Caras Feras de Analytics: Marcelo Soares, Lagoon Insights
Demystifying Taxonomies, Ontologies and Data Models Conflicting definitions of taxonomies, ontologies and data models abound, leading to imperfect or incoherent practices. This research clarifies the differences for data and analytics technical professionals, thereby improving databases, knowledge bases, website navigation and requirements gathering.
Quick Answer: Why Are Analytics Becoming Invisible for Users? Finance Data and Analytics Primer for 2024 Solution Criteria for Data Integration
Domain Data & Analytics
Network Gartner
Steve Heck: I love the professional development angle for AI leaders. As a former Data Scientist, you come to the role for your technical proficiency but being effective is more about your communication and business acumen. Those two competencies don’t tend to travel together.
Quick Answer: How to Determine the Right Team Size in Data and Analytics (gartner.com)
Not sure if you guys saw these recent pieces, but they are awesome: Solution Path for Building Modern Analytics and BI Architectures and Data Engineering Essentials, Patterns and Best Practices. They are GTP research.
GEN AI, comentário do analista → Still need a human to define what good looks like
Data pipeline → Comparison to Oil.. built to last for years
Key Frases:
Dinheiro não estã no dado, estã no uso do dado.
How Gartner Supports this Domain
Key analysts:
Lista de analistas que clientes gostaram sobre temas em Data:
Mark Beyer - Data Fabric & ETL Tools
Guido Di Simone - Metadata catalogues (how does the catalogue play a role in Data literacy?)
Allan D Duncan - Data Literacy
Donna Medeiros - Data Literacy (creating a program to train everyone on data literacy)
Saul Judah - Governance (create a program to incorporate everyone in the company)
Donald Feinberg — Data Management (Data Plex & Places)
Robert Thanaraj - ETL Tools, SLT, Google’s ETL
Donald Feinberg - Moving SAP to HANA, HANA on Google, Data Fabric, Data Lake, Data Ecosystem
Denis Torii - S4HANA, everything SAP related
D&A Budget & Efficiency Benchmark
DNA Strategy
Ignition Guide to Build a Data and Analytics Strategy in the Age of AI Tool: Data and Analytics Strategy and Operating Model Health Check Toolkit: Examples of Data & Analytics Strategy and Operating Models
Tool: Data, Analytics and AI Strategic Plan on a Page
Toolkit: Examples of Data & Analytics Strategy and Operating Models (gartner.com)
Quick Answer: How to Create a Data-driven Enterprise
How D&A Leaders Should Articulate the Business Value of Their Investments
Creating a Modern, Actionable Data and Analytics Strategy That Delivers Business Outcomes
Ultima revisão para o Sandro:
The State of D&A Organizations and Roles Is in Flux: A Gartner Trend Insight Report
CDO Success Factors: Change Management and Communication Unlock Data-Driven Business Value
Tool: Data and Analytics Strategy Template
Once you have established these components of your strategy, you can self-assess for completeness using the Checklist: D&A Strategic Planning Review below.
D&A Strategic Planning Checklist
From https://www.gartner.com/document/3985724?ref=sendres_email&refval=77733516
Driving the Business Value of Data and Analytics: A Gartner Trend Insight Report
-
- This tool can help clients in mid-size enterprises develop and organize their D&A strategy!
How to Persuade Someone in 50 Words or Less | Inc.com
How to Craft a Modern, Actionable Data and Analytics Strategy That Delivers Business Outcomes
Data & Analytics Sample Strategy Presentation
[Creating a Data Strategy - DZone Big Data](https://dzone.com/articles/creating-a-data-strategy?edition=448195&utm_source=Zone Newsletter&utm_medium=email&utm_campaign=big data 2019-01-31)
Practical Data and Analytics Strategy for Midsize Enterprises
Presentation: The Foundation of a Modern Data and Analytics Strategy
Toolkit: Best of … Data and Analytics Strategies - How do I create a data and analytics strategy
Vision
Views from the front lines of the data-analytics revolution | McKinsey
Mobilizing your C-suite for big-data analytics | McKinsey
DNA Culture - Resistence
Culture, Resistance blocck
How Mastercard’s AI-Driven Forecast Launch Led to 100% Adoption
DNA Investments, Value
Enterprise Value Equation (EVE)
The Gartner Enterprise Value Equation: It’s Time to Rethink Outdated Enterprise Value Formulas
I believe the Value Prism is a great companion tool to the EVE model: Toolkit: How to Rank and Prioritize Your Use Cases With a Gartner Prism
DNA Trends
Predicts 2022: Analytics, BI and Data Science Ecosystems Drive New Perspectives
DNA Organization and Roles
hub and spoke means developing D&A capabilities centrally (Hub) and executing in the BUs (Spoke)
DNA Leadership - Chief Data Office (CDO) / Chief Data Analytics Office (CDAO)
McKinsey - How leaders in data and analytics have pulled ahead
3 things:
- Data culture
- Data literacy
- A modern data architecture
Real examples from the CDOs of ZF Group (Gahl) and USAA (Farouk – former Gartner EITL partner) on how the role is evolving to product management and P&L responsibilities
The average tenure of a CDO is 2-2.5 years
DNA Organizational Structured
Toolkit: Data and Analytics Governance Organizational Structures
Data and Analytics Org Model Benchmarks: A Survey of D&A Functions
DNA xOPs & Operating Model
Understanding MLOps to Operationalize Machine Learning Projects
Demystifying XOps: DataOps, MLOps, ModelOps, AIOps and Platform Ops for AI
Maximize the Value of Your Data Science Efforts by Empowering Citizen Data Scientists
DNA Roles / People / Team / Staff
Toolkit: Create a RACI Matrix For Your Data and Analytics Initiatives
Data and Analytics Job Description Library
Data and Analytics Job Description Library
Organizing Your Teams for Modern Data and Analytics Deployment
Must-Have Roles for Data and Analytics, 2018
Capability-Based Talent Implementation Guide: Job Functions (Stats NZ)
Data Analyst vs Business Analyst. Here’s the Difference:
Differences
- A Data Analyst will get the data
- A Business Analyst will use that data to make business decisions
- A Data Analyst will work more with technical people within the company
- A Business Analyst will be more cross-functional, working with stakeholders
- A Data Analyst will focus more on SQL and data tables
- A Business Analyst will focus more on the metrics themselves and visualizing them
- A Data Analyst is more heads-down
- A Business Analyst is more customer-facing
From https://towardsdatascience.com/data-analyst-vs-business-analyst-heres-the-difference-e702f288aaa3
Data Scientist vs Data Analyst | Which Is Right For You? - Video
Data Steward
Toolkit: Data and Analytics Governance Role Descriptions includes a Data Steward job description. A Day in the Life of an Information Steward provides further insight into the role.
Also, you may be interested in Dama, see http://www.dama.org.br/. This organization offers data management professionals training and certification.
Metrics
Create a Data & Analytics Dashboard to Track Business Outcomes
DNA Technologies & Solutions
Executive Essentials: Deploy Data and Analytics, Artificial Intelligence and Digital Platforms
CIOs must take advantage of digital platforms to achieve their data and analytics (D&A) and artificial intelligence (AI) goals to accelerate business growth. They must build their updated vision and strategy in consideration of the impact and value these disciplines bring to the portfolio. Published: 13 Apr 2023 Albert Gauthier
DNA and SAP
Henry cook just published a note on SAP BW and D&A architecture for SAP
DNA and Cloud
Strategic Roadmap for Migrating Data Management to the Cloud
Solution Criteria for Cloud Analytical Data Stores
A&BI platforms / Analytics & Business Intelligence Platforms
Evolving Capabilities of Analytics and Business Intelligence Platforms (gartner.com)
Augmented Analytics Tools
Market Guide for Augmented Analytics Tools
DNA Concepts
Infographic: Strategic Comparison of Data Mesh and Data Fabric
Convergence of Analytics and Business Intelligence, Data Science and AI
Business Intelligence
Is Your Business Intelligence Enabling Intelligent Business? Analytics and BI have long been equated with generating reports and dashboards, not directly utilizing the full potential for the business. Data and analytics leaders should evaluate the visions on ABI solutions for business and create an optimal analytics experience to enable intelligent business.
Augmented Analytics
Maximize the Value of Your Data Science Efforts by Empowering Citizen Data Scientists
3 Steps to Scale Analytics Adoption Through Automation and Augmentation
How to Apply DevOps and Value Stream Mapping to Data, Analytics and AI
Data Fabric
WHAT TO EXPECT: By 2023, augmented data management will reduce the reliance on “data” specialists for repetitive and low impact data management tasks thereby freeing up to 20% of their productive time for collaboration, training and high-value data management tasks. The vast majority of augmented data management solutions require combinations and analysis of many different types of metadata from multiple data management tools and platforms. Organizations are looking for data fabric designs led by active metadata and machine learning insights to simplify and consolidate their architectures and to increase automation in their redundant data management tasks.
[10/5 4:01 PM] Yakimov,Gary Hi Team - Is there a list of “Top Data Fabric Vendors” out there, or should I just send an old school magic quadrant? If the latter, which one is best for this topic?
[10/5 4:13 PM] Nimer,Fernando Yakimov,GaryHi Team - Is there a list of “Top Data Fabric Vendors” out there, or should I just send an old school magic quadrant? If the latter, which one is best for this topic? my understanding is that data fabric is more of concept of an integration layer, than of specific tools; in searching our portal I found some references to ‘old school’ that support that - e.g.: https://www.gartner.com/document/4017744?ref=solrSearch&refval=341918734 Gartner Login
[10/5 4:14 PM] Nimer,Fernando apparently, the use cases define the (traditional?) tools
[10/5 4:14 PM] Nimer,Fernando hope this helps
[10/5 4:28 PM] Marwah,Raman Gary, the Data Fabric is an amalgamation of multiple technologies - Data Cataloging, Knowledge Graphs, Orchestration, Recommendation Engine etc. As yet, there is no single vendor that can do all these things. In fact, this aspect is mentioned as one of the drawbacks of a data fabric in Gartner Research as well.
[10/5 4:29 PM] Marwah,Raman I am putting a chart here that displays the multiple technologies involved.
[10/5 4:29 PM] Pignatello,Joe the data fabric is a composable architecture
[10/5 4:30 PM] Alaybeyi,Saniye Correct. Data Fabric is an architecture that will require more than one tool, not necessarily from vendors, could be open source as well. IN fact, better if open source.
[10/5 4:31 PM] Marwah,Raman In the chart above, the tools/software required for each component of the Data Fabric are mentioned.
[10/5 4:41 PM] Yakimov,Gary Thanks all, my client has already had multiple calls with Mark Beyer so they have the concept down, but they want to hire a vendor to help implement their Data Fabric. I will send them the diagram that Raman provided as a starting point.
[10/5 4:46 PM] Newman,David Gary - this should help you. (Data Fabric Offerings) [10/5 4:47 PM] Newman,David It’s got the vendors and guidance on how to move through the data fabric morass [10/5 4:48 PM] Newman,David there are some different approaches to take first — data catalog for instance. This deck should help you give them some practical guidance on how to move forward. This deck is an piece of a session I did for AmerisourceBergen. Let me know if you want talk about it further [10/5 5:08 PM] Yakimov,Gary ABSOLUTE GOLD David, particularly the first and third slides. They have an RFP that has already been reviewed by Gartner, now they are trying to build a list to send the RFP to. This will be very helpful!!!
Data Ingestion
Data Literacy
Semantic Layer
Reminder for today’s tradecraft meeting, Joe Pignatello will lead a discussion on Semantic layer. Please join and we’ll see you soon!
Why a Semantic Layer Discusison:
Data and analytics professionals are struggling to deliver self-service analytics that balance agility and control. This session will open discussion to compare options for building and deploying the semantic layer, which can be a valuable enabler of self-service analytics.
Areas of discussion:
- Centralized data and federated through semantic views
- Using Views vs Materialized views.
- Benefits of semantic views when data centralized is on the cloud.
- Open
DNA Products
Dashboards
Building a modern data dashboard Using Python and the Gradio library
DNA Practices
Self-Service BI
-
Self-Service BI
-
-
Governança
-
- Catálogo
-
Ciclo de vida da informação
-
Ferramentas de visualização
-
Equipe e perfis
-
How to Balance Control and Agility in Your Self-Service Analytics
Data and Analytics Essentials: Self-Service Analytics Operating Model
Personalization
Critical Capabilities for Personalization Engines
Data engineering
The goals of data engineering are:
- Onboard variety of internal and external data sources
- Decrease the time to onboard
- Ensure data quality with data validation and verification
- Develop data pipelines quickly
- Enable analytic teams to develop business logic
- Provision data infrastructure for ingest, storage and processing
- Automate, orchestrate and monitor data pipelines
From Data Engineering Essentials, Patterns and Best Practices
Data Engineering Is Critical to Driving Data and Analytics Success
Data Management
Hype Cycle for Data Management, 2024 Cool Vendors in Data Management: GenAI Disrupts Traditional Technologies
Decision Intelligence
Combine Predictive and Prescriptive Analytics for Better Decision Making (gartner.com)
How to Use Machine Learning, Business Rules and Optimization in Decision Management (gartner.com)
Decision Models That Fit Your R&D Profile (gartner.com)
Crowdsourcing
Integrating Crowdsourcing in Data and Analytics Environments
DNA in Areas
I&O
The 4 Core Foundations for Building Advanced Analytics Capabilities in I&O
DNA in Industries
Manufacturing
DNA Use Cases & Cases
Leverage Inspiring Data and Analytics Case Studies and Use Cases to Create Business Value Data and analytics is pervasive across enterprises, business processes and industries — from addressing macro world disruptions to supporting personal financial goals. Data and analytics leaders should use this research to create D&A business value based on real-world case studies and use cases.
CDAO Industry Review: 6 Case Studies for Building a Data-Driven Healthcare Organization
France finds a rather unexpected form of AI surveillance - swimming pool detection
DNA Service Vendors
Brazil
DataLake → JumpLabel → Jump Label Solutions
The following service providers sponsored the D&A summit in Brazil last year.
I have not checked them for their data governance capabilities:
- CSC Brazil
- Evolution IT Services
- Keyrus
- Tenbu
- Wipro
- KPMG
- PWC
Wipro, KPMG and PWC are in the MQ for D&A service providers
Other D&A service providers in Brazil (again not specific for data governance):
-
Consist
-
Neoris
-
Protiviti
-
Stefanini
Hope this helps,
Jorgen
https://www.lagomdata.com.br/coronavirus
Oi Vitor, tudo bem?
Obrigado pelo email!
Segue meu contato abaixo. Além disso, acho que para um primeiro overview o site https://www.quantumblack.com/ pode ajudar bastante! Se quiser entrar no detalhe de alguma indústria especifica podemos marcar uma conversa, talvez seja mais fácil.
Grande abraço,
Thomaz Cortes
+55(11)97143-4371
+55(11)3790-4126
DNA Data Governance
I couldn’t make it yesterday, but I’m listening to the recording right now! To the conversation on client asks for example governance charters/policies/standards, etc I use the following for D&A governance conversations:
- The word document (attached) comes from a former security advisor, Zaira, and she created this to help define the different terms.
- Our Ignition Guide includes a template for data governance standards at step 4.1
- I’ve also been collecting links for publicly available documents for when clients ask for them. Of course, I always share a disclaimer that these are not Gartner endorsed, but rather an incomplete listing of what can be found online that they can reference and determine the relevance to their context. I also tell them to look at their own state/province/etc government websites for public data governance docs because they do typically exist. My collection is here, and do feel free to add to it if you’d like! https://docs.google.com/document/d/1q4RTAotYnvwQRKQaHHhG8HDanfzDDJ5_mcvq61FxFo4/edit
[3/30 1:04 PM] Nelson,Grant Faulkner
Here’s another: Toolkit: Data Governance Roles and Committee Charter for Finance
like 1
Gartner Login
Types of Analytics
Lema de 2023
Não contrate um Designer — Use @canva
Não contrate um Desenvolvedor — Use @carrd
Não contrate um Representante — Use @gumroad
Não contrate um Copywriter — Use @copy_ai
Não contrate um serviço de Marketing — Use @buffer
É 2023 — você pode começar um negócio com $0.
DNA Jokes and Images
Data & Analytics Product Management productmanagement
Quick Answer: How to Adopt Data and Analytics Product Management