COMMON SENSE
Data & Analytics Dubai 2022

POWERED BY
Google Cloud
Atlantis, The Palm, Dubai, August 30 - September 1, 2022, Successfully held

A conversational conference for Data Leaders focused on transformative data infrastructure and the analytic value that transformation creates.

COMMON SENSE
Data & Analytics Dubai 2022

POWERED BY
Google Cloud
Atlantis, The Palm, Dubai, August 30 - September 1, 2022, Successfully held

A conversational conference for Data Leaders focused on transformative data infrastructure and the analytic value that transformation creates.

What we discussed at
COMMON SENSE

Data & Analytics Dubai

The avalanche of data generated by companies and their customers should be a competitive advantage, but most companies are struggling to manage it all. There aren’t any simple answers, but emerging frameworks are helping us catch up with the growth in data. In this conference we’ll focus on the role of new models for data infrastructure and the new ways and reasons to invest in data analytics.

Who IS AttendING

Our conference is designed for 70 IT leaders responsible for their company’s data infrastructure, data strategy and analytics strategy, including CTO’s, Chief Architects, Heads of Software Engineering, Data Analytics leaders, Chief Data Officers and Chief Digital Officers from innovative enterprises in the Middle East region.

AGENDA

16:00 – 18:00

Registration

18:00 – 20:00

Welcome Reception

7:30 – 8:20

Breakfast

8:20 – 8:30

Keynote Address

8:30 – 8:55

Presentation by
Google Cloud

9:00 – 9:50

Panels

Theme – Data Infrastructure

Moderated by 

Inspira
The data lake didn’t kill data warehouses. Will the lakehouse finish the job?

The data lakehouse contains the best of both data warehouses and data lakes, with plenty of advantages. Are those advantages sufficient to move companies off legacy data warehouses?

Who will build the next bridge to data consumers? Data consumers were actually more self-serve prior to the advent of the modern data stack: Where is the Excel-like interface that knowledge workers can use to seamlessly interact with data in the modern data stack in a horizontal way?

Theme – Data Analytics

Why you should be recruiting analytics engineers instead of data scientists

The data scientist role is starting to lose its magic. Companies have failed to operationalize models, universities have produced coders who don’t understand business context, and data scientists spend hours on the dull work of dealing with messy, disparate data.

There’s an urgent need for insights to drive action, leading to the rise of the analytics engineers who create data products that include both business insights and the rigor of backend engineering processes.

An analytics engineer is more technical than an analytics translator and has deep knowledge of SQL. They combine that knowledge with an understanding of the business, a skill that many junior data scientists lack.

10:00 – 10:50

Panels

Theme – Data Infrastructure

Moderated by 

Inspira
Is Data platform as a service (dPaaS) right for you?

Many organizations, especially those starting their data journey, prefer someone to build and manage the data platform for them. Another option that’s gaining popularity is the data platform as a service so that in-house data engineers and analysts can quickly provision data pipelines while avoiding server installations, backups, and monitoring.

How are companies making these choices? Is there a case for making no choice, or boot-strapping an internal initiative? We’ll discuss the pros and cons of each in this session.

Theme – Data Analytics

Evaluating data lineage solutions

Implementing data governance policies intended to assert control and oversight over the quality of data assets is nearly impossible without knowing data lineage.

Lineage records simplify the analysis of root causes of data quality issues and the impact of changes to data sets in source systems. There are plenty of software solutions for data lineage; in this session, we’ll discuss what to look for in evaluating data lineage solutions.

11:00 – 11:50

Panel Session

Theme – Data Infrastructure

Tackling the 3 biggest data governance challenges

Robust data governance is the foundation for data trust, accurate analytics and regulatory compliance, but many companies are still in early in their journey. In this session we’ll discuss the 3 main obstacles to effective data governance and how companies mare overcoming them.

  1. Demonstrating business value
  2. Supporting self-service analytics
  3. Big data governance

12:00 – 13:00

Lunch

13:10 – 14:00

Panel Session

Theme – Data Analytics

Moderated by 

Google Cloud
Will cloud ecosystems make insight to action a reality in 2022?

Insights disconnected from action aren’t very valuable. If insights from ingested data don’t feed back into operational systems like CRM or marketing automation, the insights either go unused or require massive manual effort. Modern cloud ecosystems and APIs are now at the point where this problem can be solved without a massive development effort.

14:10 – 15:00

Panels

Theme – Data Infrastructure

Choosing the right MLOps solution

Machine learning operations, or MLOps, documents, manages and optimizes the full lifecycle of ML development from ideation to deployment.

MLOps exists not only to improve the quality and security of ML models, but also to document best practices in a way that makes machine learning development more scalable for ML operators and developers.

Some MLOps tools specialize in one core area, like data or metadata management, while other tools offer an MLOps platform to manage several pieces of the ML lifecycle.

In this session, we’ll discuss the MLOps landscape and how companies are selecting tools for their businesses. Key criteria include:

  • Data management
  • Modeling and design
  • Deployment and ongoing maintenance of ML models
  • End-to-end lifecycle management, which is usually available in full-service MLOps platforms
  • Project and workspace management

Theme – Data Analytics

Re-thinking data governance in the age of the modern data stack

Without good governance, more data creates more chaos, eroding trust in the data.

Data governance includes many use cases including the discovery of data assets, viewing lineage information, and providing data consumers with the context needed to navigate huge data footprints inside the modern organization. It’s been made more painful by the modern data stack that has made it so easy to ingest, model, and analyze more data.

How are organizations planning and implementing a more holistic view of data governance to get ahead of exponential data growth in the enterprise?

15:00 – 17:00

Networking and Sponsor Meetings

17:30 – 18:30

Cocktail reception

18:30 – 20:00

Dinner

8:00 – 9:00

Breakfast

9:00 – 9:50

Panels

Theme – Data Infrastructure

Identifying root causes of data quality problems

When incorrect data is discovered, companies react with data profiling and data cleansing and typically build a plan to avoid repeating the mistakes that led to it.

However, data quality issues are much more challenging to resolve when they span the enterprise. 3 common challenges create data quality issues and make it difficult to resolve them:

  • Not treating data as an enterprise asset.
  • Executive management indifference
  • No time to implement & monitor best practices

Theme – Data Analytics

Moderated by 

Inspira
Why AI/ML advanced analytics projects fail

Advanced analytics projects using ML are inherently complex with multiple points of failure. Companies can improve the likelihood of success by avoiding the most common stumbling blocks. Some of these include:

  • Model maintenance challenges
  • Lack of data governance
  • Model testing & validation

10:00 – 10:50

Panel Discussion

Theme – Data Analytics

7-step data quality improvement roadmap

Bad data is like a virus: It will spread into dashboards, spreadsheets and other analytics tools across your company. The key is planning for data quality instead of reacting when things go wrong. Here are 7 steps companies can take to ensure data quality up front:

  1. Begin by building a data quality mindset in the C-Suite
  2. Enlist data stewards and quality champions
  3. Hire the right skills, or train them
  4. Decide what metrics will usefully measure data quality in your organization
  5. Seek and destroy data silos
  6. Ensure data quality procedures begin at ingestion
  7. Deploy data quality management tools

11:00 – 11:15

Closing remarks

Some of our Speakers

Armin Kech
Head of Data Governance at MBC
LinkedIn
Imran-Chowdhury_347x347
Global Head of Data Protection & Governance at Al Jazeera Media Network
LinkedIn
Michael Vessey
Chief Data Officer MENAT at HSBC
LinkedIn
Mohammed Muzamil Sadiq
Head of Digital Analytics, BI & Automation at Al Rajhi Takaful
LinkedIn
Tamara-AlBsoul_347x347
Head of Business Analytics & Data Science at Zain Jordan
LinkedIn
Waliur-Rahman_347x347
Head of Consumer Strategy & Analytics – Middle East, North Africa & South Asia at BAT
LinkedIn
Michael-Naumov_347x347
Head of Analytics and Data at Teva Digital Health (Teva Pharmaceuticals)
LinkedIn
Kader
Head of AI & Smart Data Practice, Etisalat
LinkedIn
Oan Ali
Head Of Architecture at Ahli United Bank
LinkedIn
Padam Kafle
Head Of Information Technology And Automation at Aster Hospitals UAE
LinkedIn
Saurabh-Kapoor_347x347
Director, Data and AI at KPMG Lower Gulf
LinkedIn
Shahar-Zamir_347x347
CTO at Carasso Motors
LinkedIn
Akshay Saraf
Head of Analytics, Majid Al Futtaim
LinkedIn
Sri Lakshmi
VP, Head of Analytics and Artificial Intelligence, First Abu Dhabi Bank (FAB)
LinkedIn
Mohammed Junaid
Director, IT Operations (Infra, Network, Data Management, Cybersecurity and Service Management) at SAL – Saudi Arabian Logistics
LinkedIn

Transformative Data Infrastructure

The data lake didn’t kill data warehouses. Will the lakehouse finish the job?
Is Data platform as a service (dPaaS) right for you?
Metadata management and data catalogs
Re-thinking data governance in the age of the modern data stack

New Possibilities in Data Analytics

Why you should be recruiting analytics engineers instead of data scientists
Will cloud ecosystems make insight to action a reality in 2022?
Is data sharing essential for data-driven companies?
Why people analytics should be your top analytics priority

Transformative Data Infrastructure

The data lake didn’t kill data warehouses. Will the lakehouse finish the job?
Is Data platform as a service (dPaaS) right for you?
Metadata management and data catalogs
Re-thinking data governance in the age of the modern data stack

New Possibilities in Data Analytics

Why you should be recruiting analytics engineers instead of data scientists
Will cloud ecosystems make insight to action a reality in 2022?
Is data sharing essential for data-driven companies?
Why people analytics should be your top analytics priority
VENUE

Atlantis, The Palm, Dubai

Crescent Rd – The Palm Jumeirah
Dubai, United Arab Emirates

Quick Facts about
COMMON SENSE

Data & Analytics Dubai

VENUE & REGISTRATION

Dubai

Atlantis, The Palm, Crescent Rd – The Palm Jumeirah – Dubai – United Arab Emirates

TALK TO US

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Registration for the conference has been closed because we have reached the maximum number of registrations for this event. Thank you for your interest!

OUR CURRENT SPONSORS 

Google Cloud
Inspira

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