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Understanding Data-as-a-Product (DaaP): Principles, Implementation, and Benefits

Treating data as a product means viewing it as a valuable asset that can be curated, managed, and monetized just like a physical product.

Why should marketers care about the data-as-a-product (DaaP) concept?

Treating data as a product ensures it is accurate, consistent, and up-to-date, leading to better decisions and ultimately driving higher revenue and ROI. Reliable, well-managed data allows marketing teams to gain deeper insights into customer behavior. This helps optimize targeting and segmentation, and personalize marketing efforts to enhance customer engagement and conversion rates. Accurate data enables more precise tracking of campaign performance, allowing marketers to allocate budgets more efficiently and focus on high-return strategies.

Now that you know the why, let’s dive into the fundamentals, how to implement it in your company, and key considerations.

What Is Data-as-a-Product (DaaP)?

Data-as-a-Product (DaaP) is an approach to data management where data is treated as a product that is curated, maintained, and delivered to users with the same level of quality and care as a physical product.

DaaP involves rigorous data governance, comprehensive documentation, and user-friendly interfaces, making data easily discoverable and usable for various applications. This approach ensures that data is not just a byproduct of operations but a valuable asset that is carefully managed to support data-driven decision-making.

Data Products vs. Data-as-a-Product (DaaP)

Diving deeper into the topic, we need to distinguish between two related but distinct concepts: data product and data-as-a-product.

DaaP is a holistic approach to data management that covers the entire data lifecycle, from creation and processing to maintenance and delivery.

Data products are specific tools or outputs derived from data, such as dashboards, reports, predictive models, and customer segments. These products are the end results that marketing teams use to inform strategies, track performance, and make decisions. They are tangible, ready-to-use assets that provide insights and drive actions.

Often marketing teams view data products as isolated outputs rather than part of a holistic data management system. Marketers might spend excessive time cleaning and preparing data for each project instead of adopting a consistent approach like DaaP. This leads to delays and increased operational costs.

Aspect Data products Data-as-a-product (DaaP)
Purpose Designed to solve specific problems or deliver specific insights. Manages data with a broad, strategic perspective, aiming to make it accessible and useful across the organization.
Scope Often limited to specific functions or insights; tailored to particular business processes. Encompasses the entire lifecycle of data, from creation to delivery.
User engagement Targeted at specific user groups, such as marketing analysts, managers, or specific business units. Requires engagement from various levels of the organization, promoting a broad adoption of data-centric practices.
Management Project-based and situational, focusing on delivering specific functionality or outcomes. Involves ongoing management akin to traditional product development, with iterative improvements.
Strategic value Provides value through targeted applications and insights, often in a specific operational context. Enhances the overall data culture and strategic capabilities, positioning data as a core organizational asset.
Integration Integration is usually within defined operational contexts. Requires integration across various business domains and functions.
Lifecycle The lifecycle might be finite, concluding with the end of the project or solution lifecycle. Has a continuous lifecycle that requires regular updates and management to stay relevant and useful.
Outcome Orientation Directly tied to business outcomes linked to specific tasks or processes. Oriented towards creating a sustainable, scalable, and efficient data environment that supports multiple outcomes.

Core Principles of Data-as-a-Product

So now that you understand what data-as-a-product is and what it encompasses, let's dive into the core principles that make data a valuable asset for your marketing strategies. These principles ensure that data is treated with the care and attention it deserves, turning it into the new oil for your business. 

1. Data quality

Data quality is the foundation of data-as-a-product. High-quality data is accurate, consistent, and up-to-date, ensuring that all marketing decisions are based on reliable information. 

To ensure data is of high quality, start from the very beginning, with data collection and processing. Use ETL (Extract, Transform, and Load) tools to streamline the data preparation process. These tools automate the extraction of data from diverse sources, transform it into a consistent format, and load it into a centralized system for analysis. This automation significantly reduces the manual effort and likelihood of errors.

Improvado is a marketing data pipeline and analytics platform.
A schematic representation of how Improvado ETL works

Improvado provides a solid data foundation for a cohesive analytics framework. The platform aggregates data from 500+ marketing and sales platforms, internal systems, and offline sources, automatically prepares it for analysis, and securely loads the data to a data warehouse or a BI tool of your choice. Improvado helps brands establish the foundation of DaaP and derive real-time, actionable insights from their data.

2. Data accessibility

Data should be easily accessible to everyone who needs it. This means having user-friendly platforms and tools that allow marketing teams and other business users to quickly find and use the data they need. For instance, an analytics tool with natural language processing that marketing specialists can access without technical assistance ensures that campaign adjustments can be made swiftly and based on real-time data insights.

Improvado AI Agent can handle the majority of questions you would typically ask your data team.

Improvado AI Agent is a conversation analytics and self-service BI platform that enables seamless data exploration, analysis, and visualization through commands in plain English. The agent is connected to your marketing data set and has a chat interface where you can ask any ad-hoc questions, build dashboards, analyze data, and more. 

3. Data governance

Data governance is another core principle of data-as-a-product. It involves setting policies and procedures to ensure data is managed correctly and securely. This includes defining who has access to data and what they can do with it, compliance with regulations, and adherence to privacy standards. 

Consider a scenario where different team members are responsible for various marketing channels, product lines, regions, or clients. Without data governance, each person might interpret what to track and how to record it differently. This inconsistency makes it difficult to compare performance across different segments of the business accurately. This could lead to misguided strategies that misallocate resources, overlook potential opportunities, or fail to address underperforming areas.

One example of a marketing analytics data governance tool is Improvado Workspaces. Workspaces allow users to create separate child environments within a single, overarching parent environment. These child environments can be tailored to specific accounts or data sources, and the admin can manage who has access to which data.

For instance, you might have an Improvado analytics environment for the entire brand, but separate analytics for each product line in distinct workspaces.

To monitor adherence to data governance standards, consider leveraging an automated solution like Cerebro. Cerebro is an AI-powered data governance platform that monitors compliance with operational and business data guidelines and alerts you to deviations from established rules. All rules are set using natural-language input, in plain English.

4. Data consistency

Consistency in data means that the same data is available and identical across all platforms and tools. This prevents discrepancies that can lead to misinformed decisions. For example, if the sales and marketing departments use different data sources with inconsistent information, it can result in misaligned strategies. Consistent data ensures that all teams are on the same page.

5. Data usability

Another core principle of data-as-a-product is data usability which ensures that data is well-organized and easy to analyze. 

Usable data should be presented in a format that allows marketing analysts to extract actionable insights quickly. For instance, dashboards that visualize key performance indicators (KPIs) in an easily digestible format help marketing specialists track campaign performance and make data-driven decisions efficiently.

6. Data lifecycle management

Managing the data lifecycle means overseeing data from creation to deletion. This includes data collection, processing, storage, and eventual disposal.

Effective lifecycle management ensures that outdated or irrelevant data is not clogging up systems, allowing marketing teams to focus on the most current and valuable information. For instance, conducting regular audits of marketing databases to remove obsolete campaign data can enhance system performance and ensure that analysts are working with the most up-to-date information. Implementing data classification systems can help categorize data based on its relevance and usage frequency, making it easier to identify which data should be prioritized and which can be archived or deleted.

Another example is the use of version control for marketing materials and content assets. By managing different versions of data and keeping only the most current and relevant versions readily accessible, marketing teams can avoid confusion and ensure consistency in their campaigns.

7. Data integration

Integrating data from various sources ensures a comprehensive view of the customer journey. This means combining data from CRM systems, social media, website analytics, and more to create a unified view. This holistic perspective allows marketing analysts to understand customer behavior better and tailor strategies accordingly.

By following these core principles, marketing teams can leverage data-as-a-product to enhance their strategies, optimize campaign performance, and drive better business outcomes.

Challenges and Solutions in Implementing Data-as-a-Product (DaaP)

Implementing data-as-a-product can be challenging due to technical complexities and the need for organizational adaptation. However, with targeted strategies, these challenges can be effectively managed to maximize the benefits of DaaP.

Technical and organizational readiness

Adopting Data-as-a-Product (DaaP) requires a robust technical infrastructure that supports large data sets and complex analytics. This often means upgrading existing systems, which can be costly and time-consuming. Additionally, the integration of advanced analytics tools and ensuring their compatibility with current systems can pose significant challenges. To address this, organizations should consider investing in scalable, cloud-based infrastructure that can grow with their data needs. 

Alongside technical upgrades, fostering a data-driven culture is crucial. Training programs and workshops can help ease the transition, encouraging employees to embrace data-driven decision-making processes. Leadership should also champion the use of data in strategic planning and daily operations to reinforce its importance and integrate data-centric thinking into the company culture. 

Aligning data strategy with business goals

Ensuring that data strategies align with overall business goals can be challenging. Misalignment can lead to wasted resources, as data initiatives that do not directly contribute to business objectives can consume valuable time and budget without delivering tangible benefits. 

For example, a company might allocate significant resources to gather and analyze social media data to enhance brand awareness metrics, but if the current business objective is to increase sales conversions through targeted email campaigns, this data initiative may not directly contribute to achieving that goal. As a result, the effort and budget spent on social media analytics might not deliver tangible benefits related to the primary business objective, leading to wasted resources.

Involve key stakeholders in the data strategy planning process from the outset. This includes executives, department heads, and other decision-makers who understand the core objectives and priorities of the business. Regularly review and adjust data initiatives to ensure they support business objectives. 

Ensuring Real-Time Data Availability

Many business decisions require real-time data availability, but ensuring that data is continuously updated and accessible can be technically challenging. A significant number of companies still rely on post-campaign optimization because they can't aggregate and map data quickly enough to make timely adjustments during the campaign. This delay in data processing and availability can lead to missed opportunities, as decisions are made based on outdated information, potentially resulting in suboptimal campaign performance and wasted resources.

Automated data processing tools tailored to specific use cases, like Improvado, can significantly enhance real-time data availability. Improvado is a marketing analytics platform with native data connectors to over 500 marketing and sales platforms, along with pre-built data models that map and transform data efficiently. This allows for the presentation of analysis-ready data in a near-real-time manner. Setting up real-time dashboards and alerts with these tools can provide immediate visibility into key metrics and issues, enabling more agile and informed decision-making.

What DaaP Means for the Future of Your Brand

Adopting a data-as-a-product approach represents a transformative shift in how organizations manage and leverage their data. By treating data with the same rigor and strategic importance as any other product, companies can create a more agile and responsive marketing function that is capable of adapting to real-time insights and rapidly changing market conditions.

Adopting a data-as-a-product approach positions companies to be more proactive rather than reactive. With real-time data insights, businesses can anticipate market trends, identify emerging opportunities, and make informed decisions quickly. This forward-looking capability can give organizations a competitive edge, enabling them to stay ahead in a dynamic and fast-paced market landscape.

Frequently Asked Questions

What is data-as-a-product (DaaP)?

Data-as-a-product (DaaP) is an approach where data sets are treated as standalone products, focusing on quality, usability, and user satisfaction throughout their lifecycle. It applies product management principles to make data accessible and actionable for end users like business analysts, marketers, and senior management.

How does DaaP differ from traditional data products?

Unlike traditional data products like a dashboard or report that are designed to address specific issues or deliver insights, DaaP adopts a holistic approach to managing data across its entire lifecycle. It aims to make data easily accessible and useful across an organization, enhancing the strategic value and integration of data into daily operations.

What are the core principles of Data-as-a-Product?

The core principles include user-centric design, quality and reliability, lifecycle management, scalability, and strong security and governance measures. These principles ensure that data products are effective, secure, and consistently meet user needs.

What challenges might organizations face when implementing DaaP?

Challenges include managing the complexity of integrating various data sources, ensuring data privacy and security, and adapting organizational culture to a data-centric approach. Solutions involve strategic planning, investment in technology, and fostering a culture that embraces data-driven decision-making.
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