innovation cycle

Knowledgent Announces Data Innovation Labs

Knowledgent is pleased to announce our recently updated Knowledgent Labs and focus on innovation. Knowledgent Labs are our facilities where we help clients innovate through experimentation. This is all guided by the Knowledgent Innovation Cycle, known as KLIC.

“Knowledgent delivers innovation in data and analytics with rapid ideation and experimentation to identify the business applications that will yield the most value for our clients. This is followed by rigorous scale-up and operationalization to ensure that the solution is institutionalized as part of their process and technology fabric,” said Shail Jain, Chief Executive Partner at Knowledgent. “This ensures that value is captured and the solution is sustainable. To address this paradigm, Knowledgent has developed KLIC to guide the innovation efforts.”

Knowledgent is constantly Technology Sensing through active engagement with leading-edge data, analytics, robotics, artificial intelligence, machine learning, and cognitive computing software providers, and open-source projects. The goal is to rapidly understand tangible capabilities and applicability to real-world problems. We combine this with ideation sessions where we identify actionable and impactful business solutions. This is enabled by thought leadership gained through hundreds of successful data and analytics projects, a library of industry use cases, engagement of outside advisors, and business value models and supporting metrics.

Knowledgent Protocycles solutions to prove their viability. Protocycles can be executed in 1 of 2 ways. Lab-based lean experimentation utilizes Knowledgent Labs to develop a proof-of-concept (POC) that is developed and tested using simulated data with a small group of client stakeholders. Field-based experimentation is integrated with a client team with the intent of solving a real-world problem in near real-time. Using real data, a POC quickly gains broad exposure and feedback is continuously integrated into the process to test the business outcome.

Once the solution has been engineered, it is ready for scale-up, which is the deployment of successful POCs into a production environment so capabilities and resulting insights can be operationalized to capture and sustain value. Finally, the insights are operationalized by embedding analytic insights into business processes in order to capture sustained value and innovation. An analytic operating model is delivered to manage change, establish clear organization responsibilities, and promote governance.

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