Data_and_Analytics_Essentials_Architect_an_Analytics_Platform.pptx
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Data_and_Analytics_Essentials_Architect_an_Analytics_Platform.pptx
- 1. © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. This presentation, including all supporting materials, is proprietary to Gartner, Inc. and/or its affiliates and is for the sole internal use of the intended recipients. Because this presentation may contain information that is confidential, proprietary or otherwise legally protected, it may not be further copied, distributed or publicly displayed without the express written permission of Gartner, Inc. or its affiliates. Julian Sun, Anirudh Ganeshan Last Updated: 22 April 2022 Data and Analytics Essentials: Architect an Analytics Platform
- 2. 1 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. How to Architect an Analytics Platform Objectives This document helps data and analytics leaders design, develop, deploy and evolve the architecture of an analytics platform, from data and technology components to the support of business processes, information consumers and decision makers. It includes answers to questions such as: • What components and constructs are required for a successful analytics architecture? • How to optimize the interaction between the analytics architecture and the business? • How to select and plan the deployment of new analytics capabilities? • How to plan and structure the integration and application of Analytics Capabilities in a cohesive architecture? Data and analytics leaders can present these slides in their current format or use them as templates or components to support their analytics strategies. Most of the diagrams can be decomposed and customized according to the reader’s needs.
- 3. 2 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Choosing the Components and Constructs Required for an Analytics Architecture
- 4. 3 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Choosing the Components and Constructs Required for an Analytics Architecture Objectives This section provides the foundational concepts that will be used and detailed throughout the rest of the Toolkit. It introduces and discusses: • The analytics capability as the basic component with which to transform data into analytics outputs within the analytics architecture. It gives a framework for organizations to design the building blocks, analytics packaged business capabilities (PBC). • The analytics capability components (data management, people, analytics, governance and processes). • The analytics domains (including the information portal, analytics workbench, data science laboratory and artificial intelligence hub), their data requirements, and the types of analytics outputs. Data and analytics leaders should use this section to explain the overall data and analytics landscape and its building blocks to users within the organization.
- 5. 4 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Analytics Capabilities Analytics Capabilities are granular analytics functions, and their technical and organizational components are able to transform data into analytics outputs to support business outcomes. It is the foundational building blocks to shape the analytics packaged business capability (PBC). Geospatial Analytics Prescriptive Modeling Predictive Modeling Advanced Analytics Data Visualization Examples of Analytics Capabilities: Analytics Capability Data Analytics Outputs Analytics Capability Reporting
- 6. 5 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Analytics Domain Components Analytics Capabilities are highly diversified but can be grouped into clusters of similar characteristics called analytics domains to simplify deployment and operation
- 7. 6 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Analytics Capability Components Analytics Capabilities are composed of several components that are required for their deployment and operation. Misuse, lack of alignment or gaps in these components will usually lead to the failure or suboptimized use of Analytics Capabilities. Analytics, processes, data management and governance can be a packaged business capabilities (PBCs), enabling composable business. Governance Analytics Outputs People Analytics Data Management Data Processes Results generated by analytics processes; can interface directly with users, be operationalized through integration into systems or used as inputs for other analytics processes. Series of steps or actions required to deploy and operate the components on the analytics capability and generate the expected analytics outputs. Set of data and analytics roles needed to support or execute the processes on the analytics capability; can also be called “people capabilities” or “skills.” Analytics technologies and techniques used to transform data into insights or decisions. Technologies to collect, process, organize and make data available to analytics processes and users. The main components are often data repositories. Set of rules that guarantee the integrity, security, privacy, availability and usability of data for the analytics processes. Diverse types of inputs for the Analytics Capabilities operation, generated by entities such as internal business applications, external ecosystems, connected “things,” customers or users. Data Management Analytics Data Analytics Outputs Processes People Governance Analytics Capability Data and Analytics Essentials: Gartner Analytics Atlas
- 8. 7 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Data Processes Analytics Processes Recommended Reading People (Analytics) People (Data) Data Management Analytics Data Analytics Outputs Sample Vendors Sample Analytics Capability Profile: Interactive Visualization Profile Interactive visualization enables the exploration of data via the manipulation of chart images, with the color, brightness, size, shape and motion of visual objects representing aspects of the dataset being analyzed. These products provide an array of visualization options that go beyond those of pie, bar and line charts, including heat and tree maps, geographic maps, scatter plots and other special-purpose visuals. These tools enable users to analyze the data by interacting with a visual representation of it. • Enable business users to extract advanced analytics insights by interacting with data sets and dashboards. • Improves user experience and analytics adoption across the organization. • Allows business users to organize and present findings in new ways and generate previously unconsidered insights. • Monitor standard KPIs • Communicate with business units to understand which users need interactive visualization capabilities and which can rely on pre-built interactive content delivered by others. • Train and support the users on the autonomous exploration of data using the appropriate tools. • Define and apply processes to certify user-built analytics content to guarantee trust on data. • Define processes to provide datasets for analysis, leveraging the data engineer skills for quick delivery. • Create processes to promote analytics datasets to more robust data management repositories, for permanent access. • Tableau, Microsoft, Qlik, TIBCO Spotfire, Pyramid Analytics • “Magic Quadrant for Analytics and Business Intelligence Platforms” • “Critical Capabilities for Analytics and Business Intelligence Platforms“ • “Communicate Insights Effectively With Augmented Data Visualization and Storytelling” Interactive Visualization Data Modeler Data Engineer Analytics Support Business Analyst Data Warehouse Data Hub Analytics Sandbox Data Mart Departmental Tabular Data Corporate Tabular Data Ad Hoc Data Sales Data External Data CRM Data Business Applications Data Interactive Visualization Dashboards Data Preparation Data Steward Information Consumer Interactive Visualizations Dashboards Data Stories Insights Datasets Business Analytics Capability
- 9. 8 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. Analytics Domains Inputs (data) and analytics outputs will vary from one domain to the other, creating unique use cases, with different types of data inputs and analytics outputs. Information Portal Analytic Workbench AI & Data Science Lab Monitor Explore Investigate Analytics Capabilities Analytics Outputs Data People (Data) Data People (Analytics) Analytics Alerts Recommendations Datasets Data Stories Interactive Dashboards Insights Dashboards Reports Forecasts Predictions Conversations Unstructured Data Streaming Data Text Ad Hoc Tabular Data Corporate Tabular Data Departmental Tabular Data Sensors Data Language Audio Image Vision Reporting & Dashboarding NLG Conversational Analytics Augmented Analytics Data Preparation Geospatial Analytics Graph Analytics Data Visualization Predictive Modelling Advanced Image/Video Analysis Prescriptive Modelling NLP Augmented DSML Stream Analytics
- 10. 9 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Business Requirements for Analytics Domain Customer Service Engage Customer Check-In Customer Gratify Customer Finance Operations Make Reservation Offer Services Check-in Customer Organizations have historically deployed Analytics Capabilities from the IT team and then expanded into lines of business. As organizations increasingly seek to incorporate analytics capabilities into business process and align with value streams, the requirements for analytics domains will become more business-oriented. Value Stream Map Business Process
- 11. 10 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Optimizing the Interaction Between the Analytics Architecture and the Business
- 12. 11 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Optimizing the Interaction Between the Analytics Architecture and the Business Objectives The main objective of this section is to describe how the analytics architecture can align with business needs to maximize its impact. It introduces and discusses: • The business-technology cycle and how analytics operates to enhance it. • From business analytics capabilities to Analytics PBCs and how to start with business processes to generate them for business impact. • The analytics domains supporting business analytics capabilities. Data and analytics leaders should use this section of this Toolkit to explain why the business requires a broad range of analytics use cases, and why a broad portfolio of analytics capabilities is needed to support these use cases.
- 13. 12 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Technology Platforms Sample Business Processes Technology platforms support business processes. Business processes generate data, managed by technology platforms. IT Systems Customer Experience Internet of Things Partner Ecosystems Product Development Customer Support Financial Planning Sales and Marketing HR Management Technology Management . . . Business Context Technology Context Analytics Block Data Analytics Outputs The Technology-Business Cycle Business processes in organizations are supported by technology platforms and generate data that is collected, stored, processed and managed by those platforms to support their operation. The analytics architecture (and all its components), is at the core of this cycle, helping to implement, support, optimize or even create new business processes.
- 14. 13 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Technology Platforms Sample Business Processes Technology platforms support business processes. Business processes generate information that is managed by technology platforms. IT Systems Customer Experience Internet of Things Partner Ecosystems Product Development Customer Support Financial Planning Sales and Marketing HR Management Technology Management . . . Analytics Block Data Analytics Outputs Analytics Block Data Analytics Outputs Analytics Block Data Analytics Outputs Analytics Block Data Analytics Outputs How to Impact the Business With Analytics? Impacting the Business With Analytics Analytics Capabilities collect data from technology platform sources and transform it, generating analytics outputs. The challenge is how to transform those outputs into business outcomes that can positively impact the business and encapsulate them into business analytics capabilities.
- 15. 14 © 2020 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. Identify Business Processes That Require Business Analytics Capabilities Business Analytics Capabilities Business Process Business Process Business Goal Business Analytics Capabilities Business Analytics Capabilities Business Analytics Capabilities Business Analytics Capabilities Business Analytics Capabilities Steps to Find Business Analytics Capabilities • What business processes will be supported (or impacted) by data and analytics? • What decisions will rely on analytics outputs? • Who are the stakeholders, and what is their participation in analytics? • What specific metric is the analytics platform providing or helping to improve? • What data can be collected and explored from inside and outside the organization? • How will the organization access and process the new data sources needed to generate new analytics outputs? • How will the organization connect to and integrate disparate data sources into a cohesive architecture? • What systems will require outputs from the analytics platform and how will we interface with them?
- 16. 15 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Alerts Recommendations Datasets Data Stories Interactive Dashboards Insights Dashboards Reports Forecasts Predictions Conversations Business Analytics Capabilities Analytics Outputs Analytics Capabilities Monitor customer churn metrics per segment and region. Recommend what to offer to customers at risk, to retain them. Reduce Customer Churn Monitor customer churn Understand and predict churn risk Run campaigns for customers with high churn risk Business Processes Target Business Outcome Predict customer churn probability. Generate campaign target lists of customers with high churn probability. Characterize profile of customers that churned in previous months. Alert segment managers when churn levels increase. 1 0 1 0 0 1 0 1 Information Portal Analytic Workbench AI & Data Science Lab Monitor Explore Investigate Reporting & Dashboarding NLG Conversational Analytics Augmented Analytics Data Preparation Geospatial Analytics Graph Analytics Data Visualization Predictive Modelling Advanced Image/Video Analysis Prescriptive Modelling NLP Augmented DSML Stream Analytics Unstructured Data Audio Speech Video Text Ad Hoc Tabular Data Corporate Tabular Data Departmental Tabular Data Image Sensors Data Streaming Data Data 1 0 1 0 0 1 0 1 Business Outcome: Reduce Customer Churn
- 17. 16 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Analytics Packaged Business Capabilities Are the Building Blocks of Composable Architecture Packaged business capabilities (PBCs) are encapsulated software components that represent a well-defined business capability, recognizable as such by a business user and packaged for programmatic access. Optional User Interface Source: Gartner Key Types of PBCs • Encapsulated Business Object (e.g., Account Management, Purchase Order Management) • Process Management (e.g., Order- to-Cash, Credit Approval) • Digital Twin (e.g., Turbine Management, Elevator Management) • Data Reference (e.g., Exchange Rate Lookup, Keyword Search) • Analytical Insight (e.g., Sentiment Analysis, Credit Assessment) APIs Event Channels SVC SVC SVC Internal Data and Metadata
- 18. 17 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Encapsulate Business Analytics Capabilities to Analytics PBCs Analytics Packaged Business Capability (PBC) Recommend the Next-Best Offer Predict Customer Churn Rate Examples Characterize Profile of Customers Data Management Analytics Data Analytics Outputs Processes People Governance Business Analytics Capabilities Business Analytics Capabilities Business Analytics Capabilities Start with business process to use Analytics Capabilities to generate more business analytics capabilities and encapsulate them into PBCs for faster and adaptive composition by business PBC PBC PBC PBC PBC
- 19. 18 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Interactive Visualization Data Preparation Reporting Simulation and Optimization Text Analytics Improve Demand Forecasting Dashboards Interactive Visualization Data Preparation Graph Analytics Reduce Customer Churn Predictive modeling Identify Reusable Business Analytics Capabilities to Build Analytics PBCs Increase Campaign Sales Dashboards Interactive Visualization Predictive Modeling Prescriptive Modeling Location Analytics Audio Analytics Data Preparation Prioritize simpler projects that deliver results quickly with low risk to gain credibility. Favor projects with reusability but remember that diversification creates opportunities. Take precedencies into consideration, including technology and skills. Manage risk taking: several smaller wins are better than the ultimate project that never arrives. Respect business priorities.
- 20. 19 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. Analytic Content Content Type Cert. Level Rating Downloads Producer Business Problems Producer Role/Domain Community Rank Skills Certification Published Analytics Content Marketing Campaign Target Lists Store Customer Profiling Monthly US Sales Detail Customer Sentiment Analysis Reports Sales Dashboard Marketing Applications Analytics PBCs Marketing Gold Gold Silver Bronze Online Campaign Execution Customer service Monthly U.S. Sales Performance Business Analytics 2 Advanced Analytics 3 Intro to Data Preparation Sales Teams Management Compensation Calculation Online Campaign Analysis Store Auditing Stocks Planning Marketing Campaign Target Lists Customer Sentiment Analysis Quarterly U.S. Sales Forecasts Customer profiling Karen Taylor Charles Smith Pierre Noah Karen Taylor 985 410 283 12 3.1 4.3 4.7 4.2 Karen Taylor Karen Taylor Charles Smith Include Analytics PBCs Into the Analytics Catalog
- 21. 20 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Selecting and Planning the Deployment of New Analytics Capabilities
- 22. 21 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Selecting and Planning the Deployment of New Analytics Capabilities Objectives This section presents two complementary approaches to the evolution of analytics platform architecture, with the identification of new analytics capabilities. It introduces and discusses: • Business-driven evolution, its counterpart, technology and data-driven evolution, and indications of when to use each. • Step-by-step descriptions and examples of business-driven evolution. • Step-by-step descriptions and examples of technology and data-driven evolution. • Gartner’s “return opportunity appetite and risk” (ROAR) portfolio optimization model, with its high- level steps. Data and analytics leaders should use this section to structure a hybrid approach to the evolution of their analytics portfolios where the data and analytics team collaborates with the business units for a more impactful analytics landscape.
- 23. 22 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. The analytics platform can evolve following two different patterns. It can be driven by the business, or driven by technology and data. These options should be used together, many organizations use only the latter approach. That is often the case when there is limited collaboration between the data and analytics team, and the wider business. The business-driven approach may generate a higher business impact and should be considered the primary source of inputs for an analytics evolution roadmap, complemented by the technology/data-driven approach. Optimization and innovation will be guided by a combination of both approaches. Analytics Architecture Evolution Use business-driven evolution when: • The business asks for support to address business challenges. • There is demand for analytics and knowledge across the organization of how it can help the business. • The organization had success with some Analytics Capabilities and wants to replicate them on new use cases. … eventually: • The data and analytics team is not very proactive on the delivery of new capabilities and the business wants to lead the process. Business-Driven Evolution Use technology and data-driven evolution when: • The organization wants to innovate, deliver business processes in new ways, or define new processes. • New functionality is offered by existing tools or vendors. • The data and analytics team knows about new analytics technologies. • The organization gets access to new data that can support new analysis, leading to better or broader support of use cases. … eventually: • The organization’s analytics maturity is low, and the business has no demand for new data analytics capabilities. Evolution Driven by Technology and Data
- 24. 23 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Business-Driven Evolution Business outcomes and the supporting processes are defined in advance. The analytics architecture is designed to optimize business processes or support business-driven innovation. Business Process Business Process Business Processes Target Business Outcome Business Goal • Identify and prioritize the organization’s business goals. • Identify the business processes required to deliver them. • List the business analytics capability required to support the business processes. • Describe the busienss analytics capability, including stakeholders and required analytics outputs. • Identify the Analytics Capabilities needed to provide the required analytics outputs. • List integration requirements between the data Analytics Capabilities. • Identify data sources for the data required for the analytics architecture. • Define data integration requirements to collect the data. Coporate Tabular Data Departmental Tabular Data Ad Hoc Tabular Data Unstructured Data Sensors Data Streaming Data Text Speech Audio Image Video • Identify and optimize the components required by each analytics capability. • Define interactions between components and design the overall architecture model. Audio Analytics Ad Hoc Data Analytics System Integrator Data Preparation Data Lake Business Analyst Data Scientist Data Warehouse Interactive Visualization Data Modeler Predictive Modeling Data Engineer Analytics Moment Analytics Moment Analytics Moment Analytics Moment Analytics Moment Analytics Moment Analytics Moment Analytics Moment Analytics Moment Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Analytics Capability
- 25. 24 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Business Outcome: Reduce Customer Churn Business users identify their critical business outcomes and the business processes able to deliver them (1). Next, with the help of the data and analytics team, they identify business analytics capabilities (2) that will help execute the business processes. The data and analytics team, can then identify the analytics outputs required for those business analytics capabilities with the Analytics Capabilities (3) that produce them, the components (4) required to implement them and the necessary data inputs (5). Business-Driven Evolution
- 26. 25 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Technology and Data-Driven Evolution The data and analytics team uses its market expertise to suggest the use of new technologies that can impact the business positively, or new data that can support new use cases (or optimize existing ones). Business Process Business Process Business Processes Target Business Outcome Business Goal • Identify enhancements to existing business processes and assess their impact on the organization’s goals. • Innovate by creating new processes. • Identify new data sources or new data types to feed to analytics processes … • ... or design new ways to collect and process data, generating inputs for analytics. Coporate Tabular Data Departmental Tabular Data Ad Hoc Tabular Data Unstructured Data Sensors Data Streaming Data Text Speech Audio Image Video • Identify business analytics capabilities or new ways to explore them for the business … • ... or find opportunities to optimize, expand, scale up or replicate the use of existing capabilities. • Identify and optimize the components required by each new analytics capability. • Define interactions between components and design the new architecture model. Audio Analytics Ad Hoc Data Analytics System Integrator Data Preparation Data Lake Business Analyst Data Scientist Data Warehouse Interactive Visualization Data Modeler Predictive Modeling Data Engineer Analytics Moment Analytics Moment Analytics Moment Analytics Moment Analytics Moment Analytics Moment Analytics Moment Analytics Moment Analytics Moment • With the users, list potential new business analytics capabilities to support the business. • Describe the business analytics capabilities to deploy, including stakeholders and business processes to support. Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Business Analytics Capability Analytics Capability
- 27. 26 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Optimization of Data and Analytics Investments Organizations need a structured framework that allows them to identify, assess and prioritize their analytics investments. Gartner’s Risk, Opportunity, Appetite, Return (ROAR) portfolio optimization model allows data and analytics leaders and other key stakeholders to: • Identify, prioritize and select the products that create measurable analytics moments. • Ensure that these analytics moments align to mission-critical business initiatives and strategic focus. • Identify and map analytics value streams, including information innovation to mission- critical business priorities and KPIs • Set target portfolio range for strategic focus • Assess and score business value for each value stream • Assess and score each value stream for data, technology and analytics, organization and culture, cost and go-to-market risks • Net business value is the business value score minus the aggregate success contributor/ inhibitor/risk score • Generate and assess portfolio combinations optimized for net business value and strategic focus targets, given available funding • Visualize sources of value and risk • Incorporate value, risk and strategic focus portfolios and capabilities gaps into strategy and roadmap • Identify measures of success, execute plans, manage portfolio Create Value Propositions Incorporate Into Strategy, Delivery Model and Roadmap Assess Net Business Value Build Portfolios Manage Execution and Results Gartner’s Risk, Opportunity, Appetite, Return (ROAR) Portfolio Optimization Model Business-Driven Evolution Evolution Driven by Technology and Data
- 28. 27 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Planning and Structuring of Analytics Capabilities Into a Cohesive Architecture
- 29. 28 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Planning and Structuring the Integration of Analytics Capabilities Into a Cohesive Architecture Objectives This final section shows the detailed components of a reference analytics architecture and explains how to organize those components in a way that will be understandable by both technology and business specialists. It introduces and discusses: • The four layers of a business-friendly analytics architecture: (1) data-related people roles, (2) data management capabilities, (3) analytics-related people roles, and (4) analytics capabilities. • The detailed components of the four analytics domains and a global reference architecture. • The application of Analytics Capabilities with a catalog approach. • A sample description of processes and governance, to be used as a template, to complement the technical and people components of the architecture. • The tool, Gartner data and analytics governance technology atlas help data and analytics leaders to understand, navigate and apply relevant technologies supporting and enabling data and analytics governance. Data and analytics leaders should use this section to identify and describe requirements for the fulfillment of their business users’ demands.
- 30. 29 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Data Management, People and Analytics Capabilities
- 31. 30 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. Information Portal The information portal is centralized and favors governance and trust on information over agility and user autonomy. Data and analytics teams play a major role in the development of structured data repositories and the design of BI content. The depictions below represent a sample of possible analytics block components but are not an exhaustive list or represented throughout each profile. Information Portal Analytics Workbench AI & Data Science Laboratory Reporting Stream Analytics Alerts BI Developer Dashboards Analytics Outputs Data Management People (Data) Analytics People (Analytics) Corporate Tabular Data Departmental Tabular Data Data Data Steward Data Modeler Data Steward Data Mart Data Warehouse Streaming Data Conversations Conversational Analytics NLG Reporting
- 32. 31 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. The Analytics Workbench User empowerment, quick access to data and easy-to-use visual interfaces are the top characteristics of an effective analytics workbench. The data and analytics team supports business users with autonomous exploration of data, production of analytics content and content certification (to gain the trust of other users). The depictions below represent a sample of possible analytics block components but are not an exhaustive list or represented throughout each profile. Information Portal Analytics Workbench AI & Data Science Laboratory BI Developer Analytics Outputs Data Management People (Data) Analytics People (Analytics) Corporate Tabular Data Departmental Tabular Data Data Data Steward Data Modeler Data Steward Data Mart Data Warehouse Streaming Data Business Analyst Analytics Support Expert Citizen Data Scientist Augmented Analytics Analytics Sandbox Ad-Hoc Data Data Engineer Ad-hoc Tabular Data
- 33. 32 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. The AI & Data Science Laboratory The AI & data science laboratory relies on experts (with deep business, analytics and technical knowledge) to explore data with a diverse set of advanced tools and methods. It can automate processes that would normally require human intervention, such as understanding an audio recording of a customer request, or operating a machine. The depictions below represent a sample of possible analytics block components but are not an exhaustive list or represented throughout each profile. Information Portal Analytics Workbench AI & Data Science Laboratory Analytics Outputs Data Management People (Data) Analytics People (Analytics) Data Data Modeler Data Steward Citizen Data Scientist Analytics Sandbox Ad-Hoc Data Data Engineer Unstructured Text Sensors Speech Audio Image Video Augmented DSML Data Scientist External Data Data Streamer Data Lake Data Architect Predictive Modelling Advanced Image/Video Analysis Prescriptive Modelling NLP Statistician Analytics System Integrator
- 34. 33 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. The Reference Data Analytics Architecture The reference data analytics architecture is a high-level representation of how to plan, deploy and operate a comprehensive data analytics portfolio. It includes data inputs, the required capabilities for data management, people and analytics, and the potential analytics outputs. Sample list of capabilities. Information Portal Analytics Workbench AI & Data Science Laboratory BI Developer Analytics Outputs Data Management People (Data) Analytics People (Analytics) Corporate Tabular Data Departmental Tabular Data Data Data Steward Data Modeler Data Steward Data Mart Data Warehouse Streaming Data Business Analyst Analytics Support Expert Citizen Data Scientist Analytics Sandbox Ad-Hoc Data Data Engineer Ad-hoc Tabular Data Unstructured Text Sensors Speech Audio Image Video Data Architect External Data Data Streamer Data Lake Data Scientist Statistician Analytics System Integrator Predictive Modelling Advanced Image/Video Analysis Prescriptive Modelling NLP Alerts Dashboards Conversations Reporting Reporting Stream Analytics Conversational Analytics NLG Augmented Analytics Augmented DSML
- 35. 34 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. The Application of Analytics Capabilities
- 36. 35 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Build the Analytics Application With a Catalog Evaluate the analytics application development modules within the analytics tools. They work as analytics application builder to assemble the essential components from the Analytics Capabilities for application. Organizations can build a data and analytics catalog which include wider reusable assets and let the users compose the application from the catalog. Information Portal Analytic Workbench Data Science Lab Monitor Explore Investigate Alerts Recommendations Datasets Data Stories Interactive Dashboards Insights Dashboards Reports Forecasts Predictions Conversations Unstructured Data Streaming Data Text Ad Hoc Tabular Data Corporate Tabular Data Departmental Tabular Data Sensors Data Language Audio Image Vision Reporting & Dashboarding NLG Conversational Analytics Augmented Analytics Data Preparation Geospatial Analytics Graph Analytics Data Visualization Predictive Modelling Advanced Analytics Prescriptive Modelling NLP Augmented DSML Stream Analytics Catalog/ Marketplace PBC Reports Dashboard Data Prep Flow Models Metrics APIs Features Roles Process Data Analytics Application Builder Low-Code | Workflow Management | Multiexperience | Value Stream
- 37. 36 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Processes and Governance
- 38. 37 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Describe Processes and Governance The analytics architecture describes the data management, people and analytics requirements to transform data into analytics outputs. Data and analytics leaders also need to define, publish and get support for the governance and processes needed to operate this analytics architecture. Data Management Analytics Data Analytics Outputs Processes People Governance Analytics Workbench Information Portal Geospatial Analytics Reporting Interactive Visualization Data Preparation Mobile BI OLAP Dashboards Tabular Ad Hoc Tabular Coporate Tabular Departmental Datasets Stories Interactive Dashboards Insights Dashboards Alerts Reports Data Warehouse Ad Hoc Data Data Mart Analytics Sandbox Data Engineer Data Steward Data Modeler BI Developer Business Analyst Analytics Support Expert Data Management Analytics Analytics People Data People Data Analytics Outputs
- 39. 38 © 2020 Gartner, Inc. and/or its affiliates. All rights reserved. Gartner is a registered trademark of Gartner, Inc. and its affiliates. Implement Data and Analytics Governance Data and analytics governance, when implemented, provides a decision support system that leverages decision rights, accountabilities, and behaviors for the valuation, creation, consumption and control of data and analytics. Identify Data Sources Curate Data Apply Workflow Drive Harmonization Report/Visualize Business Use Case By collaboration Prepare data for analysis Analytics Discover data Understand data and its relationships Profile data Notify the anomalies Recommend changes By operations Analytic models Data lineage Impact analysis Auditing Remediate the issues • Architect • Data Steward • Data quality expert • Process/ Data Owner • Expert user or SME • Business process Manager • Data Steward • Data Engineer • Data Scientist • Analyst • Consumer of a data marketplace • Data Steward Modeler/ Analyst /Architect/Data scientist who are is the owner of a dataset but drive semantics Key stakeholder(s) Tool: The Gartner Data and Analytics Governance Technology Atlas
- 40. 39 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. Recommendations Categorize your analytics portfolio under the analytics domains model to provide a business-friendly, structured view of analytics capabilities to the organization. Develop a hybrid evolution model for your analytics platform architecture where the data and analytics team and the business collaborate and contribute to the implementation roadmap (but still prioritize the fulfillment of business needs). Execute a structured approach to link business processes and needs to analytics capabilities and the components required for their implementation. Detail the architecture — including data, analytics, people, governance and processes — on your analytics landscape diagrams, thereby offering a characterization and description of requirements that the business can read.
- 41. 40 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. For information, please contact your Gartner representative. Recommended Research “Tool: The Gartner Data and Analytics Governance Technology Atlas” Guido De Simoni (G00736612 ) “Critical Capabilities for Analytics and Business Intelligence Platforms” Kurt Schlegel (G00467560) “Critical Capabilities for Data Science and Machine Learning Platforms” Pieter den Hamer (G00467488) “Achieve DSML Value by Aligning Diverse Roles in an MLOps Framework” Anirudh Ganeshan (G00755653) “How to Balance Control and Agility in Your Self-Service Analytics” Austin Kronz (G00730070) “Composable Analytics Shapes the Future of Analytics Applications” Julian Sun (G00732056) “How to Enable Self-Service Analytics to Ensure D&A Success” Anirudh Ganeshan (G00748717) “Tool: A Step-By-Step Guide to Begin Your Analytics Initiative” Kurt Schlegel (G00375992)
- 42. 41 © 2022 Gartner, Inc. and/or its affiliates. All rights reserved. For information, please contact your Gartner representative. Recommended Research “Align D&A With Value Streams to Optimize Decision Making and Business Value Creation” Joao Tapadinhas(G00755132 ) “CDOs Can Use This 6-Step Approach to Obtain Buy-In and Sell Data and Analytics to Stakeholders” Alan D. Duncan (G00751258) “Tool: Board-Ready Slides for Educating the Board on Data and Analytics” Frank Buytendijk (G00754316) “Top Trends in Data and Analytics, 2022” Rita Sallam (G00763301) “IT Score for Data & Analytics” Chief Team (G00738086) “What Are Must-Have Roles for Data and Analytics?” Jorgen Heizenberg (G00752065) “Applying AI — A Framework for the Enterprise” Bern Elliot (G00725152) “Drive the Analytics Economy to Multiply Business Value” Julian Sun (G00719570)