Solutions
Solutions for all your Big Data Challenges
We design our solutions to address our clients’ most significant challenges, and opportunities within the organization, including but not limited to strategy, technology, operations, sales, marketing, and advanced analytics.
We bring deep expertise spanning across multiple industries to our solutions, based on leading big data and analytics technologies. With a holistic approach, we strive to bring accelerated data-driven value to your organization.
Pick your Solution Today
Data Science Value Acceleration
Timeline: 4-6 weeks
Workshops
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- Align data goals
- Identify use case for data driven opportunities
- Use case summary & tech recommendations
Data Diligence
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- Explore data sources for use case
- Identify business value & implementation effort
- Produce solution road map
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Pilot
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- Demonstrate solution capability & feasibility of implementation
- KPI-driven testing
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Production
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- Implementation of pilot in production environment
- Relevant KPI integrated into organizational score card
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Workshops
-
-
- Align data goals
- Identify use case for data driven opportunities
- Use case summary & tech recommendations
-
Data Diligence
-
-
-
- Explore data sources for use case
- Identify business value & implementation effort
- Produce solution road map
-
-
Pilot
-
-
-
- Demonstrate solution capability & feasibility of implementation
- KPI-driven testing
-
-
Production
-
-
-
- Implementation of pilot in production environment
- Relevant KPI integrated into organizational score card
-
-
Building Organizational Value
As an organization becomes more organizationally mature, productionalized solutions become more integrated into their operations, which in turn generates more organizational value.
Communication
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- Model results are accepted and used in business decisions but require human intervention to generate results and execute processes
- Typically delivered by whitepapers, presentations, spreadsheets, etc.
- Tools include:
- R Markdown
- Shiny Apps
- Jupyter Notebook
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Integration
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- Model results are automatically pushed to data for accelerating other business decisions
- Examples include:
- Daily scoring customers in database for cross sell and CLV
- Notifications for inventory reorder times
- Tools include:
- Integrated R in MSSQL
- Rserver
- Plumber
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Automation
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- Model results are generated and actions executed automatically
- Examples include:
- Recommendation engines
- Fraud alerts
- Automatic reordering
- Tools include:
- rJava
- Spark Streaming
- Nifi, Atlas, Storm
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