Is your legacy data architecture holding you back?

Is your legacy data architecture holding you back? Tips to avoid common pitfalls while building your modern data lake house.

Data lake houses have emerged as pivotal platforms for harnessing the potential of big data. They serve as a centralised nexus where all data-producing and data-consuming applications converge to access raw data in unadulterated form. This streamlined accessibility expedites application development, whether transactional or analytical, by furnishing developers with standardized interfaces for data input and retrieval.

However, when left unattended, data lakes can swiftly transform into liabilities, burdening organizations with maintenance costs while failing to deliver anticipated value. Download the whitepaper to explore the critical pitfalls that can render your data lake ineffective. Accelerate your path to

Personalisation and Data Security: Personalisation and data security are crucial for a data lake. Without proper measures, sensitive information can be compromised. For instance, a financial institution’s data lake may implement personalized access controls for different departments. While traders may need access to real-time market data, compliance officers may only require access to aggregated reports. The institution protects sensitive financial data by implementing customised access controls and encryption protocols while enabling personalised user experiences.

Agile Value Delivery: Agile value delivery emphasises extracting value from the data lake iteratively. Consider a retail company using a data lake to analyse customer purchasing behaviour. Instead of waiting for a comprehensive analysis, the company regularly extracts insights from the data lake to optimize product recommendations and marketing campaigns. By continuously iterating on insights derived from the data lake, the company can quickly respond to changing market trends and customer preferences, thereby maximizing the value extracted from the data lake. More use cases can be added as the management and organization builds trust in the approach. The application of data-driven decision-making can spread out to more functions and processes in an iterative way.

Data Lake as the Sole Big Data Strategy: Relying solely on a data lake as the embodiment of a big data strategy can be shortsighted. Integrating the data lake with other big data solutions can enhance its effectiveness. For example, a healthcare provider may integrate its data lake with machine learning models to analyse patient data and predict potential health risks. By leveraging the data lake and machine learning algorithms, the provider can improve patient outcomes and reduce healthcare costs more effectively than relying on the data lake alone.

Metadata Neglect: Metadata is essential for understanding and managing data within the data lake. Consider an e-commerce platform that captures metadata such as product categories, customer demographics, and purchase history. This metadata provides valuable context for analysing sales trends, identifying popular products, and personalizing recommendations. The e-commerce platform can optimise its data lake for effective data utilisation and decision-making by ensuring the capture and management of essential metadata.

Undefined Data Standards: Establishing clear data standards is imperative for ensuring the consistency and reliability of data within the data lake. For instance, a transportation company may define data standards for tracking shipment status, including data formats, update frequencies, and quality thresholds. By complying to these standards, the firm can ensure that shipment data remains accurate and actionable, enabling timely decision-making and operational efficiency improvements.

Entity Unawareness: Capturing pertinent entities within data enriches datasets and expands potential use cases. For example, a social media platform may capture user interactions with posts, including likes, comments, and shares, along with metadata such as user IDs and timestamps. By associating interactions with specific users and content, the platform can analyse engagement patterns, personalize content recommendations, and enhance user experiences, ultimately driving user retention and platform growth.

Granularity Oversights: Maintaining appropriate granularity levels for data ensures its value and applicability. Consider a financial institution storing transaction data in its data lake. By maintaining high granularity levels, including transaction timestamps, amounts, and transaction types, the institution can conduct a detailed analysis of financial transactions, detect fraudulent activities, and improve risk management practices, safeguarding financial assets and maintaining regulatory compliance.

Lack of Auditability: Comprehensive audit trails are essential for tracking data usage and ensuring accountability within the data lake. For example, a cybersecurity firm may implement audit trails to monitor access to sensitive security data, including user authentication logs, network activity records, and system configuration changes. By maintaining detailed audit logs, the firm can detect security breaches, investigate incidents, and demonstrate compliance with regulatory requirements, thereby enhancing trust and confidence in its data lake infrastructure.

Insufficient Data Lake Services: Mere data accumulation without accompanying services for consumption can lead to underutilization of the data lake. For instance, a manufacturing company may deploy data analytics tools and visualization platforms to complement its data lake infrastructure. The company can empower decision-makers to derive actionable insights from the data lake, optimize manufacturing processes, and drive operational excellence by providing data analytics services, including predictive maintenance models and real-time production dashboards.

Dumping Ground Syndrome: Prioritizing data quality and structure is essential for ensuring the effectiveness of the data lake. Consider a healthcare provider implementing data quality checks and data cleansing processes to improve the quality of patient data stored in its data lake. By standardizing data formats, removing duplicates, and validating data accuracy, the provider can ensure that patient records are reliable and consistent, enabling accurate diagnosis, treatment, and patient care coordination.

Abstruseness: Enhancing data discoverability and usability promotes effective data utilization within the data lake. For example, a research institution may implement a data catalogue and metadata management system to organize and annotate research datasets stored in its data lake. By providing researchers with searchable metadata attributes, such as dataset descriptions, authors, and usage permissions, the institution can facilitate data discovery, collaboration, and knowledge sharing, ultimately accelerating research outcomes and scientific advancements.

Growing Management Complexity: Managing data lakes becomes increasingly complex as data volumes and consumer interactions grow. For instance, a financial services organisation may implement automated data governance policies and data lifecycle management strategies to streamline data lake management. By automating data classification, retention policies, and access controls, the firm can ensure data quality, compliance, and security across its data lake infrastructure, thereby reducing management complexity and maximizing the value derived from its data assets.

This blog was first published in the CXOtoday.com