Data is one of the most valuable assets for modern companies. Companies collect immense volumes of data via websites, applications, CRM systems, e-commerce platforms, communications with customers, paperwork, and other activities.
But the collection of data is not all that is needed. Besides, the companies have to structure, clean, integrate, protect, govern, analyze, and manage the collected data at all stages of its life cycle.
This is where Generative AI (GenAI) can help immensely.
Companies do not need to apply AI to just one thing; they can bring Generative AI into the data management life cycle and save effort, enhance data quality, and increase the value of business data
What Is the Data Management Life Cycle?
The data management life cycle refers to the process of creation, collection, storing, usage, governance, and archiving/deletion of business data.
An ordinary data life cycle consists of the following stages:
Data Collection → Processing → Storing → Quality → Integration → Governance → Analysis → Archiving
Generative AI may be helpful at each of these stages.
1. Improved Data Discovery, Categorization and Classification
Data collection involves multiple data sources including databases, documents, emails, applications, and APIs.
Generative AI can be used to discover, categorize and classify both structured and unstructured data.
Examples include the ability of AI to detect customer information, financial information, product information, or other sensitive information and make recommendations on how to appropriately classify the discovered data.
This saves businesses the time that would have been taken by the data team in the discovery and classification process.
2. Data Cleaning
Inaccurate data leads to inaccurate reporting and decision-making within businesses.
Generative AI can be used to identify the following issues with the data:
- Duplicate records
- Missing data
- Inconsistent data
- Incorrect data format
- Outdated data
- Anomalies in data patterns
Generative AI will be able to suggest corrections to the data or flag data which requires human intervention to solve problems in data quality.
3. Automation of Metadata and Documentation
The importance of knowing what data is cannot be understated.
Generative AI can be used to auto-document data sets, tables, columns, APIs and data pipelines.
This allows businesses to generate:
- Data dictionaries
- Business glossaries
- Descriptive information for datasets
- Descriptive information for data pipelines
4. Improved Data Integration
In today’s companies, there are many different systems such as CRM, ERP, accounting, inventory, and e-commerce systems.
AI can help developers and data engineers to learn different schema structures, field mappings, transformations, and document integration procedures.
It allows saving time from manual analysis of each data source and helps to automate repetitive tasks.
5. Better Data Governance
Data governance is an important process which aims at protecting the information and using it in a proper manner.
Generative AI can help with the following data governance activities: data classification, policy interpretation, access recommendations, documentation, and compliance monitoring.
For instance, it could help an employee to know whether some dataset can be accessed according to the company’s policies.
6. Analysis of natural-language data
Another application of Generative AI that proves to be very helpful is the analysis of business data.
Users no longer have to write complicated queries because they can just ask a question in natural language such as “What products brought the most income this quarter?”
AI will translate the request into the proper query and provide the answer in an easily accessible form if the source of data is recognized and authorized.
7. Intelligent data monitoring
Generative AI also helps organizations to detect abnormal behavior in their data environment.
For instance, if a particular system regularly receives thousands of data records per day and all of a sudden receives much less than usual, AI can help find the problem.
Future of Data Management Will Be Combination of AI and Human Knowledge
Generative AI must not be treated as a full-fledged replacement for legacy data management tools or specialists working with them.
However, companies may benefit from using a combination of:
Reliable Data Infrastructure + Generative AI + Automation + Human Knowledge
While the former will bring order and consistency, AI will bring intelligence and automation to the process of data processing along with natural language interfaces.
Final Thoughts
Generative AI will allow organizations to make the data management process more efficient at all stages – from gathering and categorization to data quality improvement, integration, governance, analytics, and monitoring.
However, the goal is not to use AI just because it is trendy now. Companies need to find out what issues they have and where AI may solve them effectively.
Thus, with the right use of AI, automation, custom software development, integrations, and reliable data infrastructure, organizations can increase their data’s value.
Malind Tech provides business-oriented solutions for exploring advanced AI and software development.
