Part 1 — Building Your AI-Ready Data Stack: Define Business Objectives.

10-part series about building your first Data stack from 0 to 1, and be ready for AI implementation.

Many organizations struggle with scaling their data management as they grow, finding that Excel reports are no longer sufficient. There’s a common challenge in identifying the right tools to build efficient analytics workflows and ensuring the data stack is ready for AI applications.

Without a modern data infrastructure, you risk falling behind, as your data processes become cumbersome and inefficient. This stagnation can hinder your ability to leverage AI technologies effectively, leaving valuable insights on the table.

This is why I am excited to start 10-part series designed specifically for organizations moving from spreadsheets to scalability. This comprehensive guide will walk you through each step of building a data stack that is not only AI-ready but also adaptable to various business needs.

To make it practical, we’ll walk through everything using a data-driven marketing campaign as our example.

I also want to make this community driven, so open to shaping this series based on what you would like to learn or include any specific challenge you may have. First parts would look something like this.

1. Defining business goals and technical requirements
2. Designing Data architecture that scales with the demand
3. Setting up Data architecture in AWS — step by step tutorial
4. Build your first real time and batch data ingestion pipelines
5. Data quality and optimization
6. ???

I will be on the lookout for your responses! Meanwhile let’s dive in to today’s topic of Defining business goals and requirements.

Defining business goals and technical requirements

Before we start procuring Snowflake or Redshift, chance are your business is asking you to justify the need for the Data stack. We have Postgres right ? The key here is to not lead with the solution but rather lead with the problem. Define specific business outcome we want to achieve and how setting up scalable data platform will help us get there.

We’ll walk through a step-by-step process to help you:

1. Articulate your high-level business objectives
2. Translate those into specific AI and data goals
3. Identify the key metrics and data needed to achieve those goals
4. Outline the technical requirements and Roadmap to put your strategy into action

By the end, you’ll have a practical roadmap to harness Data to drive real business results. Let’s dive in!

Step 1: Start with Your North Star — Defining Clear Business Objectives

Throughout this series, we will work on example project called “Data driven marketing campaign analytics” for ABC corp. First question is, What are the most important outcomes you want to achieve in the next 1–2 years with your marketing strategy?

For a retailer, key objectives might be increasing sales by 15%, improving customer retention, and optimizing inventory to reduce waste. We will use SMART goals to define our objectives.

Here’s a template to help crystalize your business objectives:

In the next [timeframe], [Company Name] aims to achieve:

1. [Objective 1] as measured by [key metric]
2. [Objective 2] as measured by [key metric]
3. [Objective 3] as measured by [key metric]

For example: In the next 18 months, ABC corp aims to achieve:

1. 15% increase in sales revenue as measured by quarterly sales growth
2. 20% improvement in customer retention as measured by repeat purchase rate
3. 10% reduction in inventory waste as measured by inventory turnover ratio

With your north star objectives defined, you’re ready to translate those into concrete technical goals.

Step 2: Bridging the Gap — Translating Business Objectives into technical Goals

Next step is identifying how AI and data can help you achieve those outcomes. This is where you start bridging the gap between business strategy and technical execution.

For each business objective, brainstorm potential AI and data applications that could move the needle. Engage a cross-functional team in this process — business leaders, data scientists, engineers, domain experts. Encourage bold ideas while also being realistic about what’s achievable given your resources and timeline.

Let’s use the retail example from before to illustrate:

Business Objective: 15% increase in sales revenue Potential AI & Data Applications:

• Targeted marketing using customer segmentation models
• Recommendation engine to drive upsell/cross-sell
• Dynamic pricing optimization based on real-time demand
• Chatbots to enhance customer engagement and support

Business Objective: 20% improvement in customer retention Potential AI & Data Applications:

• Predictive models to identify at-risk customers for proactive outreach
• Personalized offers & experiences based on customer lifetime value
• Sentiment analysis on customer reviews/feedback to identify pain points
• Loyalty program optimization using customer behavior data

Business Objective: 10% reduction in inventory waste Potential AI & Data Applications:

• Demand forecasting models to optimize inventory levels
• Computer vision for automated quality control
• Supply chain optimization using IoT sensor data
• Markdown pricing optimization for end-of-lifecycle products

But everything looks sooo important! I know, I know. At this stage you need to conduct workshop with required stakeholders to prioritize the features using MoSCoW or RICE frameworks to agree on One common goal and KPI.

For example:

Goal: Implement a recommendation engine to increase average order value by 10% and sales revenue by 5% within 6 months.

Assuming you have A goal agreed with your stakeholders (seriously, let me know how you did that!) , you can dive deeper into the data and technical requirements needed to realize them.

Step 3: Garbage In, Garbage Out — Identifying Data Requirements & Gaps

Without the right data, in sufficient quantity and quality, even the most sophisticated algorithms will fall short. That’s why it’s crucial to take stock of your data assets and gaps early in the strategic planning process.

For each of your AI and data goals, map out:

• What data is needed to achieve the desired outcome?
• Do you currently capture that data? At what granularity?
• Where does the data reside? Is it easily accessible?
• What is the quality of the data? Are there gaps or inconsistencies?
• How will the data need to be transformed or enriched for AI/ML?

Let’s apply this to the recommendation engine example:

Goal: Implement a recommendation engine to increase average order value by 10% and sales revenue by 5% within 6 months.

Data Requirements:

• User profile data: demographics, preferences, behavioral segments
• Transaction history data: products purchased, prices, discounts applied
• Product catalog data: product features, categories, inventory levels
• User interaction data: clicks, product views, cart abandonment
• (Ideal) User reviews and ratings data

Current Data Assets:

• User profiles & transaction history in CRM (Salesforce)
• Product catalog in ERP system (SAP)
• User clickstream data in web analytics tool (Adobe Analytics)
• Partial product reviews in PIM (Akeneo)

Data Gaps:

• User profiles lack key preference & segmentation attributes
• Transaction history only goes back 1 year vs. ideal of 2+
• Product catalog lacks granular feature data for some categories
• Reviews data is siloed and not linked to product catalog
• No data on inventory levels by product

Data Quality Issues:

• Inconsistent product categorization between CRM and ERP
• Missing values in user demographic fields
• Duplicate user records due to varying email addresses
• Suspected bot traffic inflating some clickstream metrics

With this assessment, you have a clear view of your current data foundation and the work needed to get it AI-ready.

At this stage, do not worry about getting the right data or fixing quality gaps, that is exactly what you will learn in this 10-part series. For now, just be aware about data sources you may need and where to find them.

Step 4 : Creating the Data and AI implementation Roadmap

At this point you’ve aligned on business objectives, translated those to AI and data goals, scoped data requirements. Now it’s time to pull it all together into a phased execution roadmap.

We already selected recommendation engine as a main Objective for this example series. You can also use frameworks like this to prioritize features based on business impact, feasibility, and data readiness.

Here’s the Miro board link

For each priority use case, define:

• Goals and success metrics
• Data sources and gaps to address
• Architecture components required
• Cross-functional team (data science, engineering, domain experts)
• Timeline and milestones
• Budget and resource needs

Capture these in a phased roadmap, with quick wins in early phases to build momentum while laying the foundation for more strategic bets. Be sure to include checkpoints to measure progress, celebrate wins, and course-correct as needed.

Here’s an example phased roadmap for our retail AI strategy:

Phase 1 (0–3 months):

• Integrate CRM & ERP data into customer 360 view (Data Investment)
• Pilot product recommendation engine for one category (Quick Win)
• Enrich product catalog for high-value categories (Data Investment)
• Implement data quality initiative for user profiles (Data Investment)

Phase 2 (3–6 months):

• Expand recommendation engine to all categories (Quick Win)
• Develop predictive model for customer churn (Strategic Bet)
• Pilot demand forecasting for inventory optimization (Strategic Bet)
• Explore external data partnerships for reviews, market trends (Data Investment)

Phase 3 (6–12 months):

• Productionize customer retention program with churn models (Strategic Bet)
• Scale inventory optimization with demand forecasting (Strategic Bet)
• Augment recommendation engine with user reviews (Strategic Bet)
• Real-time personalization with streaming clickstream data (Strategic Bet)

Here’s the Miro board link

The key is to balance short-term wins to sustain momentum with building long-term strategic capabilities. Communicate the roadmap broadly and rally cross-functional teams around it. And embrace an agile mindset — the plan will evolve as you learn and adapt.

Conclusion

Remember, this isn’t a linear process — it’s an ongoing journey of refinement and evolution. Goal of this series to get you started on the journey to build your first scalable data stack and provide you with templates and resources at each stage.

Most importantly, don’t wait for perfection to get started. The AI revolution is here and moving fast. The organizations that start now, with focused goals and a bias toward action, will be the ones that harness its transformative potential.

So get out there and start putting your AI and data strategy into action! And remember, we’re here to help at every step of the way. If you have questions or get stuck, don’t hesitate to reach out. We’re in this together.

Onward!

Questions? Feedback? Connect with me on LinkedIn or contact me directly at gunjan@bytesandbayes.com!

This article is proudly brought to you by Bytes & Bayes, the consulting firm dedicated to guiding you towards data excellence. We also offer a AI Literacy for business leaders workshop that provides a deeper understanding of how to make your organization READY for AI.