Knoldus Data Science Platform
Discover actionable insights with a powerful, enterprise-ready platform.
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Knoldus Data Science Platform
Enterprises are driving real business transformation through data science and analytics
Data is everywhere and is being generated at a breakneck pace. This is creating a huge opportunity for organizations to gain new insights, make the data-driven decision and arrive at outcomes that drive success.
In the scramble to catch up, many organizations have adopted a hodgepodge of tools without a clear strategy for how each fits in the broader analytics technology stack in their environment. These dynamics affect organizations at all maturity levels; and after investing more resources in big data and data science, they are not yet realizing their anticipated return on investment.
Unable to unleash the full potential of data from inside out
Companies invested in technology that keeps them on the cutting edge by using these powerful tools to give their data science teams a leg up in the race to deliver value. However, organizations are facing below challenges with these standalone tools and without having a data science workflow.
Focus on data not action
Organizations are focusing more on data sources over capabilities that create action from insight.
Disconnected Tools and Technology
Organizations are focusing more on data sources over capabilities that create action from insight.
Poor Collaboration
Data scientists are solving similar problems over and over again in different ways due to standalone tools or in different departments.
Poor Collaboration
Data scientists are solving similar problems over and over again in different ways due to standalone tools or in different departments.
Time Consumption in data management tasks
Data scientists spend over 60% of their time on data preparation and model refinement and managing infrastructure.
Lack of engineering support
Data scientists are expert statisticians but they often aren't qualified to deploy data models into production and therefore need engineering support.
Time Consumption in data management tasks
Data scientists spend over 60% of their time on data preparation and model refinement and managing infrastructure.
Lack of engineering support
Data scientists are expert statisticians but they often aren't qualified to deploy data models into production and therefore need engineering support.
Knoldus Data Science Platform (KDSP)
Leverage KDSP to unlock value from your data in a single, integrated environment
Knoldus Data science platform uses a structured data program for the entire data science life cycle, including data integration and exploration, model development, and model deployment. within a single integrated environment. It combines open source and commercial analytic technology together to operationalize insights, solve complex business problems, and enable descriptive, predictive and prescriptive analytics-including autonomous decision-making. The KDSP delivers the best analytic functions and engines, preferred tools and languages and support for multiple data types.
Knoldus Data Science platform enables organizations to deliver a tangible business outcomes in a short period while enforcing best practices in building data programs.
Unlock the Business Values with Knoldus Data Science Platform
Having access to many advanced analytics technologies under a single visual environment, such as a Data Science Platform, will enable you:
Centralized location for data
Centralized location for data
Eliminate the need for copying & extracting data. It simplified data access also by supporting multiple data types and format.Quickly Operationalize Analytics
Quickly Operationalize Analytics
Operationalize analytics on an enterprise-ready platform to produce high-impact, trusted business outcomes.Reduce Cost
Reduce Cost
Reduce expenses associated with utilizing numerous analytics tools and database warehouse appliances without compromising data access, performance, and ease-of-useEnhance collaboration
Enhance collaboration
Enhance collaboration among departments and team and with different skill levels and locationsTechnical Specification and a brief Architecture of Knoldus Data Science Platform
KDSP is a unified analytic and data framework. But under the covers, it contains a cross-engine orchestration layer that pipelines the right data and analytic request to the right analytic engine across a high-speed data fabric. The result is a tightly integrated analytic implementation that is not bound by functional or data silos.
Data Science Platform Components
Knoldus Data Science platform enables all the 4 phases and operationalizes data programs to deliver a tangible business outcomes in a short period while enforcing best practices in building data programs.
Implementation methodology of Knoldus Data Science Platform
Sprint
Planning
Detailed stories, estimated
and sprint level planning
Program
Planning
Data Org
Structure
Technical
Architecture
Architecture beyond Knoldus
Data Science Platform
particularly integration
Product
Definition
Feature, process
and Flows
Data Org
Structure
Hierarchy and teams
Customers, Suppliers,
Business, Units, IT,
Product, Teams
Governance
& Policies
Roles/Responsibilities/
Meta Data, -
Data life-cycle,
Securities
Meta Data
Management
Interpretation of Data-Schema storage,evolution,format and data
association
Sprint
Planning
Detailed stories, estimated
and sprint level planning
Program
Planning
Data Org
Structure
Technical
Architecture
Architecture beyond Knoldus
Data Science Platform
particularly integration
Product
Definition
Feature, process
and Flows
Data Org
Structure
Hierarchy and teams
Customers, Suppliers,
Business, Units, IT,
Product, Teams
Governance
& Policies
Roles/Responsibilities/
Meta Data, -
Data life-cycle,
Securities
Meta Data
Management
Interpretation of Data-Schema storage,evolution,format and data
association
Sprint
Planning
Detailed stories, estimated
and sprint level planning
Program
Planning
Data Org
Structure
Technical
Architecture
Architecture beyond Knoldus
Data Science Platform
particularly integration
Product
Definition
Feature, process
and Flows
Data Org
Structure
Hierarchy and teams
Customers, Suppliers,
Business, Units, IT,
Product, Teams
Governance
& Policies
Roles/Responsibilities/
Meta Data, -
Data life-cycle,
Securities
Meta Data
Management
Interpretation of Data-Schema storage,evolution,format and data
association
How Knoldus Data Science Platform impacts our clients and discovers new ways to monetize data
We help organizations with their journey from challenges to high-performance siness outcomes and look out for ways to leverage data science technologies along with existing systems. With a single and integrated framework that enables data to flow throughout an organization to where it is needed, and when it is needed to bring insights and value.
CASE STUDY
Data Integration and Machine Learning for Deeper Insights
CASE STUDY
Accelerate digital transformation journey with a unified data processing platform
Our Digital Team Structure
Our diverse wrokforce to challenge old practices and drive exceptional performance.