It is a well-known fact that data lies at the core of the decision-making process. But all these years, organizations have had to invest in sophisticated special purpose data analytics tools to make sense of the data generated from their systems, tools, and applications. Imagine if there was a way for these systems to internally process the data they generate without integrating with advanced analytics platforms or solutions!
This future has already been carved in the evolution of data products. Learn how the emergence of data products has made building and testing them a real challenge.
Data Products Have Become a Mainstay
Today, organizations want to embed data in every decision, interaction, and process. But traditional approaches to analyzing and processing data via advanced analytics tools are becoming extremely sluggish and time-consuming. Instead of solving critical business problems that take months, today’s decision-makers need to embrace innovative data techniques that resolve challenges in weeks, days, or even hours.
As data-driven decision-making becomes the only way to propel a business toward growth and success, data products have become extremely popular. By building data capabilities into user workflows and product features, these products can:
- Enable non-technical users to directly solve business problems without having to rely on skilled data engineers or scientists.
- Deliver high-quality, in-demand data to decision-makers and uphold the required levels of quality and accuracy of enterprise data.
- Eliminate barriers between those who understand data and those who understand the business use case while allowing them to maintain centralized governance and control.
Benefits of Emergence of Data Products
The rise of data products has transformed how organizations operate, offering numerous advantages. These products, derived from large datasets and advanced analytics, enable businesses to make informed decisions, enhance customer experiences, and drive innovation. Here are some key benefits:
- Informed Decision-Making: Data products provide actionable insights, enabling businesses to make data-driven decisions with confidence.
- Enhanced Customer Experience: By analyzing customer data, organizations can tailor their offerings to meet customer needs more effectively.
- Increased Efficiency: Automation and optimization through data products streamline operations, reducing costs and improving productivity.
- Innovation and Competitive Advantage: Leveraging data products allows companies to stay ahead of market trends and develop innovative solutions.
- Risk Management: Data products help in identifying and mitigating risks by providing predictive analytics and real-time monitoring.
But Building and Testing Them Doesn’t Come Easy
Products that weave in an element of data can take business decision-making to great levels. But creating, testing, and delivering such products require addressing the entire life cycle of data—from requirements to creation, usage, and eventually to end of life. Let’s look at the top challenges of building and testing data products:
1. Ensuring Ease of Use
Data products are used by users from different departments for different purposes and with different maturity levels. Building products that fit unique use cases while offering the same level of convenience to technical and non-technical users isn’t easy.
2. Maintaining Quality
A key challenge most enterprises face when it comes to data is trust. Since business users have little faith in the quality and accuracy of enterprise data while building data products, it becomes critical to leap from software testing to quality assurance. As data products collate data from various sources, organizations must invest in modern data quality approaches to detect and fix anomalies before sending them to production.
3. Enabling Self-Service
Data products must allow non-technical users to interact with data in a user-friendly way. Enabling a self-service digital storefront allows them to easily search, preview, and filter data quickly. However, constantly updating the knowledge base, monitoring and analyzing usage, and managing feedback can get extremely challenging.
4. Managing Multiple Products Across Their Lifecycle
As organizations realize the importance of data products, the enterprise ecosystem is brimming with such products. Managing multiple products across multiple use cases requires a centralized approach to data creation, usage, and consumption. Organizations must craft a consistent process for creating, publishing, customizing, and delivering data and consistently act on reports for future optimization.
5. Ensuring Compliance
Data products, once implemented, find their way to an extended enterprise of suppliers, partners, distributors, and even customers. To restrict data from falling into the wrong hands, high levels of authentication and authorization are needed. Organizations need to adopt DevSecOps to:
- Enable policy-driven data product management
- Have full control of who can view, use, and export data
- Maintain an updated audit trail of their activity
6. Making Data Discoverable
Data products need to be highly reusable. For instance, if an organization has invested in developing a data product that captures and analyzes customer data in real-time, it should be leveraged by various departments. For this to happen, data products must be stored in a registry with adequate metadata descriptions. Since data constantly evolves, several changes to the schema need to be made to avoid errors.
Seamlessly Build and Test Data Products with Forgeahead
Organizations with a data-driven culture can create truly differentiated customer and employee experiences and enable growth in profound ways. But unlocking the value of data that is expected to grow to 180 zettabytes by 2025 is exceptionally daunting for enterprises.
As data volumes grow, product architectures become ever more complex, and skilled data engineers become hard to find, data products offer a great way to quickly act on data. But building and testing these data products is not everyone’s cup of tea. If you want to consistently develop and deploy cutting-edge data products to increase the agility of decision-making, you need to engage with expert partners who understand the nuances of data management.
Being a leading digital product development company, Forgeahead is adept at meeting complex data requirements while ensuring the right levels of software quality. With expertise in cloud architectures, DevOps, automation, and serverless technologies, we can help in crafting fully-responsive, cross-platform data products.
Begin your journey of developing data products with Forgeahead! Explore our range of development capabilities. Or contact us to speak to our expert!
FAQ
Why are data products important?
Data products are important because they transform raw data into actionable insights, enabling businesses to make informed decisions, optimize operations, personalize customer experiences, and create new revenue streams.
What are some common examples of data products?
Common examples include business intelligence dashboards, recommendation engines, predictive analytics tools, data marketplaces, and IoT analytics platforms.
How do data products differ from traditional software products?
Data products focus on utilizing data to provide insights or functionality, often involving complex data processing and analytics. Traditional software products may not inherently focus on data but rather on performing specific tasks or functions.
What skills are needed to develop data products?
Developing data products typically requires skills in data science, data engineering, software development, machine learning, and domain-specific knowledge.
What is the future outlook for data products?
The future outlook is highly promising, with advancements in AI and machine learning, increased data availability, and growing demand for data-driven insights driving continued innovation and adoption of data products.
What role does user experience play in data products?
User experience is critical, as it ensures that the insights and functionality provided by the data product are accessible, understandable, and actionable for the end users, thereby driving adoption and value.
