Once in the past, routing team at HERE Technologies was in the situation when routing backend was in danger of failing SLA. It’s triggered a massive work to improve its performance. As a side effect of this work, https://github.com/heremaps/flatdata was born: zero-copy memory-mapped data storage.
This talk will be about sharing an experience of creation of Go implementation for Flatdata: zero-copy memory-mapped data storage.
We will compare Go implementation with implementations for other languages. I will show in which cases Go has advantages compared to other languages and opposite cases (hi, generics!). We will look also at the performance aspect of different implementations.
Enabling Edge-Cloud Duality of Time Series DataInfluxData
In this session, learn about the new feature in InfluxDB: Edge Data Replication! Discover how to automatically replicate data from an InfluxDB instance to InfluxDB Cloud. This will provide developers with insights into all assets at the edge — including sensors, servers, networks, and apps. InfluxDB is the centralized hub for collecting, storing, and analyzing time-stamped data collected from the edge, cloud and on-premises. InfluxDB will automatically copy the data from the source and send it to InfluxDB for all engineers, data scientists and business analysts to utilize.
Sam Dillard will discuss the growing needs and challenges of edge computing. Applications have become more distributed and data volumes keep increasing. Sam will discuss InfluxDB’s new edge data replication feature that leverages existing capabilities of the time series platform in order to enable edge-cloud data pipelines that fit any business needs and constraints. This feature automatically streams data on-write from an edge dataset to a cloud one of the user’s choosing. Adding to this automatic replication of writes is a durability designed to withstand network outages. This feature lays the groundwork for a much larger story about how the edge and cloud can work together to produce global time series data architectures! Sam will cover:
Methodology for improving IIoT monitoring at the edge with a time series platform with nanosecond precision
The importance of centralized visibility into all assets to meet business requirements
How to use InfluxDB and Flux to reduce latency and cloud operational costs
3 Benefits of Multi-Temperature Data Management for Data AnalyticsMapR Technologies
SAP® HANA and SAP® IQ are popular platforms for various analytical and transactional use cases. If you’re an SAP customer, you’ve experienced the benefits of deploying these solutions. However, as data volumes grow, you’re likely asking yourself: How do I scale storage to support these applications? How can I have one platform for various applications and use cases?
The internet has evolved from a human-centric client-server based architecture to one where humans and assets (or things) are equal stakeholders. Do our databases, middleware, and client applications still stand up? In this talk, Brian Gilmore outlines the unique challenges of data operations and analytics in the IoT environment, examines how human and machine interactions drive architecture and deployment, and identifies where we could work to improve our data strategies to fully leverage the IoT opportunity.
Why time series data is the secret to success when it comes to Industry 4.0
How an IoT data platform fits in with any IoT architecture to manage the data requirements of every IoT implementation
Effectively collect, manage, and analyze time series data to drive your ability to improve operations
How Service Mesh Fits into the Modern Data StackFabian Hardt
The modern data stack has become increasingly popular in the analytics community. Patterns like domain-driven design, known from classical software development, are finding their way into analytics contexts. This is the basis of a new paradigm, like Data Mesh. In a Data Mesh, every domain - like a different department for example - wants to solve similar problems with their own business data. Therefore, it’s vital to implement a flexible, lightweight, and manageable, but also secured and monitorable central self-service data platform. With the containerization of services, and using Kubernetes as a runtime, you can build flexible data architectures. Data visualization, data ingestion, orchestration, and ETL tools, as well as Cloud Data Warehouses, should all live together in a kind of a mesh. In this session, learn how Kong's CNCF Sandbox, project Kuma, provides the next level of security when handling data, other business domains, and exchanging data with external systems. Uncover the advantages of end-to-end tracing, data collection, and external access from outside of the mesh using Data APIs.
Once in the past, routing team at HERE Technologies was in the situation when routing backend was in danger of failing SLA. It’s triggered a massive work to improve its performance. As a side effect of this work, https://github.com/heremaps/flatdata was born: zero-copy memory-mapped data storage.
This talk will be about sharing an experience of creation of Go implementation for Flatdata: zero-copy memory-mapped data storage.
We will compare Go implementation with implementations for other languages. I will show in which cases Go has advantages compared to other languages and opposite cases (hi, generics!). We will look also at the performance aspect of different implementations.
Enabling Edge-Cloud Duality of Time Series DataInfluxData
In this session, learn about the new feature in InfluxDB: Edge Data Replication! Discover how to automatically replicate data from an InfluxDB instance to InfluxDB Cloud. This will provide developers with insights into all assets at the edge — including sensors, servers, networks, and apps. InfluxDB is the centralized hub for collecting, storing, and analyzing time-stamped data collected from the edge, cloud and on-premises. InfluxDB will automatically copy the data from the source and send it to InfluxDB for all engineers, data scientists and business analysts to utilize.
Sam Dillard will discuss the growing needs and challenges of edge computing. Applications have become more distributed and data volumes keep increasing. Sam will discuss InfluxDB’s new edge data replication feature that leverages existing capabilities of the time series platform in order to enable edge-cloud data pipelines that fit any business needs and constraints. This feature automatically streams data on-write from an edge dataset to a cloud one of the user’s choosing. Adding to this automatic replication of writes is a durability designed to withstand network outages. This feature lays the groundwork for a much larger story about how the edge and cloud can work together to produce global time series data architectures! Sam will cover:
Methodology for improving IIoT monitoring at the edge with a time series platform with nanosecond precision
The importance of centralized visibility into all assets to meet business requirements
How to use InfluxDB and Flux to reduce latency and cloud operational costs
3 Benefits of Multi-Temperature Data Management for Data AnalyticsMapR Technologies
SAP® HANA and SAP® IQ are popular platforms for various analytical and transactional use cases. If you’re an SAP customer, you’ve experienced the benefits of deploying these solutions. However, as data volumes grow, you’re likely asking yourself: How do I scale storage to support these applications? How can I have one platform for various applications and use cases?
The internet has evolved from a human-centric client-server based architecture to one where humans and assets (or things) are equal stakeholders. Do our databases, middleware, and client applications still stand up? In this talk, Brian Gilmore outlines the unique challenges of data operations and analytics in the IoT environment, examines how human and machine interactions drive architecture and deployment, and identifies where we could work to improve our data strategies to fully leverage the IoT opportunity.
Why time series data is the secret to success when it comes to Industry 4.0
How an IoT data platform fits in with any IoT architecture to manage the data requirements of every IoT implementation
Effectively collect, manage, and analyze time series data to drive your ability to improve operations
How Service Mesh Fits into the Modern Data StackFabian Hardt
The modern data stack has become increasingly popular in the analytics community. Patterns like domain-driven design, known from classical software development, are finding their way into analytics contexts. This is the basis of a new paradigm, like Data Mesh. In a Data Mesh, every domain - like a different department for example - wants to solve similar problems with their own business data. Therefore, it’s vital to implement a flexible, lightweight, and manageable, but also secured and monitorable central self-service data platform. With the containerization of services, and using Kubernetes as a runtime, you can build flexible data architectures. Data visualization, data ingestion, orchestration, and ETL tools, as well as Cloud Data Warehouses, should all live together in a kind of a mesh. In this session, learn how Kong's CNCF Sandbox, project Kuma, provides the next level of security when handling data, other business domains, and exchanging data with external systems. Uncover the advantages of end-to-end tracing, data collection, and external access from outside of the mesh using Data APIs.
Bee is an engine to build, deploy and manage microservices from composing event and data message handling. It supports smart scalabilities and distributed computing patterns, like mesh networks.
Integrating Semantic Web in the Real World: A Journey between Two Cities Juan Sequeda
Keynote at The 9th International Conference on Knowledge Capture (KCAP2017), Austin, Texas, Dec 2017
An early vision in Computer Science has been to create intelligent systems capable of reasoning on large amounts of data. Today, this vision can be delivered by integrating Relational Databases with the Semantic Web using the W3C standards: a graph data model (RDF), ontology language (OWL), mapping language (R2RML) and query language (SPARQL). The research community has successfully been showing how intelligent systems can be created with Semantic Web technologies, dubbed now as Knowledge Graphs.
However, where is the mainstream industry adoption? What are the barriers to adoption? Are these engineering and social barriers or are they open scientific problems that need to be addressed?
This talk will chronicle our journey of deploying Semantic Web technologies with real world users to address Business Intelligence and Data Integration needs, describe technical and social obstacles that are present in large organizations, and scientific challenges that require attention.
Webinar: How Banks Use MongoDB as a Tick DatabaseMongoDB
Learn why MongoDB is spreading like wildfire across capital markets (and really every industry) and then focus in particular on how financial firms are enjoying the developer productivity, low TCO, and unlimited scale of MongoDB as a tick database for capturing, analyzing, and taking advantage of opportunities in tick data.
Best Practices & Lessons Learned from Deployment of PostgreSQLEDB
This talk will review best practices and lessons learned from working with large and mid-size companies on their deployment of PostgreSQL. We will explore the practices that helped industry leaders move through the stages of PostgreSQL adoption and get as much value out of their deployment as possible without incurring undue risk.
Uma introdução à malha de dados e as motivações por trás dela: os modos de falhas de paradigmas anteriores de gerenciamento de big data. A proposta de Zhamak Dehghani é comparar e contrastar a malha de dados com as abordagens existentes de gerenciamento de big data, apresentando os componentes técnicos que sustentam a arquitetura de software.
Battling the disrupting Energy Markets utilizing PURE PLAY Cloud ComputingEdwin Poot
Disruption can be intimidating. You may even be losing business to one or more rising competitors. You may be wondering how you could possibly compete. Rest assured, this disruption doesn’t mean you need to turn your business upside down. But just be smart in how you engage your business using innovation without the need for huge changes, high risks or large investments.
Il contenuto riguarderà il Software ERP
in Cloud #1 al Mondo (Gartner), in
questa sessione enfatizzeremo la
connessione tra le esigenze delle
imprese innovative e NetSuite. Oltre le
funzionalità, un passaggio importante è
relativo ai nuovi paradigmi (Cloud, SaaS,
ecc.) a reale supporto della strategia
aziendale (no version locking,
customization carry forward, major
releases per year, ecc.)
contrapponendoli agli ostacoli alla
crescita aziendale e del business.
Presentation given by our CTO, Dr. Stéphane Barbey, at the AdaCore Tech Days 2015.
Short introduction to Paranor followed by a description of ZVIS - a financial system developed in Ada. The presentation answers the question, how a >4 millions lines-of-code application can be supported by tools and other means to ease the maintenance of the application and shift focus to innovation.
Building a Pluggable Analytics Stack with Cassandra (Jim Peregord, Element Co...DataStax
Element Fleet has the largest benchmark database in our industry and we needed a robust and linearly scalable platform to turn this data into actionable insights for our customers. The platform needed to support advanced analytics, streaming data sets, and traditional business intelligence use cases.
In this presentation, we will discuss how we built a single, unified platform for both Advanced Analytics and traditional Business Intelligence using Cassandra on DSE. With Cassandra as our foundation, we are able to plug in the appropriate technology to meet varied use cases. The platform we’ve built supports real-time streaming (Spark Streaming/Kafka), batch and streaming analytics (PySpark, Spark Streaming), and traditional BI/data warehousing (C*/FiloDB). In this talk, we are going to explore the entire tech stack and the challenges we faced trying support the above use cases. We will specifically discuss how we ingest and analyze IoT (vehicle telematics data) in real-time and batch, combine data from multiple data sources into to single data model, and support standardized and ah-hoc reporting requirements.
About the Speaker
Jim Peregord Vice President - Analytics, Business Intelligence, Data Management, Element Corp.
Demystifying Data Warehousing as a Service (GLOC 2019)Kent Graziano
Extended deck from the 2019 GLOC event in Cleveland. Discusses what a DWaaS is, the top 10 features of Snowflake that represent that, and a check list for what questions to ask when choosing a cloud based data warehouse.
Deep dive on cloud economics and how to provide customers with TCO analysis and pricing on AWS. We will also share best practices for building out a profitable solution and services partnership with AWS.
How to reinvent your organization in an iterative and pragmatic way? This is the result of using our digital toolbox. It allows you to transform your business model, expand your ecosystem by setting up your digital platform. This reinvention is also supported by the adaptation of your governance allowing you to innovate while guaranteeing the performance of your organization. For any information / suggestion / collaboration - william.poos@nrb.be
Comment réinventer votre organisation de manière itérative et pragmatique ? C'est le résultat de l'utilisation de notre boîte à outils digitale. Elle vous permet de transformer votre modèle métier, d'étendre votre écosystème en mettant en place votre plateforme digitale. Cette réinvention est également supportée par l'adaptation de votre gouvernance vous permettant d'innover tout en garantissant la performance de votre organisation. Pour toute information / suggestion / collaboration - william.poos@nrb.be
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Bee is an engine to build, deploy and manage microservices from composing event and data message handling. It supports smart scalabilities and distributed computing patterns, like mesh networks.
Integrating Semantic Web in the Real World: A Journey between Two Cities Juan Sequeda
Keynote at The 9th International Conference on Knowledge Capture (KCAP2017), Austin, Texas, Dec 2017
An early vision in Computer Science has been to create intelligent systems capable of reasoning on large amounts of data. Today, this vision can be delivered by integrating Relational Databases with the Semantic Web using the W3C standards: a graph data model (RDF), ontology language (OWL), mapping language (R2RML) and query language (SPARQL). The research community has successfully been showing how intelligent systems can be created with Semantic Web technologies, dubbed now as Knowledge Graphs.
However, where is the mainstream industry adoption? What are the barriers to adoption? Are these engineering and social barriers or are they open scientific problems that need to be addressed?
This talk will chronicle our journey of deploying Semantic Web technologies with real world users to address Business Intelligence and Data Integration needs, describe technical and social obstacles that are present in large organizations, and scientific challenges that require attention.
Webinar: How Banks Use MongoDB as a Tick DatabaseMongoDB
Learn why MongoDB is spreading like wildfire across capital markets (and really every industry) and then focus in particular on how financial firms are enjoying the developer productivity, low TCO, and unlimited scale of MongoDB as a tick database for capturing, analyzing, and taking advantage of opportunities in tick data.
Best Practices & Lessons Learned from Deployment of PostgreSQLEDB
This talk will review best practices and lessons learned from working with large and mid-size companies on their deployment of PostgreSQL. We will explore the practices that helped industry leaders move through the stages of PostgreSQL adoption and get as much value out of their deployment as possible without incurring undue risk.
Uma introdução à malha de dados e as motivações por trás dela: os modos de falhas de paradigmas anteriores de gerenciamento de big data. A proposta de Zhamak Dehghani é comparar e contrastar a malha de dados com as abordagens existentes de gerenciamento de big data, apresentando os componentes técnicos que sustentam a arquitetura de software.
Battling the disrupting Energy Markets utilizing PURE PLAY Cloud ComputingEdwin Poot
Disruption can be intimidating. You may even be losing business to one or more rising competitors. You may be wondering how you could possibly compete. Rest assured, this disruption doesn’t mean you need to turn your business upside down. But just be smart in how you engage your business using innovation without the need for huge changes, high risks or large investments.
Il contenuto riguarderà il Software ERP
in Cloud #1 al Mondo (Gartner), in
questa sessione enfatizzeremo la
connessione tra le esigenze delle
imprese innovative e NetSuite. Oltre le
funzionalità, un passaggio importante è
relativo ai nuovi paradigmi (Cloud, SaaS,
ecc.) a reale supporto della strategia
aziendale (no version locking,
customization carry forward, major
releases per year, ecc.)
contrapponendoli agli ostacoli alla
crescita aziendale e del business.
Presentation given by our CTO, Dr. Stéphane Barbey, at the AdaCore Tech Days 2015.
Short introduction to Paranor followed by a description of ZVIS - a financial system developed in Ada. The presentation answers the question, how a >4 millions lines-of-code application can be supported by tools and other means to ease the maintenance of the application and shift focus to innovation.
Building a Pluggable Analytics Stack with Cassandra (Jim Peregord, Element Co...DataStax
Element Fleet has the largest benchmark database in our industry and we needed a robust and linearly scalable platform to turn this data into actionable insights for our customers. The platform needed to support advanced analytics, streaming data sets, and traditional business intelligence use cases.
In this presentation, we will discuss how we built a single, unified platform for both Advanced Analytics and traditional Business Intelligence using Cassandra on DSE. With Cassandra as our foundation, we are able to plug in the appropriate technology to meet varied use cases. The platform we’ve built supports real-time streaming (Spark Streaming/Kafka), batch and streaming analytics (PySpark, Spark Streaming), and traditional BI/data warehousing (C*/FiloDB). In this talk, we are going to explore the entire tech stack and the challenges we faced trying support the above use cases. We will specifically discuss how we ingest and analyze IoT (vehicle telematics data) in real-time and batch, combine data from multiple data sources into to single data model, and support standardized and ah-hoc reporting requirements.
About the Speaker
Jim Peregord Vice President - Analytics, Business Intelligence, Data Management, Element Corp.
Demystifying Data Warehousing as a Service (GLOC 2019)Kent Graziano
Extended deck from the 2019 GLOC event in Cleveland. Discusses what a DWaaS is, the top 10 features of Snowflake that represent that, and a check list for what questions to ask when choosing a cloud based data warehouse.
Deep dive on cloud economics and how to provide customers with TCO analysis and pricing on AWS. We will also share best practices for building out a profitable solution and services partnership with AWS.
How to reinvent your organization in an iterative and pragmatic way? This is the result of using our digital toolbox. It allows you to transform your business model, expand your ecosystem by setting up your digital platform. This reinvention is also supported by the adaptation of your governance allowing you to innovate while guaranteeing the performance of your organization. For any information / suggestion / collaboration - william.poos@nrb.be
Comment réinventer votre organisation de manière itérative et pragmatique ? C'est le résultat de l'utilisation de notre boîte à outils digitale. Elle vous permet de transformer votre modèle métier, d'étendre votre écosystème en mettant en place votre plateforme digitale. Cette réinvention est également supportée par l'adaptation de votre gouvernance vous permettant d'innover tout en garantissant la performance de votre organisation. Pour toute information / suggestion / collaboration - william.poos@nrb.be
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
