This document discusses three ways to improve healthcare data quality when implementing an Enterprise Data Warehouse (EDW). It recommends establishing realistic expectations about data quality issues, understanding the operational causes of data problems, and improving data quality upfront through data governance, identifying subject matter experts, and standardizing the data model. Addressing data quality proactively can help ensure a successful, efficient EDW implementation.
20 Years in Healthcare Analytics & Data Warehousing: What did we learn? What'...Health Catalyst
The enterprise data warehouse (EDW) at Intermountain Healthcare went live in 1998. The EDW at Northwestern Medicine went live in 2006. Dale Sanders was the chief architect and strategist for both. The business inspiration behind Health Catalyst was, in essence, to create the commercial availability of the technology, analytics, and data utilization skills associated with these systems at Intermountain and Northwestern. Lee Pierce assumed leadership of the Intermountain EDW in 2008. Andrew Winter assumed leadership of the Northwestern EDW in 2009, and transitioned leadership of the EDW to Shakeeb Akhter in 2016. This webinar is a fireside chat among friends and colleagues as they look back across their healthcare IT decisions to answer these questions:
What did we do right and what did we do wrong?
What advice do we have for others in this emerging era of Big Data?
What does the future of analytics and Big Data look like in healthcare?
Getting Ahead Of The Game: Proactive Data GovernanceHarley Capewell
Data today is getting bigger, more widely available and
changing more quickly than ever before. Data Governance
coach Nicola Askham shares her advice on why you
need to embrace Data Governance NOW and what good
governance looks like.
The Data Driven University - Automating Data Governance and Stewardship in Au...Pieter De Leenheer
Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve data trust between business and IT communities in the organization. University divisions operate highly autonomously and decentralized, and are often geographically distributed. Hence, they benefit more from an collaborative and agile approach to Data Governance and Stewardship approach that adapts to its nature.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data being shared: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of growing data pain as an organization expands, and we map each phase on this spectrum of semantics.
Next, we introduce our principles and framework for business semantics management to support Data Governance and Stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with use cases from Stanford University, George Washington University and Public Science and Innovation Administrations.
Part 2 - 20 Years in Healthcare Analytics & Data Warehousing: What did we lea...Health Catalyst
Lessons learned over 20 years. This time we focus on technology lessons learned from experience at Intermountain Healthcare, Northwestern Medicine and Cayman Islands Health Authority
20 Years in Healthcare Analytics & Data Warehousing: What did we learn? What'...Health Catalyst
The enterprise data warehouse (EDW) at Intermountain Healthcare went live in 1998. The EDW at Northwestern Medicine went live in 2006. Dale Sanders was the chief architect and strategist for both. The business inspiration behind Health Catalyst was, in essence, to create the commercial availability of the technology, analytics, and data utilization skills associated with these systems at Intermountain and Northwestern. Lee Pierce assumed leadership of the Intermountain EDW in 2008. Andrew Winter assumed leadership of the Northwestern EDW in 2009, and transitioned leadership of the EDW to Shakeeb Akhter in 2016. This webinar is a fireside chat among friends and colleagues as they look back across their healthcare IT decisions to answer these questions:
What did we do right and what did we do wrong?
What advice do we have for others in this emerging era of Big Data?
What does the future of analytics and Big Data look like in healthcare?
Getting Ahead Of The Game: Proactive Data GovernanceHarley Capewell
Data today is getting bigger, more widely available and
changing more quickly than ever before. Data Governance
coach Nicola Askham shares her advice on why you
need to embrace Data Governance NOW and what good
governance looks like.
The Data Driven University - Automating Data Governance and Stewardship in Au...Pieter De Leenheer
Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve data trust between business and IT communities in the organization. University divisions operate highly autonomously and decentralized, and are often geographically distributed. Hence, they benefit more from an collaborative and agile approach to Data Governance and Stewardship approach that adapts to its nature.
In this lecture, we start by reviewing 'C' in ICT and reflect on the dilemma: what is the most important quality of data being shared: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of growing data pain as an organization expands, and we map each phase on this spectrum of semantics.
Next, we introduce our principles and framework for business semantics management to support Data Governance and Stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with use cases from Stanford University, George Washington University and Public Science and Innovation Administrations.
Part 2 - 20 Years in Healthcare Analytics & Data Warehousing: What did we lea...Health Catalyst
Lessons learned over 20 years. This time we focus on technology lessons learned from experience at Intermountain Healthcare, Northwestern Medicine and Cayman Islands Health Authority
Because everyone matters.
IBM Health and Social Programs Summit, October 2014
Stephen Morgan
Senior Vice President and Chief Medical Officer
Carilion Clinic
Jianying Hu
Research Staff Member and Manager of Healthcare Analytics Research
IBM
Paul Grundy
Global Director of Healthcare Transformation
IBM
Beyond Firefighting: A Leaders Guide to Proactive Data Quality ManagementHarley Capewell
Tired of trying to fight data quality issues in a sporadic,
reactive fashion? This white paper by data quality expert
Dylan Jones draws on his many years of experience
helping organisations adopt a more proactive, holistic
view of data quality management.
Data Governance PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Governance Powerpoint Presentation Slides. This PPT deck displays twenty five slides with in depth research. Our topic oriented Data Governance Powerpoint Presentation Slides deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Data Governance Powerpoint Presentation Slides. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
Legal entity data is one of the top areas of focus in many data programs throughout the industry due to the criticality for risk, regulatory reporting, and operations. Legal entity data in legacy systems is still a significant challenge that can lead to costly projects, errors, and unnecessary rework.
Dive into the details of legal entity data and some of the best practices on how to identify data quality issues and proactively manage legal entity data quality. This presentation will focus on topics including duplication, industry classification, hierarchy management, special entity types, globalization issues, and many more topics.
Business impact without data governanceJohn Bao Vuu
Presentation on common business issues and challenges in organizations that do not have formal data governance practices. Data management on the whole has evolved over the years, but data governance is still one of the greatest constraints in strategic transformation and operational effectiveness.
1. What is Data Governance?
2. Business Impact without Data Governance
3. Benefits of Data Governance
4. Implementing Data Governance
AMCTO presentation on moving from records managment to information managementChristopher Wynder
This presentation was given to AMCTO zones 1 and 4/5. It presents how to use the records classification as the core for a faceted classification schema that can be used to enable workflow and processes across the organization.
Presentation on using workflow to implement a highly used ECM system.
Provides a step-by-step outline how to understand user needs through marketing techniques such as user journeys and persona building.
Introduces the concept that ECM is an organically growing system rather than an architected software solution.
This presentation contains our view on how data can be Strategically managed and stewarded in an organization, and the categories where rules can be applied to facilitate that process.
Precision medicine has profound implications for patient care and clinical outcomes, and is already beginning to impact everyday medical practice. However, implementation faces several obstacles, including overstated claims, resistance among clinical medicine thought leaders and providers, and concerns about costs, data overload, and interoperability. This webinar will address five key concerns, challenges, and barriers among clinicians and IT professionals struggling to determine the value and limitations of implementing precision medicine, and offer tangible recommendations to help drive toward precision medicine adoption.
Learning Objectives:
Identify obstacles that impede the implementation of precision medicine in clinical practice.
Contrast population-based medicine and precision medicine.
Demonstrate the real world benefits of precision medicine in today's healthcare setting.
Role of Operational System Design in Data Warehouse Implementation: Identifyi...iosrjce
Data warehouse designing process takes input from operational system of the organization. Quality
of data warehousing solution depends on design of operational system. Often, operational system
implementations of organizations have some limitations. Thus, we cannot proceed for data warehouse
designing so easily. In this paper, we have tried to investigate operational system of the organization for
identifying such limitations and determine role of operational system design in the process of data warehouse
design and implementation. We have worked out to find possible methods to handle such limitations and have
proposed techniques to get a quality data warehousing solution under such limitations. To make the work based
on live example, National Rural Health Mission (NRHM) Project has been taken. It is a national project of
health sector, managed by Indian Government across the country. The complex structure and high volume of
data makes it an ideal case for data warehouse implementation.
Change Management: The Secret to a Successful SAS® ImplementationThotWave
Whether you are deploying a new capability with SAS® or modernizing the tool set that people already use in your organization, change management is a valuable practice. Sharing the news of a change with employees can be a daunting task and is often put off until the last possible second. Organizations frequently underestimate the impact of the change, and the results of that miscalculation can be disastrous. Too often, employees find out about a change just before mandatory training and are expected to embrace it. But change management is far more than training. It is early and frequent communication, an inclusive discussion, encouraging and enabling the development of an individual, and facilitating learning before, during, and long after the change.
This paper not only showcases the importance of change management but also identifies key objectives for a purposeful strategy. We outline our experiences with both successful and not so successful organizational changes. We present best practices for implementing change management strategies and highlighting common gaps. For example, developing and engaging “Change Champions” from the beginning alleviates many headaches and avoids disruptions. Finally, we discuss how the overall company culture can either support or hinder the positive experience change management should be and how to engender support for formal change management in your organization.
Because everyone matters.
IBM Health and Social Programs Summit, October 2014
Stephen Morgan
Senior Vice President and Chief Medical Officer
Carilion Clinic
Jianying Hu
Research Staff Member and Manager of Healthcare Analytics Research
IBM
Paul Grundy
Global Director of Healthcare Transformation
IBM
Beyond Firefighting: A Leaders Guide to Proactive Data Quality ManagementHarley Capewell
Tired of trying to fight data quality issues in a sporadic,
reactive fashion? This white paper by data quality expert
Dylan Jones draws on his many years of experience
helping organisations adopt a more proactive, holistic
view of data quality management.
Data Governance PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Governance Powerpoint Presentation Slides. This PPT deck displays twenty five slides with in depth research. Our topic oriented Data Governance Powerpoint Presentation Slides deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Data Governance Powerpoint Presentation Slides. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
Legal entity data is one of the top areas of focus in many data programs throughout the industry due to the criticality for risk, regulatory reporting, and operations. Legal entity data in legacy systems is still a significant challenge that can lead to costly projects, errors, and unnecessary rework.
Dive into the details of legal entity data and some of the best practices on how to identify data quality issues and proactively manage legal entity data quality. This presentation will focus on topics including duplication, industry classification, hierarchy management, special entity types, globalization issues, and many more topics.
Business impact without data governanceJohn Bao Vuu
Presentation on common business issues and challenges in organizations that do not have formal data governance practices. Data management on the whole has evolved over the years, but data governance is still one of the greatest constraints in strategic transformation and operational effectiveness.
1. What is Data Governance?
2. Business Impact without Data Governance
3. Benefits of Data Governance
4. Implementing Data Governance
AMCTO presentation on moving from records managment to information managementChristopher Wynder
This presentation was given to AMCTO zones 1 and 4/5. It presents how to use the records classification as the core for a faceted classification schema that can be used to enable workflow and processes across the organization.
Presentation on using workflow to implement a highly used ECM system.
Provides a step-by-step outline how to understand user needs through marketing techniques such as user journeys and persona building.
Introduces the concept that ECM is an organically growing system rather than an architected software solution.
This presentation contains our view on how data can be Strategically managed and stewarded in an organization, and the categories where rules can be applied to facilitate that process.
Precision medicine has profound implications for patient care and clinical outcomes, and is already beginning to impact everyday medical practice. However, implementation faces several obstacles, including overstated claims, resistance among clinical medicine thought leaders and providers, and concerns about costs, data overload, and interoperability. This webinar will address five key concerns, challenges, and barriers among clinicians and IT professionals struggling to determine the value and limitations of implementing precision medicine, and offer tangible recommendations to help drive toward precision medicine adoption.
Learning Objectives:
Identify obstacles that impede the implementation of precision medicine in clinical practice.
Contrast population-based medicine and precision medicine.
Demonstrate the real world benefits of precision medicine in today's healthcare setting.
Role of Operational System Design in Data Warehouse Implementation: Identifyi...iosrjce
Data warehouse designing process takes input from operational system of the organization. Quality
of data warehousing solution depends on design of operational system. Often, operational system
implementations of organizations have some limitations. Thus, we cannot proceed for data warehouse
designing so easily. In this paper, we have tried to investigate operational system of the organization for
identifying such limitations and determine role of operational system design in the process of data warehouse
design and implementation. We have worked out to find possible methods to handle such limitations and have
proposed techniques to get a quality data warehousing solution under such limitations. To make the work based
on live example, National Rural Health Mission (NRHM) Project has been taken. It is a national project of
health sector, managed by Indian Government across the country. The complex structure and high volume of
data makes it an ideal case for data warehouse implementation.
Change Management: The Secret to a Successful SAS® ImplementationThotWave
Whether you are deploying a new capability with SAS® or modernizing the tool set that people already use in your organization, change management is a valuable practice. Sharing the news of a change with employees can be a daunting task and is often put off until the last possible second. Organizations frequently underestimate the impact of the change, and the results of that miscalculation can be disastrous. Too often, employees find out about a change just before mandatory training and are expected to embrace it. But change management is far more than training. It is early and frequent communication, an inclusive discussion, encouraging and enabling the development of an individual, and facilitating learning before, during, and long after the change.
This paper not only showcases the importance of change management but also identifies key objectives for a purposeful strategy. We outline our experiences with both successful and not so successful organizational changes. We present best practices for implementing change management strategies and highlighting common gaps. For example, developing and engaging “Change Champions” from the beginning alleviates many headaches and avoids disruptions. Finally, we discuss how the overall company culture can either support or hinder the positive experience change management should be and how to engender support for formal change management in your organization.
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
Data governance is a bunch of strategies and practices that ensure high quality through the complete lifecycle of your data. Data Governance is a practical and actionable framework to assist a wide range of data stakeholders across any organization in identifying and meeting their data requirements.
Data integration and governance drive value by enabling organizations to achieve more accurate, reliable, and comprehensive insights from their data. Learn about different approaches and best practices to enhance your data integration and governance strategy.
Click here to download your eBook:
https://resources.pixentia.com/how-data-integration-and-governance-enables-hr-to-drive-value
Standards make it easier to create, share, and integrate data by making sure that there is a clear understanding of how the data are represented and that the data you receive are in a form that you expected. Data standards are the rules by which data are described and recorded. In order to share, exchange, and understand data, we must standardize the format as well as the meaning. Simply put, using standards makes using things easier. If different groups are using different data standards, combining data from multiple sources is difficult, if not impossible.
Targeting towards the health and human services communities, this presentation covers the importance of a data-driven culture, how to identify areas where data can be used to innovate and how to recognize the operational processes you must have in place to fully utilize your data.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Hcd wp-2012-better dataleadstobetteranalytics
1. Better Data Leads to Better Analytics:
Three Ways to Improve Healthcare Data Quality
in an EDW
Written by
Jason B. Buskirk
Chief Operating Officer
Health Care DataWorks
2.
2
Better Data Leads to Better Analytics:
Three Ways to Improve Healthcare Data Quality in an EDW
Too often, organizations embark on Enterprise Data Warehouse (EDW) projects with the notion
that all their data needs will be met once the implementation is complete. It is understandable
why this thinking becomes pervasive throughout the organization. Typically, organizations have
decided to take on such projects after lengthy and time-intensive meetings, presentations
and reviews to bring together the myriad interests of its key stakeholders, followed by the due
diligence necessary to secure the funding and select the technology partner. Expectations begin
to run very high.
While an EDW undoubtedly will empower organizations to do more with their data than ever before
and the investment will pay dividends in terms of the value it brings, an EDW is only as good as the
data that is fed into it. Every organization will encounter data quality issues during or leading up to
EDW implementation, and these issues can negatively affect the timeline of the implementation. If
there are issues with data quality, the organization will find that, when it comes time to extract the
data, it will not be as useful as expected. It is important to discover and address data quality issues
as early as possible. Not doing so becomes expensive, both in terms of the developers’ time and
the lack of trust that could occur within the organization. Think of it this way: If you put bad data in,
you get bad data out, and the sooner you find the bad data, the better off your project will be. This
white paper details three ways to improve data quality in an EDW.
Establish realistic expectations
Improving data quality starts with understanding the data challenges and proactively
communicating and working with stakeholders to address potential pitfalls. Taking these steps
will contribute to a successful, cost-efficient and relatively smooth implementation that can
achieve results at a quicker pace.
It is important that everyone in the organization knows that the EDW will only be as effective
as the data that goes into it. This will help manage expectations and reduce potential frustration.
Everyone wants access to data that is relevant, understandable and, ultimately, results in
actionable knowledge. But the reality is an organization will not know how bad its data is until
it begins the task of profiling the data that is to be extracted.
3.
3
Know the causes of data issues
Virtually all the data issues encountered with a data warehouse implementation are not
technological in nature – they are operational. These operational causes of data issues generally
fall into two broad categories:
• Data collection requirements. Organizations have multiple systems capturing and
storing their electronic medical records, financial records and human resource information.
But these systems tend to operate in silos. This often contributes to issues around when
and if data is collected. Some systems may require data elements to be populated, while
others may not make them mandatory for data capture. This leads to sparse data sets that
could have limited usefulness in the future.
• Lack of standardization. Because myriad systems are in use and individual departments
can track data in different ways, problems with standardization often arise and take many
forms. For example, two units within a health system track the same information – patient
gender. In one system, the information is input and categorized as “male” or “female.” In
the other system, gender is input as a “1” or “2.” Even though these issues can and
should be fixed during the extract process, the time needed to identify these issues and
decide how the data should be stored in the data warehouse is something the organization
needs to take into consideration when planning the data warehouse project.
Improve data quality
By taking the following steps before the implementation process begins, organizations can cleanse
and improve the quality of the data, positioning the organization for a successful enterprise data
warehouse project.
• Establish a governance body or data quality group to create consistent standards.
Most organizations do not have this in place prior to an EDW implementation. The body
or group should be comprised of stakeholders who know which data is being collected,
how it is being categorized, how and where it is stored, and all the other details critical
to establishing an organization-wide standard. The goal should be to identify “bad” and
non-standardized data. Doing this sooner rather than later can ensure the most
cost-efficient implementation.
4.
4
Organizations have
two options: They
can build their own
data model or buy
one. Health Care
DataWorks, for
instance, offers a
mature data model
that is proven over
many years of
effective use.
• Identify subject matter experts to play an ongoing role in the implementation
process. These should be individuals who understand the data and know how it can
be used. Make them part of the implementation team. They are valuable resources in
that they not only know the data, but also understand how existing operational systems
work. By including them on the team, you will identify data quality issues earlier in your
implementation. Their involvement will also help provide built-in credibility when it
comes time to go live.
It’s also important to remember that these subject matter expert resources be freed up
from a time commitment standpoint to devote the required attention to the implementation
process. It is an in-kind investment that is worthwhile because of the positive outcome
that will result.
• Standardize your data model up front. Having a
data model up front will not only accelerate the data
warehouse’s implementation timeline, it also will assist
the organization with the data issues mentioned earlier
by connecting multiple and disparate source systems.
Remember, data elements will be captured
inconsistently by different operational systems. When
the data model is populated, it will have a place to store
each data element regardless of the source system.
Data quality rules can be implemented to populate the
data based on data availability in each source system.
In the example of gender mentioned earlier, the same
data elements may be stored using different data values.
Possessing and populating a robust data model will
force an organization to standardize these data
elements and serve as a blueprint for how these data
elements should be handled. In this example, the data
model will have a conformed dimension to standardize
gender values.
5.
5
Organizations have two options for obtaining a data model: They can build their own data
model or buy one. Health Care DataWorks, for instance, offers a mature data model that
is proven over many years of effective use. Regardless of how the organization proceeds,
the data model needs to be in place up front in order for an organization to be ready for
the data quality issues that it should expect.
Conclusion
Organizations can expect data quality challenges when undertaking an EDW implementation. But
when they understand the potential pitfalls, remain committed to improving the quality of data, and
involve their internal experts and users in the process, they will be well on the way to adding value
to the entire organization in the most cost-effective and timely manner.
6.
6
About the Author
Jason Buskirk is responsible for managing the day-to-day operations of Health Care DataWorks
(HCD) and leading product strategies for the company's pre-built analytics applications. He is one
of the company's founders.
Prior to HCD, Buskirk worked for Deloitte Consulting, where he implemented analytic applications
built using Oracle's Business Intelligence Enterprise Edition. Buskirk also served as Manager of
the Information Warehouse and Research Information Systems at the Wexner Medical Center at
The Ohio State University.
Buskirk holds a bachelor's degree in computer information systems from DeVry University.
About Health Care DataWorks
Health Care DataWorks, Inc., a leading provider of business intelligence solutions, empowers
healthcare organizations to improve their quality of care and reduce costs. Through its pioneering
KnowledgeEdge™ product suite, including its enterprise data model, analytic dashboards,
applications, and reports, Health Care DataWorks delivers an Enterprise Data Warehouse
necessary for hospitals and health systems to effectively and efficiently gain deeper insights
into their operations. For more information, visit www.hcdataworks.com.