Data Products-Your Blueprint to Maximizing ROI

A survey by Harvard Business Review Analytics Services revealed that 89% of business leaders believe data products and analytics are crucial for their organization's digital transformation efforts.

According to a study by McKinsey, companies that leverage data-driven insights are 23 times more likely to acquire customers, six times more likely to retain customers, and 19 times more likely to be profitable.

A survey by BARC Research found that 83% of organizations consider data products and analytics as assets essential for their digital transformation initiatives.

It's evident from the statistics above that data products will continue to serve as the cornerstone for informed decision-making and sustainable business growth in 2024 and beyond. Data products are not just assets but catalysts driving business growth. The journey to sourcing reliable data products in driving informed decision-making for growth and innovation is fraught with several challenges. This article discusses data products in depth, their features, advantages, and the challenges business leaders face in sourcing reliable data products in an organization.

What is a Data Product?

Data products are valuable reusable assets engineered to serve specific purposes effectively. These products play a crucial role in the data-driven landscape by integrating information from relevant source systems, processing the data, ensuring compliance, and making it instantly accessible to authorized users. The primary function of data products is to shield data consumers from the complexities of underlying data sources, making the data easily discoverable and accessible as a valuable asset.

 Here is a brief by Scott Middleton highlighting what is a data product and why are they important for business growth.

Data Product Definition

A data product is a reusable data asset that bundles data together with

everything needed to make it independently usable by authorized consumers. -K2View

A data product is a broad definition that includes any product or feature that utilizes data to facilitate a goal. -ThoughtSpot

You can think of a data product as a self-contained data “container” that directly solves a business problem or is monetized. -Forbes

Typically corresponding to one or more business entities, data products are designed to be independently usable by authorized consumers. Examples of data products include tools or applications that process data and generate insights to aid in decision-making. The concept of data products is integral to the data mesh framework, emphasizing decentralized, domain-oriented data ownership, self-service platforms, and federated data governance.

A data product is thus a reusable, self-contained unit that adheres to best practices for delivering data, creating meaningful insights, and driving informed business decisions in the ever-evolving landscape of data analytics and business intelligence.

Now that we know precisely what data products are, let's talk about why they're super important for businesses to work well.

Why do businesses need quality data products?

Many reasons drive the demand for quality data products, each contributing significantly to the success and sustainability of businesses in this data-driven era. The majority of them have been discussed below.

• Quality data products serve as reliable business guides, facilitating informed decision-making with accurate and timely information.

• These products enhance operational efficiency and save costs by rectifying errors in data and streamlining work processes.

• They uncover new revenue opportunities by studying accurate and reliable data; guiding smart business moves for growth and financial success.

• Quality data products act as guards, finding and fixing mistakes to ensure compliance and mitigate risks, preventing legal and financial troubles.

• They play a crucial role in maintaining customer trust and brand reputation by ensuring accurate customer information and smooth operations, building a positive image in today's data-centric environment.

As we talk about why businesses need top-notch data products, we must address a common mix-up: the confusion between data and data treated as a product. Let's unravel this distinction and understand why it matters for businesses.

Data Product vs. Data as a Product

A data product is a valuable output created by processing and transforming raw data into a meaningful and actionable format. It goes beyond information collection, incorporating machine learning algorithms and artificial intelligence to generate insights, predictions, or recommendations. Examples of data products include recommendation engines, predictive analytics tools, and personalized user experiences. Organizations create data products for specific purposes within teams, providing tangible value by leveraging data for automated decision-making and improved operational efficiency. 

On the other hand, "Data as a Product" refers to treating data itself as a valuable commodity that can be packaged, marketed, and sold to external entities. In this context, raw data or datasets are products with inherent business value. An organization's data scientists can monetize their data by offering it to external parties, such as researchers, businesses, or developers, who find value in using that data. Unlike data products that focus on insights and applications, data as a product emphasizes the raw information's intrinsic value and marketability as a standalone entity.

Here is another common misconception cleared by Anuj Agarwal around ‘data’ and ‘data products’.

Hoping the confusion around the two terms has been cleared up; it is time to explore the characteristics of a data product that make it so valuable for business users and data teams.

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Features of a Data Product

When building a data product, an organization must keep in mind a set of crucial features to ensure its effectiveness and value:

• Quality assurance is fundamental for reliable and accurate insights from data products, addressing issues proactively, and prioritizing data quality as a business concern.

• Data products are built to handle varying data volumes and complexities, ensuring scalability and flexibility to adapt to the evolving requirements of different domain data teams.

• Integration capabilities enable seamless integration with existing systems, databases, or applications, facilitating smooth data flows and interoperability across different platforms.

• User-friendly interfaces in data products make them accessible to a wide range of users, including non-technical stakeholders, simplifying data exploration for informed decision-making.

• Continuous improvement and iteration are inherent in data products, refining functionality, accuracy, and relevance over time based on feedback and evolving business requirements.

• Security measures, crucial for self-service analytics, control access and adhere to privacy standards, ensuring only authorized individuals access sensitive data for compliance.

• Discoverability is inherent to data products, ensuring easy utilization across organizational departments and utilizing clear descriptions and data catalogs to enhance accessibility.

All these features of a data product collectively contribute to the effectiveness, usability, and value of data products in driving informed decision-making and achieving business outcomes.

Data Product Examples

Let us explore a few examples of data products that highlight the diverse applications of data products across various industries, showcasing their versatility and impact on user experiences and business operations.

• Netflix revolutionized the entertainment industry by leveraging data to provide personalized content recommendations, enhance user experience, and set a benchmark for streaming services.

• HealthifyMe utilizes data to offer personalized health and fitness solutions, providing users with tailored meal plans, exercise routines, and insights for managing their well-being.

• Amazon leverages data productization for personalized recommendations, efficient supply chain management, and targeted marketing, making it a prominent example in the e-commerce industry.

• PayPal data engineers deploy data products for fraud detection, risk management, and personalized financial insights, contributing to secure and seamless online transactions.

• Google Search employs sophisticated data algorithms to deliver highly relevant search results, showcasing the power of data products in information retrieval and user experience enhancement.

• Uber relies on data products and data pipelines for dynamic pricing, route optimization, and predictive analytics, shaping the future of transportation services and enhancing efficiency for drivers and riders.

Having explored real-world examples of data products across industries, let's now shift our focus to the tangible advantages that these innovative solutions bring to enterprises.

Advantages of a Data Product

Data products have emerged as powerful assets, offering a range of advantages that distinguish them from traditional data projects. From an enterprise perspective, data products offer the below-mentioned distinct advantages that align closely with organizational objectives.

• Data products are inherently business-driven and outcome-focused. Their development aligns closely with business objectives and product management principles, ensuring that the outcomes generated contribute directly to better business outcomes.

• The agile nature of data products enables incremental value delivery. They can adapt to changing requirements, allowing for the incremental delivery of features and functionalities, ensuring that value is continuously provided.

• One of the standout advantages of data products is their reusability. Built once but used repeatedly, they contribute to operational efficiency by eliminating the need to recreate solutions for similar requirements.

• Data products are designed to be future-proof in terms of data architecture. This forward-looking approach ensures that the underlying data infrastructure can adapt to technological advancements and evolving business needs.

• The trust and integrity of data are paramount, and data products play a crucial role in enhancing both. By consistently delivering high-quality, accurate information, these products build and maintain trust in the data.

• Data products foster collaboration by creating a common language between business and IT stakeholders. This collaborative approach ensures that both sides can effectively communicate and understand the intricacies of data utilization.

After recognizing the perks of data products, let's shift our focus to tackling challenges in finding dependable ones.

Addressing Challenges in Finding Reliable Data Products

Let's consider the example of an e-commerce company with multiple departments, each responsible for different aspects of the business, such as sales, marketing, supply chain, inventory management, etc. Each department generates its data, including sales transactions, customer demographics, inventory levels, etc. Suppose the sales team needs to analyze customer purchasing patterns to optimize their marketing campaigns. The sales team will need data on transaction histories, product preferences, and other demographic data. Other than this data, they would also want to leverage models built by the marketing department for segmentation and targeting, those from the finance department for customer profitability analysis, and those from the operations department for inventory management and demand forecasting.

Every department could have its isolated data warehouse, making it difficult for teams to share and collaborate on data-driven initiatives. For example, the marketing team may have built predictive models based on the customer demographics to target specific segments, while the sales team has insights from sales transactions. However, these models and insights are not easily accessible by other departments, leading to duplication of efforts, increased costs,  and missed opportunities for cross-functional collaboration.

Models built by one team can have valuable insights relevant to other departments in the company. In our example above, the inventory management data team may benefit from insights from the sales team's predictive models to optimize stock levels and inventory turnover. Here is a list of other challenges that the teams are likely to face along the way:

1. Each department has its data warehouse, making it hard for teams to share and work together on data-related projects easily.

2. Teams find it difficult to access and use insights and models created by other departments because there is no central place for everyone to share.

3. Different departments cannot work together without a shared platform and benefit from each other's insights and models.

4. Teams may end up doing the same work independently because there is no system to let them know what others are working on.

5. Necessary resources like models and insights are not used effectively across the company because there's no easy way to share them.

6. Departments need help to improve their operations as they can't use insights from models created by other teams to manage things like stock levels and inventory turnover.

7. The way data is stored and shared makes it hard for everyone in the company to make good decisions based on data, as the information they need is only readily available to some.

Thus, without a centralized platform for accessing and sharing models and insights, valuable resources remain underutilized across the organization. An efficient data management system, therefore, becomes a requirement for such organizations. The question that is now likely to pop up in the mind of a data product owner is whether a dedicated platform exists that eases the process of deploying data products and their integration. The answer to it can be found in the next section.

ProjectPro’s Enterprise Data Marketplace for Sourcing Reliable Data Products

Building upon our example of the e-commerce company struggling with diverse data warehouses and models, it is clear why having a centralized data product platform to access data assets is essential. ProjectPro's Data Products Marketplace is a centralized data access point that consolidates data assets from across the organization into a single, accessible platform, helping data product owners within the organization to break down silos and make the most of their data. With ProjectPro, the sales team's predictive models and other valuable data assets become readily available to all relevant stakeholders when needed. This seamless accessibility fosters collaboration, enables cross-functional insights and accelerates data-driven decision-making processes. The centralized nature of ProjectPro's data marketplace ensures data integrity, security, and governance, mitigating risks associated with data silos and unauthorized access.

About the Author

Manika

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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