DCDAO Online A/NZ 2021 (2-days conference)
I would like to thank Craig for the invitation, hopefully by this reminder on urging us to be more data-centric and less model-centric. Here is what I observe and absolutely fantastic discussion and people representing their competitive products, as a reminder, this would be a post I can read back for future research.
Welcome from Corinium platform on virtual and online events including Keynote Insights! Yes, I am intrigued with the exposure of panel speakers and power of the enterprise adoption tools that enhance the data and technological aspects of their products, each speaker live up to their work and show them exceptionally! Unfortunately I am unable to attend all seminar due to other commitments :)
tableau Data Culture & Tableau Business Science
— — they are absolute tools for essential business people who are new to data visualisation and a good news they will leads to AI & Machine learning sector — — — —
Data Dialogue: Building Business Resilience with DataAshley Howard-Neville, Senior Evangelist, TableauJeremy Blaney, Director, Tableau Blueprint Product Manager, Tableau
We couldn’t be more excited to establish this class of AI-powered analytics with Salesforce’s Einstein Discovery engine in Tableau. Built with transparent and ethical AI, it’s a proven technology that gives business teams visibility into key drivers behind the results and the potential for bias, allowing for deeper understanding and confidence in decision-making.
Democratizing AI and ML comes with important considerations and responsibilities This is an emerging field, so it’s unclear how the landscape will change as new technologies and use cases are discovered, and as ethics standards and regulations become more commonplace. AI technology will further be shaped by forthcoming regulations from governing bodies in the European Union, and as more state and federal governments in the US implement new artificial intelligence controls. Given the evolving landscape and level of disruption seen in the past year, organizations are being put to the test: digital transformation is no longer on the horizon, but is here. Enterprises are figuring out ways to manage the current influx of data and AI innovation and its effective applications — including how to use AI to be more agile and resilient.
Meet Tableau Blueprint
We curated Tableau best practices and the expertise of thousands of customers to help you turn repeatable processes…
Powering the ESG Revolution: Agile + Collaborative Data Science (Dataiku)
- How important is the funding model to support agile innovation POCs versus operationalisation. How are POCs to be funded (innovation budget) vs BAU (operationalisation budget)?
- Sorry didnt articulate it better… u said we need to define the measures for the data that we are going to incorporate in our hypothesis, so is there a framework that one follows for defining those measure along the way when we are doing our requirements analysis phase etc.. ? makes sense ? and how to validate that if it is a correct measure? and not too much of a deviation from where we want to be and track our progress against any clearer ?
- Ah got it, yes defining metrics is very important, and the place to start is to see what data is available and also the data closest to business metrics meaning the KPIs that are being used to evaluate business performance.
When I asked about the cloud transformation and techniques onto the cloud native manifesto:
Excited to hear that, Novia. You can apply complex business transformations on the data using standard ANSI SQL.
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how do you balance data discovery and agility with the risk that people may connect to imperfect datasets? On the mentioned above from another researcher of data-centric shift
“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”
Andrew Ng, Co-founder and head of Google Brain and was the former Chief Scientist at Baidu
What is data cloud?
Data Dialogue: Snowflake — The Data Cloud Interview James Harnishmacher, Regional Manager, Sales Engineer, Snowflake
Connect this with snowflake account…
- Great question, Abhi. Most customers create multiple domains in Snowflake and handover the control to each domain. You can then share data in a secure manner between multiple domains. For example, you might have a Snowflake account for finance, one for marketing and one for the data analytics team and share data between them.
so how about a Common Data Model to address that data democratize to give that visibility and access to data to every person ? kinda DW / data mart concept but more to be built as codeless access
Data science is “37.7914° N, 122.3951° W + 2,03 x1⁰⁵mm in 44,7° => 37.7932° N, 122.3947° W,” where Business Science is “take the third left, mind the traffic, and you have arrived at One Market Street.”
Hi Tym, agreed Informatica is not directly involved in building the AI/ML model, but AI/ML models are as good as the data that is fed into, this is where data management and data governance solutions come to the fore. IAG was just an example used to highlight the value Analytics bring to drive business value. No reference made to say that Informatica played a role.
Data Dialogue: Responsive not Reactive- The Data Offense in Modern Data GovernanceAnand Ramamoorthy, Director, APJ Head of Data Governance and Data Security, Informatica
I have a question: Anand mentioned IAG being able to settle a claim using AI in only 24 min. What role did Informatica play in that achievement? I understand Informatic does data management, but I didn’t think you delivered AI / ML solutions?
Data Driven & Customer Centric — The Ultimate Goal -
James Bailey, Director of Education, Data Science and Ai Association of Australia (DSAi),
- Virginia Wheway, Vice President, Data & Analytics, koala,
- Todd Stevenson, Chief Member Outcomes Officer, Colonial First State,
Moving from legacy system from we build them, but now we lead to plug and play to play with the experience.. So we implement and to know that great to know that black box data is being reviewed in real time!
- Joy King, VP Product & GTM Strategy, Vertica
Read the report to learn how Vertica in Eon Mode:
- Achieves the best performance in all benchmark tests for scale and concurrency
- Runs the most queries per hour, at every level of scale and concurrency
- Cuts performance costs 45% — 73% over Amazon Redshift
- Slashes performance costs 84% — 92% over the unnamed data cloud platform
Workload management — troubleshooting in data warehousing
Data Dialogue: Rise to the Modern Data Warehouse Challenge David Thompson, Director Professional Services, Cloudera ANZ and Justin Hayes, Senior Director, Product Management, Cloudera
- Top Three Issues Facing the Modern Data Warehouse
- SMARTOFFLOAD: Migrate Your Data Warehouse to Cloudera
- “What differentiates a good deployment is the quality of data; everyone can get their hands on pre-trained models or licensed APIs.” The competitive advantage and significant improvements are usually found by increasing availability of high quality data. Improvements in data quality normally dwarf improvement in enhancing the modeling algorithms, since the teacher in the process is the data during training!
What are your biggest challenges in data in a post covid world?
- from a data perspective it’s more important than ever to be easily able to join in store purchase behaviours to online purchase behaviours.
Data Dialogue: Applying Data in New Ways Aaron Pratt, AI & Advanced Analytics Manager, David Jones & Country Road Group
In pandemic situation, mostly on retail industry, we are now focusing in e-commerce and delivering the online delivery..
- Market Basket Analysis in Retail — Application of Association Rule Mining Technique
- Customer Sentiment analysis for an e-commerce retailer
- Predict Holiday Sales for A Retail Client — Application of Linear Regression
- Industry vertical exposure across Banking, Retail, Manufacturing, etc.
- Industry standard virtual assessments and long-term platform access to enable one’s analytics & AI journey.
- Career assistance via portfolio showcase and recruiter interface; Social Recognition and Personal Brand Creation.
Unlocking the Power of The Data Cloud & Top 6 Data Science & Analytics Trends for 2021
This is some cool digital brochure I am willingly
Six Strategic Steps to Democratizing DataThe Eight Essential Elements of Data Empowerment
Powering the ESG Revolution: Agile + Collaborative Data Science from Data Governance to AI Governance
- Data wrangling
- Data governance
Data Dialogue: Where Next Meets Now: Data Visibility and Transparency Bee Ser Chan, Senior Channel Marketing Manager, Quest | erwin by QuestSam Benedict, Vice President — Solution Strategy, Quest | erwin Data Intelligence Suite
What’s the value of data governance and how do you quantify it?The Evolving Role of the CDO at Financial Organizations: 2021 Chief Data Officer
Data Dialogue: Data Analytics — Making a DifferenceCraig Napier, Chief Data Officer, University of Technology SydneyKwame Wetsi, recently Chief Data Officer, Catholic Schools NSW
Data Virtualization The Modern Data Integration Solution DV for Dummies
Data Dialogue: Data Democratisation for Faster Decision-making and Business AgilityChris Day, Solution Architect, DenodoKatrina Briedis, Solution Architect, Denodo
D&A Innovators: D&A Skills-Sets and Capabilities
Giving Voice to… Teams, Talent & Diversity — Upskilling Staff & Yourself Guest Host: Rolee Satyam, Partner, Customer Data, Analytics & Insights, BigW Panellists:
Building on the hub and spoke model to support. Like Optus, they do partner with uni to run some hackathon competition to enhance open source, approach to analytics course content and also external traininf from freelance :) This is so cool knowing company do like to skill up their employee
Team culture and curiosity, also patience on timing to develop and unfold. After that, the real challenge are to retain ‘trained’ employee so they become fulfilled!
Give analysts deep business acumen and focus to access develop their own ‘personalised’ career plan
COE — helps to identify talent within organisation, they believe using the internal business people who understand would be much easier to train than simply recruiting outsiders
this process also helps to do pilot and roll out or simply to have analytics roles such as agile — running in sprint and each sprint are celebrates with a small win.
Angela Kim, Head of Analytics & AI, Teachers Health
Ji-Hyun Kim, Director, Automation, Data & Analytics, Optus
Harini Bharadwaj, Senior Manager — Data Platforms and Architecture, SBS
“It is almost as an art managing data-centric management and its people”
Thanks for inviting close of event from Corinium
Recording for the session
https://cdao-anz.coriniumintelligence.com/replay-the-sessions#utm_source=Reply Sessions&utm_medium=Survey Monkey&utm_campaign=0624 -CDAO Online A/NZ&utm_content=Reply
It’s a great question. At the end of the day, I strongly encourage organizations to create governance processes for both data and content. This includes developing guidelines around what is meant by imperfect data as well as what needs to be done to convert it into perfect data.
(External Resources) — Handouts
- Powering the ESG Revolution: Agile + Collaborative Data ScienceFrom Data Governance to AI Governance
- What’s the value of data governance and how do you quantify it?The Evolving Role of the CDO at Financial Organizations: 2021 Chief Data Officer
- Top Three Issues Facing the Modern Data WarehouseSMARTOFFLOAD: Migrate Your Data Warehouse to Cloudera
- Data Virtualization: Achieve Better Business Outcomes More QuicklyThe Business Value and Benefits of Master Data Management