An introduction to AI DataSense™ technology

Introduction to AI DataSense™ technology
November 11, 2020 | Data Catalog

An introduction to AI DataSense™ technology

Introduction to DataSense Technology

Artificial Intelligence is changing how data management and analysis is handled in the age of big data. Organizations are generating more data than ever before and the process of accessing data using traditional methods is not only time consuming but also poses a threat to the data sets. To handle this challenge, AI changes the way we view data in terms of structure, usability, and access. To understand data sense, we’ll take a brief detour and view how human senses work:

Sense and Perception

As human beings, we interact with the world using our five basic senses of sight, smell, touch, hearing, and taste. Each of these senses has an associated organ that sends a signal to the brain which in turn creates the perception that allows us as human beings to understand the world we are in.

We know that the goals of artificial intelligence are perception, reasoning, and learning. In data management, Artificial intelligence goals remain the same, and thus the birth of data sense technology. DataSense technology focuses on detecting data patterns, correlations, outcomes, and inference within the focus data. All the above parameters help to create perception which is the algorithms utilize to create structures, indexing, tagging, classifications among others. The third step is learning from how the users interact with the data whose perception has been generated through actions such as search, access, among others. DataSense technology has modernized metadata, catalog, and taxonomy management.

AI DataSense and Metadata

Traditional data analytics had a limitation in the types of files from which accurate metadata could be successfully extracted. However, AI Datasense can extract metadata from multiple file types in different formats, thus helping provide a better sense of what is in the data. This helps power a faster and smarter search performance.

How DataSense handles Metadata;

  • By using statistical learning
  • By creating neural networks after sifting through the data
  • Deep learning from patterns and comparisons made when sifting through data
  • Utilizing natural language processing just a human brain would when looking for patterns

AI DataSense and Catalog

DataSense helps create a smart catalog that is important in selecting the right datasets to be used in the creation of machine learning algorithms. It creates indexes that make filters that are far-reaching, adaptive, and effective. It also helps user’s search to be smarter thereby saving time and recommending the data they might want to take a look at based on their job responsibilities. Recommendations made for search are usually based on business terms, contacts, departments among others.

Smart data catalogs assist in:

  • Shows previous searches
  • Shows what other users searched for
  • Understanding how data is used
  • Give accurate meaning to datasets

AI DataSense and Taxonomy Management

AI DataSense helps create categories and sub-categories thereby providing a unified view of the data. By detecting common semantics and terminologies across multiple data sets in the same or different formats. It also creates a hierarchy for the metadata that helps in controlling access and makes search faster. AI DataSense helps taxonomy management comply with data laws such as GDPR. For instance, the customer’s details can be categorized as Category A and the order details can be categorized as Categorized B together with their payment method. In today’s world where data protection laws are in almost all countries, avoiding litigation is a top priority for most organizations. By creating security levels for the data, chances of accidental noncompliance are greatly reduced.

Benefits of AI DataSense Technology

  • Continuous data enrichment: By continuously learning how the data is being used, the algorithms make the right adjustments to increase the accessibility and accuracy of the data. It also creates metadata for files in different formats thereby increasing the metadata available to manage the data sets. With time the neural networks increase as the algorithm creates relationships within the data sets. With time, an AI driven DataSense will easily detect irregular movement of data and alert the admin of suspicious activity. By adding natural language to how the data is managed, non-technical people have an easy time successfully querying the data. This increases the ways in which the data can be successfully queried, organized, or analyzed.
  • Usable data structure view: AI driven DataSense technology will create a visualization of the data that makes it easy for the users to understand. This visual presentation can be based on data usage as well as data creation. In other words, it helps make available the available data from the perspective of a data user. This can help an interested user understand the organization they are and the role that the data they create plays. The right structure and filters increase the perspectives from which data can be analyzed or organized.
  • Ease in data classification: AI driven DataSense technology helps in classification data in a way that is logical to even the non-technical users. It is difficult and time consuming to use traditional data analytics to make sense of common data in different data sets. The result of this is that fewer patterns and relationships are identified and the final analysis will fail to be deep enough. As the algorithm sifts through the data, it not captures all patterns and correlations but also learns from it to deliver better results on search and filters.
  • Faster search: By optimizing the search process through smart metadata and catalogs, users have a higher chance of getting the best results on search. In the age of big data where a search can yield tens or hundreds of results, having an ability to get the right results saves time and increases efficiency within an organization.

Conclusion

AI DataSense technology has transformed how data is viewed and accessed by users. Metadata, catalogs, and taxonomy management have been made more efficient enabling ease in how data is accessed and shared. With machine learning capabilities, the algorithms used continue to increase their understanding of the organization’s data. Just like a human brain, AI driven DataSense technology has introduced perception, reasoning, and learning into data analytics.

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