Data Management
Data Management is integral to the success of large enterprises, impacting various aspects of their operations, from decision-making and compliance to innovation and growth. It is a foundational element that supports the overall effectiveness and sustainability of regional and international organizations.
Smart Enterprise
Similar to our personal smartphones, companies possess applications, processing power (compute), and memory (storage). Transitioning between phones or altering hardware and software necessitates swift data migration. While a company might not actively monitor aggregated enterprise compute and storage metrics, the analogy holds true on a larger scale within both local and cloud landscapes, encompassing considerations of switching costs, operating expenses, and overall performance.
Data serves diverse purposes in analytics, primarily categorized into "Entries" and "Events." In the realm of Ecommerce, real-time event tracking is crucial, capturing data that directly influences the store's performance. This practice is mirrored in major companies with their enterprise systems and web applications. Events, whether components of larger processes or standalone significant occurrences (e.g., Sales Order Line Item Delivered), play a pivotal role in shaping insights through analytics.
Here are the organized Entry and Event areas for data. Effective organization and tracking of events are fundamental for analytics:
Data Integration
How does a Business Model and supporting Business Processes become a Data Model?
The computation and storage aspects have evolved into significant considerations, encompassing both cloud and onsite expenses. The construction of extensive database reporting usually occurs gradually, aligned with the initial plans. Repetitive low-grain queries can incur costs in terms of both computation and time. Additionally, ensuring timely synchronization of the database and comprehensive incorporation of "company dimensions" for complete Integrated Development Environment (IDE) multidimensional use is essential. This allows for potential improvements or the adoption of a new structural approach.
Seven Levels for gauging Enterprise Data Maturity and Optimization
Orchestrated EDW | EDW² 
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Machine and External Data
As Enterprise Data spans across numerous applications and databases, a crucial aspect is comprehending both current business data and machine data. This understanding serves as a bridge, connecting the business-side with diverse formats of technical data, ranging from simple text, time, and numerics to digital data.
Experience Curve
Alexicon has a 25 experience curve with progressive enterprise applications, data management and analytics solutions.
Enterprise. Orchestrated.