The 4 most challenging (yet critical) capabilities to build within a data product

Data products like customer data platforms, IoT applications, supply chain analytics, recommendation engines, and the like are essential to modern data-driven businesses. However, building helpful and robust capabilities within these products can be complex and demanding. 

Even if certain capabilities are critical to the success of a data product, yet pose seemingly insurmountable challenges to launch, a DevOps-aligned approach can bring them to fruition.

Common data product complexities

First, a rundown of the primary complexities of the most common data products.  

Customer data platforms

These platforms consolidate data from websites, apps, and service records to provide a unified view of each customer. The main challenges include maintaining high data quality and ensuring sensitive information (PII) is protected. 

Recommendation engines

These tools analyze user behavior to deliver personalized suggestions. Building effective recommendation engines requires scalable data processing, reliable AI models, and real-time data capabilities to ensure accuracy and relevancy.

Fraud detection systems

Designed to monitor transactions and identify suspicious activity, these systems face the dual challenge of implementing robust security measures and integrating real-time data streaming with advanced machine learning algorithms to maintain compliance.

Business intelligence (BI) tools

These tools collect, analyze, and visualize data from various sources to support data-driven decision-making. Integrating and synchronizing data from multiple inputs can be a complex process. So can building traceability to back up any reliability or accuracy concerns from BI analysts or executives. 

Supply chain analytics platforms

These platforms improve logistics, forecasting, and risk management by processing vast amounts of data captured at, ideally, every stage of the supply chain. The challenge lies in managing the volume of data efficiently while ensuring timely insights. These platforms also deal with complex physical sensors requiring maintenance and synchronization. 

Broadly speaking, the complexities of building these internal data products often revolve around four core pillars of effective data products:

1. Advanced data analytics
2. Real-time data processing
3. Data governance and security
4. Intuitive user experiences

Understanding these pillars can guide the teams that build, manage, and use these data products toward optimizing their overall data pipelines and achieving better outcomes.

1.   Advanced data analytics

Advanced data analytics — those fuelled by the predictive power of AI — are an essential part of effective, modern data products. These products work incredibly fast, processing huge amounts of data much more efficiently than any human could. 

This capability allows the data product to process and analyze large datasets to uncover patterns, trends, and insights that can drive strategic decision-making. For example, in the retail industry, data products with advanced analytics can predict customer preferences and optimize inventory levels, reducing waste while increasing sales. 

To implement this capability effectively, businesses should invest in powerful analytical tools and platforms that support machine learning and artificial intelligence. This ensures that data products not only handle complex queries but also provide predictive insights that add significant value.

2.   Real-time data processing

The ability to process data in real-time (as it’s received) is a game-changer for any data product. In sectors like finance, EdTech, and e-commerce, where decisions need to be made in milliseconds, real-time data processing is indispensable. It enables continuous data flow and instant analysis, tying into the advanced data analytics above. 

To build this capability, organizations can leverage scalable cloud-based solutions that offer real-time data streaming and processing. Technologies like Apache Kafka and AWS Kinesis are popular choices that help manage data velocity and ensure that systems remain responsive under high loads.

3.   Automating data governance and security

With data breaches becoming increasingly common, advanced data security is a capability with endless applications. Data products must be designed to protect sensitive information against unauthorized access and ensure compliance with regulatory standards such as GDPR and CCPA.

Effective implementation involves adopting comprehensive security measures like encryption, access controls, and regular security audits — and the driving force of all these processes is automation. Additionally, integrating data security into the product’s architecture from the ground up ensures that protective measures are not merely an afterthought but a core component of the system's design.

Automating data governance across the pipeline must also include the foundational database layers. As database schemas (structures) evolve and update to meet the needs of rapid data processing and newly aggregated sources, they need a change management approach that integrates with the rest of the deployment process.

4.   Intuitive user experience

A data product’s value depends on how easily users can interact with it. An intuitive user experience ensures effortlessly navigating the product, interpreting the data, and deriving actionable insights without a steep learning curve. 

For instance, a business intelligence dashboard with clear visualizations and interactive elements can empower users to make data-driven decisions quickly. One report found that “up to 73% of data collected within an enterprise goes unused.” It’s essential to make datasets more accessible so information can be utilized effectively by the relevant departments.

Involving end-users in the design process through feedback and usability testing helps in understanding user needs and refining the interface to ensure it’s both functional and engaging.

What makes a good data product?

A solid, reliable data product can serve many purposes for a business, from turning raw data into valuable insights to simplifying complex data. This provides clarity and a top-level oversight into the intricacies of all of the data owned by a company.

A good data product should be built for discoverability and seamless interoperability, leveraging machine interfaces to support real-time operations. It needs to be comprehensive enough to accommodate a domain-specific universal schema and deliver clean, accurate data. 

Robust data control and monitoring systems are crucial, alongside adherence to global best practice standards for data processing. Most importantly, a great data product isn’t static — it should be continuously updated to stay accurate and relevant over time. This must include the ability to support building draft pipelines to meet a business objective. 

“When building a draft data pipeline to achieve a specific data product, your process needs to be flexible and easy to change,” explains Jenn Lewis, Sr. Technical Solution Engineer at Liquibase and longtime data pipeline problem-solver. “You also need a way to see the history of changes over time. Getting the most out of your data product requires a strategic approach to data pipeline change management.”

Getting the most from your data products

Incorporating these four pillar capabilities can significantly enhance the effectiveness of a data product. By focusing on these areas and coordinating them into a united, streamlined environment that embraces DevOps, database DevOps, and DataOps principles, organizations can develop data products that solve business challenges and provide a competitive edge for innovation and growth.

The first step towards this data-driven scalability is managing the increasingly more frequent changes to database structures. Data pipeline change management incorporates automation, governance, and observability with a collaborative culture focused on continuous optimization.