above), a major disadvantage of using it is that if the content is not properly managed and tagged , it becomes very difficult to process the content. Searching for specific content is tedious, which is why automatic classification and document tagging are needed to improve the ability of typical CMS to accurately search.

Gartner positions intelligent document processing (IDP) as a necessary practice in the coming years due to its ability to capture, digest and reprocess complex documents into actionable data, while NLP and knowledge graphs will is widely used for this function. ​​

4. Data governance

Use semantics as data management One of the big advantages of the strategy is that it prioritizes metadata. Simply put, metadata is data that provides information about other data. For example: a novel can be described by genre, author, paperback vs. hardcover, publishing company, and copyright date, which are all examples of various forms of metadata.

Taxonomies, concept tags, and knowledge graphs facilitate the creation and maintenance of metadata, which is important for data governance. Data governance, a framework that defines how data is handled based on internal data standards and policies, is highly favored in the data management community.

In its predictions for this year’s trends, Dataversity claims that “data security, data auditing, and data quality are becoming increasingly complex. As a result, organizations are developing more comprehensive data governance strategies ."

#In addition to helping comply with regulations and business needs, data governance also helps assess the impact of changes in data sources. By establishing standardized data models, security and risk professionals can categorize data based on risk and security needs to stay ahead of potential issues. ​

#5. Semantic artificial intelligence in 2022 and beyond

Enterprises will increasingly rely on semantic artificial intelligence to satisfy their needs needs, especially around unstructured data and repairing data silos.

Graph databases and semantic AI are proving to be such high-performance methods for collecting, managing, and ingesting data that they will not only become a data management trend in 2022, but also in the future. became mainstream over the years.

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