Bridging the Data Gap: Unifying Datasets and Streamlining Delivery

The data landscape is vast, complex, and full of opportunity, but it’s also plagued with challenges that slow progress and reduce the effectiveness of data-driven decision-making. From the difficulty of data delivery to the complexities of mapping disparate datasets, data’s value is often lost in translation. This article explores the key challenges in the data value chain and offers insights into how organizations can overcome these hurdles to unlock the full potential of their data investments.

Gated delivery: The challenge of getting data into the right hands

Why is data delivery still a major challenge?

A key challenge is the complexity of gated data delivery. Unlike many other digital products, data isn’t something that can simply be emailed or shared through a spreadsheet. Data delivery often involves navigating a complex tech stack and understanding the tools that the client has in place to manage and ingest the data.

For data buyers—whether they are hedge funds, corporates, or other organizations—this can be a significant barrier. Clients are often faced with integrating multiple data sources into workflows, each requiring different tools, formats, and processes. This makes data delivery a critical pain point because it can slow down operations and stop companies from being able to use the data they purchased. 

Steps to simplify data delivery

To overcome the challenges of gated delivery, companies need to prioritize flexibility and user-centric design for their data offerings:

• Provide multiple integration options: Offering data in various formats and through different integration methods (e.g., APIs, Data Feeds, or direct integration with tools like Power BI) ensures that clients can choose the method that works best for their existing tech stack.
• Use tailored datasets: Providing pre-built datasets (website datasets and app datasets, for example) that are ready to be integrated into workflows reduces the time and effort clients spend on data preparation.
• Ensure transparency and support: Clear documentation and dedicated support for clients during the onboarding process make data integration smoother, allowing users to derive insights more quickly.

Mapping datasets together: Creating a cohesive view

The complexity behind mapping disparate datasets

Another significant challenge in the data value chain is the complexity of mapping multiple datasets together. In the financial services sector, for example, firms often rely on a mosaic research approach, combining various data points from different sources—both qualitative and quantitative—to assemble the complete picture. However, stitching these datasets together is often time-consuming and labor-intensive, requiring significant data engineering resources.

Mapping disparate datasets into a cohesive whole is both an art and a science, especially when managing a vast array of data sources with different structures, formats, and collection methods. Each dataset presents unique challenges, from aligning varying structures to harmonizing data collected at different times, frequencies, or levels of granularity. These datasets need to be stitched together.

At Similarweb, we invested significantly in mapping products for various datasets in order to provide a cohesive view of the digital ecosystem. These products determine:

1. All of a parent company’s subsidiaries
2. Which domains belong to the parent company and which belong to the subsidiaries
3. Which apps belong to the parent company and which belong to the subsidiaries 
4. How key metrics drive traffic to these websites and how consumers engage with the apps 

Mapping is key to gaining a better understanding of the competitive landscape. Most companies don’t have the resources to do this on their own, so they have to rely on the data provider. It is a rigorous process that involves months of work to ensure the mapping is complete and accurate. Then, it must be constantly monitored as the data is dynamic and changes frequently.

The data mapping challenge

Entity mapping and enrichment are essential to unify datasets for delivery. At Similarweb, we mapped key data points, such as companies with subsidiaries, associated domains and apps, and ticker symbols. This comprehensive dataset ensures that users access a unified view of an entity’s digital presence.

For example, a company might operate under different legal names in different countries and use distinct doing business as (dBA). These may have a separate domain for each region, apps for different platforms, and multiple stock tickers tied to their corporate structure. The mapping involves connecting the company to its subsidiaries, parent companies, regional entities, and digital assets (apps and domains). This approach provides a unified view of the organization, enabling more accurate analysis and deeper insights.

To ensure accurate mapping and effective use of the data, there are two critical focus areas:

• Establishing a robust data policy: A well-defined data policy sets the foundation for consistent and reliable mapping. This includes defining how frequently the mapping is updated, maintaining detailed logs to track changes, determining the appropriate level of granularity, and standardizing schema and primary keys to seamlessly connect datasets internally.
• Standardizing entities and enriching data: Once entities are mapped, they can be enhanced with industry classifications, traffic metrics, user engagement data, audience insights, app usage, and more. These enrichments transform raw data into actionable intelligence, providing deeper context and utility.

By focusing on these areas, organizations can create a unified data ecosystem that delivers a comprehensive 360-degree view of a company’s digital presence, empowering businesses to make more informed, strategic decisions.

The future of data integration: Collaboration and standardization

Collaboration is key

Today, many data buyers face significant challenges because individual datasets are not built to work together. This lack of standardization makes it difficult for companies to integrate data from multiple vendors.

The solution lies in data providers collaborating to create standardized formats and build easily integrated data solutions. This kind of collaboration benefits data buyers and allows data vendors to offer a more complete, robust product, adding more significant value.

How to foster data collaboration

• Partner integrations: Building integrations with key platforms like AWS, Google Cloud, Databricks, and Snowflake allows clients to access data directly within their existing infrastructure. This reduces the friction involved in data integration and helps clients derive value faster.
• Comprehensive datasets: Creating datasets that incorporate different data points relevant across industries helps ensure that the data can easily complement and be integrated with other datasets that clients may be using.

Overcoming data integration challenges

Data delivery, integration, and mapping challenges are significant barriers that often prevent organizations from fully utilizing third-party data. However, with the right tools and approaches provided by Data-as-a-Service solutions, these challenges can be overcome. By focusing on making data easy to access, understand, and act on, organizations can unlock the full value of their data investments—turning data into actionable insights that drive business success.

Are you ready to transform the way you use data? By adopting best practices in data delivery and integration, you can empower your team to focus on what really matters—deriving insights and making data-driven decisions that propel your business forward.

FAQs

Why is data delivery such a big challenge for companies?
Data delivery isn’t as simple as sending an email attachment. It often requires navigating technical infrastructure, dealing with client-specific tools, and ensuring compatibility with existing workflows. Different companies use different platforms, and without flexible integration options like APIs or direct connections, data delivery can be slow and inefficient.

What does it mean to “map datasets together”?
Mapping datasets means connecting different datasets to create a unified, holistic view. Since datasets come in various formats, structures, and frequencies, aligning them requires technical effort. For example, linking web traffic data with app performance data involves matching company entities, app IDs, and domain names to a single parent company structure.

How can companies simplify the mapping of multiple datasets?
Companies can simplify dataset mapping by:

• Entity mapping and enrichment (like connecting tickers, product hierarchies, and company structures).
• Using unified data feeds to streamline integration and avoid manual data stitching.
• Standardizing formats to create consistency across datasets.

How does unified mapping improve competitive intelligence?
Unified mapping connects multiple sources of competitive data (like website traffic, app usage, and product data) into a single view of a company’s performance. This makes it easier to benchmark performance, track competitor strategy, and identify market opportunities.

by Omri Shtayer

VP of Data and DaaS Products

Omri Shtayer is the VP of Data and DaaS Products. He is known for leading innovation initiatives across the company and scaling the data business of Similarweb. Omri was the CEO and Co-founder of Lagoon, launched in May 2020 which helped investors make better decisions with instant access to high-quality data.