Last updated on August 31st, 2023
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.
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.