Want To Build Successful Data Products? Start With Ingestion and Integration
If you want to create data products that are successful, it's important to begin with proper ingestion and integration of data.
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In today’s world of fragmented, ever-increasing volumes of data, the need for real-time or near-real-time access to data is paramount.
Data is your lifeline for improving business outcomes and depending on your organization’s business strategy. Plus, it can also be monetized. Data products are a foundational building block for any modern data architecture patterns such as data fabric or data mesh.
The Need for Data Products
Data products tailored for specific business purposes are becoming increasingly popular because of their emphasis on addressing user requirements. This includes timely access to relevant information and role-based visualization of the datasets. They begin with a minimum viable approach and then undergo evaluation and iterative improvements.
By definition, data products combine data and virtually all the functionalities needed to achieve business objectives using data by making it available to those who need it at the right time.
In my 20+ years of data management experience, I have seen some great examples of data products. These include 360-degree views (such as a customer’s evolving expectations using a single, actionable, best version of truth across organizations), next best offering (recommending items or content to users based on past behavior), and demand forecasting (forecasting product demand based on buying history and trends with a predictive analytics platform).
Like any product development, data product follows a process from its inception to retirement. This lifecycle involves various stages, each of which plays a crucial role in the creation, management, and eventual discontinuation of a data product. As shown in Figure 1, once you identify the requirements of a data product and complete the design and modeling, you must focus on building the data product before you deploy and iterate for further improvements. However, building these data products is not without its challenges.
Figure 1: Data product lifecycle
The Data Dilemma
55% of data leaders report having more than 1,000 sources of data in their organization, and 91% predict increases in data sources.
Imagine a scenario where an organization is sitting on a goldmine of data — customer information, transaction records, website interactions, and more. They recognize this data can potentially drive personalized marketing campaigns, optimize their supply chain, and identify trends that could give them a competitive edge by building data products. However, there is a significant roadblock in their path: The data is scattered across various departments and systems, residing in different formats and structures.
This situation can cause several complications:
- Data silos: Data is trapped in silos, often in legacy applications, making it difficult to access and integrate for meaningful analysis.
- Data volume and variety: Large volumes of data with newer data types make it time consuming to replicate and ingest.
- Trustworthiness and quality: The data is often riddled with inconsistencies, inaccuracies, and missing values, undermining its reliability.
- Delayed insights: The organization struggles to provide real-time insights to respond swiftly to market changes and customer demands.
- Operational inefficiency: Manual data integration processes are time-consuming, error-prone, and hinder operational efficiency.
- Data governance and compliance concerns: Ensuring data security and compliance with data privacy regulations becomes increasingly challenging as data sprawls across the organization.
- Point solution roadblocks: Organizations often accumulate a complex and fragmented data infrastructure as they incorporate various point solutions for tasks like data ingestion, integration, quality, cataloging, and governance. While individually valuable, these point solutions can create disconnected systems that hinder collaboration and increase data product development effort substantially, often doubling or tripling the workload
What can help resolve these data dilemmas so data products can be built? Enter data ingestion and integration.
Laying the Groundwork for Building a Robust Data Product Foundation
Data ingestion and integration are essential components in the process of building data products. They serve as the foundational steps that enable organizations to leverage their data effectively. Let’s walk through the processes now:
Data ingestion is the process of collecting, importing, and storing data from various sources into a centralized repository. It’s like gathering all the ingredients you need for a recipe in one place. A data ingestion solution is critical because it addresses the problem of data accessibility by eliminating the need to search for data across multiple systems and databases. A robust data ingestion solution can help you ingest and replicate data from files, applications, databases (often called as change data capture), and streaming systems in real-time.
Data Integration and Quality
A data integration tool goes a step further. It is the art of combining data from disparate sources, transforming it into a common format, and ensuring its consistency and quality. Data integration is like mixing those ingredients mentioned above into a delectable dish: Each element contributes its unique flavor to create something extraordinary.
Plus, data cataloging is just as important. Because you have so much data, you must make it discoverable by cataloging and tagging everything you ingested. Ideally, this would be done automatically. A data catalog solution will help you understand what data you have and understand the data lineage, data glossary, and definition.
Now, let us dive into why data ingestion and integration are so critical to create data products.
The Advantages of Data Ingestion and Integration
Data ingestion and integration play a crucial role in data product development by offering several advantages. Here are the key benefits of each of these processes:
- Data accessibility: Data ingestion brings all your data sources under one roof, making it easily accessible to your data engineers and analysts. No more treasure hunts for scattered data!
- Data quality: As data flows into the central repository, you can clean, enrich, and validate it, ensuring your data is trustworthy and reliable.
- Real-time updates: Need up-to-the-minute information for your data product? Data ingestion can be configured to provide real-time or near-real-time updates, keeping you coordinated in today’s dynamic market.
- Scalability: Your data needs room to grow. Efficient data ingestion systems can scale to accommodate increasing data volumes, ensuring that your data product stays agile as your business expands.
- Operational efficiency: Automation is the key to efficiency. Data ingestion processes can be automated, reducing manual efforts, errors, and operational costs.
- Data governance: Data ingestion can enforce data governance policies, ensuring that sensitive data is handled with care and in compliance with regulations.
- Holistic insights: Data integration combines data from various sources, offering a holistic view of your subject matter. It’s like assembling the pieces of a puzzle to reveal the big picture.
- Data consistency: When data is integrated, it becomes consistent across different systems and sources. This consistency is essential for accurate reporting and analysis within your data product.
- Flexibility: Your business environment is dynamic, and so are your data sources. Data integration pipelines can adapt to changing data sources and formats, keeping your data product up to date.
- Advanced analytics: Data integration ensures that your data is in the right format and structure for advanced analytics. This enables data scientists and analysts to perform complex analyses and build predictive models with ease.
- Faster time-to-insight: With efficient data integration, you can reduce the time it takes to integrate and analyze data, empowering you to respond swiftly to market changes and customer demands.
But wait, there’s more to the story.
Achieve More With Less: A Unified Approach
Instead of many point solutions, a unified modern data management platform that is integrated and interoperable can help fast track your data product development and save costs and effort in the long term.
Here are the benefits of a unified approach:
- Common metadata foundation: Unified metadata foundation for AI (artificial intelligence) and data intelligence helps you to develop a data product catalog. According to renowned industry analyst Sanjeev Mohan, Principal at SanjMo, this single pane of glass can help you discover data products and its associated metadata.
- Fully integrated and interoperable: From first mile to the last mile, a unified platform enables an integrated data lifecycle that is easier and faster to use, manage, and secure.
- Flexibility: A common platform provides flexibility. You can start with a limited scope during prototype and a minimal viable phase while you gradually add newer capabilities.
- Autonomous and augmented data management: An AI-enabled modern data platform can help automate thousands of manual data management tasks, increasing productivity by up to 100 times.
- Optimized data processing engine: A common platform helps you optimize computations to support various data processing methods across hybrid and multi-cloud environments. This includes extract, transform, load (ETL), extract load transform (ELT), data engineering, data preparation, and more.
The Journey to Successful Data Products
In the ever-evolving landscape of data-driven decision-making, data ingestion and integration transform raw data into valuable insights. They break down data silos, enhance data quality, provide real-time updates, and improve operational efficiency. Data ingestion opens the door to accessible data, while data integration gathers and extracts the relevant information from these sources to make it available for data product users.
At the same time, a common data platform provides flexibility to add newer capabilities, which removes the additional overhead of building interfaces within disparate point solutions. This allows you to focus on optimizing data products versus low-priority tasks.
So, the next time you think about data products, remember that their success begins with effective data ingestion and integration with a unified data platform. They are the alchemists turning your data into gold, helping you make informed decisions and deliver exceptional customer experiences — must haves to effectively compete in today’s digital economy.
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