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Forbes
15-05-2025
- Business
- Forbes
What It Takes To Foster A Data-Driven Culture
Written by Christina Egea, MVP, Product Management, Enterprise Data, Capital One It's well known that having plentiful, high-quality data is necessary to becoming a data-driven organization. After all, data powers strategic decisions across any business function, ranging from marketing to risk management, and it serves as the foundation for enhancing customer experiences. But at the heart of the most successful organizations that use and elevate data is one thing – a strong data culture. Gorodenkoff Productions At Capital One, data has been at the heart of our business strategy since our founding days. As a company, we've long adopted the mindset that data-driven decision making is a key business unlock. And, that's why we continually invest in data and foster a culture that encourages people across all levels and areas of the business to make valuable use of data. After reflecting on what's worked at Capital One, a few investments emerge as fundamental to fostering this culture: building a platform-based data ecosystem, bringing a product mindset to data, and elevating data talent. Adopting these elements are paramount for organizations looking to build their own thriving data culture. Ask any business leader if they want to use data to make decisions – the answer is always yes. But the path to getting data, especially readily available, high-quality data, is often the opposite of easy. Tackling this challenge is at the heart of a data platform strategy, which develops the collection of platforms that enable users across the company to find, understand and make use of data to power decisions. Building a central set of platforms requires investment in company-wide standards for data development. These standards should define the data lifecycle, establishing protocols for how data is gathered, stored, and shared. This framework can be enforced across the organization through central platforms that enable automation, supporting the lifecycle from the moment data is created to when and where it gets used. Investing in central platforms democratizes data access, while ensuring data is well-governed across the lifecycle, even as it's locally owned. Democratizing data access is essential to a thriving data culture. Platforms should empower every employee—given appropriate access controls are in place—to use data with minimal friction. This can make leveraging data a natural extension of every employee's daily life. On the shoulders of great platforms, organizations must invest in the underlying quality and definition of data itself. Too often organizations have held a mindset of treating data as a "byproduct" of other business or system processes. We can quickly, especially in today's age, end up in a world where our data lake becomes a data swamp full of mass quantities of data that's hard to use or understand. To ensure you're making valuable data available to employees, you should treat data as you do any other product. Data isn't a secondary asset that happens by accident, it's a vital resource. Bringing a product mindset to data requires building an understanding of your customer, current and potential, and curating data to meet their needs. Those customers aren't always external – most data customers will be your own employees. You need to think about their pain points, what they need, and where data can support them. Managing data as a product requires the same investment as any other product you build. You wouldn't expect a great product to appear without product managers there to problem solve and identify use cases. The same goes for data. Organizations need a clear accountability structure and investment in the teams that are managing and making data available. It can't be a side of desk job or afterthought, but rather a primary focus of investment. This is how you ensure that the data being made available on the established platforms is of the best possible quality. The ultimate data strategy and culture comes to life through people. An organization with a thriving data culture invests in developing the best talent to fuel its ecosystem with the right data – and even more talent to engage with that data. This is why a strong data culture calls for companies to invest in data talent and education. Everyone, from senior leaders to new junior hires, should understand the power of data-driven decision making and the ecosystem that enables it. Part of this will come through developing roles that attract strong data talent across a variety of disciplines. It's not enough to simply add data as a focus to pre-existing roles – you need roles where data is the sole focus. This requires building specific job profiles for these data roles that work back from business needs and recruiting specifically for those roles. Beyond hiring, you also need ongoing training and upskilling to cultivate talent, especially given how rapidly data technologies change. Data roles are constantly evolving, and even more specialties are emerging as AI advances. Staying on top of those trends and adapting to them is key. There are many strategies for doing this. For example, an in-house learning platform can engage employees with classroom-based learning. Meanwhile, internal workshops and forums, alongside external events, can help talent share best practices and build knowledge. At Capital One, our data strategy is our business strategy. This alignment ensures that data initiatives are baked into real-world objectives and that business decisions are consistently backed by data-derived insights. Cultivating a strong data culture like this doesn't happen overnight or by accident. It requires a commitment to building a platform-based data ecosystem that lowers the barrier to data access, promoting a product mindset to develop high-quality, trustworthy data, and investing in the data talent that fuels this ecosystem. These interconnected elements of a strong data culture must be continuously reinforced. It's not easy, but it's well worth the effort. When data is celebrated, curiosity is encouraged, and employees are empowered to do more with data, new possibilities for driving business value and innovation open up across every function.
Geeky Gadgets
07-05-2025
- Business
- Geeky Gadgets
Why Your Data Lake Strategy is Failing and How Apache Iceberg Can Save It
What if the very foundation of your organization's data strategy was holding you back? For years, data lakes have promised flexibility and scalability, yet many businesses find themselves grappling with fragmented systems, unreliable analytics, and outdated schemas. Enter Apache Iceberg—a innovative open table format that's quietly transforming the way we think about data lake management. Originally developed by Netflix to tackle the inefficiencies of traditional architectures, Iceberg introduces relational database-like capabilities to data lakes, offering a structured, scalable, and consistent framework for modern data needs. It's no wonder the tech world is abuzz with its potential. In this overview, the team at Confluent Developer explore why Apache Iceberg is becoming a cornerstone of modern data architectures. You'll uncover how it solves persistent challenges like schema evolution, data consistency, and transactional limitations—issues that have long plagued traditional data lakes. From its snapshot-based consistency to seamless integration with tools like Kafka and Spark, Iceberg is more than just a technical upgrade; it's a paradigm shift. Whether you're managing streaming data, real-time analytics, or massive datasets, Iceberg's innovative approach could redefine how your organization handles data. So, what makes this technology so indispensable in today's data-driven world? Let's unravel the story. Apache Iceberg Overview Challenges in Traditional Data Lakes The transition from data warehouses to data lakes introduced greater flexibility in handling raw, unstructured data. However, this shift also brought significant challenges that limited the effectiveness of traditional data lakes. These challenges include: Schema Management: Traditional data lakes often struggle with schema evolution, making it difficult to update schemas without breaking existing queries or workflows. Traditional data lakes often struggle with schema evolution, making it difficult to update schemas without breaking existing queries or workflows. Data Consistency: Making sure consistent data operations across distributed environments has been a persistent issue, leading to unreliable analytics and processing. Making sure consistent data operations across distributed environments has been a persistent issue, leading to unreliable analytics and processing. Transactional Limitations: Many data lakes lack robust support for updates, deletes, or upserts, which are critical for maintaining data accuracy and integrity. These limitations have made it challenging for organizations to maintain data integrity, perform advanced analytics, and support real-time processing. As a result, many data lake implementations have become fragmented and inefficient, requiring innovative solutions to address these shortcomings. Core Features of Apache Iceberg Apache Iceberg was designed to overcome the limitations of traditional data lakes by introducing a range of advanced features that enhance scalability, consistency, and usability. Key features include: Open Table Format: Iceberg provides a standardized framework for managing data in distributed file systems, making sure compatibility across tools and scalability for growing datasets. Iceberg provides a standardized framework for managing data in distributed file systems, making sure compatibility across tools and scalability for growing datasets. Schema Evolution: Iceberg allows seamless schema updates without disrupting existing queries, allowing organizations to adapt to changing data requirements effortlessly. Iceberg allows seamless schema updates without disrupting existing queries, allowing organizations to adapt to changing data requirements effortlessly. Snapshot-Based Consistency: By using snapshots, Iceberg ensures reliable and consistent data operations, even in complex distributed environments. By using snapshots, Iceberg ensures reliable and consistent data operations, even in complex distributed environments. Logical Data Organization: Data is structured into columnar formats like Parquet, with changes tracked using JSON metadata. This approach integrates with catalog systems for efficient table management. These features make Apache Iceberg a robust and versatile solution for managing large and evolving datasets, empowering organizations to unlock the full potential of their data lakes. Apache Iceberg Explained Watch this video on YouTube. Below are more guides on artificial intelligence (AI) from our extensive range of articles. How Apache Iceberg Operates The architecture of Apache Iceberg is built on three interconnected layers, each serving a critical role in its functionality: Data Layer: This layer stores raw data in columnar formats such as Parquet, optimizing both storage efficiency and query performance. This layer stores raw data in columnar formats such as Parquet, optimizing both storage efficiency and query performance. Metadata Layer: Iceberg tracks data and schema changes over time using manifest files, manifest lists, and metadata files. This ensures consistency, traceability, and efficient data management. Iceberg tracks data and schema changes over time using manifest files, manifest lists, and metadata files. This ensures consistency, traceability, and efficient data management. Catalog Layer: The catalog layer maps table names to metadata files using systems like Hive Metastore or JDBC databases, simplifying data discovery and allowing seamless querying. This layered design ensures that Iceberg remains lightweight, flexible, and capable of handling the demands of large-scale datasets while maintaining high performance and reliability. Flexibility and Ecosystem Integration One of Apache Iceberg's most notable strengths is its flexibility and ability to integrate seamlessly with a wide range of tools and platforms. Unlike traditional systems, Iceberg is a specification rather than a server, making it highly adaptable. Key integration features include: Multi-Language Support: Iceberg is compatible with popular programming languages such as Java, Python, Flink, and Spark, allowing developers across ecosystems to use its capabilities. Iceberg is compatible with popular programming languages such as Java, Python, Flink, and Spark, allowing developers across ecosystems to use its capabilities. Advanced Querying: Iceberg integrates with tools like Presto and Trino, allowing users to perform complex analytics, joins, and aggregations with ease. Iceberg integrates with tools like Presto and Trino, allowing users to perform complex analytics, joins, and aggregations with ease. Catalog Integration: By integrating with Hive Metastore and JDBC databases, Iceberg ensures compatibility with existing infrastructure, reducing the need for extensive reconfiguration. This adaptability makes Iceberg a versatile choice for modern data architectures, allowing organizations to build scalable and future-proof systems. Relational Capabilities for Data Lakes Apache Iceberg bridges the gap between traditional data lakes and relational databases by introducing relational semantics to data lake environments. With support for operations such as updates, deletes, and upserts, Iceberg enables precise and reliable data management. These capabilities, traditionally associated with transactional databases, empower organizations to maintain data accuracy and integrity at scale. Additionally, Iceberg supports real-time data processing, making it well-suited for streaming use cases where data freshness is critical. By allowing real-time updates and schema changes, Iceberg ensures that data remains consistent and up-to-date, even in dynamic and fast-paced environments. Applications in Streaming Data Apache Iceberg is particularly effective in streaming data scenarios, where real-time processing and consistency are essential. For example, Iceberg integrates seamlessly with tools like Kafka to enable real-time updates and schema evolution. Confluent's 'table flow' feature, for instance, maps Kafka topics directly to Iceberg tables, eliminating the need for batch processing. This integration ensures that data remains consistent and accessible for analytics, even as it evolves in real time. By supporting streaming data workflows, Iceberg enables organizations to build systems that can handle dynamic environments and deliver actionable insights without delays. The Role of Apache Iceberg in Modern Data Architectures Apache Iceberg is emerging as a cornerstone of modern data architectures. Its support for advanced features such as transactional operations, schema evolution, and real-time processing positions it as a versatile solution for a wide range of use cases. Whether your focus is on batch processing, real-time analytics, or streaming data, Iceberg provides the tools and flexibility needed to meet the demands of today's data-driven world. As organizations continue to adopt streaming systems and real-time analytics, the importance of Iceberg's capabilities will only grow. By addressing critical challenges in data lake management, Iceberg enables you to build scalable, reliable, and efficient data architectures that are ready for the future. Media Credit: Confluent Developer Filed Under: Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
