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Start-up Reboot for Data Scaling


CLIENT PROFILE

An online national document filing service company that specializes in the formation of start-up business entities.


CHALLENGE

Legacy reporting could only utilize data stored in an on-prem, transactional system built to service their primary application. Queries and reports could take up to 30 minutes to execute. Business decisions required faster analysis and integrating data from third-party partners with operations and financial data.


The client required a cloud-based data mart with the ability to easily integrate new data to enable faster business analysis. The new data warehouse


CHALLENGES

  • B2B and/or B2C

  • Limited or highly complex integrations

  • Highly customized source solutions

  • Disparate systems with limited integration

  • Interoperability and hyper-scale

  • Geographically sensitive requirements

  • Highly regulated controls

  • 3rd party meta-analysis capabilities (psychographic, demographic, industry analysis).

  • Customized modeling engines (dynamic demand planning, sensitivity analysis, forecast modeling)

ACTIONS

  • Comprehensive assessment & requirements.

  • Develop tailored project sequence, milestones, and delivery schedule.

  • Adaptable agile development framework.

  • Rapid prototyping with fail-fast method for uncharted greenfield development.

  • Iterative sprint project deliverables.

  • Strategic integrations as needed, scalable to current needs and 3-year growth estimates.

  • Post-project managed services available.

​IMPACT

  • Rapid Prototyping - reduce risk & cost charting new territory.

  • Quick-Win Delivery - reduce dev expense & prioritized what matters most to the business.

  • Made-Ready Handoff- flexible choices with 'owned code options'.


Data solutions requiring expertise in data at least one of following: systems, technology, analysis techniques, or business domain areas. Anything that doesn't fit into the above categories is described as a customized solution. Keywords: Data Warehouse, Power BI

 

DEEPER DIVE


ORGANIZATIONAL PURPOSE

A national document filing service company strictly specializing in the formation of business entities, this client assisted in the formation of nearly 1 million Corporations and LLCs. Easy online ordering provides incorporation within minutes. In addition to incorporation services, the client is also a nationwide provider of Registered Agent services.


They specializes in the formation of these entities:

  • C Corporation

  • Limited Liability Company (LLC)

  • S Corporation

  • Nonprofit Corporation


BUSINESS CHALLENGE

Legacy reporting focused solely on transactional data (Atomic) rather than an optimized ecosystem designed for functional business reporting needs. Ultimately, this led to a difficult and slow reporting system requiring complex SQL queries beyond their team's ability to make data ready reporting.


CLIENT PAIN POINTS

  • Reporting required complex SQL to generate usable data sets

  • Data not organized for reporting

  • Inconsistency in naming conventions

  • No dedicated environment for all available data – Atomic + API + web


TECHNOLOGY CHALLENGE

Additionally, data existed in different levels of detail lacking standard naming conventions making navigation unnecessarily challenging. Furthermore, reporting needs required data from outside sources be integrated with the existing transaction data, but there were no processes in place to land, transform, and integrated that data.


The reporting tables and columns had little continuity so that the same data is referenced the same way throughout the various tables (ex. “Order ID” is referenced as “OrdID,” “orid,” “tran,” and “tranFKey”).


SOLUTION

The development of a data mart that functions as the reporting layer for all data. The data in this layer is automatically extracted from a homegrown transaction system, transformed, and integrated with other data structures in Microsoft's Azure cloud enabling integration with Power BI.

  • Automated data extraction, transformation

  • Data integration / reporting tables

  • Standardized naming conventions

  • Uncovered data quality opportunities

Indicator columns and other helpful data were added to increase reporting speed. Additionally, third-party data is integrated into the reporting environment. Lastly, as part of these efforts, has helped uncover opportunities to improve data quality.

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