We have a “GOBI” Data Focus
There are lots of amazing resources out there on Management information systems with normal tabular data with a temporal, financial or taxonomic (categories) dimension. There are ERP, CRM, Financial, HR, and many other common MIS solutions. Yet there are 4 data types that are creepingin and changing organizations quickly in data practices – Thiis is our Geospatial – Open – Big – Iot(Sensors) Data focus.
- Yet, more and more, as data moves into decision support, analytics modeling, detail on where is needed for spatiotemporal ‘asks’. When 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.
- Then, with the cost data acquisition, curation, creation, and maintenance, people want to re-use quality data. This is open data.
- On top of those two, 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.
- Finally, not only is the data getting big, yet 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.
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.
- 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
Top 5 Challenges Geospatial Data Face
- Geospatial data has a large variety of themes, standards, as it is a horizontal to integrate with various themes. This provides a challenge in how to integrate.
- Geospatial data as a horizontal is new to integrating with temporal and financial content that adds a newer dimension to solutions.
- Geospatial data has various levels of coverage from very detailed, large scale to national and global small scale without a clear vision of where the organization should play.
- Geospatial data has been treated largely as a separate group to solve GIS Survey, map making, and has not been opened up to integrate to support.
- Geospatial requires end to end understanding of data supply chains, production management, product generation, service delivery platforms, and application integration.
Geospatial Data Project Examples
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.
- Open Data Platforms (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
Top 5 Challenges Open Data Face
- The old nerdy sarcastic adage – “The great thing about standards is that there are so many”. Aggregation solutions need adaptive transformation, presentation, and publication rules.
- Open data policy, practices and solutions for fluid, big, and fast is non-existent. The policy is for traditional compliance more recently required, yet still early, scientific publication to support datasets.
- Discoverability solution still relies on metadata, and rules-based filtering, publication, weighting and gamification. This still makes finding the lost ark more likely than an open dataset in the wild.
- Semantic data feature mapping still is a dream as the focus is on getting the open data out there.
- Focus is on open datasets, not open data assets. Undiscoverable data assets are applications, products, maps, galleries, collections. These need aggregated registry solutions .
Open Data Project Examples
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.
- 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
Top 5 Challenges Big Data Face
- Big Data’s first challenge is most organizations have traditional relational database and warehouse architecture approaches which need to revisit the vision, roadmap, and investment plans for the path to transform to support this new scale.
- Big Data is such a large investment that assessing the Portfolio Management needs to identify and prioritize the knowledge asks prior to investing in the data supply needs, existing data integration and readiness gaps.
- Big Data architectures require some data literacy, outreach, and sponsorship alignment prior to such an investment.
- Big Data with high velocity and geospatial require additional integration considerations.
- Big Data is intimidating to the level of data and information overload. It requires thoughtful consideration to increase the density to be of value for analytics.
IoT & Sensing 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.
- 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)
Top 5 Challenges IoT Data Face
- IoT feeds require both high performance computing and storage solutions to avoid the data lakes turning into data marshes.
- Organizations have increasingly started looking at storing big data collected using IoT devices rather than rely on vendors.
- IoT devices may provide unstructured data formats, may be dirty, and require edge filtering, adaptive transformation, and other deferred load solution as area matures.
- Data hubs are needed to handle IoT, remote sensing, and other massive fast or big feeds.
- No current toolsets are available to easily assess the effectiveness of IoT sensor project installation projects.