Data engineering teams and data producers can find it hard to collaborate within an organization for a number of reasons.
Poor data quality is one of them.
For example, a customer’s address may be recorded incorrectly, which can affect their delivery. Sometimes, there are inconsistent data formats (e.g., inconsistency between two datasets). These issues can make data integration tricky and hamper data analytics. As a consequence, you risk dealing with outages and lose a lot of time that goes into fixing things.
That’s precisely why you need to gain a thorough understanding of data producers, assess their relationship with data consumers, and follow best practices for data producers to minimize data quality issues and improve your data management.
A data producer is anything that collects, processes, generates, and stores data that’s relevant to your organization and makes it available for data consumers. This can be a user interface, service, device, system—or human.
Data producers serve a key role in data lifecycle as the main source of truth. This information is fed into analysis and decision-making processes. Since data producers often generate unstructured data or raw data, it needs to undergo further processing in order to derive meaningful insights from it.
For example, a point-of-sale (PoS) system is a data producer for a retail company. It generates raw data in the form of sales transaction records, including customer information, product ID, and purchase details. You can perform processing and analysis on this data via customer segmentation or sales trend analysis to understand consumer buying behavior.
Software engineers, on the other hand, are also data producers because they develop and maintain systems and applications that generate large amounts of data. They create transactional databases, message queues like Kafka topics, and other tools that produce data.
While data producers generate data, a data consumer is an entity that uses it. Data consumers copy this data, perform data transformations on it, or send it to other systems. Some examples of data consumers include marketing automation platforms, BI platforms, data analysts, and data scientists. In the case of data analysts, Python, SQL, and other tools can analyze, transform, or visualize data.
Consider an ecommerce website that uses a database to store product information, customer profiles, and transaction records. The website's API serves as the intermediary, enabling seamless communication between the database and the front-end interface. The website provides users with a user-friendly interface to browse products, add items to their cart, and complete purchases.
When it comes to data producers, your data follows a unidirectional path. In other words, data only flows upstream or downstream at any given time. In this example, the data follows a unidirectional flow, starting from the database, where product information and customer details are stored. The API then processes this data and delivers it to the front-end interface, allowing users to view product listings, add items to their cart, and make purchases. User interactions with the front-end interface, such as selecting products and completing payments, are returned to the API and then stored in the database.
What you should keep in mind is that a data producer can function as a data consumer in a different context. The database functions as the data producer when it generates and stores data for the website. The API is the data consumer when it retrieves the data from the database.
However, the API becomes the data producer when the front-end interface (acting as a data consumer) calls it to retrieve data. Similarly, the front-end interface can become a data producer for the customer who is now the data consumer that goes through the information provided on the website.
Data producers can consist of any of the following:
Struggling to maintain standardized data formats and failing to streamline data integration can leave your organization behind the curve and impact your decision-making processes. Data engineering managers should consider the following best practices for data producers to improve data management within their organization.
1. Understand your data sources
You need to understand your data sources when it comes to data producers. When you assess how the data was created and its context, you can identify potential inaccuracies, biases, or limitations.
For instance, an automated employee attendance tracking system is a data producer. The system serves as the data producer by generating and recording employee attendance data. Understanding the data source—in this case, a biometric attendance system—allows the office management to identify any potential inaccuracies or limitations. They might recognize that the system may not be able to capture nuanced attendance details, such as the reasons for late arrivals. By reviewing these limitations, office management can implement relevant initiatives such as periodic manual checks to provide more accurate information for data producers.
2. Introduce metadata management
Metadata management provides context and information about the data. It helps data producers understand the structure, meaning, and relationships within a dataset. For instance, data producers, such as customer service agents, need to know how the customer data is organized with the CRM system.
Metadata management includes documenting and categorizing different data types within the CRM system via a data catalog. This can help agents understand whether the data is numerical, textual, or categorical. They can also learn about relationships between different data entities. For example, it can uncover how customer data is linked to past purchases or interactions.
3. Implement data quality assurance
Data quality assurance is an ongoing process to ensure data is accurate, reliable, consistent, and relevant. It includes using a wide range of techniques and practices to maintain and improve the quality of data that producers generate. This process involves validating data at the point of entry, identifying and resolving errors within it, and ensuring it remains consistent across different systems throughout the organization.
Consider a financial firm where a transaction monitoring system serves as a data producer. The system generates data on customer transactions, account activities, and financial operations. Implementing data quality assurance in this context includes validating how accurate the transaction data is, ensuring data is standardized and consistent across different accounts, and detecting any potential fraudulent activities. This can be done through real-time transaction monitoring, anomaly detection algorithms, and anti-money laundering (AML) compliance checks.
4. Use a data collaboration management platform
Gartner analysts predict that by next year, 50% of businesses will embrace a modern data quality solution.
After GitHub was launched in 2008, it quickly became a must-have tool for software development teams throughout the world for companies of all scales. GitHub helped teams to manage and track code effectively in a collaborative environment.
Similar to how GitHub was highly needed for software teams, there’s a need for a collaboration management tool for data teams. Fortunately, Gable.ai fills this vacuum. Gable.ai can bridge the gap between data producers and data consumers. By introducing data contracts and a data collaboration system, the platform can help to improve data quality, consistency, and communication.
For example, if you have an order processing system as a data producer and an inventory management system as a data consumer, Gable.ai can create data contracts for them. These contracts define specific data parameters that are required by the inventory system and ensure that the order processing system produces data according to its needs.
By following these best practices for data producers, data engineering managers can make data flows through their infrastructure more seamless, enhance data integrity, and improve the reliability of data-driven insights produced from their systems. With Gable.ai in your toolkit, you can eliminate data silos and empower your team, stakeholders, data producers, and data consumers to collaborate with data more effectively. Join our product waitlist and learn how our solution can transform your data infrastructure for the better.
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