E-commerce professionals have probably encountered this scenario: your company wants to monitor competitor pricing, only to find that the marketing department has one dataset, operations has another, and engineering has yet another. The three datasets are in different formats with mismatched metrics, and just aligning them takes two weeks. By the time the data is finally ready, your competitors have already changed their prices three times over.

This isn’t a joke—it’s a daily reality for countless businesses.

Companies don’t lack data. What they lack is data that can flow freely and be put to use.

What exactly is a data silo? What problems can a data middle platform solve? And what role does Data as a Service (DaaS) play? Answering these three questions will help you understand the core logic behind enterprise data transformation today.

What Is a Data Silo, and Why Is It a Problem?

A data silo is a state in which enterprise data is scattered across different systems and departments, unable to be interconnected, shared, or uniformly accessed.

Here’s an analogy: in your living room, you have three remotes—one for the TV, one for the cable box, and one for the sound system. They all control entertainment devices, but none of them can control each other. To watch a movie, you have to pick up all three remotes. That’s exactly what a data silo feels like. Your ERP system manages sales data, your CRM manages customer data, and your finance system manages accounting data. Each operates independently, ignoring the others.

The harm caused by data silos comes down to three things:

First, decision-making is always a step behind. By the time departments align their data and generate reports, the market window has already closed. Your competitors have finished their promotions, and you’re still waiting for data validation.

Second, resources are wasted through duplication. Three departments each buy their own BI tools and hire their own data analysts—doing the same work, spending three times the money.

Third, customer experience is fragmented. A customer service agent calls a user, unaware that the same user submitted a support ticket just five minutes ago on the website. Because the data isn’t connected, the user is left feeling that your organization doesn’t communicate internally.

So, data silos are not fundamentally a technical problem—they are an organizational and management problem. Technology merely exposes the inefficiencies that already exist within the organization.

Data Middle Platform: The “Central Kitchen” That Breaks Down Silos

How do we solve data silos? The most widely discussed and heavily invested solution in recent years is the data middle platform.

The best way to understand the data middle platform is through the “central kitchen” analogy.

Consider a restaurant chain. If every branch had to purchase, wash, and prepare its own ingredients, not only would costs be high, but the taste would also vary from location to location. That’s why chains build central kitchens—unified procurement, processing, and distribution, with each branch receiving semi-finished ingredients ready to cook.

A data middle platform does exactly what a central kitchen does.

Its workflow generally looks like this:

Data collection: Gathering data from various sources such as ERP, CRM, financial systems, and log systems.

Data cleaning and standardization: Unifying formats, deduplicating, and correcting errors across different datasets.

Data storage and modeling: Reorganizing data around business topics to create usable data models.

Data service and output: Delivering data to front-end business units through standardized APIs or reports.

The core value of a data middle platform can be summed up in one sentence: it turns data from a “liability” into an “asset.” Business units don’t need to worry about where data comes from or how it’s cleaned—they just use it directly.

However, a data middle platform primarily addresses internal enterprise data. Your ERP, CRM, and financial systems are all internal, and the middle platform can connect them.

But what if the data your business needs comes from external sources?

For example, you want to know what your competitors are pricing today, monitor ranking changes of products on a cross-border platform, or gather public industry data for trend analysis. None of this data lives in your internal systems, and no matter how powerful your data middle platform is, it can’t conjure it out of thin air.

That’s when another player enters the picture.

Data as a Service (DaaS): Making External Data as Convenient as Utilities

As mentioned earlier, the data middle platform solves the problem of “internal disconnection.” But what about external data? The answer is Data as a Service (DaaS).

DaaS is a capability that delivers data via APIs or subscription models. You don’t need to buy your own servers, write scrapers, or maintain data updates—just call what you need on demand.

Let’s continue with analogies to make it clearer:

The data middle platform is the central kitchen: it processes internal enterprise data into semi-finished ingredients for use across departments.

DaaS is a bottled water company: you don’t need to drill your own well, purify the water, or test its quality. You just turn on the tap (API) and get clean water.

The relationship between the data middle platform and DaaS is not one of replacement, but of complementarity.

Here’s a table to clarify the differences:

Aspect	Data Middle Platform	Data as a Service (DaaS)
Data source	Internal (ERP, CRM, logs, etc.)	External (public data, third-party data, etc.)
Core mission	Break internal silos, standardize data	Acquire external data on demand, fill information gaps
Delivery method	Internal APIs, reports, data products	External API subscriptions, pay-per-use
Analogy	Cleaning up the inside	Bringing outside information in

A complete data architecture should be driven by both the middle platform and DaaS—like two wheels on the same axle. Internal data is unified by the middle platform, and external data is brought in through DaaS. Together, they deliver a complete business picture.

Consider a concrete scenario. A cross-border retail company has internal ERP (inventory) and CRM (customer) data managed by its data middle platform. But it also needs real-time pricing data on Amazon for similar products, BSR ranking changes, and user review trends. That external information must come through DaaS.

Only by combining internal and external data can the company make truly accurate pricing and inventory decisions.

That raises the question: where does DaaS providers’ data actually come from?

The Prerequisite for DaaS Success: High-Quality External Data Acquisition

DaaS providers typically acquire external data through two main channels:

The first is purchasing licensed data from third-party data agencies, such as industry reports or market indices. This approach is legally compliant but expensive, and the data is often outdated.

The second is collecting public data through technical means, such as e-commerce pricing or job board postings. This approach offers strong real-time capabilities and relatively controllable costs, but it demands significant technical expertise.

Many companies choose to collect public data themselves for its timeliness and flexibility. But this task is far more difficult than it seems.

There are three core challenges:

First, access stability is hard to guarantee.

Target platforms enforce strict security policies against high-frequency, concentrated data requests. Companies collecting data on their own often encounter blocked access, CAPTCHA challenges, or even temporary disconnections due to suspicious request patterns, severely impacting the continuity and completeness of data acquisition.

Second, localized data acquisition is difficult.

Many platforms tailor content based on the user’s geographic location. The same product may display completely different results when viewed from China versus from the United States. Without a localized network environment, the data a company collects may be skewed or incomplete, making decisions based on it risky.

Third, technical maintenance is costly.

Building your own data acquisition infrastructure requires purchasing server resources, maintaining the availability of IP pools, and continuously adapting to evolving platform policies. These tasks are tedious and repetitive, consuming engineering teams’ bandwidth that should be spent on core business and product development. That overhead is a hidden drain on resources.

So, if a company chooses to collect public data on its own, a reliable IP infrastructure is a prerequisite.

Take Novproxy, for example. Its residential IP services are designed precisely to address these challenges:

Global reach: Residential IP resources covering multiple countries and regions worldwide, with city-level geo-targeting.

Real residential IPs: IPs originate from real home networks and behave like ordinary users, ensuring stable and reliable data collection.

Automated rotation: Supports custom IP rotation intervals from 1 to 120 minutes, or rotation per request, with no manual intervention required.

Seamless integration: Provides API interfaces supporting both username-password authentication and IP whitelisting for flexible integration.

At its core, Novproxy makes data acquisition simpler. Companies don’t need to build and maintain complex network infrastructure on their own. They can focus their energy on extracting value from data—and that’s where the real competitive advantage lies.

If you need any help with IP procurement or usage, feel free to contact us:

Email: [email protected] (95% off coupon: QKUrIIOdux)

Conclusion

To put it simply, the data middle platform handles internal connectivity, while DaaS handles external data intake. Their directions differ, but they share the same end goal: making data flow. In reality, however, most companies’ data remains scattered across the organization—unavailable, incomplete, and slow to access.No matter how powerful AI becomes, it can’t escape a fundamental rule: the quality of your output is determined by the quality of your data input.

We all know this. But in day-to-day operations, can the data you need actually reach you reliably and in time?

If this problem goes unsolved, buzzwords like “smart decision-making” and “data-driven” will remain empty promises.

Novproxy helps companies get the “data acquisition” part right. Once that’s solid, let the data do the rest of the work.