{"id":198266,"date":"2024-12-13T16:49:38","date_gmt":"2024-12-13T16:49:38","guid":{"rendered":"https:\/\/www.similarweb.com\/blog\/?p=198266"},"modified":"2025-03-27T11:33:29","modified_gmt":"2025-03-27T11:33:29","slug":"unifying-datasets-streamlining-delivery","status":"publish","type":"post","link":"https:\/\/www.similarweb.com\/blog\/daas\/data-analysis\/unifying-datasets-streamlining-delivery\/","title":{"rendered":"Bridging the Data Gap: Unifying Datasets and Streamlining Delivery"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">The data landscape is vast, complex, and full of opportunity, but it&#8217;s also plagued with challenges that slow progress and reduce the effectiveness of <\/span><a href=\"https:\/\/www.similarweb.com\/blog\/research\/market-research\/dddm\/\"><span style=\"font-weight: 400;\">data-driven decision-making<\/span><\/a><span style=\"font-weight: 400;\">. From the difficulty of data delivery to the complexities of mapping disparate datasets, data&#8217;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.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Gated delivery: The challenge of getting data into the right hands<\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">Why is data delivery still a major challenge?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">A key challenge is the complexity of gated data delivery. Unlike many other digital products, data isn&#8217;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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For data buyers\u2014whether they are hedge funds, corporates, or other organizations\u2014this 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.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Steps to simplify data delivery<\/span><\/h3>\n<p><img decoding=\"async\" class=\"alignnone wp-image-198267 size-full\" src=\"https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2024\/12\/Data-Delivery-Simplified.png\" alt=\"Image showing how data delivery can be simplified\" width=\"1200\" height=\"628\" srcset=\"https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2024\/12\/Data-Delivery-Simplified.png 1200w, https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2024\/12\/Data-Delivery-Simplified-300x157.png 300w, https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2024\/12\/Data-Delivery-Simplified-1024x536.png 1024w, https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2024\/12\/Data-Delivery-Simplified-768x402.png 768w, https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2024\/12\/Data-Delivery-Simplified-512x268.png 512w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">To overcome the challenges of gated delivery, companies need to prioritize flexibility and user-centric design for their data offerings:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Provide multiple <a href=\"https:\/\/www.similarweb.com\/corp\/daas\/integrations\/\">data integration solutions<\/a><\/b><span style=\"font-weight: 400;\">: Offering data in various formats and through different integration methods (e.g., <\/span><a href=\"https:\/\/www.similarweb.com\/blog\/daas\/data-analysis\/what-is-an-api\/\"><span style=\"font-weight: 400;\">APIs<\/span><\/a><span style=\"font-weight: 400;\">, <\/span><a href=\"https:\/\/www.similarweb.com\/corp\/daas\/data-feeds\/\"><span style=\"font-weight: 400;\">Data Feeds<\/span><\/a><span style=\"font-weight: 400;\">, or direct integration with tools like Power BI) ensures that clients can choose the method that works best for their existing tech stack.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Use tailored datasets<\/b><span style=\"font-weight: 400;\">: Providing pre-built datasets (<\/span><a href=\"https:\/\/www.similarweb.com\/corp\/daas\/datasets\/websites\/\"><span style=\"font-weight: 400;\">website datasets<\/span><\/a><span style=\"font-weight: 400;\"> and <\/span><a href=\"https:\/\/www.similarweb.com\/corp\/daas\/datasets\/apps\/\"><span style=\"font-weight: 400;\">app datasets<\/span><\/a><span style=\"font-weight: 400;\">, for example) that are ready to be integrated into workflows reduces the time and effort clients spend on data preparation.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Ensure transparency and support<\/b><span style=\"font-weight: 400;\">: Clear documentation and dedicated support for clients during the onboarding process make data integration smoother, allowing users to derive insights more quickly.<\/span><\/li>\n<\/ul>\n<h2><span style=\"font-weight: 400;\">Mapping datasets together: Creating a cohesive view<\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">The complexity behind mapping disparate datasets<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">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\u2014both qualitative and quantitative\u2014to assemble the complete picture. However, stitching these datasets together is often time-consuming and labor-intensive, requiring significant data engineering resources.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">All of a parent company&#8217;s subsidiaries<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Which domains belong to the parent company and which belong to the subsidiaries<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Which apps belong to the parent company and which belong to the subsidiaries\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">How key metrics drive traffic to these websites and how consumers engage with the apps\u00a0<\/span><\/li>\n<\/ol>\n<p><img decoding=\"async\" class=\"alignnone wp-image-195293 size-full\" src=\"https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2024\/09\/Similarweb-Stock-Intelligence-Ticker-Mapping.png\" alt=\"Image showing how Similarweb's Ticker Mapping works\" width=\"960\" height=\"540\" srcset=\"https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2024\/09\/Similarweb-Stock-Intelligence-Ticker-Mapping.png 960w, https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2024\/09\/Similarweb-Stock-Intelligence-Ticker-Mapping-300x169.png 300w, https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2024\/09\/Similarweb-Stock-Intelligence-Ticker-Mapping-768x432.png 768w, https:\/\/www.similarweb.com\/blog\/wp-content\/uploads\/2024\/09\/Similarweb-Stock-Intelligence-Ticker-Mapping-512x288.png 512w\" sizes=\"(max-width: 960px) 100vw, 960px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Mapping is key to gaining a better understanding of the <\/span><a href=\"https:\/\/www.similarweb.com\/blog\/marketing\/marketing-strategy\/competitive-landscape-analysis\/\"><span style=\"font-weight: 400;\">competitive landscape<\/span><\/a><span style=\"font-weight: 400;\">. Most companies don&#8217;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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The data mapping challenge<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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&#8217;s digital presence.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To ensure accurate mapping and effective use of the data, there are two critical focus areas:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Establishing a robust data policy:<\/b><span style=\"font-weight: 400;\"> 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.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Standardizing entities and enriching data:<\/b><span style=\"font-weight: 400;\"> 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.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">By focusing on these areas, organizations can create a unified data ecosystem that delivers a comprehensive 360-degree view of a company&#8217;s digital presence, empowering businesses to make more informed, strategic decisions.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">The future of data integration: Collaboration and standardization<\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">Collaboration is key<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">How to foster data collaboration<\/span><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Partner integrations<\/b><span style=\"font-weight: 400;\">: 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.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Comprehensive datasets<\/b><span style=\"font-weight: 400;\">: 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.<\/span><\/li>\n<\/ul>\n<h2><span style=\"font-weight: 400;\">Overcoming data integration challenges<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">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 <\/span><a href=\"https:\/\/www.similarweb.com\/corp\/daas\/\"><span style=\"font-weight: 400;\">Data-as-a-Service solutions<\/span><\/a><span style=\"font-weight: 400;\">, 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\u2014<\/span><a href=\"https:\/\/www.similarweb.com\/blog\/research\/market-research\/actionable-data\/\"><span style=\"font-weight: 400;\">turning data into actionable insights<\/span><\/a><span style=\"font-weight: 400;\"> that drive business success.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">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\u2014deriving insights and making data-driven decisions that propel your business forward.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">    <div class=\"post-banner post-banner--base\">\n        <div class=\"post-banner__wrapper\">\n            <div class=\"post-banner__text\">\n                                    <p class=\"post-banner__title\">Got data?<\/p>\n                                    <p class=\"post-banner__subtitle\">Unlock the power of Similarweb and transform how you do business.<\/p>\n                                <div class=\"post-banner__button-wrapper\">\n                                            <a class=\"swui-button swui-button--solid swui-button--primary post-banner__button js-post-banner\"\n                           href=\"https:\/\/www.similarweb.com\/corp\/daas\/contact-us\/\"\n                           data-disable-dynamic-tracking\n                        >Talk to a data expert<\/a>\n                                    <\/div>\n            <\/div>\n                    <\/div>\n    <\/div>\n<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">FAQs<\/span><\/h2>\n<p><b>Why is data delivery such a big challenge for companies?<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\">Data delivery isn&#8217;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.<\/span><\/p>\n<p><b>What does it mean to &#8220;map datasets together&#8221;?<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\">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.<\/span><\/p>\n<p><b>How can companies simplify the mapping of multiple datasets?<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\">Companies can simplify dataset mapping by:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Entity mapping and enrichment<\/b><span style=\"font-weight: 400;\"> (like connecting tickers, product hierarchies, and company structures).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Using unified data feeds<\/b><span style=\"font-weight: 400;\"> to streamline integration and avoid manual data stitching.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Standardizing formats<\/b><span style=\"font-weight: 400;\"> to create consistency across datasets.<\/span><\/li>\n<\/ul>\n<p><b>How does unified mapping improve competitive intelligence?<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\">Unified mapping connects multiple <\/span><a href=\"https:\/\/www.similarweb.com\/blog\/marketing\/marketing-strategy\/competitive-intelligence-sources\/\"><span style=\"font-weight: 400;\">sources of competitive data<\/span><\/a><span style=\"font-weight: 400;\"> (like <\/span><a href=\"https:\/\/www.similarweb.com\/website\/\"><span style=\"font-weight: 400;\">website traffic<\/span><\/a><span style=\"font-weight: 400;\">, <\/span><a href=\"https:\/\/www.similarweb.com\/app\/\"><span style=\"font-weight: 400;\">app usage<\/span><\/a><span style=\"font-weight: 400;\">, and product data) into a single view of a <a href=\"https:\/\/www.similarweb.com\/corp\/stocks\/company-performance\/\">company&#8217;s performance<\/a>. This makes it easier to benchmark performance, track competitor strategy, and <\/span><a href=\"https:\/\/www.similarweb.com\/blog\/research\/market-research\/market-opportunities\/\"><span style=\"font-weight: 400;\">identify market opportunities<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The data landscape is vast, complex, and full of opportunity, but it&#8217;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&#8217;s value is often lost in translation. This article explores the key challenges in the data [&hellip;]<\/p>\n","protected":false},"author":540,"featured_media":198269,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[8500,8504,8502],"tags":[],"class_list":["post-198266","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-daas","category-data-analysis","category-data-basics"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Bridging the Data Gap: Unifying Datasets and Streamlining Delivery | Similarweb<\/title>\n<meta name=\"description\" content=\"Understand the challenges of data delivery and data integration and solutions that can provide easily accessible, understandable, and useable data.\" \/>\n<meta name=\"robots\" content=\"max-snippet:-1, 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