Big data tools allow users to integrate, access, prepare, and blend data from any source without coding. They also enable users to analyze and visualize large amounts of data at scale. These tools streamline the process of working with big data.
Reusable datasets, pipelines, and dashboards for collaboration. The document describes ShareInsights, a big data analytics platform that provides reusable datasets, pipelines, and dashboards to enable collaboration. It has features like Jupyter notebooks, a drag-and-drop designer, self-service capabilities, and machine learning support. ShareInsights provides a unified approach to data preparation, analysis, visualization, and machine learning on big data.
Liberate Legacy Data Sources with Precisely and DatabricksPrecisely
Mainframe and IBM i data continues to be prevalent in several industries including financial services, insurance, and retail where critical customer information lives on legacy systems. In fact, in 2019 alone, studies show that there was a 55% increase in transaction volumes on the mainframe across all industries. To thrive in highly competitive markets, you must quickly break down legacy data silos to swiftly gain a full picture of data for insights for strategic action.
Traditional storage solutions that are mainframe proprietary struggle to scale for high data volumes and real-time analytics use cases. This results in increased costs, diminished performance, and missed SLAs. To solve this, Precisely and Databricks provide a modern approach for organizations to optimize volumes of data by leveraging the massive scalability of the cloud to power high-performance analytics, AI, and machine learning, regardless of where data lives.
In this webinar, we discuss:
- Quickly ingesting data from on-premises sources – such as mainframe and IBM i – to the cloud with the Databricks Unified Data Analytics Platform and Delta Lake
- Modernizing ETL processes and reduce development costs with visual data pipelines that uses the elastic scalability of Databricks
- Empowering business users with the most up to date data by populating Delta Lake with realtime data changes from legacy systems
View this webinar on-demand to see a live demo of the joint solution and how it can modernize your legacy infrastructure
Data engineering at the interface of art and analytics: the why, what, and ho...Data Con LA
- Data Engineering and Analytics at Netflix combines engineering and insights work to enable a data-driven culture and provide a single source of truth across the business.
- Data engineering at Netflix focuses on building meaning from data at scale through shared abstractions, rather than optimizing individual views, in order to highly align teams while keeping them loosely coupled and innovating faster.
- Leadership and ownership are valued for all engineers and data scientists at Netflix through relatively flat structures and career paths that decouple pay from title to focus on enabling experts to excel.
Building a Consistent Hybrid Cloud Semantic Model In DenodoDenodo
This webinar covers Denodo's product deep-dive series, focusing on their modern data virtualization architecture and capabilities. It discusses Denodo's approach to building a consistent semantic data model in a hybrid cloud, including connecting to various data sources, transforming and combining data, publishing results, and development/operations functions. A demonstration of Denodo's semantic data modeling capabilities is also provided.
Cloud Data Services - from prototyping to scalable analytics on cloudWilfried Hoge
Presentation from the German customer conference of IBM's Technical Expert Council. It shows how IBM's cloud data services could be used to explore data for new insights or business models.
#ibmbpsse18 - The journey to AI - Mikko Hörkkö, Elinar IBM Sverige
Elinar Oy Ltd is a system integrator for IBM Analytics products in Finland, Sweden and Norway with over 30 personnel and annual turnover of 3.9 million euros. Elinar helps organizations turn their data into business value using enterprise content management with analytics and artificial intelligence. Elinar's AI Miner tool was selected as one of the top three solutions out of hundreds of entries in the IBM Watson Build Challenge 2017 for extracting critical business information from unstructured data. Elinar offers AI Miner and additional regulatory technology and analytics offerings that combine AI Miner with IBM tools.
Bas van Dorst discusses how Agder Energi is embracing renewables and distributed energy resources through digital transformation. Some key points:
- Digital transformation requires adopting new mindsets and operational approaches to drive efficiency, reduce defects, and increase customer satisfaction.
- The market opportunity for digital innovation, connected devices, data, and analytics is massive, projected to be over $1 trillion by 2018 and include over 80 billion connected devices by 2025.
- Agder Energi's digital transformation involves moving workloads to Azure in 5 phases to establish a cloud native platform, integrate with IT systems, launch new services, scale workloads, and continuously enable business innovation.
Reusable datasets, pipelines, and dashboards for collaboration. The document describes ShareInsights, a big data analytics platform that provides reusable datasets, pipelines, and dashboards to enable collaboration. It has features like Jupyter notebooks, a drag-and-drop designer, self-service capabilities, and machine learning support. ShareInsights provides a unified approach to data preparation, analysis, visualization, and machine learning on big data.
Liberate Legacy Data Sources with Precisely and DatabricksPrecisely
Mainframe and IBM i data continues to be prevalent in several industries including financial services, insurance, and retail where critical customer information lives on legacy systems. In fact, in 2019 alone, studies show that there was a 55% increase in transaction volumes on the mainframe across all industries. To thrive in highly competitive markets, you must quickly break down legacy data silos to swiftly gain a full picture of data for insights for strategic action.
Traditional storage solutions that are mainframe proprietary struggle to scale for high data volumes and real-time analytics use cases. This results in increased costs, diminished performance, and missed SLAs. To solve this, Precisely and Databricks provide a modern approach for organizations to optimize volumes of data by leveraging the massive scalability of the cloud to power high-performance analytics, AI, and machine learning, regardless of where data lives.
In this webinar, we discuss:
- Quickly ingesting data from on-premises sources – such as mainframe and IBM i – to the cloud with the Databricks Unified Data Analytics Platform and Delta Lake
- Modernizing ETL processes and reduce development costs with visual data pipelines that uses the elastic scalability of Databricks
- Empowering business users with the most up to date data by populating Delta Lake with realtime data changes from legacy systems
View this webinar on-demand to see a live demo of the joint solution and how it can modernize your legacy infrastructure
Data engineering at the interface of art and analytics: the why, what, and ho...Data Con LA
- Data Engineering and Analytics at Netflix combines engineering and insights work to enable a data-driven culture and provide a single source of truth across the business.
- Data engineering at Netflix focuses on building meaning from data at scale through shared abstractions, rather than optimizing individual views, in order to highly align teams while keeping them loosely coupled and innovating faster.
- Leadership and ownership are valued for all engineers and data scientists at Netflix through relatively flat structures and career paths that decouple pay from title to focus on enabling experts to excel.
Building a Consistent Hybrid Cloud Semantic Model In DenodoDenodo
This webinar covers Denodo's product deep-dive series, focusing on their modern data virtualization architecture and capabilities. It discusses Denodo's approach to building a consistent semantic data model in a hybrid cloud, including connecting to various data sources, transforming and combining data, publishing results, and development/operations functions. A demonstration of Denodo's semantic data modeling capabilities is also provided.
Cloud Data Services - from prototyping to scalable analytics on cloudWilfried Hoge
Presentation from the German customer conference of IBM's Technical Expert Council. It shows how IBM's cloud data services could be used to explore data for new insights or business models.
#ibmbpsse18 - The journey to AI - Mikko Hörkkö, Elinar IBM Sverige
Elinar Oy Ltd is a system integrator for IBM Analytics products in Finland, Sweden and Norway with over 30 personnel and annual turnover of 3.9 million euros. Elinar helps organizations turn their data into business value using enterprise content management with analytics and artificial intelligence. Elinar's AI Miner tool was selected as one of the top three solutions out of hundreds of entries in the IBM Watson Build Challenge 2017 for extracting critical business information from unstructured data. Elinar offers AI Miner and additional regulatory technology and analytics offerings that combine AI Miner with IBM tools.
Bas van Dorst discusses how Agder Energi is embracing renewables and distributed energy resources through digital transformation. Some key points:
- Digital transformation requires adopting new mindsets and operational approaches to drive efficiency, reduce defects, and increase customer satisfaction.
- The market opportunity for digital innovation, connected devices, data, and analytics is massive, projected to be over $1 trillion by 2018 and include over 80 billion connected devices by 2025.
- Agder Energi's digital transformation involves moving workloads to Azure in 5 phases to establish a cloud native platform, integrate with IT systems, launch new services, scale workloads, and continuously enable business innovation.
The document discusses how connecting devices and collecting data through the Internet of Things (IoT) can create unprecedented value for businesses but also poses challenges. It argues that harnessing 50 billion connected devices generating 35 zillion bytes of data daily could enable new revenue streams, cost savings, and efficiency gains through personalized, ubiquitous, and automated solutions. However, integrating legacy systems, a lack of standards, security issues, and failing to analyze data effectively could limit these opportunities. The document proposes that Intel can help businesses overcome these challenges through solutions that connect, manage, analyze and secure IoT infrastructure and data to optimize industrial systems and unlock manufacturing intelligence.
The document discusses the future of IT infrastructure from the perspective of a CIO. It notes that IT must enable the business by embracing change through cloud adoption, which shifts spending from capital expenditures to operating expenses, improving efficiency, cash flow, agility, and innovation while avoiding write-downs of legacy systems. Emerging IT trends include everything-as-a-service, mobile technologies, APIs, analytics, and security through cloud identity and authentication. The roadmap for IT involves attracting top talent, ambitious planning, data-driven decisions, and prioritizing security.
The document describes a mobile data collection solution that allows users to map, track, and manage assets from mobile devices. It simplifies data entry in the field, allows real-time syncing to databases, and provides inventory management, reporting, asset mapping, document management, and data analysis capabilities from a cloud-based platform. The solution runs on iOS and Windows Embedded operating systems and uses an annual/monthly subscription business model.
A Successful Journey to the Cloud with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3mPLIlo
A shift to the cloud is a common element of any current data strategy. However, a successful transition to the cloud is not easy and can take years. It comes with security challenges, changes in downstream and upstream applications, and new ways to operate and deploy software. An abstraction layer that decouples data access from storage and processing can be a key element to enable a smooth journey to the cloud.
Attend this webinar to learn more about:
- How to use Data Virtualization to gradually change data systems without impacting business operations
- How Denodo integrates with the larger cloud ecosystems to enable security
- How simple it is to create and manage a Denodo cloud deployment
This document provides an overview and agenda for a presentation on product data management using Neo4j graph databases. The presentation will include an introduction to graph databases and Neo4j by Bruno Ungermann from Neo4j, followed by a discussion of using graph databases for product data management by Dr. Andreas Weber from semantic PDM. Examples will be provided of graph models and how they can be used for various domains including logistics, manufacturing, and customer relationships. Attendees will have an opportunity to ask questions and discuss use cases.
Big data analytics involves capturing, storing, processing, analyzing, and visualizing huge quantities of information from a variety of sources. This data is characterized by its volume, variety, velocity, veracity, variability, and complexity. Traditional analytics are not suited to handle big data due to its size and constantly changing nature. By analyzing patterns in big data, businesses can gain insights to improve processes and campaigns. However, specialized software is needed to make sense of big data's different types and formats from numerous sources. The right big data solution depends on an organization's specific data, budgets, skills, and future needs.
Enterprise Ready: A Look at Neo4j in Production at Neo4j GraphDay New York CityNeo4j
This document contains the agenda for an enterprise Neo4j training session in New York City on April 18, 2017. The agenda includes sessions on using graphs with Neo4j, working examples of transforming data, and a look at deploying Neo4j in production environments. Lunch is from 12:30-1:30 and a training session runs from 1:30-5:00pm.
Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)Denodo
Watch full webinar here: https://bit.ly/3idAnbf
Heute werden hochwertige Daten schnell und integriert benötigt, mittlerweile häufig auch über unterschiedliche Clouds hinweg.
Datenvirtualisierung kann hier als logische Datenschicht wahre Wunder wirken und die Modernisierung der Datenarchitektur drastisch beschleunigen.
In unserem kostenlosen Webinar interviewen wir den Experten Otto Neuer von Denodo, der die hier nur angerissenen Gedanken weiter ausführt. Er wird uns Einblicke in den Wandel von Datenarchitekturen geben und wie aus seinem Blickwinkel die nächste Phase der Business Intelligence aussieht.
Was Sie mitnehmen:
- Was sind die Herausforderungen und Limitierungen traditioneller Datenarchitekturen
- Wie können mit modernen Architekturen diese Limitierungen aufgehoben werden
- Welche Rolle spielt Datenvirtualisierung bei modernen Datenarchitekturen
- Was ist die nächste Phase der Business Intelligence
Erfahren Sie am 23. September 2020, den Experten Otto Neuer von Denodo zusammen mit unserem Partner QuinScape GmbH wird uns Einblicke in den Wandel von Datenarchitekturen geben und wie aus seinem Blickwinkel die nächste Phase der Business Intelligence aussieht.
Sie haben Interesse? Dann melden Sie sich am besten direkt an - die Plätze der Veranstaltung sind begrenzt.
DataHero spoke at Strata+Hadoop World on architecting for cloud data, the opportunities and challenges it poses. Big data isn't the only way to get data-driven insights anymore. Data is an integral part of an organization but a lot of this data now comes from cloud services. We need to find a way to make accessing and charting cloud data easier and faster. Chris Neumann, CEO and Cofounder of DataHero, outlines how.
Idera live 2021: Why Data Lakes are Critical for AI, ML, and IoT By Brian FlugIDERA Software
Find out why your AI (Artificial Intelligence), ML (Machine Learning) and IoT (Internet of Things) strategy is dependent on a robust data platform. We will explore why Data Lakes are the future of EDP (Electronic Data Processing), Data Warehouse and Data Archive, and how Qubole enables this via their open and secure cloud computing data platform. Learn why Data Lakes are critical for your business to keep your customer’s personal information security when under attack.
About the presenter:
Brian Flūg has decades of worldwide demonstrated experience as a pioneering Technologist with expertise in big data, analytical, distributed intelligent cloud computing, HPC, IoT wizard, HPC/ML/AI data storage, data intelligence and CAE/CAD/CAM/CFD. He has experience in life sciences, medical, financial, entertainment, gaming, manufacturing, defense, DOE, DOJ, automotive and consumer goods industries.
Brian is a Solutions Strategist with Qubole who has demonstrated success in computational solutions, from supercomputing, cluster and grid computing, to pre and post cloud computing, research, business intelligence, scientific analytics and engineering. He brings a wealth of knowledge to his role supporting Qubole customers and ensuring they are maximising their return from the tool.
Idera live 2021: Managing Digital Transformation on a Budget by Bert ScalzoIDERA Software
Digital transformation efforts, and developing data maturity often lead to organizations requirements changing. This means new databases - both cloud and on-premises - need to be implemented, and new types of data need to be processed and managed. Key here, is a flexible tool for managing multiple databases. In this session, Bert Scalzo explains the benefits of Aqua Data Studio - one tool to manage a wide array of databases environments whether cloud, on-premises or hybrid. Meaning you can manage more, with less.
About the presenter:
Bert Scalzo is an Oracle ACE, blogger, author, speaker and database technology consultant. He has worked with all major relational databases, including Oracle, SQL Server, DB2, Sybase, MySQL, and PostgreSQL. Bert’s work experience includes stints as product manager for multi-database tools such as DBArtisan and Aqua Data Studio at IDERA, and chief architect for the popular Toad family of products at Quest Software. He has three decades of Oracle® database experience and previously worked for both Oracle Education and Oracle Consulting. He holds several Oracle Masters certifications and his academic credentials include a BS, MS and Ph.D. in computer science, as well as an MBA.
Read the full post at https://www.fourquadrant.com/gartner-go-to-market-strategy/
Gartner's IT Predictions
Key technology drivers that will impact go to market strategy and tactics include: intelligent things, collecting massive amounts of data, artificial intelligence and machine learning.
Gartner identifies 3 key themes that form the basis for the Top 10 strategic technology trends:
- Intelligent
- Digital
- and Mesh
The technologies noted above are at the front-end of the technology adoption curve but are expected to break out of an emerging state and stand to have substantial disruptive potential across industries.
Read Pragmatic Posts on B2B Marketing - https://www.fourquadrant.com/marketing-resource-blog/
Download Go to Market Templates (FREE) - https://www.fourquadrant.com/marketing-tempates/
View the Go to Market PowerPoint Slide Library - https://www.fourquadrant.com/marketing-slides/
Leverage Proven Go to Market Planning Templates - https://www.fourquadrant.com/products/
Presented at the Artificial Intelligence for Knowledge Management (AI4KM), 2017, Melbourne, Australia
Artificial Intelligence, Semantic Data Lake, Social Analytics
There is an increasing backlog of digital data waiting to be processed. OBACS aims to process and visualize data as fast and accurately as possible using their SCINET Scientific Software Framework. SCINET is a suite of advanced software libraries and components that provide an ideal environment for developers to perform numerical computations, visualize and analyze data, develop algorithms, and create new software applications rapidly. SCINET features include an integrated all-in-one framework, object-oriented architecture, high performance computing with 80-bit precision, and compatibility with .NET programming languages.
The document discusses how connecting devices and collecting data through the Internet of Things (IoT) can create unprecedented value for businesses but also poses challenges. It argues that harnessing 50 billion connected devices generating 35 zillion bytes of data daily could enable new revenue streams, cost savings, and efficiency gains through personalized, ubiquitous, and automated solutions. However, integrating legacy systems, a lack of standards, security issues, and failing to analyze data effectively could limit these opportunities. The document proposes that Intel can help businesses overcome these challenges through solutions that connect, manage, analyze and secure IoT infrastructure and data to optimize industrial systems and unlock manufacturing intelligence.
The document discusses the future of IT infrastructure from the perspective of a CIO. It notes that IT must enable the business by embracing change through cloud adoption, which shifts spending from capital expenditures to operating expenses, improving efficiency, cash flow, agility, and innovation while avoiding write-downs of legacy systems. Emerging IT trends include everything-as-a-service, mobile technologies, APIs, analytics, and security through cloud identity and authentication. The roadmap for IT involves attracting top talent, ambitious planning, data-driven decisions, and prioritizing security.
The document describes a mobile data collection solution that allows users to map, track, and manage assets from mobile devices. It simplifies data entry in the field, allows real-time syncing to databases, and provides inventory management, reporting, asset mapping, document management, and data analysis capabilities from a cloud-based platform. The solution runs on iOS and Windows Embedded operating systems and uses an annual/monthly subscription business model.
A Successful Journey to the Cloud with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3mPLIlo
A shift to the cloud is a common element of any current data strategy. However, a successful transition to the cloud is not easy and can take years. It comes with security challenges, changes in downstream and upstream applications, and new ways to operate and deploy software. An abstraction layer that decouples data access from storage and processing can be a key element to enable a smooth journey to the cloud.
Attend this webinar to learn more about:
- How to use Data Virtualization to gradually change data systems without impacting business operations
- How Denodo integrates with the larger cloud ecosystems to enable security
- How simple it is to create and manage a Denodo cloud deployment
This document provides an overview and agenda for a presentation on product data management using Neo4j graph databases. The presentation will include an introduction to graph databases and Neo4j by Bruno Ungermann from Neo4j, followed by a discussion of using graph databases for product data management by Dr. Andreas Weber from semantic PDM. Examples will be provided of graph models and how they can be used for various domains including logistics, manufacturing, and customer relationships. Attendees will have an opportunity to ask questions and discuss use cases.
Big data analytics involves capturing, storing, processing, analyzing, and visualizing huge quantities of information from a variety of sources. This data is characterized by its volume, variety, velocity, veracity, variability, and complexity. Traditional analytics are not suited to handle big data due to its size and constantly changing nature. By analyzing patterns in big data, businesses can gain insights to improve processes and campaigns. However, specialized software is needed to make sense of big data's different types and formats from numerous sources. The right big data solution depends on an organization's specific data, budgets, skills, and future needs.
Enterprise Ready: A Look at Neo4j in Production at Neo4j GraphDay New York CityNeo4j
This document contains the agenda for an enterprise Neo4j training session in New York City on April 18, 2017. The agenda includes sessions on using graphs with Neo4j, working examples of transforming data, and a look at deploying Neo4j in production environments. Lunch is from 12:30-1:30 and a training session runs from 1:30-5:00pm.
Datenvirtualisierung: Wie Sie Ihre Datenarchitektur agiler machen (German)Denodo
Watch full webinar here: https://bit.ly/3idAnbf
Heute werden hochwertige Daten schnell und integriert benötigt, mittlerweile häufig auch über unterschiedliche Clouds hinweg.
Datenvirtualisierung kann hier als logische Datenschicht wahre Wunder wirken und die Modernisierung der Datenarchitektur drastisch beschleunigen.
In unserem kostenlosen Webinar interviewen wir den Experten Otto Neuer von Denodo, der die hier nur angerissenen Gedanken weiter ausführt. Er wird uns Einblicke in den Wandel von Datenarchitekturen geben und wie aus seinem Blickwinkel die nächste Phase der Business Intelligence aussieht.
Was Sie mitnehmen:
- Was sind die Herausforderungen und Limitierungen traditioneller Datenarchitekturen
- Wie können mit modernen Architekturen diese Limitierungen aufgehoben werden
- Welche Rolle spielt Datenvirtualisierung bei modernen Datenarchitekturen
- Was ist die nächste Phase der Business Intelligence
Erfahren Sie am 23. September 2020, den Experten Otto Neuer von Denodo zusammen mit unserem Partner QuinScape GmbH wird uns Einblicke in den Wandel von Datenarchitekturen geben und wie aus seinem Blickwinkel die nächste Phase der Business Intelligence aussieht.
Sie haben Interesse? Dann melden Sie sich am besten direkt an - die Plätze der Veranstaltung sind begrenzt.
DataHero spoke at Strata+Hadoop World on architecting for cloud data, the opportunities and challenges it poses. Big data isn't the only way to get data-driven insights anymore. Data is an integral part of an organization but a lot of this data now comes from cloud services. We need to find a way to make accessing and charting cloud data easier and faster. Chris Neumann, CEO and Cofounder of DataHero, outlines how.
Idera live 2021: Why Data Lakes are Critical for AI, ML, and IoT By Brian FlugIDERA Software
Find out why your AI (Artificial Intelligence), ML (Machine Learning) and IoT (Internet of Things) strategy is dependent on a robust data platform. We will explore why Data Lakes are the future of EDP (Electronic Data Processing), Data Warehouse and Data Archive, and how Qubole enables this via their open and secure cloud computing data platform. Learn why Data Lakes are critical for your business to keep your customer’s personal information security when under attack.
About the presenter:
Brian Flūg has decades of worldwide demonstrated experience as a pioneering Technologist with expertise in big data, analytical, distributed intelligent cloud computing, HPC, IoT wizard, HPC/ML/AI data storage, data intelligence and CAE/CAD/CAM/CFD. He has experience in life sciences, medical, financial, entertainment, gaming, manufacturing, defense, DOE, DOJ, automotive and consumer goods industries.
Brian is a Solutions Strategist with Qubole who has demonstrated success in computational solutions, from supercomputing, cluster and grid computing, to pre and post cloud computing, research, business intelligence, scientific analytics and engineering. He brings a wealth of knowledge to his role supporting Qubole customers and ensuring they are maximising their return from the tool.
Idera live 2021: Managing Digital Transformation on a Budget by Bert ScalzoIDERA Software
Digital transformation efforts, and developing data maturity often lead to organizations requirements changing. This means new databases - both cloud and on-premises - need to be implemented, and new types of data need to be processed and managed. Key here, is a flexible tool for managing multiple databases. In this session, Bert Scalzo explains the benefits of Aqua Data Studio - one tool to manage a wide array of databases environments whether cloud, on-premises or hybrid. Meaning you can manage more, with less.
About the presenter:
Bert Scalzo is an Oracle ACE, blogger, author, speaker and database technology consultant. He has worked with all major relational databases, including Oracle, SQL Server, DB2, Sybase, MySQL, and PostgreSQL. Bert’s work experience includes stints as product manager for multi-database tools such as DBArtisan and Aqua Data Studio at IDERA, and chief architect for the popular Toad family of products at Quest Software. He has three decades of Oracle® database experience and previously worked for both Oracle Education and Oracle Consulting. He holds several Oracle Masters certifications and his academic credentials include a BS, MS and Ph.D. in computer science, as well as an MBA.
Read the full post at https://www.fourquadrant.com/gartner-go-to-market-strategy/
Gartner's IT Predictions
Key technology drivers that will impact go to market strategy and tactics include: intelligent things, collecting massive amounts of data, artificial intelligence and machine learning.
Gartner identifies 3 key themes that form the basis for the Top 10 strategic technology trends:
- Intelligent
- Digital
- and Mesh
The technologies noted above are at the front-end of the technology adoption curve but are expected to break out of an emerging state and stand to have substantial disruptive potential across industries.
Read Pragmatic Posts on B2B Marketing - https://www.fourquadrant.com/marketing-resource-blog/
Download Go to Market Templates (FREE) - https://www.fourquadrant.com/marketing-tempates/
View the Go to Market PowerPoint Slide Library - https://www.fourquadrant.com/marketing-slides/
Leverage Proven Go to Market Planning Templates - https://www.fourquadrant.com/products/
Presented at the Artificial Intelligence for Knowledge Management (AI4KM), 2017, Melbourne, Australia
Artificial Intelligence, Semantic Data Lake, Social Analytics
There is an increasing backlog of digital data waiting to be processed. OBACS aims to process and visualize data as fast and accurately as possible using their SCINET Scientific Software Framework. SCINET is a suite of advanced software libraries and components that provide an ideal environment for developers to perform numerical computations, visualize and analyze data, develop algorithms, and create new software applications rapidly. SCINET features include an integrated all-in-one framework, object-oriented architecture, high performance computing with 80-bit precision, and compatibility with .NET programming languages.
1. The Power of
Big Data at Work
• Big Data integration with
zero coding
• Access, prepare & blend
data from any source
• Analyse & visualise data
at scale