Data Lifecycle

Master your complete data journey

Most enterprises face challenges with fragmented data landscapes where information remains siloed. This causes inconsistencies and inefficiencies and undermines trust in critical business information.

A strategic approach to managing the full data lifecycle creates order from chaos. It enables better business outcomes through improved data quality, greater accessibility, and higher-quality insights.

Understanding the enterprise data journey​

The data lifecycle represents the complete journey of information assets from creation in source systems to utilization for business intelligence and operational systems.

Key components in the data lifecycle​

Producing Systems

  • ERP (Enterprise Resource Planning): Systems managing core business processes
  • CRM (Customer Relationship Management): Platforms handling sales, marketing and customer service data
  • PLM (Product Lifecycle Management): Tools managing product development and engineering processes
  • SaaS applications: Cloud-based business applications
  • Digital assets: Images, videos, and other media files
  • Industry taxonomy: Structured classification of industry-specific data

The data that derives from these systems spans multiple data domains – product data, customer data, asset data, etc. When this data is confused, when there are different ways of formatting the data, when there’s duplicate data, or when the right data can’t be discovered easily, it becomes a massive drain on the resources of your organization. Manual checks and reconciliation points proliferate, you don’t have a clear, unified view of your customers, and chaos reigns.

Master Data Management

When it comes to actually managing this data, Master Data Management (MDM) systems are often the answer. Think of MDM as a way to create “clean water” – a single source of truth for your critical data. All your different systems feed into this MDM hub, and what comes out the other end is consistent and reliable. There are built-in workflows and approvals to guarantee the data is solid. For customer data, it acts like a filtration plant, matching and merging duplicates, and standardizing customer records to create a single, accurate view.

Data Ingestion

The process of bringing data into a managed environment via:

  • APIs (Application Programming Interfaces): Protocols for real-time data transfer
  • ETL (Extract, Transform, Load): Systems for batch data movement and preparation
  • Files: Direct ingestion from various file formats

Data Governance Workflow

The framework ensures data quality and compliance through:

  • DG rules: Data governance validation, approval, and escalation rules
  • Validation rules: Automated checks that enforce data quality standards

Master Data Management (MDM)

The technology-driven solution establishing trusted “golden records” for critical business entities like customers, products, and suppliers. Think of this as a toolset rather than a monolithic application – there are tools for workflow creation, rule validation, data model creation, etc. MDM standardizes information to enable cross-record analysis and creates a single source of truth.

Data Syndication

The distribution of accurate, consistent data to downstream systems and external sources, including retailers, ecommerce, and distributors.

Data Lake

A data lake is a repository of data ready for downstream use – including powering business intelligence and analytics – without the need for real-time retrieval from source systems. Ideally, this is high-quality data that has been processed and normalized to ensure it is consistently clean, accurate, and reliable.

  • Discovery: Exploration and understanding of available data assets
  • Data lineage: Tracing data’s origin and transformations throughout its journey
  • Data catalog: Organizing and indexing metadata to help users find and understand consistent data asset

Consuming Systems

These are platforms that utilize cleansed, governed data. Some of these will take real-time data straight from the MDM workflows:

  • eCommerce: Online sales and distribution channels
  • GDSN (Global Data Synchronization Network): Standards-based data pools for exchanging product data
  • ERP: Systems managing core business processes

While other, more sophisticated business intelligence and data processing systems will work with data from the data lake:

  • Analytics/BI: Business intelligence and reporting tools
  • AI/ML (Artificial Intelligence/Machine Learning): Advanced data processing systems
  • Planning analytics: Forecasting and scenario modeling tools
  • Data visualization: Interactive visual representation of data

When managed effectively, the data lifecycle transforms raw information into a valuable business asset that can drive innovation, efficiency, and competitive advantage. Organizations that grasp and optimize their data lifecycle gain the ability to make faster, more confident decisions based on reliable information.

A hand points at a digital interface displaying data lines and graphs, indicating analysis or interaction with the data

Optimize your data lifecycle

Is your data lifecycle healthy? DataCatalyst’s expertise spans data management, data governance and data monetization. Request a complimentary data Health Check to identify bottlenecks, improve processes, and maximize the business impact of your data.
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