Over the years, data has transitioned from being just a by-product of digitalization initiatives at enterprises to being the most critical asset of any organization. It has a direct impact on revenue, decision-making, innovation, and growth potential of the business. Studies show that data-driven businesses are 19 times more likely to be profitable than those that are not.
Today, data has become a focal point of business growth, and its importance has led to enterprises productizing their data into consumable entities that can trigger different outcomes when consumed by different business analytics services or systems.
What is a Data Product?
In simple terms, a data product can be defined as a ready-to-use data entity for different business analytics systems in different use cases on demand. They have standardized formats, interfaces for consumption by different services, and refresh cycles to maintain integrity and accuracy at any given time. A mature data product is discoverable, understandable, accessible, interoperable, and valuable.
How is a Data Product Different from Simple Business Applications?
It is natural to assume that data products are just like any other application that provides services to customers. However, there is a key difference. A simple application like a banking app may let users perform a service like sending money to someone or checking the account balance. Similarly uploading a picture on Instagram is also a simple application. But Instagram and the banking app are not to be confused as data products. A data product is something that offers services based on continuous learning and analysis of data from present, historic, and potential future scenarios. The best example would be the movie recommendations on Netflix or the continuous weather updates delivered via different weather apps.
These transactional services are not static applications, but they are products that learn, adapt, and grow with the data generated based on the preferences of the user in any given scenario. The recommendation engine in Netflix is a data product that was designed purely for continuous consumption by their movie display feed. The service constantly updates the displayed list based on the usage history of different user profiles sharing the Netflix account. Data products are designed to provide a continuously evolving and personalized transactional service and not a monotonous service routine.
The Evolutionary Roadmap of Data Products
In the initial days, data products were leveraged primarily for supplying insights that drive decision-making. While they excelled in this perspective, the principles of productizing data were in fact just the beginning of a new chapter for enterprises of using their data to create value.
Over time, data products found usage in several areas where analytical outcomes determined a course of action.
Let us explore some of the top use cases for data products today:
- Personalized Recommendations
From music to movies, e-commerce product displays, and even credit card offers, data products are leveraged by businesses in different sectors to deliver a range of personalized services to their customers. Data products can deep dive into the historical data consumption behavior of customers and create tailored recommendations that reflect their interests. This allows brands to connect more personally with customers thereby enhancing loyalty.
- Secure Experiences
Data products play a pivotal role in the financial sector by enabling better fraud detection and proactive vulnerability analysis. By constantly monitoring suspicious signals or behavior hidden within data streams, they help identify threats before they create impact. This helps in strategic risk management which is key for businesses that deal with large digital transactional experiences like banks or ecommerce.
- Proactive Diagnosis
From hospitals to manufacturing factories, data products can help decision-makers pick up signals that indicate risky or harmful impacts on patients or machinery. For manufacturers, this will help in setting up preventive maintenance cycles for optimizing machine health and ensuring disruption-free operations. In a clinical environment, doctors can catch illnesses before they start to impact normal body functions. Additionally, advanced predictive diagnostics help in designing more effective care routines that can help save countless lives.
The Future of Data Products
Data products will continue to transform the modern business landscape. With the ever-expanding digital economy, enterprises have more than enough data to use with data products and churn out innovative services for their customers. Data products will be a key competitive advantage for organizations in the coming years. Better data-driven experiences will help them get closer to customers and fulfill their demands with attention, thereby earning long-term loyalty.
However, monetizing data with data products is not without its fair share of complexities and challenges. For one, privacy and ethical concerns must be addressed when enterprises connect heaps of data streams from their digital channels to their data products to build new services and revenue streams. Additionally, technical roadblocks can create cost escalations, skill deficiencies, and other management overheads while building a successful data product.
The key to building the most sustainable and reliable data product is to have them created with a solid foundation from ground zero. This requires strategic expertise in creating data models, and acquisition approaches, versatility in understanding and connecting business outcomes to analytical frameworks, and much more. To succeed, businesses need a technology partner like Wissen to handle the end-to-end journey of their data products. Talk to us to know more.
