9 Reasons Why External Data Product is Critical

Updated August 23rd, 2023
External data product

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An external data product is a refined, packaged form of data specifically designed to be consumed by external users such as customers, business partners, or regulatory bodies.

Data products, which package and deliver data in accessible and actionable formats, have emerged as pivotal assets that can both drive internal decision-making and generate external revenue streams. Among these, external data products occupy a special position. But what exactly is an external data product?

From revenue potential to societal contributions, these products are not just an optional offering but often a critical asset in an organization’s portfolio.

In this article we will learn how external data product is becoming an future asset for the global businesses.

Let us dive in!

Table of contents #

  1. What are external data product: Explained with real-world examples
  2. Why are external data product important: 9 reasons to ponder
  3. External data product architecture: What does it entail?
  4. 9 Types of external data product
  5. Difference between external and internal data product
  6. Summary
  7. Related reads

What are external data products: Explained with real-world examples #

An external data product is a set of data that is curated, packaged, and offered for sale or licensing by a data provider to external organizations.They are created to deliver value to the customer by helping them make better decisions, improve their operations, or develop new services.

External data products can come in various forms such as databases, reports, analytics dashboards, data feeds, or APIs. These products can encompass a wide range of data types, including financial data, customer data, industry statistics, geospatial data, and more.

Let us get into understanding the real world insights of external data products in marketing and business:

Examples of external data products in marketing #

1. Market research reports

Comprehensive market research reports often include trends, customer behaviors, and competitor benchmarks. These reports can help marketing teams identify opportunities and threats in the marketplace.

Use-case: For instance, a report about social media trends can help a marketing team understand which platforms are gaining traction and where they should allocate their ad spend.

2. Consumer sentiment analysis

Some companies specialize in scraping and analyzing social media, customer reviews, or forums to gauge public sentiment about a brand or a product.

Use-case: Marketing teams can use this data to adjust their strategies, such as focusing on specific features in advertising that consumers love or fixing issues that generate negative feedback.

3. Geo-location data

Companies can purchase geolocation data to understand where potential customers are located and how they move within certain geographies.

Use cases: This data is often used in location-based marketing strategies to deliver targeted ads to users who are near a specific store location.

Examples of external data products in business #

1. Financial market data feeds

These data feeds provide real-time or historical financial market data that businesses can use for trading, risk management, or strategy development.

Use-case: Investment firms often subscribe to these services to make more informed investment decisions.

2. Industry benchmark reports

These reports provide valuable data on industry performance metrics, such as average revenue per user (ARPU), customer lifetime value (CLV), or operating margins.

Use-case: Businesses can compare their performance against industry averages to identify areas for improvement or investment.

3. Supply chain data

Some companies offer detailed data on raw material prices, lead times, and supplier performance.

Use-case: Manufacturing companies can use this data to optimize their supply chain, negotiate better contracts, and predict potential disruptions.

4. Employee satisfaction data

Some firms specialize in collecting and analyzing employee satisfaction data across various industries and regions.

Use-case: Companies can use this data to benchmark their employee satisfaction scores and devise better human resources strategies.

External data products offer an excellent opportunity for businesses to enrich their own data and analytics capabilities without the overhead of collecting and processing the data themselves. They serve a valuable role in enhancing decision-making, strategic planning, and operational efficiency across various sectors and functions.

Why are external data products important: 9 reasons to ponder #

External data products have gained considerable importance for several reasons. Here are some key factors that underline their significance:

  1. Revenue generation
  2. Competitive advantage
  3. Brand equity and thought leadership
  4. Customer engagement
  5. Scalability
  6. Business partnerships
  7. Regulatory compliance
  8. Societal and global impact
  9. Innovation catalyst

Let us understand each of them in detail:

1. Revenue generation #

  • One of the most straightforward reasons for the importance of external data products is their potential to be a significant revenue stream.
  • Companies can monetize their data by offering it as a product to external clients. This could be in the form of subscription-based access, licensing, or one-time purchases.

2. Competitive advantage #

  • Data has become a critical asset in today’s business landscape, and those who can effectively package and sell data can achieve a competitive advantage.
  • Data products can offer unique insights or functionalities that are not readily available elsewhere, making the company that offers them more competitive in the marketplace.

3. Brand equity and thought leadership #

  • High-quality, reliable data products can enhance a company’s reputation as an industry leader and innovator.
  • Organizations can use external data products to showcase their expertise, thereby enhancing brand image and establishing themselves as thought leaders in their respective fields.

4. Customer engagement #

  • Well-designed external data products can improve customer engagement and satisfaction.
  • By delivering valuable insights in a user-friendly format, companies can increase customer loyalty, reduce churn, and potentially uncover opportunities for up-selling and cross-selling.

5. Scalability #

  • External data products can often be more easily scaled compared to other types of products or services.
  • As digital assets, they do not require manufacturing, storage, or shipping, making them relatively easier and more cost-effective to distribute to a larger audience.

6. Business partnerships #

  • Offering valuable external data products can open doors to new business partnerships.
  • These products can serve as a point of connection with other industry players, potentially leading to collaborative efforts, joint ventures, or other mutually beneficial arrangements.

7. Regulatory compliance #

  • In some cases, producing external data products may align with regulatory requirements or standards, making them not just a business choice but a compliance necessity.
  • For example, some industries may require transparency in specific types of data, and a well-designed data product can serve to meet these regulatory needs effectively.

8. Societal and global impact #

  • Some external data products have the potential to create societal benefits, such as promoting sustainability or improving public health.
  • For example, data products related to climate change can help organizations to make better environmental choices, thus contributing to societal well-being.

9. Innovation catalyst #

  • The process of creating an external data product often requires innovation in data collection, analysis, and presentation.
  • This innovation can have a ripple effect throughout the organization, inspiring new ways to approach problems, discover insights, and create value.

The importance of external data products lies in their ability to generate revenue, offer new market opportunities, enhance brand equity, and even make societal contributions. As the data economy continues to grow, the significance of these products is only likely to increase.

External data product architecture: What does it entail? #

The architecture of an external data product consists of several layers and components designed to capture, process, and deliver data in a way that can be consumed by external customers. The architecture is generally built to ensure scalability, security, accuracy, and ease of use.

Below are some of the key architectural components of an external data product:

  1. Data sources
  2. Data ingestion layer
  3. Data processing and transformation
  4. Data storage
  5. Data analysis and intelligence
  6. Data presentation layer

Let us understand each of them in detail:

1. Data sources #

Data sources are the origins from which raw data is gathered. They can range from databases, web scraping, IoT devices, public records, to APIs from other services.

Importance: Establishing a reliable and relevant data source is fundamental for the quality of the external data product.

Challenge: Careful vetting is required to ensure that the data sources are credible and that their use complies with legal regulations like GDPR, CCPA , or other data protection laws.

2. Data ingestion layer #

This layer is responsible for importing or collecting raw data from the data sources and bringing it into the data processing pipeline.

Methods: Data ingestion can be batch, real-time, or streaming.

Tools: Popular tools for this phase include Apache Kafka, Apache NiFi, and Flume.

3. Data processing and transformation #

This phase involves cleaning, transforming, and enriching the raw data. Data can be filtered, aggregated, or reformatted during this stage.

Data cleaning: Removing anomalies, missing values, and irrelevant information.

Data transformation: Changing the format, structure, or values of data to meet desired criteria.

Enrichment: Adding new data elements to existing data to improve its quality or to make it more contextually relevant.

4. Data storage #

Once processed, data is stored in a structured or semi-structured format that allows for efficient querying and further processing.

  • Database types: Can range from traditional relational databases like MySQL to NoSQL databases like MongoDB, or data lakes and warehouses like Amazon S3 or Google BigQuery.

  • Security: Ensuring encryption and compliance with data protection regulations is crucial at this stage.

5. Data analysis and intelligence #

Data analytics tools and algorithms are applied to the processed data to derive actionable insights, which form the core value of many external data products.

Techniques: This could involve statistical analysis, machine learning algorithms, or more complex data science techniques.

Outcome: The goal is to create insights that are unique, valuable, and actionable for the consumers of the data product.

6. Data presentation layer #

This is where the data or insights are made accessible to the end-user, often through a user interface, API, or downloadable reports.

  • Formats: Data can be visualized through dashboards, presented in tables, or made accessible through APIs for more technical users.

  • Usability: The focus should be on making the data as understandable and actionable

9 types of external data product #

External data products come in a variety of forms to serve different needs across industries and use-cases.

Below are some of the most common types:

  1. Raw data feeds
  2. Aggregated data sets
  3. Analytical reports
  4. Dashboards
  5. APIs (Application Programming Interfaces)
  6. Machine learning models
  7. Geospatial data products
  8. Benchmarking tools
  9. Real-time alert systems

Let us understand each of them in detail:

1. Raw data feeds #

Raw data feeds offer a direct stream of raw data from the source to the end user. The data is often unprocessed or minimally processed.

Use-case: This type is suitable for organizations that have strong data engineering capabilities and want the flexibility to process and analyze data in their own way.

Example: Financial market data feeds that provide real-time stock prices.

2. Aggregated data sets #

These are collections of processed and summarized data, often organized around specific themes or industries.

Use-case: Useful for organizations looking for industry benchmarks, trends, or competitive analysis.

Example: A dataset that aggregates customer reviews for a particular type of product across various e-commerce platforms.

3. Analytical reports #

These are comprehensive reports based on thorough analyses of data, offering insights and recommendations.

Use-case: Suitable for businesses that need ready-made insights without the need for further analysis.

Example: A market research report that analyzes consumer behavior in the online grocery shopping space.

4. Dashboards #

Dashboards provide a graphical representation of key performance indicators (KPIs) and metrics, often updated in real-time.

Use-case: Useful for organizations that need to monitor key metrics continually and prefer a visual representation of their data.

Example: A real-time marketing dashboard that shows website traffic, user engagement, and conversion rates.

5. APIs (Application Programming Interfaces) #

APIs provide a way for businesses to query for specific data programmatically, often in real-time.

Use-case: Suitable for businesses that require seamless and real-time integration of external data into their own applications.

Example: A weather data API that provides real-time weather information for different locations.

6. Machine learning models #

These are pre-trained machine learning models that businesses can integrate into their own systems to perform specific tasks.

Use-case: Useful when an organization wants to implement machine learning but lacks the expertise or data to train their own models.

Example: A sentiment analysis model trained on customer reviews across different industries.

7. Geospatial data products #

These products provide data related to locations, such as maps, traffic, and demographic information.

Use-case: Suitable for businesses in logistics, real estate, or any industry where location data is crucial.

Example: A geospatial dataset that shows foot traffic patterns in different commercial areas.

8. Benchmarking tools #

These tools compare an organization’s performance metrics against industry standards or competitors.

Use-case: Useful for businesses that want to understand their performance in the context of broader industry trends.

Example: A tool that benchmarks advertising ROI against industry averages.

9. Real-time alert systems #

These systems send real-time alerts based on specific conditions or triggers in the data.

Use-case: Suitable for businesses that need to respond rapidly to changing conditions, like stock price changes or social media sentiment.

Example: A cybersecurity alert system that notifies companies of potential security breaches based on unusual data patterns.

Each type of external data product serves a different need and offers varying levels of value and complexity. Businesses must carefully evaluate their specific requirements, resources, and capabilities to choose the most appropriate data product.

Difference between external and internal data product #

External and internal data products serve different audiences, have distinct objectives, and are often constructed with differing considerations in mind. Below are the key differences:

CriteriaExternal data productInternal data product
AudienceExternal customers, businesses, or agenciesEmployees, departments, or internal decision-makers
Commercial AspectDesigned for revenue generation through sales, subscriptions, or licensingAimed at improving operational efficiency, not revenue generation
ComplexityGenerally plug-and-play with minimal customizationOften highly customized to meet specific organizational needs
Data Governance and SecurityStrict security and compliance due to legal obligationsLess stringent as data remains within organizational control
User InterfaceUser-friendly, intuitive interfaceMay be more utilitarian; users are trained

Summarizing it all together #

An external data product can range from simple datasets to complex analytics platforms and are made to be consumed by parties outside of the originating organization.

They are often monetized and are aimed at solving particular business problems or offering specific insights into market trends, consumer behaviors, or operational efficiencies.

In a world increasingly driven by data, external data products serve as a multifaceted asset that can not only generate significant revenue but also offer a slew of other advantages.

As organizations aim to thrive in this data-driven economy, the role of external data products as a critical business asset is undeniably compelling.

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