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

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

12 - 14 weeks

High value use cases

  • Use case delivery/ Roll out

  • Launch in multiple departments / groups / countries in waves

Iterate

Self sufficiency

  • Onboarding of new use cases / Country with minimal technology effort

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