In the past and even today, data is often seen as a cost center. Businesses collect data, but they don’t do much with it. However, with technology, better Data management, Advance Analytics, and AI there has been a growing recognition of the value of data. A cultural shift towards data-driven decision-making has transformed data into a valuable asset. Now, businesses want to use data to drive decision-making, understand customers better, innovate, and gain a competitive edge.

For example, think of your bank that has data about your spending habits and patterns. By analyzing this data the bank could offer personalized budgeting tips or suggest the best investment options based on individual financial goals. This new service may be called Budgeting or financial insights. The bank may ask you to pay for this valuable information or this can be positioned as a value-added service by the bank to attract new customers.

We have numerous examples of Data products from financial companies like checking your credit score for free, Some financial companies offer online tools or calculators that assist users in creating personalized financial plans. Some offer a better way to manage or improve savings. These products are getting popular and offer a huge retention opportunity for customers.

DAAP is the future of data-driven organizations. As businesses become more data-driven and efficient in Data management they will need to find ways to monetize their data. DAAP provides a way to do this. By treating data as a product, businesses can generate new revenue streams and improve their bottom line

What is Data as a Product? How does it work?

Data as a product means treating data like a product or service that can be sold or shared to make money. What the enterprise is actually selling is access to its data or insights it generates from the data.

A good example is Facebook or Instagram segmenting customers on demographics, spending habits, and location with the advertiser to help them target their ads more effectively. Similarly, a lot of data sharing can happen between Financial services companies and healthcare providers to improve the quality of care to help them identify patients who are at risk for certain diseases. or retail company might share data on customer shopping habits with a transportation company to help them optimize delivery routes. If you are considering treating your data as a product, there are a few things you need to do:

• Identify your data assets: What data do you have that could be valuable to others? Like your financial data for millions of customers or their spending habits.
• Assess the quality of your data: Is your data accurate, complete, and up-to-date?
• Analyse Data: Here comes the exciting part – analyzing the data to unveil valuable insights and discover opportunities for creating value. This involves using advanced data analytics tools, clever machine learning algorithms, and other techniques to uncover patterns, trends, and correlations.
• Develop Data Products: Building tangible data products means the data insights are packaged into user-friendly formats like interactive dashboards, detailed reports, powerful APIs, or Data models.
• Develop a monetization strategy: The big question is, how will you sell or license your data? offer subscription fees for access to Data for a limited period of time.
• Build a data governance framework: How will you protect the privacy of individuals and businesses?
• Secure your data: How will you protect your data from unauthorized access?

By embracing the DaaP strategy and following these steps, businesses can unlock the full potential of their data, creating valuable products and services that truly make a difference in the market.

Mckinsey Defines Data as a product in five primary consumption archetypes. Read Unlock the full value of Data.

Data products are designed to facilitate specific methods of usage, or “consumption.” They are integrated with the necessary architecture that enables various business systems, including digital applications or reporting systems, to utilize or “consume” the data. Each business system possesses unique requirements for data storage, processing, and management, which are referred to as “consumption archetypes.”

An organization may have numerous use cases planned, but they generally align with one of the five primary consumption archetypes. By developing data products that cater to these archetypes, they can be repurposed for multiple business applications with similar archetypes with ease. Thus, data products are not only versatile but also adaptable, facilitating various methods of consumption across different business scenarios.

Benefits of Data as a Product

Thinking about data as a product, one could venture into the realm of symbiotic relationships in nature for an unusual perspective. In nature, we witness organisms working together for mutual benefit. The same principle can be applied to data. Data, when treated as a product, become akin to the “bees of the digital ecosystem.” Just as bees pollinate flowers while gathering nectar, leading to the propagation of plant species, data too circulates through systems, benefiting each touch point. As it travels, it spreads value through insights, driving business decisions, catalyzing innovation, and fostering new collaborations, just like bees fostering new growth.

The data’s journey, like that of the bees, results in rich, honey-like deposits of enhanced customer experiences and new revenue streams. As it buzzes from one business operation to another, it aids risk management and encourages data-driven decision-making. So, data as a product is not just a static asset but a dynamic entity facilitating symbiotic relationships in the digital ecosystem, analogous to the vital role bees play in nature.

Data as a product provides a framework for organizations to monetize their data by developing data products that provide value to customers. By treating data as a product, organizations can develop new revenue streams and improve customer retention by providing valuable insights and analytics.

The Role of AI and Machine Learning in Data as a Product

let’s look at it from the perspective of AI and Machine Learning as not just tools but active participants in the creation and evolution of Data as a Product.

1. Data Cultivators: Like a farmer tending to crops, AI and ML don’t just process the data, they ‘cultivate’ it. Through the training and refinement of models, they play a key role in nurturing raw data into a valuable data product, rich with insights.
2. Conversationalists: In interactive data products, AI and ML are not just responding based on data, they are ‘conversing’ with users. As they interact, they learn and adapt, making these conversations more engaging and informative over time, just like a human conversation evolves.
3. Predictive Storytellers: ML algorithms tell the ‘future stories’ of businesses by leveraging historical data. They can paint vivid pictures of possible future scenarios, just like a good storyteller, thus guiding businesses in their strategic planning.
4. Quality Guards: AI and ML also act as vigilant guards, keeping an eye on the quality of the data. They detect anomalies, fill gaps, and ensure that the data product is reliable and error-free.
5. Innovation Companions: As AI and ML continue to evolve, they open up new opportunities for the creation of innovative data products. They are like companions to businesses in their journey of exploration and innovation, guiding them to unseen paths and new possibilities.

From this perspective, AI and Machine Learning are not just passive tools in the process of creating and refining Data as a Product, but they are active participants and partners in the journey, playing crucial roles in nurturing, protecting, conversing, narrating, and innovating. This symbiotic relationship between AI/ML and data creates a dynamic, ever-evolving landscape of opportunities and possibilities.

Future Trends in Data as a Product.

Data as a Product (DaaP) is a rapidly evolving field, with new trends and technologies emerging all the time. In this section, we will discuss some of the future trends in DaaP.

Increased Use of Edge Computing: Edge computing refers to the practice of processing data at the edge of the network, closer to where the data is generated. As the volume of data continues to grow, organizations are looking for ways to process data faster and more efficiently. Edge computing can help to reduce the latency associated with transmitting data over long distances, which can improve the speed and accuracy of data processing.

Growth of Data Marketplaces: Data marketplaces are platforms that enable organizations to buy and sell data products. As more organizations recognize the value of data products, we can expect to see the growth of data marketplaces, which will provide a convenient platform for organizations to monetize their data and for customers to access valuable insights and analytics.

Increased Use of Blockchain: Blockchain technology provides a secure and decentralized way to store and share data. As concerns about data privacy and security continue to grow, we can expect to see increased use of blockchain technology in DaaP. Block chain technology can help to ensure the integrity of data products, making them more attractive to customers.

Growth of Explainable AI: Explainable AI refers to the practice of developing AI models that can explain their decision-making process. As AI becomes more prevalent in DaaP, it will become increasingly important to develop AI models that can be easily understood by humans. Explainable AI will help to build trust in AI models and ensure that customers have confidence in the insights and recommendations provided by data products.

Increased Focus on Ethical Considerations: As organizations continue to collect and analyze data, there will be increased scrutiny of the ethical considerations associated with DaaP. Organizations will need to be transparent about how they collect and use data, and ensure that they are complying with applicable laws and regulations. There will be increased focus on ethical considerations, including data privacy, bias, and fairness.

Conclusion

Data as a Product is a powerful strategy that businesses can use to unlock the value of their data. By treating data as a product, companies can create new revenue streams, improve customer engagement, and stay competitive in today’s data-driven economy. As companies continue to collect more data, we can expect to see more companies adopting this strategy to turn their data into valuable assets. Know more about SCIKIQ and learn all the platform capabilities like Data Integration, Data Governance, Data Curation, and more. Check the general FAQ on the platform.