AI data management use cases

AI data management use cases

Written by Matthew K.

Organizations today work with a lot of data, which comes to the business from multiple different sources, in multiple formats. This data is handled by various users and ends up scattered across public and private clouds, on-premises storage systems and even employees’ personal endpoints.?

It can be hard to centrally track and manage all of this data, which raises two problems.

First, an organization cannot use a dataset if it does not know that the dataset exists.?

Second, this undiscovered and unmanaged “shadow data” poses security risks. According to IBM’s?Cost of a Data Breach Report, one-third of data breaches involve shadow data. These breaches cost USD 5.27 million on average—16% more than the overall average breach cost.?

AI and ML can automate many aspects of data discovery, granting organizations more visibility into, and control over, all their data assets.

Examples of AI in data discovery

AI-powered data discovery tools can automatically scan network devices and data storage repositories, indexing new data in nearly real time.?

Automated data classification tools can tag new data based on predefined rules or machine learning models. For example, the tool might classify any nine-digit number in the XXX-XX-XXXX format as a US social security number.?

LLMs and other natural language processing tools can extract structured data from unstructured data sources, such as pulling job candidates’ contact details and past experience from text-document resumes with varying formats.

Bad data can cause more problems than no data at all. If an organization’s data is incomplete or inaccurate, then the business initiatives and AI models built on that data will also be subpar.

AI and ML tools can help identify and correct errors in organizational data, meaning users don’t need to do the time-consuming work of manual data cleansing. AI can also work more quickly and catch more errors than a human user.

Examples of AI in data cleaning

AI-enabled data preparation tools can perform validation checks and flag or correct errors such as improper formatting and irregular values. Some AI-powered data preparation tools can also convert data to the appropriate format, such as turning unstructured meeting notes into structured tables.?

Synthetic data generators can provide missing values and fill other gaps in datasets. These generators can use machine learning models to identify patterns in existing data and generate highly accurate synthetic datapoints.?

Some?master data management?(MDM) tools can use AI and ML to detect and correct errors and duplicates in critical records. For example, merging two customer records with the same name, address and contact details.?

AI-powered?data observability?tools can automatically generate data lineage records so that organizations can track who uses data and how it changes over time.

Data silos prevent many organizations from realizing the full value of their data. AI and ML can streamline?data integration?efforts, replacing siloed repositories with unified?data fabrics.?Users across the organization can access the data assets they need when they need them.?

Examples of AI in data access

AI-enabled data integration tools can automatically detect relationships between different datasets, allowing the organization to connect or merge them.?

Metadata management tools with AI capabilities can help automate the creation of?data catalogs?by generating descriptions of data assets based on tagging and classification.?

Databases and data catalogs with LLM-powered interfaces can accept and process natural language commands, allowing users to find data assets and products without writing custom code or SQL queries. Some LLM-powered interfaces can also help users refine queries, enrich datasets or suggest related datapoints.?

AI-enabled query engines can use machine learning algorithms to improve database performance by analyzing workload patterns and optimizing query execution.?

There is a business case to be made for prioritizing?data security. The average?data breach?costs an organization USD 4.88 million between lost business, system downtime, reputational damage and response efforts, according to the?Cost of a Data Breach?report.?

AI and ML can help enforce security policies, detect breaches and block unauthorized activities.

Examples of AI in data security?

AI-driven?data loss prevention?tools can automatically detect?personally identifiable information (PII)?and other sensitive data, apply security controls and flag or block unauthorized use of that data. ?

Anomaly-based threat detection tools such as?user and entity behavior analytics (UEBA)?and?endpoint detection and response (EDR)?use AI and ML algorithms to monitor network activity. They detect suspicious deviations from the norm, such as a lot of data suddenly moving to a new location.

LLMs can help organizations generate and implement data governance policies. For example, in a?role-based access control?(RBAC) system, an LLM can help the security team outline the different kinds of roles and their permissions. The LLM might also help convert these role descriptions into rules for an?identity and access management system.

AI-enabled?fraud detection?tools can use AI and ML to analyze patterns and spot abnormal transactions.

Yvonne McGinnis

DevOps Engineering, ITMLP student. l'm looking to further my educational training prior to working, or while I work. I would like to spend 4 hours in school, and 4 hours at work daily.

1 个月

Great advice!

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Looking forward to learn more about how these technologies can transform data processes!?IBM Data, AI & Automation

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