Bargaining over Data and Analytics: Sellers, Buyers and Consultants

Analytics technologies that mine large amount of structured and unstructured data to gain insights are becoming increasingly important to businesses. In particular, the growing availability of enterprise proprietary data, coupled with publically aggregated or acquired data allows analytics to gain insights not only about the enterprise itself, but also cross companies, industry, and cross industries. The impact of such analysis is that it is transforming business processes and driving strategic business decision making and business model transformations, all of which overshadow more traditional low level, siloed, tactical optimizations. Such analytics trends are driving shifts in the overall analytics ecosystem that includes data providers and aggregators, analytics technology and service providers, clients in different industries, partners, and other related communities, e.g., visualization providers, academia, open source development communities. In particular, we have observed the emergence of two new service entities in the overall ecosystem: 1) New forms of data services that aggregate and provide accesses to a wide range of public and private data by partnering with data providers, aggregators, and clients are emerging. We call such services "Data as a Service (DaaS)" in this paper. DaaS can leverage commonly managed Cloud and Web based infrastructure and tools as well as hosted and Web delivery models to offer rich set of data processing, management, and access services, in addition to in house implementations. 2) On top of DaaS, one can create high value analytics services that can boost productivity and create value for all. Such services may include Business Intelligence reporting, text analytics, and advanced analytics such as predictive modeling, all made in composable forms to allow for direct consumption, integration and customizations. We call such services "Analytics as a Service (AaaS)". DaaS and AaaS help to maximize value for the overall ecosystem by eliminating common costs and delivering high value data and analytics services. Their emergence is transforming the overall analytics ecosystem and forcing significant cost structure and productivity model shifts, i.e., where to cut cost and where to make money -- two key metrics to a business model. As a result, they are driving the emergence of new business models across the overall analytics ecosystem. In this paper, we will analyze the major analytics ecosystem trends. We show that our analysis suggests that there is an analytics ecosystem transformation undergoing. The new ecosystem will increasingly leverage new forms of data and analytics services and roles, e.g., DaaS and AaaS, to maximize value for the overall ecosystem. Such ecosystem changes drive shifts in enterprise cost structures and productivity and value creation models and creates a force for business model innovation. We will describe an evolution of business models around the analytics ecosystem and highlight the emerging business models that are enabled by the new ecosystem, many of which have an open, collaboration, and co-developing spirit. We will also present several real-world case studies to illustrate how the new ecosystem can maximize value for all by implementing innovative business models.

Analysis other than simple access and visualization is the emerging trend within the web community [Berners-Lee 2007]. With the rapid development of the Internet and web technologies, “the traditional approach to publishing maps on the Internet ‘by GIS experts, for GIS experts’ is outdated, ineffective and unusable as Web mapping becomes more and more mainstream” [van der Vlugt and Stanley 2005]. Over the last few years, we have seen more and more traditional GIS functions being integrated into web mapping/GIS applications in a way transparent to users, using distributed analytical and geoprocessing services coordinated by well-defined workflows [Yang and Raskin 2009]. Web service technology has played one of the key roles in this transition. As the service-driven paradigm continues to dominate web mapping and GIS development, web-based geospatial analytical services have drawn the attention of many in the field, hoping to build such services on top of more mature basic data services. Given the complex nature of analytical services, including modelling, simulation, function and visualization services, the orchestration of web mapping and geoprocessing services from different sources becomes increasingly important. The trend towards analytical geospatial web services continues with intelligent analytical capability, which involves indepth study on dynamic geospatial service composition, for example. It is also expected that intelligent analytical services will be enhanced with further developments in Geographic Knowledge Discovery (GKD), spatial data mining techniques and tools, web-based modelling and simulation, augmented reality applications, agent-based systems and paradigms [Hutchinson and Veenendaal 2011], etc. The articles included in this special issue contribute to the capacity building towards analytical geospatial web. The three feature articles selected for this special issue touch on web geoprocessing, visualization and thematic mapping tools. The issue begins with the article authored by Eberle and Strobl, Web-based

The DOE Analytical Laboratory Capacity Study was conducted to give EM-263 current information about existing and future analytical capacities and capabilities of site laboratories within the DOE Complex. Each DOE site may have one or more analytical laboratories in operation. These facilities were established to support site missions such as production, research and development, and personnel and environmental monitoring. With changing site missions and the DOE directives for environmental monitoring and cleanup, these laboratories are either devoting or planning to devote resources to support EM activities. The DOE site laboratories represent a considerable amount of capital investment and analytical capability, capacity, and expertise that can be applied to support the EM mission. They not only provide cost-effective high-volume analytical laboratory services, but are also highly recognized analytical research and development centers. Several sites have already transferred their analytical capability from traditional production support to environmental monitoring and waste management support. A model was developed to determine the analytical capacity of all laboratories in the DOE Complex. The model was applied at nearly all the major laboratories and the results collected from these studies are summarized in this report.

The DOE Analytical Laboratory Capacity Study was conducted to give EM-263 current information about existing and future analytical capacities and capabilities of site laboratories within the DOE Complex. Each DOE site may have one or more analytical laboratories in operation. These facilities were established to support site missions such as production, research and development, and personnel and environmental monitoring. With changing site missions and the DOE directives for environmental monitoring and cleanup, these laboratories are either devoting or planning to devote resources to support EM activities. The DOE site laboratories represent a considerable amount of capital investment and analytical capability, capacity, and expertise that can be applied to support the EM mission. They not only provide cost-effective high-volume analytical laboratory services, but are also highly recognized analytical research and development centers. Several sites have already transferred their analytical capability from traditional production support to environmental monitoring and waste management support. A model was developed to determine the analytical capacity of all laboratories in the DOE Complex. The model was applied at nearly all the major laboratories and the results collected from these studies are summarized in this report.

The concept of 'core capability' can be nebulous one. Even at a fairly specific level, where core capability equals maintaining essential services, it is highly dependent upon the perspective of the requestor. Samples are submitted to analytical services because the requesters do not have the capability to conduct adequate analyses themselves. Some requests are for general chemical information in support of R and D, process control, or process improvement. Many analyses, however, are part of a product certification package and must comply with higher-level customer quality assurance requirements. So which services are essential to that customer - just those for product certification? Does the customer also (indirectly) need services that support process control and improvement? And what is the timeframe? Capability is often expressed in terms of the currently utilized procedures, and most programmatic customers can only plan a few years out, at best. But should core capability consider the long term where new technologies, aging facilities, and personnel replacements must be considered? These questions, and a multitude of others, explain why attempts to gain long-term consensus on the definition of core capability have consistently failed. This preliminary report will not try to define core capability for any specific program or set of programs. Instead, it will try to address the underlying concerns that drive the desire to determine core capability. Essentially, programmatic customers want to be able to call upon analytical chemistry services to provide all the assays they need, and they don't want to pay for analytical chemistry services they don't currently use (or use infrequently). This report will focus on explaining how the current analytical capabilities and methods evolved to serve a variety of needs with a focus on why some analytes have multiple analytical techniques, and what determines the infrastructure for these analyses. This information will be useful in defining a roadmap for what future capability needs to look like.

Instrumental considerations for reliable fieldable VOC analyses

ABSTRACTThe increasing use of data-driven decision making and big data is leading organizations to invest in analytics software and services. However, little is known about the type of analytics capabilities within IT that are required and whether there is a common progression or development model of analytics capabilities. Also unknown is how the level of analytics capabilities and other factors influence a firm’s decision to invest in analytics. The purpose of this research is to explore the relationships between levels of distinct analytics capabilities and to understand how they and other factors influence the analytics investment decision. The findings suggest that there is a distinct progression in the development of analytics capabilities, and that firm size is associated with increased capability. The results suggest that firms more likely to invest in analytics have higher current levels of specific analytics capabilities, are larger, and are located in less-competitive industries.

The expansion of the digital economy and the rapid developments of technology induced the creation of new products and industries and drove a significant increase in data resources. The information products industry, including products based on data, information and knowledge, is intensely dynamic in terms of growth and the pace of new product introduction. The complexity in the variety of product offerings and the number of firms offering those products in this industry is increasing exponentially every day. Data-driven innovation forms a key pillar in the 21 century sources of growth. Large data sets are becoming a core asset in the economy, fostering new industries, processes, and products and creating significant competitive advantages (OECD, 2015). The past two decades have brought several reconfigurations of the information and knowledge economy. The recent technological breakthroughs have driven the emergence and the exponential growth of a digital economy with vast data assets. The changes have been accompanied by ongoing attempts to make sense of all the data through the use of analytics. Analytics add substantial value to intangible assets by making them easier to understand and apply. In a world in which information alone has become ubiquitous and somewhat commoditized, analytics provide a means of making information more useful and valuable. In this paper, we focus on new analytical capabilities and data assets that together form value-added information product offerings and new possibilities for emerging markets. These offerings are often called data products. In general, a data product is digital information that can be purchased. Data products incorporate data science into the operation of a product or service, using data in smart ways to provide value. In research, a data product is a large data set in a format that requires little or no processing or programming. Typical data products are predictive, descriptive or prescriptive models, as well as insights. The future of new product development reflects both developing new innovative products and data driven products typical for emerging markets that are large economies. For any organization, there is not only revenue, but competitive advantage to be gained in developing data products and new innovative products. Creating an effective development process for data products requires following well-established steps and data analytics helps adding a few new ones, which are explained in the paper. Further, this paper will focus on three important decisions for innovative process: decision 1- key enablers of emerging market innovation (R&D capability, market opportunity and executive champions), decision 2 - what product to develop (market need, portfolio fit, and product-capability fit) and decision 3 - how to develop the product (the decision matrix and bootstrapping). Organizations can follow several approaches to monetize their data like improving internal business processes and decisions, focusing information around core products and services, and selling information offerings to emerging and existing markets. Few remarks for our country will be made as further paths of development.

This project consists of two parts. In Part 1, well logs, other well data, drilling, and production data for the Pioneer Field in the southern San Joaquin Valley of California were obtained, assembled, and input to a commercial relational database manager. These data were used in PC-based geologic mapping, evaluation, and visualization software programs to produce 2-D and 3-D representations of the reservoir. Petrographic and petrophysical measurements made on samples from Pioneer Field, including core, cuttings and liquids, were used to calibrate the log suite. In Part 2, these data sets were used to develop algorithms to correlate log response to geologic and engineering measurements. This project provides a detailed example, based on a field trial, of how to evaluate a field for EOR operations utilizing data typically available in older fields which have undergone primary development. The approach utilizes readily available, affordable PC-based computer software and analytical services. This study illustrates the steps involved in: (1) setting up a relational database to store geologic, well-log, engineering, and production data; (2) integration of data typically available for oil and gas fields with predictive models for reservoir alteration, and (3) linking these data and models with modern computer software to provide 2-D and 3-D visualizations of the reservoir and its attributes. The techniques were demonstrated through a field trial in Pioneer Field, that produces from the Monterey Formation, a reservoir which is a candidate for thermal EOR.

This project consists of two parts. In Part 1, well logs, other well data, drilling, and production data for the Pioneer Field in the southern San Joaquin Valley of California were obtained, assembled, and input to a commercial relational database manager. These data are being used in PC-based geologic mapping,e valuation, and visualization software programs to produce 2-D and 3-D representations of the reservoir geometry, facies and subfacies, stratigraphy, porosity, oil saturation, and other measured and model parameters. Petrographic and petrophysical measurements made on samples from Pioneer Field, including core, cuttings, and liquids, are being used to calibrate the log suite. In Part 2, these data sets are being used to develop algorithms to correlate log response to geologic and engineering measurements. This project provides a detailed example, based on a field trial, of how to evaluate a field for EOR operations utilizing data typically available in older fields which have undergone primary development. The approach utilizes readily available, affordable PC-based computer software and analytical services. This study will illustrate the steps involved in: (1) setting up a relational database to store geologic, well-log, engineering, and production data; (2) integration of data typically available for oil and gas fields with predictive models for reservoir alteration; and (3) linking these data and models with modern computer software to provide 2-D and 3-D visualizations of the reservoir and its attributes. The techniques are being demonstrated through a field trial in Pioneer Field, that produces from the Monterey Formation, a reservoir which is a candidate for thermal EOR.

The purpose of this project is to provide a detailed example, based on a field trial, of how to evaluate a field for enhanced oil recovery (EOR) operations utilizing data typically available in a field that has undergone primary development. The approach will utilize readily available, affordable computer software and analytical services. For example, the GeoGraphix Exploration System software package was acquired, installed on a PC in the Subsurface Laboratory at Michigan Technological University, and is currently in use. The USGS Digital Land Grid and National Geophysical Data Center`s Gravity Data CDROM were acquired and installed on GeoGraphix. Microsoft Access databases are being developed to archive analytical data and digitized log traces. Data tables for geochemical and petrographic data, well logs, well header information, well production data, formation tops, and fault trace data have been completed. A new effort was initiated during the last quarter of 1995. The surface geological maps of the southern San Joaquin Valley were digitized and loaded into the computer drafting program Canvas where they were edited combined into one large map and colored. When completed, the integrated map will be printed in large format on the HP650C color plotter.

This project will provide a detailed example, based on a field trial, of how to evaluate a field for EOR operations utilizing data typically available in an older field which has under gone primary development. The approach will utilize readily available, affordable PC-based computer software and analytical services. This study will illustrate the steps involved in: (1) setting up a relational database to store geologic, well-log, engineering, and production data, (2) integration of data typically available for oil and gas fields with predictive models for reservoir alteration, and (3) linking these data and models with modern computer software to provide 2-D and 3-D visualizations of the reservoir and its attributes. The techniques are being demonstrated through a field trial on a reservoir, Pioneer Field, a field that produces from the Monterey Formation, which is a candidate for thermal EOR. Technical progress is summarized for the following tasks: (1) project administration and management; (2) data collection; (3) data analysis and measurement; (4) modeling; and (5) technology transfer.

Modern businesses increasingly rely on internal data products, such as curated datasets or analytical services, to drive innovation and informed decisions. Despite substantial investments in data technologies, including a global Artificial Intelligence market valued at $230 to $280 billion in 2024, large organizations struggle with inconsistent API interfaces. This inconsistency hinders efficient data exchange and robust governance. This paper tackles this challenge by proposing a framework for mandatory OpenAPI Specification (OAS) adoption and automated enforcement for all internal data products. Our approach defines clear organizational standards and implements a twostep compliance checking mechanism. This involves Static Type Analysis (STA) for foundational rule enforcement and an AI agent for nuanced, contextual validation. Integrated within CI/CD pipelines, this automated system ensures continuous adherence to design standards, enhancing data product discoverability, interoperability, and overall data governance. This work provides a practical methodology for establishing standardized control over internal data product APIs, streamlining development, and fostering a resilient data ecosystem.

SeaDataNet is a major pan-European infrastructure for managing and providing access to marine data sets and data products, acquired by European organisations from research cruises and other observational activities in European coastal marine waters, regional seas and the global ocean. Founding partners are National Oceanographic Data Centres (NODCs), major marine research institutes, and ICES. The SeaDataNet network, initiated in the nineties, expanded over time its network of data centres and infrastructure, during a series of dedicated EU RTD projects, and by engaging as core data management infrastructure and network in leading European Commission initiatives such as the European Marine Observation and Data network (EMODnet), Copernicus Marine Service (CMS), and the European Open Science Cloud (EOSC). These facilitated ongoing development and evolution of the SeaDataNet technical infrastructure, standards, tools, and services, while managing and further expanding a large network of connected data centres and data providers.SeaDataNet develops, governs and promotes common standards, vocabularies, software tools, and services for marine data management, which are freely available from its portal and widely adopted and used by many communities and projects. A core SeaDataNet service is the CDI data discovery and access service which provides online unified discovery and access to vast resources of data sets, managed by 115+ connected SeaDataNet data centres from 34 countries around European seas, both from research and monitoring organisations. Currently, it gives access to more than 3 Million data sets, originating from 1000+ organisations in Europe, covering physical, geological, chemical, biological and geophysical data, and acquired in European waters and global oceans. Standard metadata and data formats are used, supported by an ever-increasing set of controlled vocabularies to mark up the metadata profiles in a semantically controlled way, resulting in rich and highly FAIR metadata and data sets. Services include online CDI catalogue, cloud-based data cache, and online shopping mechanism. FAIRness is further amplified by machine-to-machine services.  SeaDataNet provides core services in EMODnet Chemistry, Bathymetry, Physics, and Ingestion for bringing together and harmonizing large amounts of marine data sets, which are used by EMODnet groups for generating thematic data products. Examples: Digital Terrain Model for all European seas (Bathymetry), and European aggregated and validated data collections for eutrophication, contamination, and marine litter (Chemistry). These products are very popular and find their way to many users from government, research, industry, and public. SeaDataNet is also back-office for EMODnet Ingestion, reaching out and achieving input from data providers that are not (yet) participating in the European data exchange.SeaDataNet is well engaged in EOSC projects, such as EOSC-FUTURE and the Blue-Cloud project. A pilot deployed a versatile cyber platform federating multidisciplinary data repositories, analytical tools, and computing facilities for exploring and demonstrating the potential of cloud based open science for ocean sustainability. A further evolution is underway into a Federated European Ecosystem and EOSC Blue Node to deliver FAIR & Open data and analytical services, instrumental for deepening research of oceans, coastal & inland waters. This is also highly important for the Digital Twins of the Oceans (DTO) initiative.

Objectives of the NASA Information And Data System (NAIADS) project are to develop a prototype of a conceptually new middleware framework to modernize and significantly improve efficiency of the Earth Science data fusion, big data processing and analytics. The key components of the NAIADS include: Service Oriented Architecture (SOA) multi-lingual framework, multi-sensor coincident data Predictor, fast into-memory data Staging, multi-sensor data-Event Builder, complete data-Event streaming (a workflow with minimized IO), on-line data processing control and analytics services. The NAIADS project is leveraging CLARA framework, developed in Jefferson Lab, and integrated with the ZeroMQ messaging library. The science services are prototyped and incorporated into the system. Merging the SCIAMACHY Level-1 observations and MODIS/Terra Level-2 (Clouds and Aerosols) data products, and ECMWF re-analysis will be used for NAIADS demonstration and performance tests in compute Cloud and Cluster environments.