Cliche as it may sound, a big part of “staying on top” is to always “be willing to learn new things.“  If the world of data science is continuously evolving, then we as a company need to keep learning new concepts to evolve with it. At Xentity, we strive to remain on top of trends in data science so we can better assist our clients with their various projects. And we want to take this opportunity to teach how we “stay on top”, in the hope being that we can help with our knowledge of the evolving world of data science.

Our goal is to learn and understand the newest approaches, patterns and concepts in business, technology, policy and governance that could help enhance customer capabilities in our core services based on readiness, maturity, and industry need in data science. In research, we continually re-invest in new models, patterns, and constructs that go back into our designs. We are also investigating new data solution architectures and methodologies to keep up with massive, and rapid change. Furthermore, our research will be in testing early architecture concepts and patterns that are new to client programs and organizational adoption. Also, research enhancements in management constructs. This could also result in adding credibility to newer architectural patterns, analyzing or creating methods, approaches, and maturity for readiness for client adoption in corporate and government environments.

How Can Integrating Data Enhance Products, Management, Applications, Remote Sensing, Knowledge Building, And Culture Impacts (Positive And Negative)?

We are participating in forward looking academic research looking at the next wave of architecture and management in regards to Data Science. This research involves asking questions such as:

  • Can place-based and geospatial data themes, products and services be demonstrated in business cases to truly impact all areas of business, from earth-based issues to enterprise resources to social science?
  • What is the right level of progression for metadata programs to enter into improved discovery?
  • What is the investment balance of moving into semantic web models or interpretative signals and machine learning?
  • Do we understand how national and industrial datasets guide strategic national or corporate investment in maturing data strategically?
  • How can we accelerate the induction of supply chain management concepts that have been proven for decades in capital investment and industrial engineering, into data science and data management?
  • How do we take crowdsourcing to not only enhance data, but increase the quality output of data and reducing the energy?

These are some of the questions we are actively seeking to engage in the study of data science. Our research areas seek to test early architecture concepts, patterns, and management constructs that are new to client programs and organizational adoption.

What Topics Are We Interested In?

We are actively working and establishing new research partnership academia (i.e. major university research hubs, local STEM programs), government (i.e. Federal Programs, State, Local), municipal services (i.e. water utilities), and engineering/scientific services.

In considering the types of innovative data science research that Xentity seeks to transform, the following  examples capture some of the directions Xentity is investigating. These examples include architecture solution and management research topics, and areas for information integration, information service, and analysis and synthesis research.

Data Science Solutions Research

  • Tactical Industry and Trend Context Reports: Data Visualizations
  • Implementable Changes in Industrial Engineering Practices
  • Linking Research & Commercial Industries
  • Crowd and Commercial Effectivity
  • Place-based and Geospatial business cases and impact levels by data theme, product, and service types
  • Semantic vs. machine learning applications for integrating large Corporate or Government common datasets
  • Proving existing major corporate and government datasets, social information data quality and semantic readiness, and existing or new platforms and applications to support Smart Cities in simulation environment such as urban planning, decision making, and policy/rules-based intelligence (aka Real-world SimCity and Civilization models)
  • Remote Sensing Integration with BigData Sources and Analytics
  • New Energy Model Research & Development Repository and Social Network Enhancement
  • Information Patterns & Historical Analysis
  • Integrating Computer and Library Science Techniques
  • Blending Machine Learning and Semantic Web
  • Historical Timeline Visualizations for knowledge, technical evolution
  • Roadmap Prediction Visualizations
  • AI/Robotic Integration with Decision Making
  • Data Supply Chain models analysis in support of creating data ecosystem flow for major static and real-time datasets.
  • Impacts of Next Generation or Internet2 architectures on existing content and dataset

Data Science and Architecture Management Research

  • Investigating how Bill/Policy Motives align with the Federal Portfolio
  • Leveraging Architecture concepts to advise and improve bills
  • Real-World Enterprise Architecture analysis
  • Federal readiness for architecture and change management maturity by agency using
  • Performance Measurement analysis for management and budget policies
  • Reduction and impact evaluation of burden on government agencies for data calls –
  • Value-measurement on policies and metadata
  • Strategic progression of maturing datasets (i.e. What dataset to build next and butterfly effect?)
  • Realistic blending of private sector and public sector best of breed techniques
  • Historical context analysis for current information management policy and bills for future decisions
  • Analyze policy shaping techniques (i.e. market-driven policy, policy reformation, protectionism policy, new value transition or adoption) diversity by industry.
  • Improving Product Management Subjectivity
  • Agile Project Management
  • Architecture Methodology
  • Data Supply Chain Management efficiency patterns
  • Integrating Geospatial Architectures into Industry
  • Industry Acceleration & Stabilization Evaluations
  • Gaming theory application readiness for Corporate and Government policy and increasing energy and quality output (i.e. MMORPG, Social Network, Strategy games, incentive models, talent/skill development, state of integration such as Mechanical Turk models)