Adroitent

Scalable BI & Data Lake for an Employment Provider | Adroitent

Customer Story · Business Intelligence

From data silos to one scalable data lake

How Adroitent modernized the BI ecosystem of a leading American employment services provider — consolidating fragmented Oracle databases into a centralized data lake powering 190+ Tableau reports and dashboards.

Scalable BI and analytics data lake with Tableau dashboards

The inflection pointA BI ecosystem that couldn't scale

The customer is a globally renowned American provider of employment services connecting people and jobs — transforming the recruiting industry for over 30 years with intelligent digital, social and mobile solutions. Their existing data infrastructure needed transformation to support growing analytics demands.

  • Fragmented data sources: data distributed across multiple Oracle databases, creating inefficiencies in analytics processes
  • Complex reporting requirements: the existing system supported over 190 reports and dashboards, needing a streamlined approach for future scalability
  • Lack of centralized architecture: a consolidated data lake was needed as a robust, scalable foundation for Tableau-based analytics
Analytics can't scale on fragmented foundations.

The interventionA centralized data lake for analytics

Adroitent partnered with the customer to provide end-to-end consulting and implementation services across architecture, data transformation and visualization delivery.

Solution architecture

  • Consulted on a scalable, cost-effective data store and analytics architecture
  • Thought leadership and technical guidance ensuring best practices in architecture and development

ETL & data consolidation

  • Designed and implemented extraction and transformation workflows into a centralized data lake
  • Streamlined data processes to enhance accessibility and reliability for analytics

Tableau visualization

  • Collaborated to define reporting and visualization requirements
  • Designed and delivered Tableau dashboards and reports to agreed schedules and priorities

Agile delivery model

  • Sprint-based delivery plan with weekly progress reviews
  • Sprint-specific calls to track deliverables and address escalations

The game changer

One data lake, 190+ reports

  • Consolidated multiple Oracle databases into a unified, analytics-optimized data store
  • A centralized data lake as the foundation for the whole BI ecosystem
  • Tableau reports and dashboards aligned to evolving business needs
  • Architecture designed for both scalability and cost-efficiency
Data LakeTableauApache SparkAmazon EMRETL

By the numbers

Scalable architecture

Enhanced analytics

Optimized reporting

Competitive advantage

By the numbers

Scalable architecture

Enhanced analytics

Optimized reporting

Competitive advantage

The payoffScalable, insightful, competitive

Scalable architecture
Established a centralized, scalable data lake to serve as the foundation for the customer's BI ecosystem.
Enhanced analytics
Delivered efficient data transformation processes, improving analytics readiness and performance.
Optimized reporting
Developed intuitive Tableau-based dashboards and reports tailored to business needs, enabling informed decision-making.
Streamlined collaboration
Fostered a collaborative environment with clear workflows, improving project efficiency and delivery timelines.
Enabled competitive advantage
Informed strategies delivered through BI insights helped the customer gain a better market position.

The stack behind itTools & technology

MySQLAmazon RDSETLApache SparkAmazon EMRTableauData Lake

This engagement was delivered as part of Adroitent's Business Intelligence Services practice.

Good to knowFrequently asked questions

What is a data lake?
A data lake is a centralized repository that stores large volumes of structured and unstructured data in its raw form. It provides a single, scalable foundation that analytics, BI and machine-learning workloads can all draw from, instead of querying scattered source systems.
How does a data lake differ from a data warehouse?
A data warehouse stores structured, pre-modelled data optimized for reporting. A data lake stores raw data of any type at lower cost and applies structure when it's read. Many organizations use both — the lake for scale and flexibility, the warehouse for curated reporting.
What is ETL in business intelligence?
ETL stands for Extract, Transform, Load — pulling data from source systems, cleaning and reshaping it, then loading it into a target store for analytics. It's what turns fragmented operational data into consistent, reliable data for reporting.
Why do companies use Tableau for dashboards?
Tableau connects to many data sources and lets teams build interactive visual dashboards without heavy coding. It makes trends and outliers easy to see, supports self-service exploration, and helps business users make decisions from data quickly.
How do you modernize a legacy BI ecosystem?
Typically by consolidating fragmented sources into a centralized, scalable store such as a data lake; rebuilding ETL pipelines for reliability; rationalizing existing reports; and delivering modern, self-service dashboards — usually in agile, sprint-based increments to limit risk.

Give your analytics a real foundation.

Data lakes, pipelines and dashboards built to scale with your business.

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