We have a "GOBI Voice" Data Focus
There are 4 data types that are creepingin and changing organizations quickly in data practices. Geospatial - Open - Big - Iot(Sensors) - Voice - which can be integrated into warehouses, data lakes, MIS workflow, and analytics toolsets to extend augment traditional data assets (ERP, CRM, Financial, HR) to move beyond normal tabular, taxonomic and temporal data.
As data moves into decision support, analytics modeling, detail on where is needed for spatiotemporal 'asks'. When and where data is available, it is usually in pieces covering smaller areas, and not an integrated fabric covering the area of interest. This is geospatial data.
Adding the where dimension to your reporting, analytics, and monitoring adds immense value to operations, decisions, and planning. It permeates everywhere, yet is elusive in most organizations. It requires organization the community of supply capabilities driven by the community of use needs and questions sought.
Clients look for geospatial data strategy, design, and solutions by some of the largest and most complex geospatial programs around the world.
- Massive data variety complicates integration
- Temporal and Financial Integration
- Attaining complete coverage
- Integrating newer platforms to make easy for the non-GIS
- Maintenance requires true data supply chain investment
Geospatial Platforms (ESRI, FOSS4g, Analytics) (Design, Dev)
Geospatial Supply Chain Pipelines (Design, Dev)
Geospatial Data and Application Portfolio Rationalization (Strategy)
Geospatial & Cartographic Data Product Maintenance
Organizations can leverage existing and maturing quality national and local data being managed to accelerate data acquisition, curation, creation, and maintenance.
It's about Increasing Transparency and Access to valuable data goes beyond compliance. It involves making data re-usable, service-able, and business cases have shown can power economic and scientific advancement.
Clients look for high-visibility open data portals, aggregations, and community development that spans Federal, State, Local, and International Experience.
- Too many Standards and early policy complicates discovery and access
- Discoverability based on metadata makes finding the lost ark more likely than an open dataset in the wild.
- Semantic data feature mapping still is early alpha.
- The focus is on open datasets, not open data assets. These need aggregated registry solutions.
Open Data Platforms (ESRI, CKAN, Socrata, ODS, GeoNetwork, more) (Design, Dev)
Open Data ETL Supply Chain Integration(Design, Dev)
Open Data Vision and Roadmap Consulting (Strategy)
Open Data Pipeline and Metadata Operations & Maintenance
Data exhaust - logs of usage, traffic, interactions, transactions, search trends - is at such a granular level that before what was considered data exhaust is now considered data gold to help advise in micro-reactions, trends, and guidance to influence the smallest of interactive, search, algorithm, and model signals. This is Big Data.
Former ‘data exhaust’ and metadata is now fueling new data flow constructs to maintain the fidelity. We help determine new patterns and solutions to feed this knowledge-first trend.
Clients wish to move to platforms to integrate traditional MIS and data warehouse with new data pipelining architectures to feed their data-driven ambitions.
- Requires move to data lakes and ELT from traditional RDBMS and ETL.
- Large Compute investment
- Require data literacy, outreach, and sponsorship alignment prior to such an investment.
- Requires Portfolio Management to prioritize needs
- High velocity and geospatial require additional integration considerations.
- Big Data is intimidating to the level of data and information overload.
Analytics Integration (Develop)
Data Analytics Solutions Architecture (Lake, Warehouse, Graph, Real-time) (Design/Dev)
Use Case Analysis and Portfolio Planning (Strategy)
Data Hub and Lake Operations
IoT & Sensing Data
The amount of sensors has grown so much that 20 years ago, computer scientists had to design a new internet protocol foreseeing that the previous would exhaust the amount of connected devices. This is the Internet of Things or IoT world - with sensors connected within the enterprise and publicly creating a wave of noisy, loud, fast, and voluminous data requiring new architectures and patterns in edge computing, cleaning algorithms, storage, notification triggers, and integration solutions. This is IoT & Sensor Data.
Beyond Big Data, sensor data – remote or IoT, big and/or fast, requires edge and centralized new data flow patterns to trigger the real-time monitoring to support operation and decision making at the data-driven organization.
Our clients in transportation and science are leading the way in sensor integration and knowledge products.
- Requires high performance computing and data hubs to handle massive, fast feeds.
- Requires smart ways of storing big data collected using IoT devices
- Data comes in unstructured data formats, may be dirty, and require edge filtering, adaptive transformation, and other deferred load solution as area matures.
- No current toolsets are available to easily assess the effectiveness of IoT sensor project installation projects.
Agile Applicatoin Integration with IoT/Sensor Data and Enhancement (Development)
Real-Time Data Hub Architecture and Development (Design and Dev)
Remote Sensing Large Portfolio Rationalization Tools and Consulting (Strategy)
Real-Time Data Lifecycle Governance Development and Architecture Advisory (Strategy)
Voice & Language Data
From documents to transcriptions, voice data can with localized glossaries assist in uncovering patterns, expediting workflow, and inform NLP and AI for deeper pattern analysis. This can support hands-free, lower cost transcription, increased NLP eDiscovery, advertising multi-channels, and new training/education models.
Our clients are looking for the full lifecycle of voice solutions from voice data NLP pipelines to training data quality building to integrating in voice applications with MIS workflow as well as education.
- Voice App Market influx
- Voice data can be muddled (e.g bad microphones)
- Increasing Confidence in NLP
- Iterative process to get to trained data
- Configurable Voice Apps
- Trained Vocabularies
- NLP Workflow Integrations
- STEM Language Extractions (e.g. Geospatial)