Using AI in the Data Production Lifecycle

Many businesses struggle because their data systems are too complex. Data collection, organization, and analysis can be a headache.

It hinders quick decision-making and staying ahead in the market.

At Brainforge, we understand these challenges. That's why we're using AI to simplify every step of the data process.

We help you turn messy data into clear, actionable insights. We use AI to avoid the usual hassle.

In this blog post, we'll show you how AI can make handling data easier. We'll explain how AI can potentially solve tough data tasks. It can turn your business questions into answers and provide deeper insights. Our aim is to help you see how AI can truly make a difference in your everyday work.

Key Takeaways

• Simplify Data Tasks: AI automates data work, saving time and reducing mistakes.

• Improve Data Structures: AI makes data easier to analyze and understand.

• Get Quick Insights: AI offers analyses and suggestions for better decisions.

• Increase Team Productivity: AI helps small teams work better and faster.

• Enhance Data Protection: AI uses secure platforms and limits data movement to keep data safe.

1. The Problem with Current AI in Data Analysis

When tools like ChatGPT first came out, many businesses were excited to use AI for data analysis. It seemed like a quick and easy way to get insights from data without much effort. People thought they could just upload a spreadsheet and get all the answers they needed.

But in practice, these AI tools often provided very simple summaries. For example, they might tell you, "You had 10 orders yesterday." It's true. But, it doesn't help you understand trends, customer behavior, or areas for improvement. It's like scratching the surface without digging deeper.

Businesses soon realized that these basic insights weren't enough to make important decisions. Complex problems require detailed analysis, and AI wasn't delivering that on its own. The main issue was that AI tools were trying to analyze data without a strong foundation.

For AI to provide insights, your data must be well-organized and structured. This means having good data engineering practices. It involves cleaning data, ensuring consistency, and setting up reliable systems to manage it. Without this groundwork, even the smartest AI can't provide meaningful analysis.

In short, relying on AI without a solid data foundation is like building a house on sand. It might stand for a while, but it's not stable or reliable. To truly benefit from AI in data analysis, we need to start by strengthening the base.

2. Simplifying Data Engineering with AI

Data engineering is often a complex and time-consuming task. It involves collecting data from various sources. Then, clean it and prepare it for analysis. This process can be tricky. You're dealing with different systems, formats, and technologies.

For example, you might need to gather data from:

• Internal databases like PostgreSQL

• Cloud services like Google Ads, Shopify, etc

• Third-party vendors with unique APIs, such as GraphQL interfaces

Each of these sources can have its own quirks and challenges. Keeping up with new tools and technologies can also be overwhelming. Every time a new system comes out, your team has to learn how to use it, which takes time and resources.

How AI Helps:

• Creating Data Pipelines: AI can quickly create pipleine code to various data sources. It can handle complex API calls and ensure that data is fetched correctly given API docs.

• Learning New Tools Easily: AI lets you quickly adopt new tools, like Snowflake or Dagster, without a lot of time spent learning them. AI can guide you through setting them up and using them effectively using all the helpful docs the tools already have up as context

3. Improving Data Modeling with AI

Data modeling is the process of organizing your data so it's easy to understand and use. It means creating clear structures and formats. Everyone should know where to find information and how data relates to each other.

However, data modeling can be challenging because:

• Need for Consistency: Inconsistent data structures can lead to confusion and mistakes. If different teams organize data in different ways, it is hard to combine and analyze it.

• Complex Setup: Building data models manually takes time and requires specialized knowledge. Small errors can cause big problems down the line.

• Changing Business Needs: As your business grows, your data needs change. Updating models to reflect new products, services, or markets can be a heavy task.

How AI Helps:

• AI can quickly examine your data tables to see their structure (DDL, table references). It can identify inconsistencies and suggest ways to standardize them.

• Automating Model Creation: Instead of setting up data models manually, tell the AI what you need. For example, "I want to analyze sales by region and product type." The AI will build the foundational data structures for you given your raw tables.

• Cleaning and Organizing Data: AI can fix data formats, fill in missing values, and remove duplicates. It can handle these tedious tasks. This ensures your data is clean and ready to use.

• Translating Business Questions into Data Models: If you have questions, AI can design data models to help you find answers. This bridges the gap between business needs and technical implementation.

Example Scenario:

Imagine you want to understand why a new product isn't selling as well as expected. Traditionally, a data expert was needed to set up models that combined sales data, marketing efforts, and customer feedback.

With AI:

1. You Explain the Issue: You tell the AI, "I want to analyze sales data, marketing campaigns, and customer reviews for our new product. I have access to these five source tables. Here are their dbt table definitions"

2. AI Builds the Model: The AI reviews data sources. It organizes them into a model and ensures the data is compatible.

3. Data is prepared: it cleans the data, so there are no missing pieces or errors.

4. Ready for Analysis: Now, you can easily run analyses to see patterns and insights.

Benefits:

• Saves time by automating complex setup tasks

• Reduces errors in data modeling

• Adapts quickly to changing business needs

• Makes data accessible to non-technical team members

4. Getting Better Data Insights with AI

So far, we've discussed how AI can simplify data engineering and improve data modeling. Let's explore how AI can improve data analysis.

Challenges in Data Analysis:

• Communication Gaps: Often, data engineers, modelers, and analysts work separately. This can cause misunderstandings and delays. Information doesn't flow smoothly between teams.

• Slow Response Times: When a business question arises, it can take days or weeks to get an answer. The question passes through multiple people, each adding time to the process.

• Shallow Insights: Without deep analysis, you might get only basic answers. They won't help you make informed decisions.

How AI Helps:

• Bridging the Gaps: AI can act as a link between different data roles. It knows the tech and the business. So, it aligns everyone from Github to Slack to your Rill Dashboards

• Faster Answers: AI gives you quick responses to your questions. There's no wait for data to move through multiple hands.

• Deeper Analysis: AI can quickly process large data sets. It can find patterns and trends that human analysts might miss. Using new image processing APIs you can pass in the dashboards themselves to the LLM for understanding.

Example Scenario:

Imagine you're noticing a drop in sales for one of your top-selling products. You want to understand why this is happening and what you can do about it.

With AI:

1. You describe the problem: You tell the AI, "Sales for Product A have dropped recently. Can you find out why? Look into sales data, marketing efforts, pricing changes, and customer feedback."

2. AI Gathers Data: The AI automatically accesses all relevant data. This includes sales figures, marketing campaigns, price history, and customer reviews.

3. AI Analyzes the Data: It looks for patterns and correlations. For example, it might notice that:

   • The price of the product increased last month.

   • Marketing efforts for the product were reduced.

   • Customer reviews mention that the product feels overpriced now.

4. AI Provides Insights and Recommendations: The AI summarizes its findings and suggests actions, such as:

   • Offering a promotional discount to boost sales.

   • Increasing marketing efforts to highlight new features or benefits.

   • Reviewing supplier costs to see if the price can be reduced without hurting profits.

Benefits:

• Get comprehensive insights quickly

• Make informed decisions based on deep analysis

• Understand complex issues with clear explanations

• Receive actionable recommendations to address challenges

5. Making Data Teams More Efficient with AI

In many businesses, data projects involve multiple teams. These include data engineers, data modelers, data analysts, and project managers. While having specialists is important, this structure can lead to several problems:

Challenges with Traditional Data Teams:

• Slow Responses: With many people involved, getting answers can take a long time.

• Communication Gaps: As information moves between team members, it can be lost or misunderstood.

• Bureaucracy: More people mean more meetings, emails, and approvals, which can slow down progress.

• Lack of Accountability: When responsibilities are spread thin, it's hard to know who is responsible for what.

How AI Helps:

• Smaller Teams: AI enables one person to do the work of many.

• Broader Skills: AI helps team members learn new skills, cutting handoff needs.

• Quicker Progress: Fast decisions keep projects moving, avoiding delays.

• Improved Accountability: A smaller team with clear responsibilities ensures efficiency.

Benefits for the Team:

• Increased efficiency and productivity

• Better communication with fewer people involved

• Enhanced professional growth for team members

• Ability to focus on meaningful, strategic work

Benefits for Clients:

• Simplified communication with a dedicated point of contact

• Faster project completion and results

• Reduced costs without sacrificing quality

• Personalized service tailored to their needs

Further Reading:

• https://airbyte.com/data-engineering-resources/ai-for-data-analytics

• https://aijourn.com/role-of-ai-in-data-engineering/

• https://digitaltransformationskills.com/ai-for-business/

Conclusion

Dealing with complex data processes doesn't have to be a burden. Using AI at every stage of data production can improve your business's data handling.

Here's how AI makes a difference:

• Simplifies Data Engineering: Automates data collection and preparation, saving time and reducing errors.

• Improves Data Modeling: Creates clear data structures that make analysis simpler.

• Enhances Data Analysis: Provides deep insights and actionable recommendations quickly.

• Boosts Team Efficiency: It helps smaller teams do more. It improves communication and reduces delays.

At Brainforge, we're committed to helping you unlock the full potential of your data. We believe that using AI can solve common problems. It can speed up your processes and provide insights that drive your business.

Ready to transform your data journey?

Let's work together to make your data processes smoother, faster, and more insightful.