Modern Information Architect Skillset

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Academy of Technology Technical Whitepaper November 2020

Cross Industry

Skillset required to support new and emerging technologies and available resources

Internet of Things Blockchain

Cloud

Artificial Intelligence

Cognitive Enterprise

Data Governance

Abstract

This whitepaper is the outcome of the initiative Skill Set Recommendations: Information Architects Need to Support Emerging Technologies kicked off in April, 2019 by the Worldwide Community of Information Architects, an affiliate of IBM Academy of Technology (At). The initiative champion is Mandy Chessell, IBM Distinguished Engineer, Master Inventor, ODPi Egeria Lead, Open Metadata and Governance, and initiative Leader is Debbie Botha and co-leader is Sushma Singhal. In this era of digital transformation, more and more projects are incorporating new and emerging technologies. The Information Architecture remains either an important and major component or even serves as a foundation of these new technologies. The focus of this initiative is the new and emerging technologies to identify critical Information Architecture skills required to support projects using new and emerging technologies and how Information Architects can retool and update their skills enabling them to support projects using following technologies: Data Governance & Data Management, Artificial Intelligence, Hybrid & Multi Authors and Acknowledgement cloud, Blockchain, and Internet of Things (IoT). Initiative Champion: Mandy Chessell This whitepaper explores the trends in emerging Initiative Lead: Debbie Botha technologies and their impact on Information Initiative Co-Lead: Sushma Singhal Architecture, provides recommendations for keeping the skills current (how), and resources available Authors & Participants: Ahmad Gohar, Ahmed El- internal to IBM and externally for Information Shafie, Brunello Bonanni, Debbie Botha, Geert- Architects. Willem Haasjes, Louis Roehrs, Mohammed Aq Moini, Pat G O'Sullivan, Sonia Mezzetta, Sukumar Beri, Sushma Singhal Published Date: November 2020

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Table of Contents 1 THE COGNITIVE ENTERPRISE ...... 4

1.1 OTHER FUTURE TECHNOLOGIES ...... 5 1.2 REFERENCES ...... 5 2 DATA GOVERNANCE AND INFORMATION ARCHITECT SKILLSET...... 6

2.1 TRENDS ...... 6 2.2 IMPACT ...... 6 2.3 POTENTIAL WAY FORWARD ...... 7 2.4 SOLUTIONS - LEARNING RESOURCES ...... 8 2.4.1 IBM Internal Training Courses ...... 8 2.4.2 EXTERNAL SOURCES...... 9 3 HYBRID & MULTI CLOUD AND INFORMATION ARCHITECT SKILLSET ...... 10

3.1 TRENDS...... 10 3.2 IMPACTS ...... 10 3.3 POTENTIAL WAY FORWARD ...... 11 3.4 SOLUTIONS: ...... 11 3.4.1 IBM Internal Training Courses ...... 11 3.4.2 External Sources ...... 12 4 ARTIFICIAL INTELLIGENCE AND INFORMATION ARCHITECT SKILLSET ...... 13

4.1 TRENDS ...... 13 4.2 IMPACT ...... 13 4.3 POTENTIAL WAY FORWARD: ...... 14 4.4 SOLUTIONS: ...... 14 5 BLOCKCHAIN AND INFORMATION ARCHITECT SKILLSET ...... 15

5.1 TRENDS ...... 15 5.2 IMPACT ...... 15 5.3 POTENTIAL WAY FORWARD ...... 16 5.3.1 External Sources ...... 16 6 INTERNET OF THINGS (IOT) AND INFORMATION ARCHITECT SKILLSET ...... 17

6.1 TRENDS ...... 17 6.2 IMPACT ...... 17 6.3 POTENTIAL WAY FORWARD ...... 18 6.4 SOLUTIONS - LEARNING RESOURCES ...... 19 6.4.1 IBM Training Courses ...... 19 6.4.2 References ...... 19 7 THE INFORMATION ARCHITECTURE SKILLS CITY METRO MAP ...... 20 8 SUMMARY & CONCLUSION ...... 22

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1 The Cognitive Enterprise The Cognitive Enterprise, using next- generation applications that are fueled by data, A new era of business reinvention is dawning. empowered by exponential technologies, Organizations are facing an unprecedented activated by cognitive-enabled enterprise convergence of technological, social and regulatory workflows, and powered by an ecosystem of forces. As artificial intelligence (AI), automation, Internet of Things (IoT), blockchain and 5G business platforms makes possible the culture become pervasive, their combined impact will of agile innovation that will drive the next reshape standard business architectures. The digital transformation era. “outside-in” digital transformation was the trend of This generational shift will take the digital wave of the past decade. Nowadays the focus is on the businesses and governments and surf it to the next “inside-out” potential of data exploited with these level, and transform the way that employees add exponential technologies. value and sustain their differentiation. The This is about letting data drive changes to workflow Cognitive Enterprise will leverage proprietary data, and is going to need a business platform to connect unique platforms and specialized expertise to these two together. It will be fueled by data. It will achieve its goals. But what will the journey look like have AI infused in all its different workflows, and and what are the factors for success? people will feel that technology really empowers Data – automated, powered by artificial them in approaching this next-generation business intelligence (AI) and machine learning shared by model. 5G channels – augments the capabilities of every We call this next-generation business model the enterprise employees in a deepener customers’ Cognitive Enterprise. engagement. Expertise, together with the capacity to learn quickly, sustains the rapid lifecycle of innovation and iteration on business platforms. New workflows, leveraging exponential technologies, extend the organization’s capacity to create new value coming out from Internal and external Data. Once organizations have clearly identified their next core business leveraging on exponential technologies, they need to rethink what they do and how they do it, looking well beyond current market dynamics by leveraging information

Figure 1 - Capability layers for the Cognitive Enterprise insights. Effective business transformation integrates

exponential technologies with pools of expertise and proprietary data to serve customers better and drive new value. The Information Architects need to align their competencies to modern skills to be those experts which are the driving force behind the transformation.

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• Be an Agile leader and champion of the team. • Drive solution implementation from analysis to design through all delivery phases. • Expertise in supporting the delivery of complex systems, integration projects involving multiple platforms, applications and environments among Internal and External data. • Expertise in Business Information Architecture, specifically unstructured data, workflow, customer experience and human engagement patterns. Figure 2 - exponential technologies and data integration • Comfortable in both well-defined and ambiguous To ensure success in the Information Architect Role solution situations especially those that belongs it is fundamental to acquire competences in the to unstructured data contents in a daily changing world. following modern skills (those related to the • Expertise in Garage Architectures design and exponential technologies that will be detailed later methods to involve the customers in a co-creation in this paper). for a Cognitive Enterprise handover for the future. • Be an Agile advocate by having a strong

understanding of Agile processes and procedures. 1.1 Other Future Technologies emerging into the mainstream or some more time is needed to determine the precise way in which In addition to the specific technologies covered they will impact the general Information in this document, there are also several other Architecture ecosystem. Such likely future technologies that are likely to also become technologies may include1: Quantum Computing, relevant in the future to Information Architects. Virtual/Augmented Reality, Smart Spaces, Edge However, they have not been included in this Computing. document as they are either not yet seen as 1.2 References Contents and figures extracted from the following documents: 1. Cognitive Catalyst – (Document: gbe03877usen-03_GBE03877USEN.pdf) – By IBM 2. Cognitive Enterprise – (Document: 23025023usen-00_23025023USEN.pdf) – By IBM 3. Drawing the Cognitive Enterprise Blueprint - THINK Blog.pdf – By IBM 4. IBM-Cognitive-Enterprise-Blueprint-2017.pdf – By IBM 5. The Cognitive Enterprise: Reinventing your Company with AI – (Document: 26022826usen- 02_26022826USEN.pdf) – By IBM 6. The Cognitive Enterprise Finance – (Document: gbe03910usen-05_GBE03910USEN.pdf) – By IBM

1 https://workplaceinsight.net/smart-spaces-and-the- other-top-technology-trends-for-2019/

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2 Data Governance and Information Architect Skillset Emerging technologies such as Artificial The greatest strengths of metadata are Intelligence and Machine Learning (ML), accountability, auditable compliance and making Blockchain, IoT, and Cloud present enormous data discoverable. Semantic metadata and opportunities for innovation. But to maximize the ontological models take metadata to the next level potential of these technologies, businesses must by providing better means for representing data radically shift their approach to Data Governance relationships including many to many, inferencing and understand how an information architect new data facts and realizing automation. provides value to a business using their data The sharing and exchanging of metadata must management skills and how they interact with have one consistent view for the Enterprise. A other roles within an organization. recent spark in this space is the ODPi Egeria, an “You can have all the right people, like plumbers open source metadata framework that and electricians, but no one builds a house automatically exchanges and federates metadata without an architect.” across data platforms and vendor catalog tools. -Mandy Chessell, IBM Distinguished While a strong metadata strategy is extremely Engineer, Master Inventor, ODPi Egeria valuable to understand, locate, and know how to Lead, Open Metadata and Governance use data, Data Quality issues can minimize its The recurrent theme in Data Governance in value. The same applies to AI. AI generated using recent conferences has been the role of bad data will not yield accurate trustworthy metadata in DG. The future of Data Governance, results. Extensive Data Cleansing would be according to Metadata and Machine Learning in required. Having data standards, data analysis Data Governance, is a centralized Data Strategy processes, and metrics that address Data Quality to promote democratic decision-making across set the path for “trusted” data. the enterprise. Gartner believes that by 2020, at Due to the increased interest in data, more and least 50 percent of Data Governance policies will more organizations are looking to become data- be driven by metadata. driven, and with this comes a renewed interest in 2.1 Trends Data Management. As a result, Information Architects are increasingly playing a big role in A data catalog consisting of business terms and providing value to a business using their data definitions, along with data lineage, are critical management skills. metadata a business needs find/use data to derive meaningful business insights. "Any 2.2 Impact governance initiative needs to start with the Artificial Intelligence (AI) and Machine authoritative definition of business terminology. Learning (ML) –Top management are pressuring If business terminology is not well defined, data their organizations to adopt advanced AI-enabled cannot get properly linked to enterprise policies, solutions, so Data Management challenges business questions, and the initiative will fail..." - continue to mount on technology teams. The Albert Maier, IBM Senior Technical Staff Member, technology decision-makers are increasingly Chief Architect Information Governance & Data realizing that without a firm Data Strategy Quality. provisioning enterprise-wide supply of data on demand, machine learning and AI adoption will

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remain just a buzz. For most businesses today, Internet of Things (IoT) - Proper data this means a Data Governance overhaul. management in IoT is challenging due to the According to How Can Machine Learning Affect massive amounts of data collected from web- Your Organizational Data Strategy, machine enabled devices. Business metadata learning (ML) solutions can deliver “the intended classification is necessary to know how and when business outcomes” only when the organizational to act on the data. Data quality is also important. Data Management landscape, at the core of Actions taken based on the data collected which is the Data Governance, is solid and assumes its high quality since gathering incorrect transparent. data is a wasted effort. Security breaches are a significant problem in this space and the A recent article on governance and machine collection of personal data must follow data learning aptly describes how the varied data regulations to avoid hefty fines (i.e. GDPR). This sources and data types have added to the data poses a strong emphasis on securing data mess already created by massive volumes. The properly. article claims that in today’s competitive business world, a strong Data Governance will Blockchain - The promise of incorporating help “strike a balance between Data Governance Blockchain in the enterprise includes better trust, and the ML capabilities.” traceability, and transparency while providing a single system of records and getting real-time The advanced AI system providers seem to think insights from all transactions. As a single source that only ML-powered solutions will ultimately of data that can be shared and replicated across satisfy both the regulatory and compliance many participants, and the data cannot be requirements. Currently, the lack of consistency altered or modified, Blockchain provides unique in data definition and quality (Metadata) is a Data Management and Data Governance in the serious deterrent to business operations across areas of security and privacy that are much the enterprise. ML can help solve regulatory and needed in the enterprise. compliance issues, specifically those related to Data Governance and data security and privacy, 2.3 Potential Way Forward faced by different divisions within an enterprise. The emerging technologies provide Information Cloud - More and more data are being collected Architects new challenges, exciting new job everywhere, both on premises and in a multi- openings, opportunities to enhance and learn cloud environment. Data security is key in the new skills, and a chance to apply these skills to a handling of sensitive data and differs based on a wide variety of new jobs: private cloud, public cloud or even on-premise. Organizations may be willing to store sensitive 1. Data Management Strategy – Information data on a private cloud but not on a public cloud Architects need a good understanding of environment. This is where a strong metadata how to develop, apply and execute data program that handles the pre-work of classifying management strategy policies in sensitive data to facilitate taking the appropriate organizations using AI/ML, IoT, and/or measures from an architecture perspective such Blockchain projects with high volume data as masking of data, proper access control or generation. preventing the downloading of data locally. 2. Data Governance – Hybrid Cloud, ML, and Blockchain projects require a deep understanding of how to prepare

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organizations, develop and apply Data battles over who controls the data, Governance (including data quality and monetizing data, and more. metadata) and Data Security to large 2. Big Data, Data Lake and Data Governance - volumes of data. A Big Data environment is characterized by 3. New Data Storage Systems – Traditional the storage of large datasets on a relational databases, Big Data storage distributed environment systems, Real-time Analytics systems, 3. Read what analysts are saying about data distributed computing and processing governance systems, and Business Intelligence 4. Read what blog authors are saying about platforms will need to work in harmony with data governance enterprise Blockchain to extend 5. Common information model for agile - Five applications and manage Blockchain- senior IBM architects show you how to use related data. information-centric views to give data a 4. Database as a Service – DBaaS is a cloud central role in project design and delivery computing service that provides database 6. IA for Data Analysis - describes how the management resources over the Internet. various Information Analyzer functions can DBaaS infuses a breath of flexibility into be used for deep Data Analysis. database management and cloud migration, 7. Data Governance Scenario and Opportunity and offers Information Architects another Development- No AI without IA. The core of scope to challenge and enhance their skills Information Architecture is data by learning about cloud computing, cloud governance. Enterprise data catalog migration, DBaaS, and database migration management, Enterprise data quality to the cloud. management, metadata management are 5. Business Analytics – To ensure that data is all the important part of IA. of the highest quality to work in automated 8. IBM Watson Knowledge Catalog Essentials analytics and BI environments, future data -IBM Watson Knowledge Catalog is a strategists will have to align Data secure enterprise catalog to index, classify, Management goals with advanced and govern your data with greater technology (machine learning) goals and efficiency. Manage your data from every practices. angle, create models, uncover insights, and 2.4 Solutions - Learning Resources enable your teams to collaborate more effectively. The following paragraphs list a few of the 9. Cloud Pak for Data - As our clients embrace education resources for Data Governance IBM's hybrid multiload strategy, our data specialization. integration and management capabilities 2.4.1 IBM Internal Training Courses will be a major differentiator from competition. 1. Data Governance - In this video, data management expert John Adler leads you through the maze of data governance issues facing companies’ todays security breaches, regulatory agencies, in-house turf

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2.4.2 External Sources 5. https://www.linkedin.com/pulse/blockchain- disrupting-data-integration-integrity-stewart- 1. Data Architecture with Data Governance: A bond/ Proactive Approach By Amber Lee Dennis on 6. https://www.dataversity.net/data- January 15, 2019 management-internet-things/ 2. https://opengovernance.odpi.org/ 7. https://securityintelligence.com/preparing- 3. https://ico.org.uk/ next-era-computing-quantum-safe- 4. https://www.cnil.fr/en/home cryptography/

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3 Hybrid & Multi Cloud and Information Architect Skillset 3.1 Trends restructuring of data so that those network charges negate any cost savings from The rise of Cloud moving to the Cloud. Any transformation Platforms changed requires a review of the data management how IT designs and operating model. Many organizations will provisions also move the responsibilities of data computing provisioning to the cloud vendors, removing infrastructure and the need for maintaining an ETL capability. how developers approach projects, architect 2. Multi-cloud Environment Design software and collaborate. It is a core practice nowadays to deliver data solutions that have Data Design needs to include cross-cloud their data on a combination of On-Premises, data policies for security, compliance and Private, Public or even Multi-Cloud platforms. data lifecycle management across multiple Over the last decade, many cloud providers cloud providers. It is important that those exponentially scaled their business, providing data design factors are integrated into a set easier access to their Cloud Platforms (the most of managed and orchestrated data popular are Amazon AWS, Microsoft Azure, workflows that can span across multiple Google Cloud, and IBM Cloud) opening huge cloud providers. shadow IT in many enterprises. 3. New Data Storage techniques Information Architects need to modernize their The data storage techniques require cloud skills and align their knowledge and skills, such as knowledge of cloud provider- methodologies to the Cloud Platform paradigms specific APIs, in order to import data into and approaches, to create solutions and deliver the cloud and be able to manipulate data. and support successful cloud implementation • Data Integration – requires design and projects. data integration to utilize this separation of compute engines in the 3.2 Impacts cloud. There are two implementation The main impacts of the recent trends on mechanisms: (1) By using containers Information Architecture are: to provide on-demand computing 1. A move to the Cloud (especially Multi-Cloud) power, lighter weight processes is a time to Transform running on smaller, lower powered Many organizations are planning to simply virtual machines, and (2) By stand- lift and shift analytic workloads to the Cloud alone code modules deployed as (and multi-cloud) with a thought of saving virtualized compute engines or Code- money. While cloud may provide cheaper as-a-Service (CaaS) as, for example, storage, organizations do not consider Amazon's AWS Lambda service. vendors charging for data transfers and • New ETL developments – requires the network usage at a much higher rate than use of cloud core principles for their internal ones. Transforming the data as-a-service offerings, such as Google environment may only be a technical Dataflow.

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4. Data Access provides design patterns around Multi- Data Access requires the adoption of various cloud design cloud application programming interfaces 2. Data Storage (structure and organization) – (APIs) which promise to connect end-users to the new data structures and storage designs; databases regardless of the underlying Data Modelling developing new data stores. infrastructure. This brings up terms like DaaS 3. Data Integration – the Data Integration tooling (Data-as-a-Service) which offer to provide on the cloud and bringing the benefits of data to the end user regardless of separation of storage and compute to their geographies or the enterprises in which it design. resides. 4. Data Access – API-based design needs 5. A DataOps approach to data processing and relevant skills in the REST/ SOA-based analytics services and an understanding of newer The data management is targeted now as design. These should then be managed in DBaaS (Database-as-a-Service) which parallel with the cloud base data access removes some classic “levers” Database patterns. Administrators had in the past to modify or 5. Data Management and Administration change the data access paths according to (DataOps) –The Data Management and specific applications needs. With cloud Administration of new kinds of repository that platforms adoption, the skills expanded on can be accomplished once they have been the query-tuning and data-modeling, as well implemented and started it up. as improving the experiences in network There is no one tailored learning module for a dependencies and security implications “Cloud Information Architect” which teaches required by cloud DBaaS. Information Architecture on Cloud, at the time of The main idea is to integrate DataOps with writing this white paper. The Information data governance processes to ensure that: Architects must learn the Cloud Exponential • Data and data science experiments are technologies by themselves, focused on trusted and can leverage the latest Information Architecture aspects (such as Data advance in hardware (such as GPU and Integration, Data Modeling, Data Governance) to TPU) in a transparent way enlarge their skill and knowledge “properly and effectively”. • Analytics processes can be modeled as multi-step pipelines. 3.4 Solutions: • Data scientists can focus on their core The following paragraphs list a few of the skills and less on new frameworks such education resources for Cloud specialization. as Kubernetes. 3.4.1 IBM Internal Training Courses 3.3 Potential Way Forward 1. Multi Cloud Management Platform (MCMP) The main trends which Information Architects 2. IBM Cloud Pak for Data – IBM Cloud Pak for should focus on are: Data is a Modularized platform, that provides 1. Multi-cloud Design – Link to the source a portfolio of opensource and IBM products. article written by Tony Giordano, Global The advantage of IBM Cloud Pak for Data is Leader Data Platform Services in IBM

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they build once run anywhere on top of 7. Extract knowledge and insights from your data OpenShift, or IBM Cloud private. with Azure Databricks 3. Multi-cloud Design provides design patterns 8. Implement a Data Streaming Solution with around Multi-cloud design. Azure Streaming Analytics IBM Cloud Platform 9. Microsoft Azure Courses 1. IBM Cloud Solutions Architect Certification Google Cloud Platform 2. IBM Cloud Essentials 1. Google Cloud Platform Fundamentals 3. IBM Data Science Professional Certificate 2. Data Engineering on Google Cloud Platform 4. Advanced Data Science with IBM Specialization Specialization 3. From Data to Insights with Google Cloud 5. NoSQL and DBaaS 101 Platform Specialization 6. Spark Fundamentals I 4. Set of Articles and Architectures patterns 7. Spark Fundamentals II 5. Google Cloud Courses on Coursera 3.4.2 External Sources Amazon Cloud Services (AWS) 1. Amazon Web Services (AWS) Cloud Practitioner Microsoft Azure Cloud Essentials 1. Azure fundamentals 2. Architecting on AWS 2. Azure Data Architecture Guide 3. Big Data Technology Fundamentals Online 3. Azure for the Data Engineer 4. Data Analytics Fundamentals 4. Work with NoSQL data in Azure Cosmos DB 5. Amazon Training Portal 5. Implement a Data Warehouse with Azure SQL 6. Exam Readiness: AWS Certified Big Data – Data Warehouse Specialty 6. Large Scale Data Processing with Azure Data

Lake

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4 Artificial Intelligence and Information Architect Skillset Artificial Intelligence has a range of definitions and 4.1 Trends associated articles. A good starting point is an article in Forbes by Technology strategist, Bernard In general, there are three main areas where AI is Marr. likely to appear in a data architecture landscape. AI is a very important emerging technology for • Automation of existing BI/IA processes. This is Information Architects to dive into. AI will create the gradual incorporation of AI capabilities into over $2T of business value in 2021, according to portions of the BI or Data Governance Gartner. Ginni Rometty has said: “AI will affect environment. This is most effective where these new AI components carry out laborious and error- 100% of jobs." prone human tasks. Such examples are: An Information Architect focused on Data can o Machine Learning (ML) and Deep Learning consider AI as the following: to automate data discovery, data Artificial intelligence is the general concept that classification and assignment of incoming machines can be “taught” to mimic human data. decision-making and learning behaviors. o Use of semantic technologies to enable a "There is no scalable Artificial Intelligence without more adaptive and reactive Data Catalog. solid Information Architecture" o The use of a combination of AI technologies While there are many potential sub-disciplines of to accelerate data mapping. AI in general, those areas that are of relevance to o Over time AI can also be used to enhance an Information Architect are : the auto-generation of some data lake Natural Language Processing3 - in general, suitable artefacts (e.g. Data Marts, APIs, etc.). for processing (spoken or written) natural language • NLP and Search: AI has transformed the search artefacts, usually supported by virtual assistance. and navigation capabilities intended for end Machine Learning4 - is a subfield of AI that has its users. NLP and associated technologies roots in statistics and mathematical optimization. empower the Data Lake access with more Machine learning covers techniques in supervised natural/human oriented languages. AI is also and unsupervised learning for applications in used to provide users with any relevant prediction, analytics, and data mining. personalized responses by adopting machine Deep Learning5 - Deep Learning is a subfield of learning techniques based on their previous machine learning concerned with algorithms behavior and choices, as well as assisting them inspired by the structure and function of the brain in doing self-service activities. called artificial neural networks. • Enhanced range of Analytics: One of the initial uses of AI was in capabilities used by Data Scientists in areas such as predictive and classification modelling, segmentation etc. 4.2 Impact There already is a significant impact of AI on the work expected to be done by Information

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Architects. AI is increasingly seen as a key • Know what Data Management technologies now underpinning of most Information Architect include AI-infused components and their components, with most technologies having some impact in terms of deployment timescales, imbedded use of AI, be it Data Quality Tools, accuracy requirements and pre-reqs. Machine Learning Data Catalogs or AI-enhanced • In some cases, it may be beneficial for end user tools. This is driving a range of new use Information Architects to get hands-on cases that the business can now exploit, such as experience with AI/ML related tooling, e.g. Customer Sentiment Analysis, Next Best action python scripts, ML models, etc. and Client Experience Personalization, as well as radically enhancing the effectiveness of existing 4.4 Solutions: use cases such as Customer Churn, Customer To get up to speed on AI for Information Architects: Segmentation, Predictive maintenance, Credit Risk • Education Courses – There is a range of AI and and Fraud. Machine Learning courses available both A key impact on data architecture is the need to internally (via Your Learning) and externally via ensure that collected information quality is vendors like Coursera. appropriated with the required accuracy to reflect o For example, Coursera has a popular ML the underlying truth that is being sought and avoid online course from Stanford University underpinning bias. There is also the impact of how o It is also advisable to get getting an to incorporate new Data Science functions in a way understanding of Python and the associated that works with the broader data management ecosystem production activities. • External Blogs and Articles – Another consideration are the cultural or o IBM Big Data Hub Blog : How to Scale the organizational impacts. The push towards Digital AI ladder: Watch these enterprises Transformation of the overall business means that o External article - What is deep learning. A any upgrading of the data architecture to simple guide with 8 practical examples incorporate AI must also be aware of the demands of these systems and not just focus on the Systems o Data Elixir – a curated weekly Data Science of Insight. Enabling such a pervasive deployment newsletter of AI and associated Data foundations will almost o Dataversity Blog on Data Architecture and always require a high degree of executive/C-suite Artificial Intelligence, which includes useful focus, most usually in the form of a Chief Data links to other blogs and articles with more Officer. detail. o TDWI Checklist Report - AI for BI: Six 4.3 Potential way forward: Strategies for Augmenting Business Having a broad understanding of the role of AI in Intelligence with AI and Machine Learning IA is no longer optional for Information Architects. o Analyst Reports such as Forrester Machine Depending on their specialization and depending Learning Data Catalogs on client needs, an Information Architect may be expected to: • Have an understanding of the different ML techniques being used by Data Scientists

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5 Blockchain and Information Architect Skillset According to IBM, a definition of blockchain is: addressed and Blockchain becomes more of a

A shared, immutable ledger that facilitates the mainstream technology. process of recording transactions and tracking 5.2 Impact assets in a business network. An asset can be tangible or intangible. Currently many organizations have yet to start to Blockchain evolved as the foundation of the Bitcoin adopt Blockchain. However, there is likely to be a crypto. However, the Blockchain shared ledger growth in the exploitation of Blockchain in general technology is separate and separable from this as a means of the overall modernization of the example – it is applicable to a whole range of operations of the enterprise as IBM and other business challenges that cross all industries. organizations provide Blockchain solutions and technologies. Blockchain technology consists of two key components: From the perspective of an Information Architect, one key aspect of the blockchain ecosystem that • smart contract, which is a piece of code that may be pertinent is in terms of the storage options encapsulates business terms executed within for Blockchain solutions, IBM Blockchain Storage transactions, and for Cloud Private being an example of this type of • shared ledger, which is basically a distributed solution. database Another area that is still emerging but may be Blockchain considerations regarding Trends, important the intersection of Blockchain and Impact, Potential and Solutions are listed below. Analytics. 5.1 Trends • Blockchain and Analytics: Growth in the analysis of patterns about the use of The overall growth of Blockchain as a mainstream blockchain by organizations but also in how technology is expected to continue to increase Blockchain can combine with AI to over the next 4-5 years with an estimated revolutionize Analytics. worldwide market value of 12.4 BUSD by 2022. • Blockchain and Data Science: There are Focus areas are in Banking/Finance (Cross border potential areas where Blockchain can be payments and settlements), Manufacturing ( Lot used to enhance areas such as predictive lineage/provenance) , Distribution (equipment and analytics. service/parts, management) and Retail, Infrastructure and Public Sector. • Social Data: There is a sense that there may be an opportunity around the Other likely trends may be the use of Blockchain to intersection of the areas of social media secure data and devices related IoT, with and Blockchain. Specifically, with Blockchains being used to log and monitor IoT Blockchain being used to solve two major communications and transactions. However, we problems with current centralized Social are also likely to see an overall maturing of the Media applications: transparency and overall Blockchain space, as the various security. technology and legal challenges are gradually

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• Data Monetization: Where organizations, o IBM Blockchain Solutions individuals, and even devices can share/sell/trade/barter their data and analytic insights directly with others. 5.3 Potential way forward Having a broad understanding of the role of Blockchain for Information Architecture is still an evolving science. However, depending on their specialization and depending on their client needs, an Information Architect may be expected to: • Have an understanding of how Blockchain in general may disrupt/enhance the client’s overall business • Know the Data and Data Management technologies that are likely to play a part in the Blockchain ecosystem • In some cases, it may be beneficial for Information Architects to get hands-on experience with some of the relevant Blockchain technologies 5.3.1 External Sources • IBM External-facing presentations and publications: o IBM Redbook - IBM Storage Solutions for Blockchain Platform Version 1.2 o IBM Big Data Hub article - Preparing data management for blockchain o Range of presentations and videos from IBM Fellow on Blockchain o IBM Institute of Business Value and Blockchain – a wealth of excellent articles and presentations on many aspects of Blockchain • IBM Blockchain Products o IBM Blockchain Platform for IBM Cloud Private delivers the components that you need to run a blockchain network on your own infrastructure through IBM Cloud Private

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6 Internet of Things (IoT) and Information Architect Skillset Given the massive number of gadgets sending use worldwide currently, up 31 percent from data across the networks through the Internet 2016, and will reach 20.4 billion by 2020.” and into the Cloud, someone needs to make (February 7, 2017). sense of this information and use it to benefit the Digital twin technology has moved beyond business. manufacturing and into the merging worlds of the The IoT Internet of Things, artificial intelligence and data Architect analytics. leads the way through 6.1 Trends “the vision, IDC predicts that the worldwide Internet of strategy, Things Ecosystem and Trends market will architecture continue to grow, with an expected market size of and $1.1 trillion in 2023. Despite the growth, the IoT shepherding of IoT solutions from inception to ecosystem is a complex market, with multiple deployment.” Information Architects are playing a layers and hundreds of players including device significant role in the area of architecture, vendors, communications service providers, IoT management and governance of data and platform and analytics vendors and IT services information in the IoT space. providers. IoT is also witnessing the collision of Andrew Sohn, in his blog on how new IoT data is operations technology and information driving your day-to-day, cites that IoT and other technology groups within enterprises. sources will generate about 44 Trillion gigabytes Within two years, IoT will be the single greatest annually. Some of this data needs to be dealt source of data on the planet, generated by with in real time. Not only will the Information billions of interconnected sensors and devices Architect need to rein in this information, embedded into the world’s physical systems. people’s lives will depend on them. Additionally, the greatest need at most times is to move data 6.2 Impact from sensor to application via gateways and Data from sensors can provide valuable insights. networks through various protocols in an efficient To use the data, it must be transmitted, stored, way. The challenge is to find the right mix analyzed, and presented in a useful way. The between the type of network, protocol and data Watson IoT platform provides a common standard (aka payload) depending on the IoT Scenario. language that devices can use to communicate The Internet of Things (IoT) is the network of with the platform over the internet. After the data physical objects that contain embedded is received, analytics can be applied and made technology to communicate and sense or interact available to applications that meet industry- with their internal states or the external specific needs. environment. The sensors gather data that IoT creates new opportunities for innovation. reflects the human activities that are related to Using the insight from IoT data represents a big the devices and how the devices work. Sensors opportunity for all types of businesses. are embedded in things everywhere. According to Businesses utilizing IoT will be able to improve Gartner.com, “8.4 billion connected things are in

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engagement by providing a rich programming means understanding Data Quality, Data platform and exploring new business models with Governance, and Metadata standards. new revenue opportunities. Keeping up to date on the latest IoT Data Build and deploy secure IoT solutions – With the Management Practices means joining an constant news of data breaches, ransomware association and sharing knowledge with other attacks, and other harmful situations involving Information Architects. Interacting with other IoT devices, IoT security provides a good Data professionals, through DAMA and the example of why keeping up with Data Governance trade association, the Internet of Things and the IoT is important. Consortium (IoTC). The IoT platform can be used for many purposes: b. Data Analysis – Information Architects can apply Data Analytics skills to analyze the • Connect and manage devices, networks, incoming data in real time on IoT platform, and gateways test it against the expected results, and • Integrate structured and unstructured trigger an alarm action if the sensor is out of information from devices, people, the range. Because the alerts are automated, weather, and the world people can act quickly to fix the problem. The • Gain insights from information by using alerts can also be shown on a sensor real-time streaming, predictive, edge, and dashboard to review and track all the sensor cognitive analytics alarms from one place. • Visualize and manage your IoT landscape c. Data Tracking – The Information Architects end-to-end, manage risk, and gain trusted can learn data tracking instrumentation to sources of IoT data with innovative support: technology such as blockchain i. Tracking / Sensing in a large area with • Automate smart processes using strength long distance like a city or farm in cognitive, analytics, security, and cloud to ii. Industries Machine/Robot/Telemetry catalyze and monetize the transformation Data of global technology iii. Edge Scenarios / High Data volume Vision 6.3 Potential Way Forward iv. Home automation / ZigBee / blew To acquire skills for supporting IoT information d. Communication and Leadership Skills - An architecture requires several strategies: Information Architect must have great 1. Learn good IoT Data Management communication and leadership skills to practices socialize well with a variety of people. Information Architects help the organization 2. Become an expert in differing solve various business problems by building technologies. discrete IoT solutions. This requires a certain a. Data Management Practices - An skill set, including collaborating with business Information Architect needs to engage in leaders to determine their top business continuous learning about IoT businesses and problems. its Data Governance. Not only is this training e. Expert in Technologies - Information necessary to gain new businesses but also to Architecture may take mechanical and prevent unintended consequences. This electrical engineering, in addition to Web API

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programming, depending on what IoT 6.4.1 IBM Training Courses product the business supports or sells. Some 1. Getting started with IoT development Information Architects may be programming 2. Learning path: Building skills in IoT experts, use open source Data Management development solutions like Hadoop, or be versatile in 3. Learning path: Mastering IoT development different programming languages like Java, Ruby on Rails, Python, CSS, HTML5 or SQL. 6.4.2 References Others may be hardware experts. 4. https://www.ibm.com/cloud/garage/archit 6.4 Solutions - Learning Resources ectures/iotArchitecture/practice_continuo us delivery_iot The following paragraphs list a few education 5. IoT reference architecture resources for Data Governance specialization. 6. Information Architecture Skills in IoT era 7. Training Courses in IoT Area

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7 The Information Architecture Skills City Metro Map IA Skills City Metro Map shown in Figure 3 is a powerful visualization for Information Architects, used to traverse through a skill journey map summarizing the four new technology areas of Hybrid-Multi Cloud, Artificial Intelligence, Blockchain, and Internet of Things (IOT) as well as the Data Governance practice area. Note that this is the first version of the IA Skills City Metro Map. It will be enhanced in the near future to include stopovers and crossovers between the various stations to show how Information Architecture and these various Emerging Technologies are interconnected. Each technology area in the metro map is represented using a distinct color and it shows several main stations, sub stations and mini stations that provide a detailed listing of skills to be acquired by information architects during that journey. Moving from a main station to a substation track indicates a deep dive and each sub-station represents a sub skill which is mapped to the main skill. This visualization helps the information architects to get a crisp, summarized view of all the essential skills that are described in the various sections of this white paper.

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Figure 3 - IA New Technology Skills City Metro Map

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8 Summary & Conclusion This paper attempts to answer the question: Market factors What are the next set of skills that Information • Steep decline in new revenue from aging Architects need to support Emerging data technologies (Hadoop has hit a peak) Technologies? • Client demand for skills and resources Our data is our most valuable asset and yet we supporting rapidly evolving data technology are mining only 2% (Source: IBM Institute for like: spark, real -time integration, NoSQL Business Value analysis: The Cognitive Design and Build Enterprise, Part 1). • Re-engineering for modern data needs: IBM Business Value Assessment recommends o Sophisticated data handling, insights/ the orchestration of people, processes, and cognitive, deep learning, new security technology that enables an organization to models leverage data as an Enterprise Asset. o Incorporation of essential technologies • With Cloud providers, software companies, for new business capabilities: and our clients are executing on their AI ➢ NoSQL, Streaming, Containers etc. journeys with data being the catalyst for ➢ Data in the Cloud for scale, growth performance, and flexibility • IBM will diversify our portfolio in 2019 to meet • New regulatory & compliance measures: the current business demands, including: o GDPR (EU), LGPD(Brazil), CCPA (USA- Spark, NoSQL, NiFi, Hybrid Cloud CA) • Future state and roadmap will enable: Emerging Technologies and Need for Information o Lowering risk of failure to meet regulatory Architects compliance through end-to-end lineage In this era of digital transformation, more and o Increasing trust in the data delivered to more projects are incorporating new and emerging reports and applications technologies. The Information Architecture o Conducting impact analysis and improving remains either an important and major component project agility or even serves as a foundation for these new o Increasing consistency of sourcing and data technologies, e.g. Internet of Things (IoT), meaning Blockchain, Artificial Intelligence, Quantum Experts predict data volume will increase Computing, Data placement in Hybrid Cloud. It is 10x by 2025 (Source: IDC Forecast) extremely important that Information Architects continue to learn and enhance their skills to keep up with the evolving technologies and meet the high demand for Information Architects in the current job market.

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Produced in the United States of America

November 2019 All Rights Reserved IBM, the IBM logo and ibm.com are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the Web at “Copyright and trademark information” at www.ibm.com/legal/copytrade.shtml. This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. THE INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided.

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