Rapid Build & Implementation

Through many deployments, we have evolved reference architecture for the DaaS. However, building an entire DaaS at once is a bad idea. We de-risk our build approach towards our reference architecture by dividing it into small parts. Each part is designed to deliver high-value use cases within a 12-week sprint. By building in small pieces towards reference architecture, we avoid creating messes and ensure relevance to the clients’ business needs.

Build in parts
to a reference
architecture

Each part delivers business value in weeks

Agile approach in
collaboration with
your organization

DaaS iteratively
completed via
sequential use cases

Is Data an asset or a liability?

Without a clear action and correct management of Data, organizations are at risk of “hoarding” Data and letting it sit idle, turning it into a cost center and risk driver: externally hackable or internally leakable. We securely add value to the Data, enabling organizations to deliver their services intelligently to a highly digitalized world.

How do you build Data as an asset?

Data as an asset becomes your organization’s “brain” by continuously capturing interactions and coding them into actionable knowledge. It makes Data more than just numbers in your reports.

Predictive Analytics

At Open Insights, we empower organizations to

Use advanced algorithms to predict various customer behaviors at micro and macro levels

Drive churn management, cross-sell, up-sell, fraud identification, multi-touch attribution, targeted campaigns, customer base activation and acquisition

Strengthen marketing, ad targeting and attribution models

BigData Implementations

Bring up on-prem or cloud-agnostic BigData infrastructure

Migrations from expensive Data warehouse appliances to affordable performant commodity or cloud-native hardware with open source software Data platforms

Augmentation of structured Data with unstructured Data sources leveraging the 90% of unstructured Data in enterprises and external Data sources

AI/Machine Learning Use-Case Implementations

Activate on-prem or cloud AI/ML/Data Science algorithms

Automation of manual operations and embedding AI in digital transformations

Deliver measurable business value after each sprint to keep the business engaged

Rapid Build and Implementation Process

Iterate

Productionize

Iterate

< 2 weeks

Hothouse

Articulation of business needs, benefits and success criteria
Roles and responsibility
Roadmap and planning
Governance setup
Metadata management approach

4 - 6 weeks

Design and prove the solution

Architecture and design
Infrastructure Setup
Data analysis
Security setup
Metadata tool
Built in Data quality
Data science and analytics

8 - 10 weeks

Iterative framework and build

DevOps and CI established
Reusable framework components build
Automated testing
Feedback from business
Iterate and enhance framework