We are Informationists
Improving Lives and Business Through Data

Knowledgent is a data intelligence company that innovates IN and THROUGH data. We eat, sleep, and breathe data to enable advanced and agile analytics, digital enterprise, and robotics. We combine our data and analytics expertise with business specific domain knowledge. We are Informationists that are passionate about data.

Knowledgent delivers innovation in data and analytics with rapid ideation and experimentation to identify the business applications that will yield the most value. This is followed by rigorous scale up and operationalization to ensure that the solution is institutionalized as part of your process and technology fabric. This ensures that value is captured and the solution is sustainable. To address this paradigm, Knowledgent has developed a framework, the Knowledgent Innovation Cycle (KLIC).

Click on each section of KLIC to learn more.


Technology Sensing Ideation Experiment Design Lab Protocycling Field Protocycling Scale Up Insight Operationalization

Digital Tour of KLIC

Digital Tour of Knowledgent Labs

Our Impact in Your Industry

Knowledgent is focused on innovating in and through data to impact your business, using Machine Learning, Artificial Intelligence, Robotics, and Natural Language Processing.

Financial Services Life Sciences Healthcare Products & Services

Cognitive Computing for Regulatory Intelligence

Cognitive computing can be utilized to research and interpret regulatory information in response to specific requests for information. It can also detect changes to the external regulatory environment in order to support strategic decision-making, facilitate compliance, and enable earlier responses to emerging regulatory risks.

This protects from large fines associated with non-compliance and protection of an organization’s reputation. The proof of concept can be built using Sinequa for search and Natrual Language Processing, and Amazon Echo for intelligent voice control.

Real World Evidence

Real world data is ingested into a Hadoop file system, then all the data sets are standardized, and patient cohorts are built as analytic-ready outputs for visualizations. Data sets such as Truven, Cerner, and IMS are included in the ingestion and standardization.

The outputs drive insights into profiling, comparative effectiveness of drugs, signal detection, treatment pathways, and prevalence vs. incidence.

Treatment Adherence

The goal of Treatment Adherence is to create a strategy to identify members with low adherence to their treatment plan and a create value-based partnership with care-providers to improve outcomes. Ensemble machine learning techniques are used to predict a patient’s level of adherence.

Supervised Machine Learning models are combined with unsupervised models to predict patient-level adherence down to the month. Machine learning models are built in R, and Tableau is used to visualize insights.

Customer Loyalty

A Customer Loyalty enterprise data strategy is delivered across information management data, technology, analytics, processes, and organization. A business case is developed that highlights the ROI achieved through a customer loyalty program, such as increased revenues through incentives to current customers. The strategy also supports the company as they seek capital funding for the project through executive management.

Digital Compliance

Leverage web crawling technology to access all relevant regulatory information in single view, and search and text analytics to index rule books and track regulatory changes. Correlate regulatory information to themes, business units, and legal entities. Automated end-to-end process of regulatory management and recommended linkages and trends helps to mitigate risk of non-compliance and resulting fees.

Clinical Trial Innovation

Establish a learning healthcare system in collaboration with clinics to gather large-scale longitudinal patient data, for example: EMR, imaging, molecular, and patient reported outcomes. The vision is created that the data will be used to create new and innovative clinical decision support tools to treat patients at the point of care. Create and deploy an iPad based application to collect and measure patient disease progression (standard tests, patient reported outcomes).

Care Management Activation

Activate Members into Care programs through more targeted and specific outreach patterns. Supervised and unsupervised machine learning techniques were utilized to drive segments and ultimately micro-segment populations based on clinical, outreach and behavioral, and attitudinal data. The program increased Care program activation by 100%.

Next Best Action

Demonstrate how Next Best Action (i.e. Right Message / Action/ Offer on the Right Channel at the Right Time) can enable highly targeted marketing, increase ROI on marketing spend, and create a more meaningful dialogue with the customer. Machine learning models are built in SAS using SAS Enterprise Miner and marketing orchestration is enabled through client’s platform and workflow management application.

Trade Lifecycle Automation

End-to-End Trade Lifecycle management along with mid and back office process automation can be put in place for Financial Services organizations. The proof of concept showcases the capability to automate high volume repetitive trade lifecycle “trade break” identification and response processes. The experiment was built using Robotic Process Automation and java-based workflow automation tools.

Scientific Search

Pharma R&D is being revolutionized by Scientific Search. It provides the ability to improve researcher efficiency and the corresponding ability to identify new compounds by enabling search, retrieval, and provisioning of a company’s full body of research information. It can also include the capability to find information related to compounds molecularly similar to the compound being researched to further expand the knowledge base.

This ultimately shortens and enriches the research lifecycle, enabling companies to succeed or fail faster, saving time and money. The semantic search is driven by multiple biological ontologies along with Named Entity Extraction. Technologies used can include GATE, UIMA, OpenNLP, SmartLogic, Sinequa, Weka, R, and Mahout.

Adverse Events Case Processing Automation

Process automation is a new technology that is taking the data and analytics landscape by storm. Automation can eliminate long lead times for ingestion and processing of serious adverse events data. It allows you to understand non-serious adverse events that came from unstructured data (such as surveys) that in the past would manually need to be entered into a digital system. This task is time consuming and low value in comparison to the insights that can be mined from that data.

Natural Language Processing tools such as GATE, OpenNLP, and UIMA can be utilized, along with workflow automation capabilities built in Java, so this unstructured data can be consumed and utilized to shape your customer and product strategies.

Customer 360

Master Data Management programs can be expanded to include enterprise customer requirements and derive relationships with other valid customer definitions. A knowledge-driven customer focus is created that connects various organizations with a 360 degree view and provides up-to-date detailed information for any customer across multiple platforms.

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