Transforming Data Life Cycle Management with Generative AI

00:00

00:00

1x

• 0.25
• 0.5
• 0.75
• 1
• 1.25
• 1.5
• 1.75
• 2

Sirojiddin Dushaev Lead Data Engineer & Cloud Solutions Architect

Oleksandr Kolosov Technical Lead of Machine Learning

Every piece of data goes through various stages in its lifetime, from creation to deletion – and handling this entire process is what data life cycle management (DLM) is all about. With the right tools and strategies, DLM helps you keep your data organized and secure, so it is always ready when your business needs it most.

Today, the question is: how can you not only track data but actually make it more reliable and easier to work with? The clear answer to this question is generative AI development, as it brings powerful tools that have the power to transform every stage of the data life cycle.  

And that’s exactly what this article is about: how you can improve the data life cycle with generative AI. We’ll walk you through the real ways GenAI is transforming DLM; and, most importantly, give you tips on implementing this transformative tool in your business.

We’ll help you rethink your data lifecycle with GenAI and build cost-effective workflows from day one.

Start with a GenAI roadmap

Article Highlights:

• AI tools like Trifacta and Pandas AI clean up messy data fast by spotting errors and suggesting smart fixes – meaning no more wasting hours on formats, gaps, or inconsistencies;
• Power BI Copilot can instantly turn complex dashboards into detailed summaries that any employee can easily understand;
• CHI Software helped a VC firm cut processing time by 50% by using a smart combination of commercial and open-source GenAI tools.  

How Generative AI Enhances Each Stage of the Data Life Cycle

Data is the foundation of generative AI – but how can you automate the data life cycle with GenAI? Let’s see how genAI can reshape the way companies manage their data.

1. Data Creation and Ingestion

Let’s start from the beginning – the moment when data first enters your system. Usually, this stage requires a great deal of manual setup: figuring out what your data should look like, building a structure, and trying to collect enough quality data sets to work with – all of which take up valuable time.

Generating Synthetic Data for Fast Testing

Some tasks require having data on hand immediately, such as testing a new application or training a machine learning model. At this stage, you can already see the benefits of generative AI in the data life cycle because you don’t need to wait for real data or risk confidential information. Gretel.ai, Tonic.ai, or Mostly AI can instantly create synthetic data that looks and behaves like the real one. 

Auto-Building Database Structures 

Instead of manually creating database tables or guessing at how to structure your data, you can describe what you need in plain language, for example, “I want to track customer orders with delivery updates”, and LangChain or Azure OpenAI can build the structure for you.

AI and Data Engineering: A Game-Changing Collaboration Read more

2. Data Storage and Organization

Generative AI in data management acts like your smart assistant and brings structure and order without manual work. 

Automatically Generating Metadata 

Imagine metadata as labels that explain what your data is about: who created it, what’s inside, when it was updated, etc. Instead of someone manually typing all this out, LLM models can automatically scan your files and generate these labels for you.

Effortlessly Classifying and Tagging Files

Any business has hundreds of spreadsheets, PDFs, and data tables. Instead of sorting them one by one, you can set up Atlan or Collibra to understand what each file contains and tag it – for example, “customer reviews” or “sales data”. 

3. Data Usage and Processing

You can incorporate generative AI in data life cycle at any stage, including data usage and processing.

Now it’s time to use your data. A lot of companies hit a wall at this stage: queries are not executed, pipelines break down, and analysts spend hours fixing confusing spreadsheets. 

Generating SQL and Python Scripts from Natural Language

You can run analytics much faster by using generative AI for the data life cycle. For example, if you need to write an SQL query or Python script but don’t have a data engineer on your team, Azure OpenAI or Snowflake Cortex can generate that code based on a simple query in English.

Automating Documentation for Every Data Step

Instead of manually tracking every step in your data pipeline, dbt Cloud with AI Assist or Notion AI can automate document generation and explain the entire path of your dataset. 

Cleaning and Transforming Your Data in Minutes

AI can understand the context of your data and use that to suggest replacements and transformations. Data Wrangler, Trifacta, and Pandas AI can detect and correct errors, fill in missing values, and convert formats in minutes. 

4. Data Sharing and Collaboration

Data sharing can be much more easier with generative AI for data life cycle.

Another important task that too often gets an asterisk is making data easy to use. No business wants to be guided by outdated documentation and error-prone communication between teams. Fortunately, data life cycle automation with AI brings improvements to both of these issues.

Using Human-Friendly Summaries

Not everyone who reads your reports is an expert data analyst. Sometimes, a sales director just wants to know what influenced the Q2 results.

Power BI Copilot can read your dashboards, tables, and reports; generating easy-to-understand summaries in natural language. A good example is generative AI for retail, where tools analyze thousands of customer reviews and transactions and then send reports or alerts about when and why your sales might have dropped.

Building a Scalable Data Warehouse Step-by-Step Read more

Automating the Generation of Documents and Data Policies

Every business needs documentation especially when it comes to confidential information. Atlan and dbt Cloud with AI Assist can automatically generate:

• Data dictionaries (what each column or metric means);
• Access policies;
• Data flow charts (how data moves from source to report).

Creating Multilingual and Accessible Content

ChatGPT with multilingual prompts or Azure AI Translator can help you translate documentation into any language and create reports that are accessible to screen readers.

You can use data extraction with generative AI to pull insights from international sources – reviews in Spanish or compliance forms in German – and then automatically translate and tag content, presenting it in the language of your stakeholders.

Seeing the potential is just the start – now let’s talk about your use case. Request a free consultation

5. Data Archiving

One of the biggest headaches for businesses is deciding which data to keep and which to move to cold storage. Generative AI for data management analyzes usage patterns, compliance needs, and long-term value, suggesting which files are still useful. 

This use case is beneficial for businesses taking advantage of big data development, where massive volumes of data arrive every day, and manual sorting can’t keep up. 

Our team advises:

• Microsoft Purview – for classifying and applying data retention policies;
• AWS S3 Intelligent – for cost-effective data storage solutions;
• Google Cloud DLP API – for scanning and marking sensitive information before archiving.
  

6. Data Deletion and Compliance

Let’s talk about something nobody can afford to mess up: data compliance. With ever-increasing regulatory requirements and the amount of data companies receive, there is very little room for mistakes.

Quickly Managing Customer Data

When a customer asks you to delete their data by the GDPR or CCPA, your company needs to act quickly and precisely. AI-powered identification tools can now scan your systems and pinpoint the personal data associated with a specific user, even if it’s hiding under slightly different formats or names. 

Automating Audit Trails 

Think about a situation when your company is being audited. You’ll need to show when data was deleted, why, and who approved it. GenAI can automatically generate audit trails in real time, minimizing legal risks and administrative work.

CHI Software recommends OneTrust to identify and manage data subject requests and BigID to discover personal data and automate compliance.

Best Practices to Start Using Generative AI in Data Management

So, you’re ready to bring GenAI into your DLM, but where do you start? CHI Software has helped clients in various industries like marketing, finance, venture capital, and more to navigate this journey — here is our recommendation list based on our experience.

Starting off is always a challenge – follow these simple tips to set up generative AI in data management.

1. Begin with High-Impact, Low-Risk Areas

Implementing a data management life cycle with generative AI is often focused on repeatable, time-consuming, and low-risk tasks — for example, documentation.

When CHI Software worked with a fast-growing food company, our team used Airflow to simplify data migration and data build tool (dbt) to organize and cleanse data. These tools helped to automatically document data processes and ensure that all data was prepared for reporting – all behind the scenes, without disrupting the company’s day-to-day operations.

Other easy wins include:

• AI-generated summaries and reports to transform raw data into short, easy-to-read insights;
• Smart tags that help organize files and make it easier to find the data you need.

2. Choose the Right Tools 

To pick a tech tool, you need to understand your team’s capabilities and the company’s needs. If your team is tech-savvy and prefers flexibility, open-source tools like LangChain and Apache Airflow could be a great choice for you. 

If you want something off-the-shelf and scalable for large teams, commercial platforms such as Databricks or Azure OpenAI have built-in security, support, and the ability to easily process large amounts of data.

It can be challenging to meet all business requirements with just one type of technical tool, so data engineering teams often mix and match them. For example, when CHI Software was working with a venture capital firm, our engineers used:

• SetFit as an artificial intelligence model to classify startups in several languages;
• Google Composer to manage all the behind-the-scenes tasks, such as fetching data for generative AI from APIs;
• BigQuery as a powerful storage and analysis engine;
• Google Sheets as a user-friendly interface to present structured insights.

Each tool had a different function, but together, they maximized the role of data in generative AI, leading to a 50% reduction in data processing time and fully automated data ingestion.

Data Engineering Strategy: Benefits, Challenges & Best Practices Continue reading

3. Build a Governance Layer

Before you scale, you need to make sure you have solid data governance in place. Who can view or edit the data? Where does the data come from, and where does it go? How will you track changes and errors?

On one of our projects for a leading mobility company, CHI Software implemented OpenMetadata to provide full visibility into data lineage and quality across departments, helping the client integrate innovations while maintaining strict control.

4. Train Team on Prompt Engineering

Using GenAI isn’t just about plugging in to ChatGPT. Your teams need to learn prompt engineering basics, understand LLM limitations, and know when human review is critical.

For example, GenAI can auto-suggest a Python script to clean your data, but if your analysts can’t spot a logical flaw in the code, that shortcut becomes a setback. We recommend making workshops on new technologies a tradition within your company.

5. Partner with Data Experts 

Luckily, you don’t have to navigate generative AI in the data life cycle alone. A trusted partner with the right services can guide you at every step of the way. 

CHI Software is here to support you from the moment you start exploring innovations for your business. Our consulting team can quickly answer your questions about technologies, help assess your business needs, and recommend the best approach for your data-driven future.

Best practices work best when they’re tailored. Build your custom plan with our help

And CHI Software doesn’t stop at development and implementation – we also make sure your team is trained to get the most out of new solutions, whether it’s machine learning in the data life cycle or full-scale GenAI adoption.

Conclusion

There’s no doubt about what GenAI can do – in this article, we broke down where generative AI makes an impact, from day-one data creation to smart compliance and cleanup. 

Now that you have our tried-and-true tips on how to get data life cycle automation with AI, remember: a strong partner can make the transition smoother and faster. At CHI Software, we don’t just talk about data innovation, but we can also make it happen. With more than six years of experience in data engineering, a team of more than 20 experienced engineers, and deep cloud expertise across AWS, Google Cloud, and Azure, we know how to design, implement, and optimize data processes that deliver real value.

Our GenAI and data engineering teams are ready to build what’s next for your business. Talk to our experts

About the author

Sirojiddin Dushaev Lead Data Engineer & Cloud Solutions Architect

Sirojiddin is a seasoned Data Engineer and Cloud Specialist who’s worked across different industries and all major cloud platforms. Always keeping up with the latest IT trends, he’s passionate about building efficient and scalable data solutions. With a solid background in pre-sales and project leadership, he knows how to make data work for business.

Oleksandr Kolosov Technical Lead of Machine Learning

Oleksandr holds a Ph.D. in Probability Theory and Math Statistics and has a strong background as both a professor and engineer. He's worked with leading services like AWS and Azure, bringing expertise in machine learning, databases, and web applications. With skills in Python, .NET, JavaScript, and more, he's well-versed in building and optimizing tech solutions.