In this series, we demonstrate how we have applied our solutions to a variety of different projects in over twenty years of business. Last time, we went over solutions in information platforms. Now, here in part 4, we will show how we have applied solutions in knowledge platforms to a variety of projects. Like before, we will list out the solutions themselves and projects that fall under each category. Furthermore, we will also address the challenges involved with these solutions.
KNOWLEDGE PLATFORM SOLUTIONS
Knowledge requires agility to gain more accurate insight into findings to answer “What is or will be happening?” A knowledge-sharing platform is a centralized repository where people can share, organize, access, and store information. They give people easy access to important information. Basically, your average apps, sites, programs, etc.
While adding in the information context of why, knowledge is in the present, focused more on monitoring real-time or near real-time for either operational or project-analysis decision support. To understand what is and will happen, solutions provide decision support, modeling, analytics and monitoring. Solutions integrate STEM data, semantic data and multiple ontologies, temporospatial data versioning solutions, and support advanced analytics on high-powered cloud or HPC. We provide solutions such as:
- Search & Discovery – Work With BLM Seta
- Orchestration (MQ, Link, Mediate, Broker, Publish, Syndicate) – Architecture and Communication Services for the National Geospatial Program
- Intelligent Sensor feed pre-processing
- Data Warehouses & Beyond (Data Warehouses, Data Marts, Data Lakes) – Health Care Support
- Analytics (Visualization, Estimation, Sensitivity, Modeling, Simulation) – Analytics Platform Optimization for Web-Cloud-Based Territory, Data Analysis for Solar Energy, Dashboard Development, Geo Services
- Modeling – Habitat Modeling App, Aquatic Data Modeling, BI Workshops
- Data STEM Tools & Workbench solutions (Notebooks, Repositories, Containers) – Hydrography Dataset ETL, Data Governance
Challenges to Address
- Improve discovery metadata, develop semantic web models, improve interpretative signals, introduce machine learning, Search Signals, increase SEO, catalog development
- Reduce the large amount of time scientists spend in data engineering and IT setup & access
- Shift these labor intensive hours to conducting advance decision support analysis and modeling.
- Reduce access, discovery, and process burden on the customers, users, and citizens
- Develop solutions collaboratively to encourage partnerships between Xentity and their clients.
- Establish Performance Dashboards to provide decision support optics
- Shift Performance reporting to include trending, predictive, or other suggestive analytics
- Align large sensor investments with analytics and use efforts
See You in Part 5
We will see you in part 5, where we will conclude this series by going over our final offered solution: Wisdom AI and learning.