Data centric business and knowledge graph trendsAlan Morrison
The deck for my kickoff keynote at the Data-Centric Architecture Forum, February 3, 2020. Includes related data, content, and architecture definitions and fundamental explanations, knowledge graph trends, market outlook, transformation case studies and benefits of large-scale, cross-boundary integration/interoperation.
Data-Centric Business Transformation Using Knowledge GraphsAlan Morrison
From a talk at the Data Architecture Summit in Chicago in 2018--reviews digital transformation and what deep transformation really implies at the data layer. Cross-enterprise knowledge graphs are becoming feasible and can be a key enabler of deep transformation.
The explosive growth of data and the value it creates calls on data professionals to level up their programs to build, demonstrate, and maintain trust. The days of fine print, pre-ticked boxes, and data hoarding are gone and strong collaboration from data, privacy, marketing and ethics teams is necessary to design trustworthy data-driven practices.
Join for a discussion on the latest trends in trusted data and how you can take critical steps to build trust in data practices by:
- Embedding privacy by design into data operations
- Respecting individual choice and optimizing the ongoing relationship with consumers
- Preparing for future data challenges including responsible AI and sustainability
Data centric business and knowledge graph trendsAlan Morrison
The deck for my kickoff keynote at the Data-Centric Architecture Forum, February 3, 2020. Includes related data, content, and architecture definitions and fundamental explanations, knowledge graph trends, market outlook, transformation case studies and benefits of large-scale, cross-boundary integration/interoperation.
Data-Centric Business Transformation Using Knowledge GraphsAlan Morrison
From a talk at the Data Architecture Summit in Chicago in 2018--reviews digital transformation and what deep transformation really implies at the data layer. Cross-enterprise knowledge graphs are becoming feasible and can be a key enabler of deep transformation.
The explosive growth of data and the value it creates calls on data professionals to level up their programs to build, demonstrate, and maintain trust. The days of fine print, pre-ticked boxes, and data hoarding are gone and strong collaboration from data, privacy, marketing and ethics teams is necessary to design trustworthy data-driven practices.
Join for a discussion on the latest trends in trusted data and how you can take critical steps to build trust in data practices by:
- Embedding privacy by design into data operations
- Respecting individual choice and optimizing the ongoing relationship with consumers
- Preparing for future data challenges including responsible AI and sustainability
Gartner analysts will further discuss how to prioritise IT initiatives, while balancing the use of resources at Gartner PPM & IT Governance Summit 2011, 14-15 June in London. For more information please visit europe.gartner.com/ppm
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.
Scaling the mirrorworld with knowledge graphsAlan Morrison
After registration at https://www.brighttalk.com/webcast/9273/364148, you can view the full recording, which begins with Scott Abel's intro for a few minutes, then my talk for 20 minutes, and then Sebastian Gabler's. First presented on October 23 at an SWC webinar.
Conclusions:
(1) The mirrorworld (a world of digital twins, which will be 25 years in the making, according to Kevin Kelly) will require semantic knowledge graphs for interaction and interoperability.
(2) This fact implies massive future demand for knowledge graph technology and other new data infrastructure innovations, comparable to the scale of oil & gas industry infrastructure development over 150 years.
(3) Conceivably, knowledge graphs could be used to address a $205 billion market demand by 2021 for graph databases, information management, digital twins, conversational AI, virtual assistants and as knowledge bases/accelerated training for deep learning, etc. but the problem is that awareness of the tech is low, and the semantics community that understands the tech is still quite small.
(4) Over the next decades, knowledge graphs promise both scalability and substantial efficiencies in enterprises. But lack of awareness of its potential and how to harness it will continue to be stumbling blocks to adoption.
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
PLEASE DOWNLOAD TO VIEW ANIMATIONS & SPEAKER NOTES.
Analyst: What would you like SharePoint to do?
Customer: Well, what can it do?
Analyst: Tons! Let me show you…
All too often, this is how conversations between analysts and customers/stakeholders begin, and it isn’t helpful to anyone. SharePoint has a vast array of capabilities, but if you start by describing or demonstrating what SharePoint can do and how the technology works, you will end up with customers who are confused and don’t know where to begin, or who have massively overblown expectations.
In this session, you will learn how to set up and conduct workshops with various stakeholders that will allow you to understand their real needs. You will then learn how to document and organize this information so that it is useful to the stakeholders and that will allow you to guide them through prioritization and planning.
You will learn when NOT to do demonstrations of SharePoint, and when and how to do demos that are powerful and effective.
Recommended for CDOs and all Data & Analytics Managers
The past 2 years have had a huge impact on organizations journeys to become data driven. Existing data architectures were disrupted; rigid structures and processes were questioned, and many data strategies were re-written.
On the one hand, the global pandemic emphasized the need for organizations to raise the bar, implement strategies, improve data literacy and culture, increase investments in data and analytics, and explore AI opportunities.
On the other, it also presented new challenges such as: the war for data talent and the wide literacy gap. Inadequate structures as well as outdated processes were exposed. Major changes in the data landscape (Data Fabric, Data Mesh, Transition to Data Clouds) will further disrupt existing data architectures and enhance the need for a new adaptive architecture and organization.
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
Gartner analysts will further discuss how to prioritise IT initiatives, while balancing the use of resources at Gartner PPM & IT Governance Summit 2011, 14-15 June in London. For more information please visit europe.gartner.com/ppm
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.
Scaling the mirrorworld with knowledge graphsAlan Morrison
After registration at https://www.brighttalk.com/webcast/9273/364148, you can view the full recording, which begins with Scott Abel's intro for a few minutes, then my talk for 20 minutes, and then Sebastian Gabler's. First presented on October 23 at an SWC webinar.
Conclusions:
(1) The mirrorworld (a world of digital twins, which will be 25 years in the making, according to Kevin Kelly) will require semantic knowledge graphs for interaction and interoperability.
(2) This fact implies massive future demand for knowledge graph technology and other new data infrastructure innovations, comparable to the scale of oil & gas industry infrastructure development over 150 years.
(3) Conceivably, knowledge graphs could be used to address a $205 billion market demand by 2021 for graph databases, information management, digital twins, conversational AI, virtual assistants and as knowledge bases/accelerated training for deep learning, etc. but the problem is that awareness of the tech is low, and the semantics community that understands the tech is still quite small.
(4) Over the next decades, knowledge graphs promise both scalability and substantial efficiencies in enterprises. But lack of awareness of its potential and how to harness it will continue to be stumbling blocks to adoption.
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
PLEASE DOWNLOAD TO VIEW ANIMATIONS & SPEAKER NOTES.
Analyst: What would you like SharePoint to do?
Customer: Well, what can it do?
Analyst: Tons! Let me show you…
All too often, this is how conversations between analysts and customers/stakeholders begin, and it isn’t helpful to anyone. SharePoint has a vast array of capabilities, but if you start by describing or demonstrating what SharePoint can do and how the technology works, you will end up with customers who are confused and don’t know where to begin, or who have massively overblown expectations.
In this session, you will learn how to set up and conduct workshops with various stakeholders that will allow you to understand their real needs. You will then learn how to document and organize this information so that it is useful to the stakeholders and that will allow you to guide them through prioritization and planning.
You will learn when NOT to do demonstrations of SharePoint, and when and how to do demos that are powerful and effective.
Recommended for CDOs and all Data & Analytics Managers
The past 2 years have had a huge impact on organizations journeys to become data driven. Existing data architectures were disrupted; rigid structures and processes were questioned, and many data strategies were re-written.
On the one hand, the global pandemic emphasized the need for organizations to raise the bar, implement strategies, improve data literacy and culture, increase investments in data and analytics, and explore AI opportunities.
On the other, it also presented new challenges such as: the war for data talent and the wide literacy gap. Inadequate structures as well as outdated processes were exposed. Major changes in the data landscape (Data Fabric, Data Mesh, Transition to Data Clouds) will further disrupt existing data architectures and enhance the need for a new adaptive architecture and organization.
Data-centric design and the knowledge graphAlan Morrison
The #knowledgegraph--smart data that can describe your business and its domains--is now eating software. We won't be able to scale AI or other emerging tech without knowledge graphs, because those techs all require a transformed data foundation, large-scale integration, and shared data infrastructure.
Key to knowledge graphs are #semantics, #graphdatabase technology and a Tinker Toy-style approach to adding the missing verbs (which provide connections and context) back into your data. A knowledge graph foundation provides a means of contextualizing business domains, your content and other data, for #AI at scale.
This is from a talk I gave at the Data Centric Design for SMART DATA & CONTENT Enthusiasts meetup on July 31, 2019 at PwC Chicago. Thanks to Mary Yurkovic and Matt Turner for a very fun event!.
Analyst Webinar: Discover how a logical data fabric helps organizations avoid...Denodo
Watch full webinar here: https://bit.ly/3zVUXWp
In this webinar, we’ll be tackling the question of where our data is and how we can avoid it falling into a black hole.
We’ll examine how data blackholes and silos come to be and the challenges these pose to organisations. We will also look at the impact of data silos as organisations adopt more complex multi-cloud setups. Finally, we will discuss the opportunities a logical data fabric poses to assist organisations to avoid data silos and manage data in a centrally governed and controlled environment.
Join us and Barc’s Jacqueline Bloemen on this webinar to get the answer and further insights on how to better avoid falling into a #datablackhole. Hope to see you connected!
Augmentation, Collaboration, Governance: Defining the Future of Self-Service BIDenodo
Watch full webinar here: https://bit.ly/3zVJRRf
According to Dresner Advisory’s 2020 Self-Service Business Intelligence Market Study, 62% of the responding organizations say self-service BI is critical for their business. If we look deeper into the need for today’s self-service BI, it’s beyond some Executives and Business Users being enabled by IT for self-service dashboarding or report generation. Predictive analytics, self-service data preparation, collaborative data exploration are all different facets of new generation self-service BI. While democratization of data for self-service BI holds many benefits, strict data governance becomes increasingly important alongside.
In this session we will discuss:
- The latest trends and scopes of self-service BI
- The role of logical data fabric in self-service BI
- How Denodo enables self-service BI for a wide range of users - Customer case study on self-service BI
Accelerating Data-Driven Enterprise Transformation in Banking, Financial Serv...Denodo
Watch full webinar here: https://bit.ly/3c6v8K7
Banking, Financial Services and Insurance (BFSI) organizations are globally accelerating their digital journey, making rapid strides with their digitization efforts, and adding key capabilities to adapt and innovate in the new normal.
Many companies find digital transformation challenging as they rely on established systems that are often not only poorly integrated but also highly resistant to modernization without downtime. Hear how the BFSI industry is leveraging data virtualization that facilitates digital transformation via a modern data integration/data delivery approach to gain greater agility, flexibility, and efficiency.
In this session from Denodo, you will learn:
- Industry key trends and challenges driving the digital transformation mandate and platform modernization initiatives
- Key concepts of Data Virtualization, and how it can enable BFSI customers to develop critical capabilities for real-time / near real-time data integration
- Success Stories on organizations who already use data virtualization to differentiate themselves from the competition.
DAS Slides: Enterprise Architecture vs. Data ArchitectureDATAVERSITY
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key inter-relationships 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.
How a Logical Data Fabric Enhances the Customer 360 ViewDenodo
Watch full webinar here: https://bit.ly/3GI802M
Organisations have struggled for years in understanding their customers, this has mainly been due to not having the right data available at the right point in time. In this session we will discuss the role of Data Virtualization in providing customer 360 degree view and look at some of the success stories our customers have told us about.
Data Virtualization. An Introduction (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3uiXVoC
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Watch on-demand this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise? Where does it fit..?
Strata and Hadoop is where data science and new business fundamentals merge. And, in Strata and Hadoop World Conference, there were many famous personalities who have given their views on Hadoop and Big data. In this PPT, you will get to know about speakers who have spoken on this topic.
Data Intelligence: How the Amalgamation of Data, Science, and Technology is C...Caserta
Joe Caserta explores the world of analytics, tech, and AI to paint a picture of where business is headed. This presentation is from the CDAO Exchange in Miami 2018.
When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Empowering your Enterprise with a Self-Service Data Marketplace (EMEA)Denodo
Watch full webinar here: https://bit.ly/3aWI8lt
Self-service is a major goal of modern data strategists. A successfully implemented self-service initiative means that business users have access to holistic and consistent views of data regardless of its location, source or type. As data unification and data collaboration become key critical success factors for organisations, data catalogs play a key role as the perfect companion for a virtual layer to fully empower those self-service initiatives and build a self-service data marketplace requiring minimal IT intervention.
Denodo’s Data Catalog is a key piece in Denodo’s portfolio to bridge the gap between the technical data infrastructure and business users. It provides documentation, search, governance and collaboration capabilities, and data exploration wizards. It provides business users with the tool to generate their own insights with proper security, governance, and guardrails.
In this session we will cover:
- The role of a virtual semantic layer in self-service initiatives
- Key ingredients of a successful self-service data marketplace
- Self-service (consumption) vs. inventory catalogs
- Best practices and advanced tips for successful deployment
- A Demonstration: Product Demo
- Examples of customers using Denodo’s Data Catalog to enable self-service initiatives
DataEd Slides: Approaching Data Management TechnologiesDATAVERSITY
Our architecturally solid stool requires three legs: people, process, and technologies. This webinar looks at the most misunderstood of these three components: technology. While most organizations begin with technologies, it turns out that technologies are the last component that should be considered. This webinar will survey a range of Data Management technologies that can be used to increase the productivity of Data Management efforts.
Empowering your Enterprise with a Self-Service Data Marketplace (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3uqcAN0
Self-service is a major goal of modern data strategists. A successfully implemented self-service initiative means that business users have access to holistic and consistent views of data regardless of its location, source or type. As data unification and data collaboration become key critical success factors for organizations, data catalogs play a key role as the perfect companion for a virtual layer to fully empower those self-service initiatives and build a self-service data marketplace requiring minimal IT intervention.
Denodo’s Data Catalog is a key piece in Denodo’s portfolio to bridge the gap between the technical data infrastructure and business users. It provides documentation, search, governance and collaboration capabilities, and data exploration wizards. It provides business users with the tool to generate their own insights with proper security, governance, and guardrails.
In this session we will cover:
- The role of a virtual semantic layer in self-service initiatives
- Key ingredients of a successful self-service data marketplace Self-service (consumption) vs. inventory catalogs
- Best practices and advanced tips for successful deployment
- A Demonstration: Product Demo
- Examples of customers using Denodo’s Data Catalog to enable self-service initiatives
A webinar on how Neo4j customers like Nasa, AirBnB, eBay, government agencies, investigative journalists and others are building Knowledge Graphs to inform today and tomorrow’s solutions.
Similar to Data-centric market status, case studies and outlook (20)
FAIR data_ Superior data visibility and reuse without warehousing.pdfAlan Morrison
The advantages of semantic knowledge graphs over data warehousing when it comes to scaling quality, contextualized data for machine learning and advanced analytics purposes.
The FAIR data movement and 22 Feb 2023.pdfAlan Morrison
To realize the promise of FAIR data, companies must be data mature. They must adopt data-centric architecture and the #FAIR (findable, accessible, interoperable and reusable) principles. When they do, the data they need will be linked and self-describing. The data when queried will tell you where it is.
A desiloed, #semantic graph data abstraction--the only feasible means behind creating FAIR data at this point--is not only the means to data discovery, but also a path to model-driven development and data sharing at scale, both of which will break an organization's habit of duplicating data and logic.
This webinar highlights fresh enterprise case studies that are starting to realize the dream of #FAIRdata, as well as how these companies are succeeding:
- Zero copy integration: How to think about eliminating #dataduplication and stop the application buying binge that only exacerbates the problem.
- Dynamic, unified data model: Standard graphs provide a means of modeling once, use anywhere, for conceptual, logical and physical purposes all at once.
- Persuasion and teamwork: The #graph approach provides an ideal way to loop business units and domain experts in and empower them to recommend model changes that are easily implemented.
The whole process is bringing #enterprises like Walmart, Uber, Goldman Sachs and Nokia into the age of #contextualcomputing. Learn how to be a fast follower by thinking big, but starting small.
Ever wonder how these concepts contrast with and yet complement each other in a next-generation system?
Enterprise semantics
Knowledge graphs
Model-driven development
Digital twins
Self-Sovereign Identity
Own your own data
Data deduplication
Autonomous agents
Large language systems
Data-Centric Architecture combines the major technologies behind each of these concepts. In fact, it’s essential to the real-world implementation of general AI, enabling the context that’s behind contextual computing, DARPA’s Third Phase of AI. To be able to deliver, DCA needs to simplify and scale data ecosystems using these pieces of the data ecosystem puzzle.
This talk will provide an overview of how these pieces of the data-centric puzzle are fitting together. It’s a best practice to see these pieces can fit together side-by-size in an enterprise context and envision next-gen systems from the viewpoint of some of the most demanding enterprise use cases.
It’s also best practice to study how one industry vertical is moving ahead and contrast that progress with your own industry. Remember, as the data-centric ecosystem emerges and the benefits of true digitization start to pay off, many more techniques can be borrowed from other verticals and used in your own vertical. This talk will summarize several powerful recent case studies and highlight the key takeaways.
Graph Foundations for Advanced Analytics and CollaborationAlan Morrison
Presentation on Knowledge Graph Foundations and how they're used.
Presented at TechTarget ML and AI Summit
September 20, 2022
View the full video recording of this deck at https://www.brighttalk.com/webcast/9059/556690
Dcaf transformation & kg adoption 2022 -alan morrisonAlan Morrison
A keynote presentation on knowledge graph adoption trends and how to do digital transformation differently.
Delivered at the Enterprise Data Transformation & Knowledge Graph Adoption
A Semantic Arts DCAF Event
February 28, 2022
LA HUG - Video Testimonials with Chynna Morgan - June 2024Lital Barkan
Have you ever heard that user-generated content or video testimonials can take your brand to the next level? We will explore how you can effectively use video testimonials to leverage and boost your sales, content strategy, and increase your CRM data.🤯
We will dig deeper into:
1. How to capture video testimonials that convert from your audience 🎥
2. How to leverage your testimonials to boost your sales 💲
3. How you can capture more CRM data to understand your audience better through video testimonials. 📊
Tata Group Dials Taiwan for Its Chipmaking Ambition in Gujarat’s DholeraAvirahi City Dholera
The Tata Group, a titan of Indian industry, is making waves with its advanced talks with Taiwanese chipmakers Powerchip Semiconductor Manufacturing Corporation (PSMC) and UMC Group. The goal? Establishing a cutting-edge semiconductor fabrication unit (fab) in Dholera, Gujarat. This isn’t just any project; it’s a potential game changer for India’s chipmaking aspirations and a boon for investors seeking promising residential projects in dholera sir.
Visit : https://www.avirahi.com/blog/tata-group-dials-taiwan-for-its-chipmaking-ambition-in-gujarats-dholera/
Premium MEAN Stack Development Solutions for Modern BusinessesSynapseIndia
Stay ahead of the curve with our premium MEAN Stack Development Solutions. Our expert developers utilize MongoDB, Express.js, AngularJS, and Node.js to create modern and responsive web applications. Trust us for cutting-edge solutions that drive your business growth and success.
Know more: https://www.synapseindia.com/technology/mean-stack-development-company.html
Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
www.seribangash.com
Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Improving profitability for small businessBen Wann
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4. PwC
• Market oriented, passive, laissez faire
• Architecture and vision free
• “The market will fix it.”
• Big vendor dominated
• Legacy of Nick Carr’s IT Doesn’t Matter
• Siloed, isolated efforts
• Startups venture funded in silos, with waves of new
silos being generated every year
• Users themselves just passive and disempowered
buyers or subscribers
• Consumer services mostly self-service, with no one
to call if a problem arises
Product-centric IT: Got an IT problem? Buy a packaged solution.
February 5, 2019Data-Centric Conference
Products and services
Data management
Applications
Computer graphics and web design
Development
IT administration
IT certifications
IT security
Service providers
Technologies and fields
Categories from wand® precision
classification and search, 2019
5. PwC
Data-centric IT: Own the problem first. Then build a solution.
February 5, 2019Data-Centric Conference
5
• In the data-centric view, every IT category is subordinated to centrally managed, model-driven data via data
strategy, GRC and data-centric architecture (DCA)
• Relationship-rich modeling leads development for reasons of efficiency and effectiveness
• Standards based, open source enabled, build versus buy
• Empowers user communities, activism, large scale collaboration, shared infrastructure
Goals for data to
be obtained,
enriched and used
Data strategy
Data governance,
risk and
compliance
Data-centric
architecture (DCA)
Strategy and planning
Execution
Data-centric infrastructure
Data and logic lifecycle management
Model-driven development
Cross-enterprise intelligence
Relationship-rich modeling
Data-centric security
Process, pipeline and delivery automation
Human and machine learning loops
Data-centric design thinking
6. PwC
Think beyond end-to-end packaged software and
SaaS solutions
• “Cognitive computing” platforms
• “Big data” platforms
• Repackaged legacy MDM
• Application-centric integration suites
• Data preparation suites
• Robotic or intelligent process automation
• Any other “AI-enabled” X, Y or Z that simply generates
lot of market noise and gets in the way
The elephant in the room is always organizational change.
However, wrong technology and data strategy approaches
prevent change also.
Those who succeed have effective strategies, means and
execution all going for them at the same time.
Consider how to get the business and IT together
building and solving foundational data problems
• Bespoke projects with worthy objectives and practical
means of success
• Human-in-the-loop computing—constructing data-
description feedback loops for knowledge foundations
• Software and SaaSes that do encourage better data
modeling and relationship-rich integration
– semantic graph databases
– smart data hubs
– NoSQL + SQL modeling—building bridges between
tabular, document and graph
– automated taxonomy and ontology generation, but
as a starting point
– Automated process mapping, but as a starting point
– Clever modeling or visualization tools to encourage
deeper, system-level understanding
Ways to think about truly data-centric opportunities
6
February 5, 2019Data-Centric Conference
7. PwC
The real inhibitors to adoption aren’t technological – they’re rooted
in tribal biases and resistance to change
7
Tribalism CollectivismIndividualism
Anarchy TotalitarianismLocus of inertia
Daniel Quinn, Beyond Civilization and Alice Linsley, “Daniel Quinn: A Return to Tribalism?”, college-ethics.blogspot.com, 2018
February 5, 2019
Data-Centric Conference
8. PwC
Tribalism – Machine learning edition
8
Source: Pedro Domingos, The Master Algorithm, 2015
More at “Machine learning evolution”: http://usblogs.pwc.com/emerging-technology/machine-learning-evolution-infographic/, PwC, 2017
Symbolists Bayesians Connectionists Evolutionaries Analogizers
Use symbols, rules,
and logic to
represent
knowledge and
draw logical
inference
Assess the
likelihood of
occurrence for
probabilistic
inference
Recognize and
generalize patterns
dynamically with
matrices of
probabilistic,
weighted neurons
Generate variations
and then assess the
fitness of each for a
given purpose
Optimize a function
in light of
constraints (“going
as high as you
can while staying
on the road”)
Favored
algorithm
Rules and decision
trees
Favored
algorithm
Naïve Bayes or
Markov
Favored
algorithm
Neural network
Favored
algorithm
Genetic programs
Favored
algorithm
Support vectors
February 5, 2019
Data-Centric Conference
9. PwC
Tribalism – Data integration edition
9
Trend toward more data centricity this way
Application-
centric
RESTful
developers
Relational
database linkers
Data-centric
knowledge graphers
Application-
centric
ESB advocates
Semantic Web Company, 2018
Computerscience
wiki.org, 2018
TIBCO, 2014
Oracle DBA’s Guide, 2018
User Scott
Select FROM emp
Local
database
PUBLIC SYNONYM
Emp - >
emp@HQ.ACME>COM
Database link
(undirected)
Remote
database
EMP table
Portals
Net
Application
B2B
Interactions
Enterprise
data
Business process
management
Web Services
Mobile
Applications
DEE
Application
ERP
CRM SFA
Legacy
System
ESBCustom environment Common environment
API
Unstructured
Data
Semi-
structured Data
Structured
Data
Schema mapping based
on ontologies
Entity Extractor informs all
incoming data streams about
its semantics and links them
Unified Views
RDF Graph
Database
PoolParty
Graph Search
February 5, 2019Data-Centric Conference
10. PwC
Crossing the chasm between the tribes
10
Reducing the amount of unfamiliarity developers confront--familiar document means to achieve comparable ends
to graph:
• Semantic suites that use the JSON format and familiar hierarchies: SWC’s PoolParty is an example
• GraphQL: A popular document shape language that talks to APIs using SELECT-like statements and tree
shapes; retrieves only the data you need, provides needed feedback to API owners, helps with API sprawl
• Accessible web as database methods: JSON-LD and Schema.org, etc. vocabularies
• Document “schemas” via data objects: JavaScript objects to developers = documents to NoSQL DB types;
Object Data Modeling instead of database semantics
• Mongoose or MongoDB JSON schema features + GraphQL: MongoDB object modeling and querying that can
be used for subdocument filtering within a GraphQL context
• HyperGraphQL: A GraphQL UI for Linked Data, restricted to certain tree-shaped queries
• Universal Schema Language: Mike Bowers’ document/graph query and modeling language still in development
• COMN: Concept and Object Modeling Notation, Ted Hills’ well-defined NoSQL + SQL data modeling notation
February 5, 2019Data-Centric Conference
11. PwC
Knowledge graphs
• Large-scale, heterogeneous
integration for data discovery and
asset tracking
• Platforms for advanced analytics
and machine learning
• Knowledge bases for intelligent
assistants
Smart data hubs
• Alternatives or adjuncts to data
warehousing, or
• Integration across both operational
and analytics data
Off-chain to on-chain data
quality for blockchain networks
• Ways to avoid garbage-in,
garbage-out
• Supply chain integration
• Smart contracts for automated
transactions and compliance
• Personal data protection
• Self-sovereign identity
– Individuals have a border and
sovereignty for personal data
control in the same way
countries exercise sovereignty
within their borders
– Peer-to-peer relationship
status with other entities on the
network
Data cataloging and auditing
• Portal-style data asset visibility
• Data inventory and curation as a
first step to privacy compliance
• Information supply chain mapping
• Back-end classification
Examples of data-centric approaches in 2019
11
February 5, 2019Data-Centric Conference
Sources; Kurt Cagle, “The Semantic Zoo,” Forbes, 18 January 2019
Bridget Botelho, “What are the main features of data catalog software?”
TechTarget, 10 May 2018
Christopher Allen, “The Path to Self-Sovereign Identity,” Life with Alacrity blog,
25 April 2016
Phil Windley, “What is ‘self-sovereignty’?” Sovrin Twitter video, 24 January
2019
Intelligent assistants
• Expanded user experience
• Cross-domain capability
Cybersecurity
• Threat intelligence, but active
measures too
• Network analysis
• Identity verification
13. PwC
Emerging technologies and the data value chain
PwC and http://doi.ieeecomputersociety.org/cms/Computer.org/dl/mags/it/2013/01/figures/mit20130100571.gif
IoT and
drone data
collection
AI AR/VR
Plan
Create
Refine
Execute
Optimize
Blockchain
(immutable ledger sharing +
autonomous process coding)
3D printing output, IoT
distribution, robotics
and drone delivery
Operational data
generation and
use
Manage
&
Monitor
February 5, 2019Data-Centric Conference
13
14. PwC
SaaSes and clouds generally are incredibly popular.
What are the implications?
14
February 5, 2019Data-Centric Conference
Trend toward owning and managing less and less of the stack
15. PwC
Enterprises used an average of 1,181 cloud services each by the
end of 2017
15
February 5, 2019Data-Centric Conference
Netskope’s 2017 Cloud Report
• Enterprises used nearly 1,200 cloud
services each in Q4 2017, according to
Netskope
• Most of these are SaaSes such as
Salesforce, Workday, SAP Success
Factors….
• Buy rather than build continues
• Even with enthusiasm for AI, data and
analytics skills continue to be scarce
16. PwC
Means of integration, and a database popularity ranking per
DB-Engines
16
February 5, 2019Data-Centric Conference
18. PwC 18
February 5, 2019Data-Centric Conference
0
20
40
60
80
100
120
1/28/2018 2/28/2018 3/31/2018 4/30/2018 5/31/2018 6/30/2018 7/31/2018 8/31/2018 9/30/2018 10/31/2018 11/30/2018 12/31/2018
Interestovertime
100=peakpopularity
Last 12 months
Popularity of the "O" word and "data lake" versus other data terms
(per Google Trends)
"data lake": (United States) ontology: (United States) "data catalog": (United States)
"data-centric": (United States) "master data management": (United States)
19. PwC 19
February 5, 2019Data-Centric Conference
0
20
40
60
80
100
120
1/28/2018 2/28/2018 3/31/2018 4/30/2018 5/31/2018 6/30/2018 7/31/2018 8/31/2018 9/30/2018 10/31/2018 11/30/2018 12/31/2018
Interestlevel
100=peakpopularity
Past 12 months
Popularity of data lake + storage terms
data lake aws: (United States) data lake azure: (United States) data lake hadoop: (United States)
20. PwC 20
February 5, 2019Data-Centric Conference
0
20
40
60
80
100
120
1/28/2018 2/28/2018 3/31/2018 4/30/2018 5/31/2018 6/30/2018 7/31/2018 8/31/2018 9/30/2018 10/31/2018 11/30/2018 12/31/2018
Interestlevel
100=peakpopularity
Last 12 months
Graph and related search term popularity
graph database: (United States) GraphQL: (United States) NoSQL: (United States)
22. PwC
Data catalogs
Open data and data catalog development sites
• World Bank
• Data.gov
• Data.gov.uk
• OpenAfrica
• Data.world
Data-centric audit and protection (DCAP)
• Can be heavily vendor-driven (Protegrity and
Informatica lead Gartner’s ranking, e.g.)
• Bespoke methods would be more in line with
data-centric build-vs.-buy principles
Open data initiatives provide examples to follow, but what about data audit?
February 5, 2019Data-Centric Conference
22
23. PwC
Gemini Data vs. Palantir
“Placed in the hands of an analyst, Gemini
[Data] allows them to start with an event,
such as an anomalous router message or a
suspicious email address, and then work out
from there. The product’s GUI guides the
analyst through possible connections to
that particular piece of data, allowing the
analyst to quickly and iteratively explore how
different pieces of data might be connected.”
--Alex Woodie, Datanami
Cybersecurity: Will black box services give way to open graphs that
ordinary analysts can use?
February 5, 2019Data-Centric Conference
23
Cybersecurity at the DNC in 2016
“Take more vulnerable organizations that feel like they don’t
have the resources. A good example from the book, the
Democratic National Committee. OK? So before the election
cycle gets going, they bring in Dick Clarke…he now runs a
cybersecurity firm. They do a quick survey of the DNC’s
computing system and they come back and they basically
say you guys are hopeless. OK? Like, you’re down in
kindergarten levels….he showed them how much it
was going to cost. And they said, great, this is too
much money, we’ll pay for it after the election. OK?
And then the FBI calls and says, by the way, the
Russians are inside your system. Well, I’m sorry. They
called and they asked to be connected to somebody to who
they could tell that to. And they got connected to the
help desk.”
--David Sanger, author of The Perfect Weapon, as quoted on
the Council of Foreign Relations website
24. PwC
Highlighted features of Jules Data-Centric Design
• Applications pull data from Jules and push the processed
data back to Jules
• Defined metadata model, lineage, ontologies, semantics
• Data controls, governance, stewarded centrally
• Data as a platform
Source: “Journey to the Centre of Data,” Giridhar Vugrala, Managing Consultant – Capital Markets,
Wipro, Sept. 13, 2018
Data-centric architecture: Designing an investment bank from
the inside out at Wipro
24
February 5, 2019Data-Centric Conference
25. PwC
At the annual Internet Identity Workshop,
members of the Decentralized Identity Foundation
and the W3C Verifiable Claims Working Group
cobbled together a standards-based self-
sovereign identity (SSI) stack, which included
JSON-LD, as well as two other options.
SSI is currently operational in the Sovrin Network,
a public, permissioned blockchain run by 60
different stewards on six different continents. That
network is largely standards based, particularly via
the W3C and Decentralized Identity Foundation.
Sources: Oliver Terbu, “The Self-sovereign Identity Stack,” Medium post, and the Sovrin website, 2019
Personal data protection:
A preliminary self-sovereign identity stack proposes JSON-LD as a messaging payload format
25
February 5, 2019Data-Centric Conference
26. PwC
Dynamic knowledge graphs: Tracking virus mutations with the
help of graph databases
26
February 5, 2019Data-Centric Conference
Kadir Bölükbasi, “One Graph to Find Them All,”
G Data Security Blog, 8 January 2019
27. PwC
Alexa +
Cortana:
Siri:
Intelligent assistants: Amazon (Alexa) and Microsoft (Cortana)
share resources, while Apple (Siri) contemplates its next move
27
February 5, 2019Data-Centric Conference
“Apple executive Bill Stasior, who has led the Siri team since joining the company in 2012, has
been removed as head of the project in a sweeping strategy shift favoring long-term research
over incremental updates, according to a report on Friday.”
Source: Mikey Campbell, “Apple removes Siri team lead as part of AI strategy shift,”AppleInsider,
Friday, February 01, 2019, 02:47 pm PT (05:47 pm ET)
“With the new Alexa + Cortana world, one could reach across the limitations of each platform
domains and access the power of each platform. This has a synergistic effect where one could at
a future date construct meta Skills/Apps that use features from both platforms
simultaneously…. I see Voice First platforms as a uniform way to access AI-assisted
ontologies, taxonomies, and domains. Apps and Skills can be seen as a domain but also an
extended taxonomy.”
Source: Brian Roemmele, “Why Are Microsoft And Amazon Joining Forces With Cortana And
Alexa?” Quora contributor on Forbes, Sep. 25, 2017
28. PwC
When will voice recognition accuracy reach 98 percent?
What happens when it does?
Should Apple re-hire Tom Gruber?
28
February 5, 2019Data-Centric Conference
31. PwC
With a knowledge graph base, companies can skate to new
business models = Deep transformation
• Once relationship data-enabled, organizations play
different roles than they've been accustomed to in the
digitized ecosystem.
• Some because of their data collection heritage can
become data providers.
• Others take up roles in the data supply chain, or
position themselves as industry platforms or
marketplaces.
• Why are top companies able to cross industry
boundaries?
• Why can unicorns extend the reach of their
business models?
31
February 5, 2019Data-Centric Conference
32. PwC
Largest changes in market cap by global company
Cross industry, 2018
32
Known
knowledge
graph builders
Known KG
builders
Operator of Taobao
and AliBot KG builder
(1)Change in market cap from IPO date
(2)Market cap at IPO date
Source: Bloomberg and PwC analysis
Company name Location Industry
Change in market cap
2009-2018 ($bn) Market cap 2018 ($bn)
1 Apple United States Technology 757 851
2 Amazon.Com United States Consumer Services 670 701
3 Alphabet United States Technology 609 719
4 Microsoft Corp United States Technology 540 703
5 Tencent Holdings China Technology 483 496
6 Facebook United States Technology 383(1) 464
7 Berkshire Hathaway United States Financial 358 492
8 Alibaba China Consumer Services 302(1) 470
9 JPMorgan Chase United States Financials 275 375
10 Bank of America United States Financials 263 307
v
• IBM and Citi are also working on cross-enterprise knowledge graphs
• Many have cross-enterprise knowledge graph ambitions, but most are focused on a single use case
• S&P does cross-enterprise data management using relational tech
February 5, 2019
Data-Centric Conference
33. PwC
Graphs (including hybrids) complete the picture of your
transformed data lifecycle and how it’s managed
33
February 5, 2019Data-Centric Conference
34. PwC
Transformation scalability – The AirBnB knowledge
graph example
“In order to surface relevant context to people, we
need to have some way of representing
relationships between distinct but related entities
(think cities, activities, cuisines, etc.) on Airbnb to
easily access important and relevant information
about them….
These types of information will become
increasingly important as we move towards
becoming an end-to-end travel platform as
opposed to just a place for staying in homes. The
knowledge graph is our solution to this need,
giving us the technical scalability we need to power
all of Airbnb’s verticals and the flexibility to define
abstract relationships.”
--Spencer Chang, AirBnB Engineering
34
Events
Neighborhoods
Tags
Restaurants
Users
Homes
Experiences
Places
Airbnb Engineering, 2018
Markets
February 5, 2019Data-Centric Conference
35. PwC
Most automated knowledge graph – Diffbot?
“Diffbot’s crawler regularly refreshes the DKG with new information and its machine learning algorithms are
smart enough to pass over sites with histories of producing ‘logically inconsistent’ facts.
“‘That’s one of the reasons why we fuse information together from different sources,’ Tung said. ‘Our scale is
such that there’s minimal potential for errors. We’d bet the business on it.’
“Diffbot launched in 2008 and counts 28 employees among its core staff of engineers and data scientists.”
--Mike Tung of Diffbot, quoted in VentureBeat
Diffbot claims an automated knowledge graph of 1 trillion + facts, designed to grow without humans in the
loop.
That compares with 1.6 billion crowdsourced facts in Google’s knowledge graph, according to VentureBeat.
35
Kyle Wiggers, “Diffbot launches AI-powered knowledge graph of 1 trillion facts about people, places, and things,”
VentureBeat, 30 August 2018
February 5, 2019Data-Centric Conference
36. PwC
Versus more explicit, precise, contextualized meaning with a
triadic, Peircean knowledge graph and less than 1M concepts?
“There are many different approaches for distinguishing a logical basis for ontologies, but Peirce basically
says to base everything around 3s, explains [Mike Bergman of Cognonto]. That is,
1.the object itself;
2.what a particular agent perceives about the object;
3.and the way that agent needs to try to communicate what that is.
‘Without that triad it’s hard to ever get at differences of interpretation, context or meaning,’ he says, whether
that be between something like events and activities or individuals and classes.
Once you adopt that mindset, a lot of things that seemingly were irreconcilable differences begin to fall away,
and the categorization of information becomes really very easy and smooth....”
--Mike Bergman of Cognonto, quoted in Dataversity
36
Jennifer Zaino, “Cognonto Takes On Knowledge-Based Artificial Intelligence,” Dataversity, 23 November 2016
February 5, 2019Data-Centric Conference
37. PwC
Contextual AI via a large knowledge graph at Fairhair.ai
37
Media Intelligence Apps
Global Monitoring Analyze & Report
Distribute Influence & Engage
New Apps
Employee
App
Freemium
At-Powered
Reporting
Outside
Insight
Enterprise
Solutions
Custom Solutions
3rd party Apps
PaaS
100M
documents ingested
daily
150 NLP/IR
pipelines
100’s Billions of
Searches
Service Layer
Context Building
Enriching & Analysis
Outside Data
Streaming, Search, Analytics, APIs
Building block to leverage the platform
Knowledge Graph
Enable cognitive applications on top of our data by connecting the dots
Data Enrichment Platform
Enrich, analyze & build by interoperating with all major players
AI-driven data Acquisition
Bring high quality outside to our repository with minimal human effort
February 5, 2019Data-Centric Conference
38. PwC
Montefiore’s semantic data lake
38
HL7
feed
Web
services
EMR LIMS Legacy
OMICs CTMS
Claims
Annotation
engine
HDFS
Hadoop
HDFS
Hadoop
HDFS
Hadoop
HDFS
Hadoop
HDFS
Hadoop
HDFS
Hadoop
HDFS
Hadoop
HDFS
Hadoop
HDFS
Hadoop
HDFS
Hadoop
AllegrographAllegrographAllegrographAllegrograph Allegrograph
SDL loader
ML-LIB/R SPARQL
Prolog
Spark
Java API
Various data sources, some
structured, some not, now all
part of a knowledge graph
with a simple patient care-
centric ontology
Hadoop cluster with high-
performance processors
and memory
Scalable graph database
supporting open W3C
semantic standards
Standard open source
querying,ML and analytics
frameworks,
API accessibility
Doctors can query the graph
or harness ML + analytics and
receive answers from the
system at the point of care via
their handhelds.
The system also acts as a giant
feedback-response or learning
loop which learns
from the data collected via
user/system interactions.
Montefiore Health, Franz, Intel and PwC research, 2017
February 5, 2019Data-Centric Conference
39. PwC
Siemens’ industrial knowledge graph
39
AI Algorithms
1 09:00 – Analyze
Turbine data hub
2 11:00 – Configure
Configure turbine
3 12:00 – Maintain
Master data Mgmt.
4 13:00 – Mitigate
Financial Risk Analysis
5 15:00 – Contact
Expert &
Communities
6 18:00 – Guide
Rules & Regulations
3
4
5
4
2
1
6
Industrial
Knowledge Graph
“Deep learning fails when it comes to context. Knowledge graphs can handle context
and enable us to address things that deep learning cannot address on its own.”
--Michael May, Head of Company Core Technology, Data Analysis and AI, Siemens
February 5, 2019Data-Centric Conference
40. PwC
Pharma knowledge graphs for patient safety
Challenges
40
Solutions
Drug safety
Heightened
focus on safety
Evolving
regulatory
demands
Increasing
public scrutiny
Focus on
analytics
Increased
sharing &
transparency
Doing more
with the same
or less
Graph integration Natural language
processing
Data cleaning
during analysis
In-memory
query engine
PwC and Cambridge Semantics, 2018
February 5, 2019Data-Centric Conference
41. PwC
NuMedii’s precision therapeutics knowledge graph
41
goTerm
Calcium ion
binding
2201
Protein
binding
Extracellular
region
ENSG
00000138829
Extracellular
matrix
disassembly
Extracellular
matrix
Organization
proteinaceous
extracellular
matrix
positive
regulation
of bone
mineralization
Fibrillin - 2
Extracellular
matrix
Structural
constituent
Extracellular
matrix
micro fibril
Camera-type
eye
development
CHEMBL_TC_
10038
Go Function
Reference_
Gosubset
prok
100001650
100001532
100001739
100000687
100002060
Ontotext and NuMedii, 2018
February 5, 2019
Data-Centric Conference
42. PwC
Thomson Reuters’ financial knowledge graph as a service
42
Thomson Reuters, 2018
February 5, 2019
Data-Centric Conference
43. PwC
“MicroStrategy 2019 introduces the industry’s first
Enterprise Semantic Graph.
• It elevates the potential of enterprise data assets, makes
true federated analytics possible, and delivers
personalized insights based on who you are, where you
are, and what you’re doing.
• It delivers powerful search capabilities on top of all
business information systems or data assets, making it
incredibly easy to find insights.
• It categorizes and federates each of your data
investments in real time, constantly enriching the
index with location intelligence and usage telemetry.
• It delivers the underlying strength to fuel AI experiences
for every role—with smart recommendations on
authoring actions for analysts who build dashboards, to
smart suggestions on content for business users who
are looking for new insights.”
--Vijay Anand,
Microstrategy blog, January 15, 2019
Data-centric, or product-centric?
43
February 5, 2019Data-Centric Conference
45. PwC
The problem: System-level complexity and disconnectedness
(product- and app-centric sprawl)
45
Hardware
DBMS
OS
Custom code
Hardware
Lots of OSes
1,000+
SQL/NoSQL DBs
Custom code
ERP+ suites
Hardware
A few more OSes
More
DBMSes
Custom code
ERP+ suites
Hardware
Lots more OSes
5,000+ databases
Componentized
suites
Custom code
Cloud layer
Hardware
More types of OSes
10,000+ DBs +
blockchains
Multicloud layer
Suites as services
Various SaaSes
Custom code
Hardware
A few
DBMSes
A few OSes
ERP+ suites
Custom code
Threat of more
application centric
sprawl
Early1990s Late 1990s 2000s 2010s1973-1990sPre 1970 2020s
February 5, 2019
Data-Centric Conference
46. PwC
The key opportunity: Large-scale integration and model-driven AI
Rule-based systems (includes KR)
“Handcrafted knowledge” is the term DARPA
uses; rule-based programming + procedure
replication in process automation, + some
knowledge representation (KR)
• Strong on logical reasoning in specific
concrete contexts
- Procedural + declarative programming +
set theory, etc.
- Deterministic
• Can’t learn or abstract
• Still exceptionally common and useful
Statistical machine learning
• Probabilistic
• From Bayesian algorithms to neural nets
(yes, deep learning also)
• Strong on perceiving and learning
(classifying, predicting)
• Weak on abstracting and reasoning
• Quite powerful in the aggregate but
individually (instance by instance)
unreliable
• Can require lots of data
Contextualized, model-driven approach
• Contextualized modeling approach—
allows efficiency, precision and certainty
• Combines power of deterministic,
probabilistic and description logic
• Allows explanations to be added to
decisions
• Accelerates the training process with the
help of specific, contextual human input
• Takes less data
Example: Consumer tax software
Perceiving
Learning
Abstracting
Reasoning
Perceiving
Learning
Abstracting
Reasoning
Perceiving
Learning
Abstracting
Reasoning
Example: Facial recognition
using deep learning/neural nets
Example: Explains first how handwritten
letters are formed so machines can decide—
less data needed, more transparency.
John Launchbury of DARPA (https://www.youtube.com/watch?v=N2L8AqkEDLs), Estes Park Group and PwC research, 2017
Previously dominant On the rise and rapidly improving Nascent, just beginning
1
Data-Centric Conference
February 5, 2019
46
47. PwC
The key means: The right level of relationship richness
47
Use tables, document trees and
graphs.
• Graphs articulate relationship-rich data
• Tables: Relationships are what’s missing from
most large-scale data, but table are too useful
andhuman-friendly to ignore at smaller scale
• Document trees (e.g., taxonomies) are a
stepping stone to graph models
• Graphs are the parents that bring the data
model family all together, Tinker Toy-style
• Bottom line: A machine readable,
extensible model of your organization
• Build and maintain your advanced
analytics/AI foundation with that graph model
Data-Centric Conference
February 5, 2019
48. PwC
How to get started: Data-centric strategy, planning, architecture and
execution
February 5, 2019Data-Centric Conference
48
• In the data-centric view, every IT category is subordinated to centrally managed, model-driven data via data
strategy, GRC and data-centric architecture (DCA)
• Relationship-rich modeling leads development for reasons of efficiency and effectiveness
• Standards based, open source enabled, build versus buy
• Empowers user communities, activism, large scale collaboration, shared infrastructure
Goals for data to
be obtained,
enriched and used
Data strategy
Data governance,
risk and
compliance
Data-centric
architecture (DCA)
Strategy and planning
Execution
Data-centric infrastructure
Data and logic lifecycle management
Model-driven development
Cross-enterprise intelligence
Relationship-rich modeling
Data-centric security
Process, pipeline and delivery automation
Human and machine learning loops
Data-centric design thinking