The document discusses becoming a data-driven enterprise by treating data as a product. It introduces a five-stage data maturity model for enterprises to transition from an ad-hoc approach to data to an industrialized one where data drives business outcomes. The stages are: ad-hoc, organize, tactical, critical, and industrial. It also discusses strategy and governance, architecture, development, regulation and ethics, and user support dimensions to consider at each stage of the data maturity journey.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Many large enterprises have begun using AWS to host development and test environments while also building greenfield applications in AWS. After realizing the benefits that AWS has to offer, many Enterprise look for ways to accelerate their migration to the cloud. In beginning this journey they are often faced with a number of challenges such as determining which applications should move, how they should move, and how can they be effectively managed in the cloud. Accenture, working with AWS Solution Architects, and AWS Professional Services have developed a framework, based on our experiences, to quickly, efficiently, and successfully move enterprise applications to AWS at scale. This session will review our approach, tools, and methods that can help Enterprises evolve their cloud transformation programs.
Many corporations recognize their future depends on digital transformation. Earley Information Science outlines the 4 step process for building a roadmap to transformation.
AstraZeneca: A Vision for a Collaborative Clinical EnvironmentVeeva Systems
Slides from AstraZeneca's 2019 Veeva R&D Summit presentation.
After receiving the 2018 Eagle Award for the best sponsor by the Society for Clinical Research Sites (SCRS), AstraZeneca is on an ongoing quest to remain a sponsor of choice for 2019 and beyond. Learn how they are leveraging technology, a unified clinical trial platform, and other strategies to simplify trial conduct at sites.
Cloud migration is more than simply a business efficiency or a cost-saving measure. It’s a critical step towards digital transformation, innovation and operational resilience that has opened up opportunities for those who’ve embraced cloud adoption.
Whether you are looking to embark on your cloud migration or scaling the number of applications you’re moving to the cloud, it does not need to be a daunting task or one that you go at alone. AWS offers 10 years of experience helping businesses to efficiently move their legacy on-premises systems to the cloud. We work closely alongside numerous local delivery partners to help you meet your business needs.
Our Cloud Migration insights forum helps you to learn how to simplify your cloud journey with AWS.
Digital Transformation And Enterprise ArchitectureAlan McSweeney
Digital transformation - extending and exposing business processes outside the organisation - by implementing a digital strategy – a statement about the organisation’s digital positioning, operating model, competitors and customer and collaborator needs and behaviour through the delivery of digital solutions defined in a digital architecture – a future state application, data and technology view to achieve digital operating status - is potentially (very) complex.
Digital architecture does not exist in isolation entirely separate from an organisation’s overall enterprise architecture. Digital architecture must exist within the within the wider enterprise architecture context.
Enterprise architecture provides the tools and the approaches to manage the complexity of digital transformation.
The management function that drives digital transformation needs to involve the enterprise architecture function in the design and implementation of digital strategy and organisation, process and policies and the creation of a digital architecture. Management must appreciate the technology focus and the benefits of an enterprise architecture approach.
The early involvement of enterprise architecture increases successes and reduces failures. Management must trust and involve enterprise architecture. The enterprise architecture function must accept and rise to the challenge and deliver. The enterprise architecture function must allow its value to be measured.
Describes what Enterprise Data Architecture in a Software Development Organization should cover and does that by listing over 200 data architecture related deliverables an Enterprise Data Architect should remember to evangelize.
Many large enterprises have begun using AWS to host development and test environments while also building greenfield applications in AWS. After realizing the benefits that AWS has to offer, many Enterprise look for ways to accelerate their migration to the cloud. In beginning this journey they are often faced with a number of challenges such as determining which applications should move, how they should move, and how can they be effectively managed in the cloud. Accenture, working with AWS Solution Architects, and AWS Professional Services have developed a framework, based on our experiences, to quickly, efficiently, and successfully move enterprise applications to AWS at scale. This session will review our approach, tools, and methods that can help Enterprises evolve their cloud transformation programs.
Many corporations recognize their future depends on digital transformation. Earley Information Science outlines the 4 step process for building a roadmap to transformation.
AstraZeneca: A Vision for a Collaborative Clinical EnvironmentVeeva Systems
Slides from AstraZeneca's 2019 Veeva R&D Summit presentation.
After receiving the 2018 Eagle Award for the best sponsor by the Society for Clinical Research Sites (SCRS), AstraZeneca is on an ongoing quest to remain a sponsor of choice for 2019 and beyond. Learn how they are leveraging technology, a unified clinical trial platform, and other strategies to simplify trial conduct at sites.
Cloud migration is more than simply a business efficiency or a cost-saving measure. It’s a critical step towards digital transformation, innovation and operational resilience that has opened up opportunities for those who’ve embraced cloud adoption.
Whether you are looking to embark on your cloud migration or scaling the number of applications you’re moving to the cloud, it does not need to be a daunting task or one that you go at alone. AWS offers 10 years of experience helping businesses to efficiently move their legacy on-premises systems to the cloud. We work closely alongside numerous local delivery partners to help you meet your business needs.
Our Cloud Migration insights forum helps you to learn how to simplify your cloud journey with AWS.
Digital Transformation And Enterprise ArchitectureAlan McSweeney
Digital transformation - extending and exposing business processes outside the organisation - by implementing a digital strategy – a statement about the organisation’s digital positioning, operating model, competitors and customer and collaborator needs and behaviour through the delivery of digital solutions defined in a digital architecture – a future state application, data and technology view to achieve digital operating status - is potentially (very) complex.
Digital architecture does not exist in isolation entirely separate from an organisation’s overall enterprise architecture. Digital architecture must exist within the within the wider enterprise architecture context.
Enterprise architecture provides the tools and the approaches to manage the complexity of digital transformation.
The management function that drives digital transformation needs to involve the enterprise architecture function in the design and implementation of digital strategy and organisation, process and policies and the creation of a digital architecture. Management must appreciate the technology focus and the benefits of an enterprise architecture approach.
The early involvement of enterprise architecture increases successes and reduces failures. Management must trust and involve enterprise architecture. The enterprise architecture function must accept and rise to the challenge and deliver. The enterprise architecture function must allow its value to be measured.
How AstraZeneca is Applying AI, Imaging & Data Analytics (AI-Driven Drug Deve...Nick Brown
Keynote AI Presentation given at AI-Driven Drug Development Summit Europe on 26th April 2023 in London. Overview around how AstraZeneca has been developing AI in the past 5+ years. Predominantly focused on R&D and how we are developing digital solutions & AI for right safety and right dose. AI examples include machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, understanding more about our drugs through advanced imaging modalities and first steps in applying AI for right dose - immunogenicity, adverse events and tolerability.
The healthcare industry has quietly shed the laggards tag and has quickly emerged as frontrunners in digitization. Hospitals are driving technology advancements by creating a digital framework for seamless integration of all aspects of patient care and administration. There are 5 major themes that are seen as critical in the hospital IT ecosystem – Smart Care, Patient Information Management, Remote Care, Medical Devices, and Intelligent Enterprise Systems.
Large enterprises such as Microsoft and Accenture are collaborating with healthcare providers to address a variety of use cases such as chronic disease management, virtual care solutions, risk scoring, patient tracking and monitoring, precision medicine, and patient on/off-boarding. Accenture and Microsoft helped Spain’s Basque Country Health Centre build a remote elderly patient monitoring system. Athenahealth’s cloud-based network system helps Minnie Hamilton Health System identify bottlenecks and streamline the revenue cycle.
Download the report as we provide an overview of the hospital IT landscape, understand digital transformation trends across these 5 major themes and the opportunities available for vendors and service providers.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
A Reference Architecture for Digital Health: The Health Catalyst Data Operati...Health Catalyst
There are essentially four strategic options to address the enterprise data platform requirements of today’s healthcare systems: (1) build your own, (2) buy from EHR vendors, (3) look to a Silicon Valley high-tech startup, and (4) partner with Health Catalyst or a handful of similar companies.
In this webinar, Health Catalyst’s CTO, Dale Sanders, comments on all four approaches, hoping to help you to assess your organization’s strategy against the options and vendors in each category.
It’s been exactly three years since Health Catalyst embarked on a major investment in its next-generation technology, the Data Operating System (DOS™) and its applications. This webinar is an update on the progress, less about marketing the technology, but rather offering DOS as a reference architecture that can support analytics, AI, text processing, data-first application development, and interoperability, as an all-in-one agile cost-savings architecture.
In addition to the successes, Dale comments on the challenges that Health Catalyst has faced under a very ambitious DOS development plan. In its current state, DOS has made some significant improvements to overcome early mistakes, and is now a very solid enterprise data platform. In the interests of industry-wide learning, Sanders will talk transparently about those mistakes and how those learnings are being applied to the DOS platform, positioning it to evolve gracefully over the next 25 years.
View the webinar to learn how the DOS reference architecture:
- Helps manage the 2,000+ compulsory measures in US healthcare
- Enables applications as varied as a real-time patient safety surveillance system, and an activity-based costing system in one platform
- Can ingest data of any type or velocity from over 300 healthcare source systems and growing
- Bundles tools, applications, and analytics that would cost 3-6x more to build on your own
- Compares to EHR vendors as an option to serve as an enterprise data and analytics platform
- Is a performant, sustainable, and maintainable platform for deploying AI models in the natural flow of the healthcare data pipeline
- Provides curated data content and models while still allowing for the agility of a late binding design option
- Functions as a reference architecture that all healthcare organizations and vendors will ultimately have to build in their pursuit of digital health
How API Enablement Drives Legacy ModernizationMuleSoft
For many organizations, legacy systems’ integration challenges have increased costs and slowed innovation. Learn how Infosys and MuleSoft partner to address these challenges through API enablement - accelerating project delivery speed while reducing costs through pre-fabricated frameworks and solutions.
In this session you will get an understanding how to evaluate your company's or applications' cloud readiness. We will cover aspects such as workload and data categorisation, automation levels, design for failure and cost-optimised architectures. We will be looking at typical application evolution paths from tightly coupled physical systems, in some cases through virtualisation, to cloud-native, or cloud-ready, loosely coupled, distributed and automated solutions.
This session will also take a look at typical enterprise business processes, from procurement to development and testing, and operations and support. We will introduce known-to-work cloud-ready business processes and new best practices, through customer use cases from companies who are cloud native, or have undergone a cloud transformation to get there.
The ArchiMate Language for Enterprise and Solution ArchitectureIver Band
The ArchiMate standard provides:
* A language with concepts to describe architectures
* A framework to organize these concepts
* A graphical notation for these concepts
* Guidance on visualizations for different stakeholders
* An open standard maintained by The Open Group
This presentation illustrates the value of ArchiMate modeling with a Big Data case study.
Using Business Architecture to enable customer experience and digital strategyCraig Martin
Digital disruption is shifting business model design from a focus on product profitability to a stronger focus on customer experience and lifetime value.
The presentation looks at environmental pressures caused by digital disruption and identifies how to use business architecture and business design to address these changes.
It covers business architecture for digital strategy, customer-driven value chains, re-writing of the 4Ps of the marketing mix, and the nine laws of disruption and how they affect business model design.Craig also investigates the changes afoot with strategic business planning and Enterprise Architecture, which are experiencing their own form of disruption. Will Enterprise Architecture as we know it become a commodity too?
This presentation was delivered as an OpenGroup webinar and is available for viewing from the www.enterprisearchitects.com web site.
A Framework for Digital Business TransformationCognizant
By embracing Code Halo thinking and a programmatic approach to business process change, organizations can better engage with customers and deliver mass-customized products and services that drive differentiation and outperformance.
Dear all
Please go through the slides if you want to know something about "Core competencies for public health informatics".
I think these slides will be useful for you.
In this session we are going to consider some lessons that Business Architecture practitioners might learn from Lean. Business Architecture has traditionally delivered ivory tower models which are slow to generate, hard to understand, and require heavy maintenance.
We'll start with a '101' level introduction to Business Architecture, discussing both the process of defining a Business Architecture and also some of the models that Business Architecture produces. We'll also try to understand the value of Business Architecture.
We'll then start to map some Lean Principles to Business Architecture and try and understand how we could potentially optimise the value stream. We'll consider where Business Architecture can be wasteful, discussing the diminishing value of information and the concept of analysis paralysis.
Finally I'll introduce you to a leaner approach to Business Architecture that focusses on rapid techniques for model generation and heavier engagement of system actors in both the development and the maintenance of models. I'll also share my technique for rapid enterprise modelling which can help you to build a capability model in hours rather than weeks/months.
Capgemini Cloud Assessment is a Cloud agnostic, vendor aware methodology that focuses on low risk, high return business transformation. Additionally, it reduces TCO and provides an early view of ROI.
This closed loop assessment leverages pre-built accelerators such as ROI calculators, risk models and portfolio analyzers utilizing our deep partner ecosystem. We deliver an end state architecture, business case and deployment roadmap in just six to eight weeks.
RWE & Patient Analytics Leveraging Databricks – A Use CaseDatabricks
Gaining insights and knowledge from real-world health data (RWD), i.e., data acquired outside the context of randomized clinical trials, has been an area of continued opportunity for pharma organizations.
What is real-world data and real-world evidence – how it is generated, what value it drives for life sciences in general and what kind of analytics are performed.
What are some considerations and challenges related to data security, privacy, and industrialization of a big data platform hosted in the cloud.
How we leveraged Databricks to perform big data ingestion – advantages over native AWS Batch/Glue Examples of some of the advanced analytics use cases downstream that leveraged DB for RWE.
Note: This solution and one of the use case leveraging the solution won the 2020 Gartner Eye for Innovation award.
https://www.gartner.com/en/newsroom/press-releases/2020-11-17-gartner-announces-winners-of-the-2020-gartner-healthcare-and-life-sciences-eye-on-innovation-awardIn this
Accenture is leading the next evolution of Regulatory by applying leading edge solutions across the drug and device lifecycle that unify innovative technologies with compliance-focused processes. Visit https://accntu.re/2YqL18r to learn more.
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
How AstraZeneca is Applying AI, Imaging & Data Analytics (AI-Driven Drug Deve...Nick Brown
Keynote AI Presentation given at AI-Driven Drug Development Summit Europe on 26th April 2023 in London. Overview around how AstraZeneca has been developing AI in the past 5+ years. Predominantly focused on R&D and how we are developing digital solutions & AI for right safety and right dose. AI examples include machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, understanding more about our drugs through advanced imaging modalities and first steps in applying AI for right dose - immunogenicity, adverse events and tolerability.
The healthcare industry has quietly shed the laggards tag and has quickly emerged as frontrunners in digitization. Hospitals are driving technology advancements by creating a digital framework for seamless integration of all aspects of patient care and administration. There are 5 major themes that are seen as critical in the hospital IT ecosystem – Smart Care, Patient Information Management, Remote Care, Medical Devices, and Intelligent Enterprise Systems.
Large enterprises such as Microsoft and Accenture are collaborating with healthcare providers to address a variety of use cases such as chronic disease management, virtual care solutions, risk scoring, patient tracking and monitoring, precision medicine, and patient on/off-boarding. Accenture and Microsoft helped Spain’s Basque Country Health Centre build a remote elderly patient monitoring system. Athenahealth’s cloud-based network system helps Minnie Hamilton Health System identify bottlenecks and streamline the revenue cycle.
Download the report as we provide an overview of the hospital IT landscape, understand digital transformation trends across these 5 major themes and the opportunities available for vendors and service providers.
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
A Reference Architecture for Digital Health: The Health Catalyst Data Operati...Health Catalyst
There are essentially four strategic options to address the enterprise data platform requirements of today’s healthcare systems: (1) build your own, (2) buy from EHR vendors, (3) look to a Silicon Valley high-tech startup, and (4) partner with Health Catalyst or a handful of similar companies.
In this webinar, Health Catalyst’s CTO, Dale Sanders, comments on all four approaches, hoping to help you to assess your organization’s strategy against the options and vendors in each category.
It’s been exactly three years since Health Catalyst embarked on a major investment in its next-generation technology, the Data Operating System (DOS™) and its applications. This webinar is an update on the progress, less about marketing the technology, but rather offering DOS as a reference architecture that can support analytics, AI, text processing, data-first application development, and interoperability, as an all-in-one agile cost-savings architecture.
In addition to the successes, Dale comments on the challenges that Health Catalyst has faced under a very ambitious DOS development plan. In its current state, DOS has made some significant improvements to overcome early mistakes, and is now a very solid enterprise data platform. In the interests of industry-wide learning, Sanders will talk transparently about those mistakes and how those learnings are being applied to the DOS platform, positioning it to evolve gracefully over the next 25 years.
View the webinar to learn how the DOS reference architecture:
- Helps manage the 2,000+ compulsory measures in US healthcare
- Enables applications as varied as a real-time patient safety surveillance system, and an activity-based costing system in one platform
- Can ingest data of any type or velocity from over 300 healthcare source systems and growing
- Bundles tools, applications, and analytics that would cost 3-6x more to build on your own
- Compares to EHR vendors as an option to serve as an enterprise data and analytics platform
- Is a performant, sustainable, and maintainable platform for deploying AI models in the natural flow of the healthcare data pipeline
- Provides curated data content and models while still allowing for the agility of a late binding design option
- Functions as a reference architecture that all healthcare organizations and vendors will ultimately have to build in their pursuit of digital health
How API Enablement Drives Legacy ModernizationMuleSoft
For many organizations, legacy systems’ integration challenges have increased costs and slowed innovation. Learn how Infosys and MuleSoft partner to address these challenges through API enablement - accelerating project delivery speed while reducing costs through pre-fabricated frameworks and solutions.
In this session you will get an understanding how to evaluate your company's or applications' cloud readiness. We will cover aspects such as workload and data categorisation, automation levels, design for failure and cost-optimised architectures. We will be looking at typical application evolution paths from tightly coupled physical systems, in some cases through virtualisation, to cloud-native, or cloud-ready, loosely coupled, distributed and automated solutions.
This session will also take a look at typical enterprise business processes, from procurement to development and testing, and operations and support. We will introduce known-to-work cloud-ready business processes and new best practices, through customer use cases from companies who are cloud native, or have undergone a cloud transformation to get there.
The ArchiMate Language for Enterprise and Solution ArchitectureIver Band
The ArchiMate standard provides:
* A language with concepts to describe architectures
* A framework to organize these concepts
* A graphical notation for these concepts
* Guidance on visualizations for different stakeholders
* An open standard maintained by The Open Group
This presentation illustrates the value of ArchiMate modeling with a Big Data case study.
Using Business Architecture to enable customer experience and digital strategyCraig Martin
Digital disruption is shifting business model design from a focus on product profitability to a stronger focus on customer experience and lifetime value.
The presentation looks at environmental pressures caused by digital disruption and identifies how to use business architecture and business design to address these changes.
It covers business architecture for digital strategy, customer-driven value chains, re-writing of the 4Ps of the marketing mix, and the nine laws of disruption and how they affect business model design.Craig also investigates the changes afoot with strategic business planning and Enterprise Architecture, which are experiencing their own form of disruption. Will Enterprise Architecture as we know it become a commodity too?
This presentation was delivered as an OpenGroup webinar and is available for viewing from the www.enterprisearchitects.com web site.
A Framework for Digital Business TransformationCognizant
By embracing Code Halo thinking and a programmatic approach to business process change, organizations can better engage with customers and deliver mass-customized products and services that drive differentiation and outperformance.
Dear all
Please go through the slides if you want to know something about "Core competencies for public health informatics".
I think these slides will be useful for you.
In this session we are going to consider some lessons that Business Architecture practitioners might learn from Lean. Business Architecture has traditionally delivered ivory tower models which are slow to generate, hard to understand, and require heavy maintenance.
We'll start with a '101' level introduction to Business Architecture, discussing both the process of defining a Business Architecture and also some of the models that Business Architecture produces. We'll also try to understand the value of Business Architecture.
We'll then start to map some Lean Principles to Business Architecture and try and understand how we could potentially optimise the value stream. We'll consider where Business Architecture can be wasteful, discussing the diminishing value of information and the concept of analysis paralysis.
Finally I'll introduce you to a leaner approach to Business Architecture that focusses on rapid techniques for model generation and heavier engagement of system actors in both the development and the maintenance of models. I'll also share my technique for rapid enterprise modelling which can help you to build a capability model in hours rather than weeks/months.
Capgemini Cloud Assessment is a Cloud agnostic, vendor aware methodology that focuses on low risk, high return business transformation. Additionally, it reduces TCO and provides an early view of ROI.
This closed loop assessment leverages pre-built accelerators such as ROI calculators, risk models and portfolio analyzers utilizing our deep partner ecosystem. We deliver an end state architecture, business case and deployment roadmap in just six to eight weeks.
RWE & Patient Analytics Leveraging Databricks – A Use CaseDatabricks
Gaining insights and knowledge from real-world health data (RWD), i.e., data acquired outside the context of randomized clinical trials, has been an area of continued opportunity for pharma organizations.
What is real-world data and real-world evidence – how it is generated, what value it drives for life sciences in general and what kind of analytics are performed.
What are some considerations and challenges related to data security, privacy, and industrialization of a big data platform hosted in the cloud.
How we leveraged Databricks to perform big data ingestion – advantages over native AWS Batch/Glue Examples of some of the advanced analytics use cases downstream that leveraged DB for RWE.
Note: This solution and one of the use case leveraging the solution won the 2020 Gartner Eye for Innovation award.
https://www.gartner.com/en/newsroom/press-releases/2020-11-17-gartner-announces-winners-of-the-2020-gartner-healthcare-and-life-sciences-eye-on-innovation-awardIn this
Accenture is leading the next evolution of Regulatory by applying leading edge solutions across the drug and device lifecycle that unify innovative technologies with compliance-focused processes. Visit https://accntu.re/2YqL18r to learn more.
Becoming a Data-Driven Organization - Aligning Business & Data StrategyDATAVERSITY
More organizations are aspiring to become ‘data driven businesses’. But all too often this aim fails, as business goals and IT & data realities are misaligned, with IT lagging behind rapidly changing business needs. So how do you get the perfect fit where data strategy is driven by and underpins business strategy? This webinar will show you how by de-mystifying the building blocks of a global data strategy and highlighting a number of real world success stories. Topics include:
•How to align data strategy with business motivation and drivers
•Why business & data strategies often become misaligned & the impact
•Defining the core building blocks of a successful data strategy
•The role of business and IT
•Success stories in implementing global data strategies
Data Analytics has become a powerful tool to drive corporates and businesses. check out this 6 Reasons to Use Data Analytics. Visit: https://www.raybiztech.com/blog/data-analytics/6-reasons-to-use-data-analytics
Business Intelligence, Data Analytics, and AIJohnny Jepp
Data is the new currency. In this session, best practices on data collection, management dashboards, and used cases will be shared using Azure Data Services.
Video accessible at bit.ly/APACSummitOnDemand
Shwetank Sheel
Chief Executive Officer
Just Analytics
Poonam Sampat
Cloud Solution Architect - Data & AI
Microsoft Asia Pacific
Data analytics can immensely impact and improve a business’s decision-making processes. From better strategies to profits, explore the full scope of analytics.
Data analytics can immensely impact and improve a business’s decision-making processes. From better strategies to profits, explore the full scope of analytics.
To Become a Data-Driven Enterprise, Data Democratization is EssentialCognizant
To optimise enterprise knowledge, organizations need a modern platform that enables data to be more easily shared, interpreted and capitalized on by internal decision makers and by business partners across the extended value chain.
Are you making money on your data assets? You could be. And there is more than one way. Boost innovation, tap into new revenue streams and industry sectors. It’s time to see the potential of big data.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Cognitive Solutions combine the power of mathematical algorithms and computing in collaboration with digital knowledge reasoning to enable intelligent insights and actions.
Data can be your key strategic asset for long-term growth. Just as the "quantified self" builds awareness of progress toward health, your comprehensive data strategy can help you lead your industry.
Advanced data services can help you unlock the full potential of your data and gain a competitive advantage. By investing in data services, you can transform your raw data into actionable insights that can be used to improve every aspect of your business.
Data is poised to play an important role in the enterprises of the future, with businesses looking to scale up production and recover costs. Visit: https://www.raybiztech.com/blog/data-analytics/what-are-big-data-data-science-and-data-analytics
This whitepaper from IBM shows how your organisation can implement a Big Data Analytics solution effectively and leverage insights that can transform your business.
From Chaos to Clarity: Crafting a Data Strategy Roadmap for Organizational Tr...TekLink International LLC
Discover the power of a data strategy roadmap and BI roadmap strategy in optimizing data utilization, informed decision-making, and achieving business objectives. Gain a competitive edge, enhance operational efficiency, and drive innovation.
how to successfully implement a data analytics solution.pdfbasilmph
The adoption of data analytics in business has demonstrated a transformative power in modern entrepreneurship. By analyzing vast reservoirs of data, businesses can make informed decisions, optimize operations and predict trends, thus fueling growth.
Open Insurance - Unlocking Ecosystem Opportunities For Tomorrow’s Insurance I...Accenture Insurance
For early adopters, open insurance offers new revenue streams, increased customer engagement and continued market relevance.
Learn more: https://www.accenture.com/us-en/insights/insurance/open-insurance
Accenture's report explains how natural language processing and machine learning makes extracting valuable insights from unstructured data fast. Read more. https://www.accenture.com/us-en/insights/digital/unlocking-value-unstructured-data
Open Insurance - Unlocking Ecosystem Opportunities For Tomorrow’s Insurance I...Accenture Insurance
For early adopters, open insurance offers new revenue streams, increased customer engagement and continued market relevance.
Learn more: https://www.accenture.com/us-en/insights/insurance/open-insurance
Accenture's report explains how creating effortless experiences are so simple and easy with our data-driven strategy framework to drive growth. Read more.
Whole-brain leadership prepares C-suites for the digital challenges ahead, ensuring seamless growth and high-value problem solving capabilities. Read more.
Wise Pivot is a strategy fit for the digital age that can help companies pursue new growth opportunities. Read more on how to choose your pivot wisely.
Wise Pivot is a strategy fit for the digital age that can help companies pursue new growth opportunities. Read more on how to choose your pivot wisely.
Accenture's Applied Customer Engagement (ACE) is a proven approach to re-thinking and revitalizing contact center operations for the digital era. Read more.
ALIP customers Get More…more product launches, more out-of-the-box functionality and efficiency, more personalized digital capabilities, more delivery “know how”. Read more.
Way Beyond Marketing - The Rise of the Hyper-Relevant CMOAccenture Insurance
Accenture's CMO Survey unveils some important insights on the role of the new CMO and how the role is changing in the digital age. Read more: https://www.accenture.com/us-en/insights/consulting/cmo
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
Digital Transformation and IT Strategy Toolkit and TemplatesAurelien Domont, MBA
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2. Today, data is everywhere—in industry, in business,
in our day-to-day lives. And it’s growing all the time.
As digitalization takes ever greater hold, and as millions of new data-rich devices connect
in new ways, the volume of data available to the enterprise is growing exponentially. It’s
enabling unprecedented levels of insight. In fact, becoming data-driven is the new table-
stakes for enterprise success.
So, what does it mean to be a data-driven enterprise? It means maximizing the value of your
data, and treating it as an asset differentiated by its completeness, lineage, and quality. It means
using data as the basis for critical business decision-making through transparent and high-
quality analytics as opposed to tribal knowledge. In short, it means thinking of your data, and
the analytics that rely on it, as a “product”—one that’s essential for driving business outcomes,
whether that’s a medical device manufacturer creating new medical-grade data services to
support patient healthcare decisions, an oil and gas producer achieving greatly enhanced
efficiency in its plants, or a media and entertainment company serving personalized content
to its customers.
Adopting a product mindset around enterprise data requires its viability, and the analytics models
that derive actionable insights from it, are examined, measured, and refreshed continuously. A
product lifecycle approach is necessary—one that considers everything from conceptualization
and design to manufacture and roll-out, as well as ongoing support. Technology-siloed,
application-focused data architectures must become outcome-centric architectures, in which
data is reused multiple times to support partner ecosystems, data-enhanced products, and
multichannel interactions with the user community.
Above all, this mindset calls for a deliberate strategy to industrialize enterprise data and models.
DATAASA
PRODUCT
2 BECOMING A DATA-DRIVEN ENTERPRISE
3. DATA
MATURITY
MODEL
It helps a business shift away from the idea of “single-use data”, where data is tightly
coupled to particular schemas and formats, requiring major rework to make it suitable
for use beyond the original business case. And it helps them instead tap into the best
available data at any time for highly relevant actionable insights to support optimized
business decision-making.
Accenture has developed a data maturity model that
charts the enterprise’s journey to data industrialization.
3 BECOMING A DATA-DRIVEN ENTERPRISE
4. 03
04
Critical.
Moving to production. The enterprise builds well-
defined and automated methods for developing its data
products, establishes a well-structured data product
catalogue, and provides self-service capabilities across
the organization. It transforms data into an independent
digital asset for the business and expands its use out
into the connected ecosystem.
Industrial.
Data as differentiator. Data is treated as a first-class
citizen, and drives optimal business outcomes.
The competitive positioning of the enterprise is
differentiated by the quality of its data products in
the digital ecosystem.
The model charts these five stages across the dimensions of strategy and governance,
architecture, development, regulation and ethics, and user support.
The data maturity model has five stages, covering initial ad-hoc data use through to a fully
industrialized approach:
00
01
02
Ad-Hoc.
Siloed activity. The enterprise lacks a product mindset
and treats its data as a second-class citizen. Data is
tightly coupled with applications, with limited capacity
for internal sharing thanks to a lack of data strategy,
architecture, delivery, or risk management.
Organize.
Sparking excitement. The enterprise recognizes the
value of its data as an asset. It begins to decouple data
from applications and develop a data-product mindset
through a business vision and data strategy.
Tactical.
Building momentum. The enterprise proves the value
of its metrics and data-driven methods, standardizing
its tools, templates, and methods to create an essential
foothold as a data business.
4 BECOMING A DATA-DRIVEN ENTERPRISE
5. Accenture’sData
MaturityModel
AD-HOC
Strategy &
Governance
ORGANIZETACTICALMISSIONCRITICAL
INDUSTRIAL
Vision
Stake-
holder
Key use
cases
Success
metrics
Cross-group
governance
Definition,
policy &
compliance
catalog
Product roadmap
& releases
Evangelize &
community
employment
Partner
ecosystem
Automated
outcome-centric
data business
Invent-
orize
Rationalize
data assets
Standardized
Methodology
Legal review
& inventory
Static portal
Subscriber
process
Reporting &
Instrumentation
Self-service
portal
Automated usage
monitoring & proactive
system updates
Community
management
Business &
operational
pattern mining
Prescriptive
Disclosures
and terms
Code of
Ethics
Transparency
in supply chain
Compliance
governance
Regular
audit
Automated
risk, reg &
compliance
Standardized
dev tools
Standardized
design
QA & review
Industrialized
life-cycle
management
Data profiling
& quality
Data pipeline
Automated
testing
Auto-modeling
Model-driven
development
Overall arch
design
Security &
entitlement
Rationalized
platforms &
playbooks
Cross
ecosystem
Cross-geo
management
Metering &
monetization
Self-configuring
back-end
automation
SLA modeling
& management
Architecture Development
Regulation
& Ethics
User Support
5 BECOMING A DATA-DRIVEN ENTERPRISE
6. HANDLINGDATAASSETSLIKEAPRODUCT.
StrategyandGovernance
A mature data strategy recognizes that data and analytics models are products
that differentiate the business in the digital ecosystem. The strategy should thus
determine the business objectives (whether that’s growing the customer base, creating
personalized recommendations, or anything else) and outline how to obtain the data
and models to best fulfill those objectives. This use of strategy to optimize delivery
drives the other dimensions of the data industrialization journey.
6 BECOMING A DATA-DRIVEN ENTERPRISE
7. 03
Critical.
Scale the data-as-a-product approach by defining a roadmap,
including subsequent launch cycles, and building a larger
partner ecosystem.
Industrial.
At full maturity, the enterprise runs its data and models like a
product, shifting to selling data outcomes, and extending out
into the digital ecosystem of related data products.
04
01
02
Organize.
Define the product-centric vision of how data and models
support business outcomes. For each data initiative, identify
the business and technical stakeholders who will buy in to
that vision’s objectives, strategy, and tactics.
Tactical.
Apply the data-product mindset to key use cases, considering
how to get the best data and models for the desired business
outcomes. Identify success metrics and build a data committee
to oversee development and release against the strategy.
Define data policy and compliance requirements.
In the context of strategy and governance, actions for the stages of the previously introduced data
maturity model can be prescribed as follows:
Consider a farming equipment manufacturer’s
journey from selling equipment and associated
maintenance services to running enterprise
data and models as differentiating products.
By adding IoT sensors to their farm equipment,
they can collect data for new remote
diagnostics, optimization, and predictive
maintenance services. By taking a product
mindset, a new digital opportunity then opens
up. With the smart farming equipment’s
unique visibility of the land being farmed –
and the farmer’s approach – they can now
provide information services on critical factors
like soil, planting, and equipment. The company
can thus become a player in an ecosystem
focused on maximizing farming productivity,
working with futures markets, chemicals
companies, insurers, and others, to transform
itself into a provider of unique farming insights
as digital products.
TRANSFORMING A BUSINESS FROM FARMING
EQUIPMENT TO FARMING INSIGHTS.
7 BECOMING A DATA-DRIVEN ENTERPRISE
8. An industrialized data architecture is data-centric rather than application-centric.
Its technology stacks, mechanisms, and orchestrations are focused on maximizing
the security and value of data for the enterprise. It uncouples valuable data from
applications, enabling its easy reuse – both by the business and by the wider
ecosystem of partners.
Architecture
FROMAPPLICATION-CENTRICTODATA-CENTRIC.
8 BECOMING A DATA-DRIVEN ENTERPRISE
9. 03
04
Critical.
Build a common methodology to right-size the level of data
management and protection for data whether it resides in
the cloud or on-premise data centers. Apply metering and
chargeback on service use and quality. Automate provisioning
and scaling mechanisms.
Industrial.
The enterprise architecture is data-centric and secured. It not
only supports publishing data out, but also enables the easy
and flexible factoring of data and models from third parties to
power a cross-ecosystem data business.
01
02
Organize.
Take an inventory of existing data and model assets and
back-end systems. Rationalize the services that will form
the components of future enterprise-level data products.
Tactical.
Standardize the overall architecture. Evaluate best practices and
technology stacks to enhance cost-effectiveness, performance,
scalability, and security. Create common configurable and
customizable methods for data onboarding and use.
A media and entertainment company
competes on its ability to provide subscribers
with content customized by genre, cast,
director, language, or numerous other factors.
It must store and process vast volumes of
data – most of which is provided by others
—adding subtitles or other local distributor
logos as necessary. With an industrialized
architecture, the company can store data in
the most cost-effective system for its needs,
factoring in geographic distribution to manage
the transmission limits of available Internet
bandwidth. Real-time updates can be handled,
so unscheduled events like overrunning
sports games can be reflected immediately
in programming guides. Data is normalized and
transformed into common schema objects,
such as linking an original English-language
movie with its Spanish version or its sequel.
Enterprise data assets are thus standardized
and suitable for reuse and discovery, enabling
highly personalized recommendations that
factor in both proprietary content and third-
party content (ratings, user-generated reviews,
interviews and articles from other platforms,
etc.). A data-centric architecture makes it easy
for the company to use and customize data
from others and thus create new and enhanced
products for their subscribers.
COMPETING ON THE MANAGEMENT OF
MEDIA AND ENTERTAINMENT CONTENT.
9 BECOMING A DATA-DRIVEN ENTERPRISE
10. Development
An industrialized development process is agile and dynamic, streamlining
innovation to create new data-powered business outcomes in a fast-evolving,
ever-changing market. A commitment to the best data and models requires an
iterative approach. It includes data and model lifecycle and DevOps processes
that allow for rapid iteration to deploy, optimize, and redeploy new datasets
and models to best support the business.
BECOMING AGILE AND DYNAMIC.
10 BECOMING A DATA-DRIVEN ENTERPRISE
11. To optimize plant operational efficiency,
an oil and gas company developed a
series of composite real-time models,
involving data from numerous sensors fed
into forecasting models, then into asset
health and performance models, and then
rolled up into an operations status used for
decision-making – all created, governed,
and maintained by cross-organizational
business owners, data scientists, and domain
experts. With each of these complex model
pipelines deployed thousands of times
across the plant, some inevitably required
rework, retraining, and feature engineering
over time. A model-specific DevOps process
was implemented to industrialize develop-
deploy-monitor cycles. Model failure was
considered from the outset, requiring data
scientists to specify the data quality conditions
and accuracy and precision evaluations used
to automatically monitor running models
and trigger actions like retraining models or
taking them out of deployment if needed.
The result: an automated system to ensure
model accuracy and a way to accelerate and
scale model development ensures trust in the
prescribed updates to plant operations.
MANAGING MODEL DEVELOPMENT AT SCALE
TO IMPROVE PLANT OPERATIONS.
Critical.
Implement DevOps processes for data products, adopting data
and model lifecycle management frameworks, configurable data
pipelines, and automated testing and release cycles. Include
continuous monitoring that guarantees data quality and model
health. Use governance and audit features to provide validation
and closed-loop feedback.
Industrial.
Custom data products are created, tested, and deployed
by leveraging configuration and model-driven automated
processes that enable the “citizen user” within the business.
Automation implements champion-challenger analytical model
comparisons scaling the number of training and retraining
experiments to improve outcomes.
03
04
01
02
Organize.
Define standard methodologies, code templates, and use cases
for the development, testing, deployment, and production of
data and models.
Tactical.
As more data and model products are created, enforce
formalized standards across architectural components,
development tools, documentation, and quality assurance.
Ensure data publication and consumption processes align
with the business vision.
11 BECOMING A DATA-DRIVEN ENTERPRISE
12. An industrialized approach to regulation and ethics supports the transparency of
data use to help the enterprise build a trusted digital reputation in the market. It
defines a code of ethics as a common language for eliminating risk, standardizing
audit processes, and leveraging new technologies like blockchain to ensure
compliance and transparency for all data stakeholders
RegulationandEthics
ACQUIRING TRUST THROUGH TRANSPARENCY.
12 BECOMING A DATA-DRIVEN ENTERPRISE
13. 03
04
Critical.
Maintain a single logical view of data through a universal metadata
model across the data supply chain. Log everything that happens to
the data and make the footprint visible to data stewards. Implement
transparency and explainability in AI models. Evaluate whether
activities conducted on the data agreed upon usage standards.
Audit regularly to assess security and risk control and monitoring.
Industrial.
Automated monitoring programmatically supports regulatory
compliance, while proactively identifying ethical risks to the
enterprise – and taking automated action to flag for human review.
Transparency is baked into data use right across the value chain.
01
02
Organize.
Create an inventory of the legal and regulatory rules applicable
to data services.
Tactical.
Provide mechanisms to ensure informed consent is acquired from
data owners and data subjects. Define a code of conduct, as well
as policies and procedures, to ensure digital trust is established
consistently for data services.
Consider a collaborative anti-money-
laundering application used by multiple banks
in a way that allows them to share costs and
improve the detection of suspicious activity. In
developing the application, guaranteeing trust
and data transparency was key, enabling each
bank to monitor and control how their data was
being used. Beyond their legal agreements,
each bank could programmatically track
the lineage of their data and its use across
the whole data supply chain. They could also
control the models that were applied, whether
they were shared models recommended by the
application and other banks, or private models
of their own. The result is a living system of trust
and transparency, in which data access can be
programmatically tracked and enforced based
upon the agreed upon rules set by each bank.
SPOTTING FINANCIAL FRAUD THROUGH
TRUSTED DATA SHARING.
13 BECOMING A DATA-DRIVEN ENTERPRISE
14. UserSupport
An industrialized approach to user support rests on a product mindset
that makes data and models easy and appealing to use. It extends self-
service capabilities beyond the data experts to the whole enterprise in
a user-friendly way, while also supporting closed-loop optimization. The
ambition: to unlock the greatest benefits for the enterprise by using
data to power insights for “citizen” users like the business analyst, the
domain expert, or the operations engineer.
OPTIMIZINGWITHSELF-SERVICE.
14 BECOMING A DATA-DRIVEN ENTERPRISE
15. Today, the process of creating an oil or gas
well design is largely expert-led. A drilling
engineer plans where and how to drill based
on his or her experience. Even the data
used to create a well offset report, which
compares the well with previous designs, is
often based on a combination of basic filter
on the geographical region and the individual
engineer’s experience. There is much value to
be had in augmenting this expert-led process
with the wealth of data that lies beyond the
engineer’s immediate experience – provided
that finding and processing that data is
simple. Internet search provides a valuable
example to follow. In automatically indexing
content and then extracting and presenting it
in a contextual way (in other words, presenting
results differently when a search is about a
company, a person, or a work of art, etc.), it
presents content in a highly accessible and
relevant way. By applying the same techniques,
an oil and gas company can create a knowledge
model that covers a well and everything that
the drilling engineer needs to consider. It can
then use automated techniques for extracting
these insights from enterprise data, creating an
Internet-search-like capability that equips the
drilling engineer with the ability to make better
data-driven decisions in their drilling plans.
PRESCRIBING OIL AND GAS WELL PLANNING VIA
INTERNET-SEARCH-LIKE EXPERIENCES.
03
04
Critical.
Scale the data-as-a-product approach by defining a roadmap,
including subsequent launch cycles, and building a larger
partner ecosystem.
Industrial.
Prescriptive insight-driven actions are enabled across the
enterprise. A mature, optimized and contextualized data/
insight service is available for all users as they need it,
supported by knowledge graphs, AI-enabled interactions,
and other technological innovations.
01
02
Organize.
Set up a static portal for users to discover and access
enterprise data, and communicate it with their teams.
Tactical.
Foster innovation by proactively supporting the development of
new use cases for the data. Calculate standardized cost-benefit
measures, and incorporate business-level tools for defined
reporting needs.
15 BECOMING A DATA-DRIVEN ENTERPRISE
16. As the volume of enterprise data explodes, and as
the number of potential data use cases accelerates
exponentially, an industrialized approach to data and
models is quickly becoming a must-have capability.
In today’s leading digital-native and data-driven enterprises, data and models are
already treated as first-class citizens—capable of taking a truly differentiated offering
to the market.
Accenture’s data maturity model charts a path for traditional enterprises to develop this
industrialized capability and take a lead in the digital ecosystem. This journey enables an
ever-greater degree of automation and rapid delivery to create the best data and the best
models to enable business outcomes.
Industrialization helps the enterprise fully leverage and monetize its data and model assets,
generating the insights that can both drive the core business forward and create the entirely
new outcome-driven digital business models that pivot into the new. In this way, companies
that were once tied to particular products or services are transformed into robust data-driven
companies. The journey to data industrialization is the key to unlocking the data-driven
enterprise of the future.
INDUST
RIALIZED
DATA
16 BECOMING A DATA-DRIVEN ENTERPRISE