The DataWeave Blog

Tag: Data Management

Fueling Agentic Commerce: Introducing DataWeave’s Data Collection API
Commerce Is Entering Its Next Chapter

Every major shift in commerce has been driven by data. A century ago, shopkeepers relied on ledgers to track sales. In the supermarket era, loyalty cards and barcodes turned transactions into insights. With the rise of eCommerce, clickstream data and online analytics reshaped how products were merchandised and sold.

Now, we are entering the next chapter: agentic commerce.

In this new paradigm, autonomous AI agents will handle the tasks that once required teams of analysts, merchandisers, and pricing specialists. Imagine an agent that monitors competitor prices across dozens of retailers, recommends adjustments, and pushes updates to a dynamic pricing engine, all in real time. Picture a shopper’s digital assistant scanning marketplaces for the right mix of price, delivery time, and customer reviews before making a purchase on their behalf.

These aren’t distant scenarios. They’re unfolding now. Industry analysts estimate the enterprise AI market at $24 billion in 2024, projected to grow to $155 billion by 2030 at nearly 38% CAGR . Meanwhile, 65% of organizations already use web data for AI and machine learning projects, and 93% plan to increase their budgets for it in 2024. The trajectory is undeniable: the next era of commerce will be built on AI-driven decision-making.

And what fuels those AI-driven decisions? Data. Reliable, structured, timely, and compliant data.

The Data Problem No One Can Ignore

Here’s the paradox: just as data has become most critical, it has also become harder to acquire.

For data and engineering leaders, the challenges are painfully familiar:
- Old school scrapers that collapse whenever a site changes its HTML or introduces new interactivity.
- Constant maintenance cycles, with engineering teams spending 20-40 hours a week debugging, rerunning, and patching scripts.
- Low success rates, with in-house approaches succeeding just 60-70% of the time.
- Complex infrastructure, from managing proxies to retry logic, pulls attention away from higher-value work.
But the costs go far beyond engineering frustration.

For retailers, broken pipelines mean competitive blind spots. A pricing team without reliable visibility into competitor moves can’t respond fast enough, risking lost margin or missed sales. Merchandising teams trying to optimize assortments are left with incomplete data, making poor stocking decisions inevitable.

For brands, unreliable data disrupts visibility into the digital shelf. Products might be misplaced in search rankings, content could be outdated or incomplete, and reviews could signal issues, but without continuous monitoring, those signals are missed until it’s too late.

For AI and ML teams, poor-quality training data means underperforming models. Without clean, consistent, and large-scale inputs, even the most sophisticated algorithms produce flawed predictions.

Finally for consulting firms and research providers, fragile collection systems can compromise credibility. Clients expect robust, evidence-backed recommendations. Data gaps erode trust.

The reality is stark: fragile pipelines don’t just waste engineering hours. They undermine competitive agility, customer experience, and business growth.

Enter the Data Collection API

DataWeave’s Data Collection API is a self-serve, enterprise-scale platform designed to deliver the data foundation today’s enterprises need, and tomorrow’s agentic AI systems will demand.

At its core, the API replaces brittle scrapers and ad hoc tools with a resilient, adaptive, and compliant data acquisition layer. It combines enterprise reliability with retail-specific intelligence to ensure that structured data is always available, accurate, and ready to power critical workflows.

Here’s what makes it different:
- Enterprise-scale throughput: The API can process thousands of URLs in a single batch or handle continuous, high-frequency scrape. Whether you need daily pulses or near real-time monitoring, it scales with you.
- Flexible access modes: Technical teams can integrate directly into internal workflows via API, while business users can configure jobs through a no-code interface. Everyone gets what they need without bottlenecks.
- Adaptive resilience: As websites evolve, the API adapts automatically. No frantic patching, no firefighting.
- Structured outputs, your way: Clean JSON, CSV, or WARC formats are delivered directly into your environment – AWS S3, Snowflake, GCP, or wherever your data stack lives.
- Built-in monitoring and self-healing: Automated retries, real-time logs, and usage dashboards keep teams in control without manual oversight.
- Compliance by design: WARC-based archiving and SOC2 alignment ensure data pipelines are auditable, trustworthy, and enterprise-ready.
This isn’t about scraping pages. It’s about creating a reliable data utility, a system that transforms raw web inputs into structured, actionable data streams that enterprises can trust and scale on.

Who It’s Built For (And How They Use It)

The Data Collection API isn’t limited to one role or industry. It’s been designed with multiple stakeholders in mind, each of whom can apply it to solve pressing challenges:

Retailers and Consumer Brands

Retailers live and die by competitive awareness. With the API, pricing teams can monitor SKU-level prices and promotions across channels, ensuring they don’t leave margin on the table. Merchandising leaders can track assortment coverage, identifying gaps relative to competitors. Digital shelf teams can measure search rankings, share of voice, and content completeness. The result is faster responses, stronger category performance, and fewer blind spots in shopper experience.

AI & Machine Learning Teams

AI teams depend on data at scale. Whether training a natural language model to understand product descriptions or a computer vision system to analyze images, the Data Collection API delivers the structured, high-quality inputs they need. Reviews, ratings, attributes, and product images can all be captured and delivered at scale. For teams building predictive models, from demand forecasting to personalization, the difference between mediocre and world-class often comes down to input quality. This API ensures AI systems are always learning from the best data available.

Retail Intelligence & Pricing Platforms

Technology providers serving retailers and brands face unforgiving client expectations. Missed SLAs on data delivery can mean churn. By using the Data Collection API as their acquisition layer, platform providers gain enterprise reliability without rebuilding infrastructure from scratch. They can scale seamlessly with client needs while maintaining the integrity of the insights their customers rely on.

Marketing & Advertising Teams

For marketing leaders, competition is visible every time a shopper searches. The API enables teams to track keyword rankings, ad placements, and competitor promotions with consistency. Instead of anecdotal data or partial coverage, marketers get a full picture of their brand’s digital presence and the strategies competitors are using to capture share of voice.

Consulting Firms & Research Providers

Consultancies and market research agencies deliver strategy. But a strategy without evidence is just opinion. The API allows these firms to back every recommendation with structured, large-scale data. Whether advising on pricing, benchmarking performance, or publishing analyst research, firms can deliver trustworthy insights without taking on the cost or distraction of building fragile data pipelines.

The diversity of these use cases demonstrates why the API is a platform for collaboration across industries, ensuring every stakeholder, from engineers to strategists, has the reliable data foundation they need.

Why DataWeave, Why It Matters

Many vendors claim to deliver web data. Few can deliver it at enterprise scale, with commerce-specific expertise, and with proven ROI.

What sets DataWeave apart isn’t just that we provide data; it’s the way we do it, and the outcomes we enable.
- Commerce expertise baked in: With 14+ years of experience powering the world’s leading retailers and brands, DataWeave brings domain-specific intelligence that generic scraping vendors simply can’t. Our schemas are designed for commerce. Our defaults are smarter because they’re informed by retail realities.
- Adaptability without firefighting: Most tools break when websites evolve. Our API adapts automatically, minimizing the need for engineering intervention. Teams stay focused on innovation, not maintenance.
- Accessible to everyone: Whether you’re a senior data engineer automating workflows or a business analyst configuring a quick scrape, the API meets you where you are with both API and no-code interfaces.
- Enterprise-grade trust: Reliability and compliance are built in, not bolted on. With SLA-backed delivery, SOC2 alignment, and audit-ready archiving, the API is trusted by enterprises that can’t afford uncertainty.
This combination makes the Data Collection API not just a technical solution but a strategic partner for enterprises preparing for the age of agentic commerce.

A Foundation for the Future

The Data Collection API is more than an answer to today’s frustrating data problems. It represents a strategic foundation for tomorrow’s growth, designed to scale alongside the increasingly complex demands of commerce in the AI era.

At the heart of DataWeave’s vision is the Unified Commerce Intelligence Cloud, a layered ecosystem that transforms raw digital signals into strategic insights. The Data Collection API is the entry point, the essential first layer that ensures enterprises have a reliable supply of the most important raw material of the digital economy: data.
- Collection: Enterprise-grade acquisition of web data at scale. From product pages and search results to reviews and promotions, enterprises can finally count on continuous, structured inputs without worrying about fragility or failure.
- Processing: Once collected, data is normalized, enriched, and matched across sources. What was once noisy and inconsistent becomes clean, comparable, and immediately actionable.
- Intelligence: On top of this foundation sits advanced analytics, solutions for pricing optimization, assortment planning, promotion tracking, and digital shelf visibility, enabling sharper decisions at the speed of the market.
This progression means enterprises don’t have to transform overnight. Many start small, solving urgent challenges like competitive price tracking or digital shelf monitoring. From there, they can expand naturally into richer intelligence capabilities, knowing that their data foundation is already strong enough to support more ambitious use cases.

And as agentic AI systems begin to take on a larger share of decision-making, the importance of that foundation grows exponentially. These autonomous systems cannot operate effectively without clean, continuous, and contextual data. Without it, even the most sophisticated AI will falter, making poor predictions or incomplete recommendations. With it, they can operate at full capacity, powering dynamic pricing, real-time demand forecasting, and personalized shopping experiences at scale.

The Data Collection API isn’t just about reducing engineering pain today. It’s about preparing enterprises to compete and win in an AI-driven marketplace that never sleeps.

Getting Started

For teams tired of fragile scrapers, this is a chance to reset. For enterprises preparing for the next era of commerce, it’s a chance to build a foundation that can scale with them.

If your teams are still struggling with generic and inflexible data scrapers, request a demo now to see the DataWeave’s Data Collection API in action.
September 2, 2025
From Raw Data to Retail Pricing Intelligence: Transforming Competitive Data into Strategic Assets
Poor retail data is the bane of Chief Commercial Officers and VPs of Pricing. If you don’t have the correct inputs or enough of them in real time, you can’t make data-driven business decisions regarding pricing.

Retail data isn’t limited to your product assortment. Price data from your competition is as important as understanding your brand hierarchies and value size progressions. However, the vast and expanding nature of e-commerce means new competitors are around every corner, creating more raw data for your teams.

Think of competitive price data like crude oil. Crude or unrefined oil is an extremely valuable and sought-after commodity. But in its raw form, crude oil is relatively useless. Simply having it doesn’t benefit the owner. It must be transformed into refined oil before it can be used as fuel. This is the same for competitive data that hasn’t been transformed. Your competitive data needs to be refined into an accurate, consistent, and actionable form to power strategic insights.

So, how can retailers transform vast amounts of competitive pricing data into actionable business intelligence? Read this article to find out.

Poor Data Refinement vs. Good Refinement

Let’s consider a new product launch as an example of poor price data refinement vs. good data refinement, which affects most sellers across industries.

Retailer A

Imagine you’re launching a limited-edition sneaker. Sneakerheads online have highly anticipated the launch, and you know your competitors are watching you closely as go-live looms.

Now, imagine that your pricing data is outdated and unrefined when you go to price your new sneakers. You base your pricing assumptions on last year’s historical data and don’t have a way to account for real-time competitor movements. You price your new product the same as last year’s limited-edition sneaker.

Your competitor, having learned from last year, anticipates your new product’s price and has a sale lined up to go live mid-launch that undercuts you. Your team discovers this a week later and reacts with a markdown on the new product, fearing demand will lessen without action.

Customers who have already bought the much-anticipated sneakers feel like they’ve been overcharged now, and backlash on social media is swift. New buyers see the price reduction as proof that your sneakers aren’t popular, and demand decreases. This hurts your brand’s reputation, and the product launch is not deemed a success.

Retailer B

Imagine your company had refined competitive data to work with before launch. Your team can see trends in competitors’ promotional activity and can see that a line of sneakers at a major competitor is overdue for sale based on trends. Your team can anticipate that the competitor is planning to lower prices during your launch week in the hope of undercutting you.

Instead of needing to react retroactively with a markdown, your team comes up with clever ways to bundle accessories with a ‘deal’ during launch week to create value beyond just the price. During launch week, your competitor’s sneakers look like the lesser option while your new sneakers look like the premium choice while still being a good value. Customer loyalty improves, and buzz on social media is positive.

Here, we can see that refined data drives better decision-making and competitive advantage. It is the missing link in retail price intelligence and can set you ahead of the competition. However, turning raw competitive data into strategic insights is easier said than done. To achieve intelligence from truly refined competitive pricing data, pricing teams need to rely on technology.

The Hidden Cost of Unrefined Data

Technology is advancing rapidly, and more sellers are leveraging competitive pricing intelligence tools to make strategic pricing decisions. Retailers that continue to rely on old, manual pricing methods will soon be left behind.

You might consider your competitive data process to be quite extensive. Perhaps you are successfully gathering vast data about your competitors. But simply having the raw data is just as ineffective as having access to crude oil and making no plan to refine it. Collection alone isn’t enough—you need to transform it into a usable state.

Attempting to harmonize data using spreadsheets will waste time and give you only limited insights, which are often out of date by the time they’re discovered. Trying to crunch inflexible data will set your team up for failure and impact business decision quality.

The Two Pillars of Data Refinement

There are two foundational pillars in data refinement. Neither can truly be achieved manually, even with great effort.

Competitive Matches

There are always new sellers and new products being launched in the market. Competitive matching is the process of finding all these equivalent products across the web and tying them together with your products. It goes beyond matching UPCs to link identical products together. Instead, it involves matching products with similar features and characteristics, just as a shopper might decide to compare two similar products on the shelf. For instance private label brands are compared to legacy brands when consumers shop to discern value.

A retailer using refined competitive matches can quickly and confidently adjust its prices during a promotional event, know where to increase prices in response to demand and availability and stay attractive to sensitive shoppers without undercutting margins.

Internal Portfolio Matches

Product matching is a combination of algorithmic and manual techniques that work to recognize and link identical products. This can even be done internally across your product portfolio. Retailers selling thousands or even hundreds of thousands of products know the challenge of consistently pricing items with varying levels of similarity or uniformity. If you must sell a 12oz bottle of shampoo for $3.00 based on its costs, then a 16oz bottle of the same product should not sell for $2.75, even if that aligns with the competition.

Establishing a process for internal portfolio matching helps to eliminate inefficiencies caused by duplicated or misaligned product data. Instead of discovering discrepancies and having to fire-fight them one by one, an internal portfolio matching feature can help teams preempt this issue.

Leveraging AI for Enhanced Match Rates

As product SKUs proliferate and new sellers seem to enter the market at lightning speed, scaling is essential without hiring dozens more pricing experts. That’s where AI comes in. Not only can AI do the job of dozens of experts, but it also does it in a fraction of the time and at an improved match accuracy rate.

DataWeave’s AI-powered pricing intelligence and price monitoring offerings help retailers uncover gaps and opportunities to stay competitive in the dynamic world of e-commerce. It can gather competitive data from across the market and accurately match competitor products with internal catalogs. It can also internally match your product portfolio, identifying product family trees and setting tolerances to avoid pricing mismatches. The AI synthesizes all this data and links products into a usable format. Teams can easily access reports and dashboards to get their questions answered without manually attempting to refine the data first.

From Refinement to Business Value

Refined competitive price data is your team’s foundation to execute these essential pricing functions: price management, price reporting, and competitive intelligence.

Price Management

Refined data is the core of accurate price management and product portfolio optimization. Imagine you’re an electronics seller offering a range of laptops and personal computing devices marketed toward college students. Without refined competitive data, you might fail to account for pricing differences based on regionality for similar products. Demand might be greater in one city than in another. By monitoring your competition, you can match your forecasted demand assumptions with competitor pricing trends to better manage your prices and even offer a greater assortment where there is more demand.

Price Reporting

Leadership is always looking for new and better market positioning opportunities. This often revolves around how products are priced, whether you’re making a profit, and where. To effectively communicate across departments and with leadership, pricing teams need a convenient way to report on pricing and make changes or updates as new ad hoc requests come through. Spending hours constructing a report on static data will feel like a waste when the C-Suite asks for it again next week but with current metrics. Refined, constantly updated price data nips this problem in the bud.

Competitive Intelligence

Unrefined data can’t be used to discover competitive intelligence accurately. You might miss a new player, fail to account for a new competitive product line, or be unable to extract insights quickly enough to be helpful. This can lead to missed opportunities and misinformed strategies. As a seller, your competitive intelligence should be able to fuel predictive scenario modeling. For example, you should be able to anticipate competitor price changes based on seasonal trends. Your outputs will be wrong without the correct inputs.

Implementation Framework

As a pricing leader, you can take these steps to begin evaluating your current process and improve your strategy.
- Assess your current data quality: Determine whether your team is aggregating data across the entire competitive landscape. Ask yourself if all attributes, features, regionality, and other metrics are captured in a single usable format for your analysts to leverage.
- Setting refinement objectives: If your competitive data isn’t refined, what are your objectives? Do you want to be able to match similar products or product families within your product portfolio?
- Measuring success through KPIs: Establish a set of KPIs to keep you on track. Measure things like match rate accuracy, how quickly you can react to price changes, assortment overlaps, and price parity.
- Building cross-functional alignment: Create dashboards and establish methods to build ad hoc reports for external departments. Start the conversation with data to build trust across teams and improve the business.
What’s Next?

The time is now to start evaluating your current data refinement process to improve your ability to capture and leverage competitive intelligence. Work with a specialized partner like DataWeave to refine your competitive pricing data using AI and dedicated human-in-the-loop support.

Want help getting started refining your data fast? Talk to us to get a demo today!
January 30, 2025
Enterprise Data Security at DataWeave: Empowering Smarter Decisions with Seamless, Secure Data Management and Integration
At DataWeave, data security isn’t just about compliance—it’s about enabling peace of mind and better decision-making for our customers. Our customers rely on us not just for competitive and market intelligence but also for the seamless integration of critical data sources into their decision-making frameworks. To achieve this, we have built a security-first infrastructure that ensures organizations can confidently leverage both external and internal data without compromising privacy or protection.

Secure Data Integration: The Foundation of Smarter Decisions

Effective decision-making in today’s digital commerce landscape depends on combining multiple data sources—including first-party customer data, pricing intelligence, and business rules—into a unified framework. However, without the right security measures in place, businesses often struggle to operationalize this data effectively.

At DataWeave, we eliminate this challenge by offering:
- Integration with Leading Data Storage Solutions: Our platform seamlessly connects with data lakes and warehouses like AWS S3 and Snowflake, ensuring that businesses can easily ingest and analyze our data in real time.
- Support for Sandboxed Environments & Data Clean Rooms: Organizations can securely merge internal and external datasets without compromising confidentiality, unlocking deeper insights for pricing and business strategies.
- Automated Data Ingestion & Management: We simplify the process of integrating first-party data alongside competitive insights, allowing customers to focus on execution rather than infrastructure management.
Our Purpose-Built Security Framework

Handling millions of data points daily demands a security framework that is not only robust but also scalable and adaptable to evolving threats. DataWeave’s multi-tenant architecture ensures seamless data security without compromising operational efficiency.
- Multi-Tenant Architecture: Our system allows multiple customers to share the same application infrastructure while maintaining complete data isolation and security.
  - Tenants share infrastructure and computing resources but remain logically isolated.
  - Application-level controls ensure privacy while maximizing cost efficiency.
  - Centralized updates, maintenance, and easy scalability for new tenants.
- End-to-End Encryption & Access Controls: Every piece of data is encrypted both in transit and at rest. Role-based access controls (RBAC) restrict visibility to only authorized personnel, ensuring minimal risk of unauthorized data access.
Active Monitoring & Automated Compliance Management: We leverage automated access controls that adjust permissions dynamically as organizational roles evolve, ensuring that compliance is continuously maintained.

Certifications That Inspire Confidence

Data security is at the core of everything we do. Our compliance with the highest industry standards ensures that businesses can trust us with their sensitive data.

SOC 2 Type II Certification: DataWeave’s SOC 2 compliance is a testament to our commitment to stringent security protocols. This certification guarantees that we adhere to strict standards in data protection, availability, and confidentiality.

We implement a phased approach to security improvement:
- Prioritizing Critical Systems: To maximize impact, we prioritized systems that had the highest data security relevance and expanded the coverage thereafter. By addressing these priority areas, we were able to make meaningful security improvements early in the process.
- Automating Monitoring and Compliance: Partnering with Sprinto streamlined the compliance journey by automating key processes. This included real-time monitoring of our cloud environments, automated generation of audit-ready evidence, and integration with critical systems like AWS, Bitbucket, and Jira. These enhancements ensured efficient management of compliance requirements while reducing the burden on our teams.
- Fostering a Culture of Shared Responsibility: We conducted organization-wide training sessions to embed compliance as a shared responsibility across all teams. By educating employees on the importance of security practices and providing them with the tools to manage compliance autonomously, we established a security-first mindset throughout the company.
This systematic method allowed us to deliver immediate improvements while aligning long-term practices with industry’s best standards.

What This Means for Our Customers

By combining robust security with seamless data integration, DataWeave empowers businesses to:
- Optimize Price Management & Modelling: With secure access to real-time data, organizations can make informed pricing decisions that enhance profitability and market competitiveness.
- Run Advanced Simulations & Testing: Reliable, secure data enables businesses to model various pricing and assortment strategies before implementation, reducing risks and maximizing returns.
- Uncompromised Data Security: SOC 2 Type II compliance ensures stringent protocols to protect your data at every stage.
- Simplified Vendor Processes: Verified security certifications reduce friction during due diligence and onboarding, making it easier to partner with us.
- Aligned Standards: Our adherence to industry benchmarks reflects our commitment to meeting your expectations as a trusted technology partner.
- Scalable Operations: Expand across regions while maintaining full confidence in data privacy and security.
- Secure Collaboration: Share insights across teams with tools designed to protect sensitive information.
Our customers are increasingly looking to integrate their internal datasets with the external competitive intelligence provided by DataWeave. This can be a complex and risky process without the right security measures in place. We remove these roadblocks by providing a secure, scalable infrastructure designed to help businesses unify data without security concerns.

By ensuring seamless compatibility with key data storage platforms, such as Snowflake and AWS S3, we enable organizations to consolidate valuable first-party data with timely market insights. This integration empowers businesses to refine their pricing, assortment, and digital shelf strategies, thereby driving superior customer experiences—without the headaches of data security risks.

Security remains a top priority in everything we do. Our SOC 2 Type II-certified framework enforces rigorous encryption, access controls, and real-time compliance monitoring. We take on the burden of data security so our customers can focus on innovation and growth.

With DataWeave, businesses can confidently leverage secure data-driven decision-making to unlock new opportunities, optimize operations, and scale without compromise.

To learn more, write to us at contact@dataweave.com or request a consultation here.
January 21, 2025