Building a successful analytics community – Fireside chat Brian Oblinger VP Global community Alteryx


Building a vibrant data driven culture within your company is one of the more challenging aspects of analytics. In this video interview with Brian Oblinger (VP Global Community Alteryx) , I’ve learnt a few tips and tricks that will help make you successful in your organisation in delivering centres of analytic excellence in your organisation.

Watch the full interview here



What is the role of “community” in the context of data and data science?

  •  Bring people together with a common goal as a team sport – Collaborating with different cross functional groups across the organisation drives superior results as staff are able to leverage each other share workflows, data and models. It also helps break down silos and builds comadre amongst staff.
  • Shifting the conversation to higher order analytics – A few years ago, conversation in the Alteryx community was focused around the data prep and blend, however todays’ discussion is focused on higher order analytics (like spatial, predictive analytics) and leveraging skills in R and Python and Jupyter notebooks to perform higher order analytics.

2.     How is Alteryx community helping to upskill analysts ?

  • Data science still does not have well established infrastructure – Alteryx believes it has a key role in developing the next generation of analysts. This is done through both online community academy, forums and offline activities as well as udacity nanodegreetraining courses.

3.      Future of Alteryx community

  •  Localisation and globalisation of the Alteryx community (both online and offline) – Alteryx has recently launched French, Japanese and German language community localisation.
  • Position analysts as thought leaders – Being a platform to enable analysts the opportunity to speak out, share their experience so that it is not lost in the organisation.

Building a successful analytics community in your organisation takes effort, focus and dedication. I am a firm believer in learning from others experience and hope you are able to leverage some of these leanings in your organisation.

Enabling analysts to achieve more – A fireside chat with Olivia Duane Adams , Chief customer officer & Co-founder Alteryx


Alteryx is a leader in the self-service data science and analytics market place, solving almost any analytical use case in so many industry verticals and companies of any size.


I have always had the thirst for knowledge and learning and was grateful to have the opportunity to learn from Olivia Duane Adams (Chief customer officer & Co-founder Alteryx) on how Alteryx is enabling a data driven culture and driving change within so many organisations.

Watch the full interview here

Here are some of her thoughts :

1.      Alteryx enables users to deliver a continuum of simple to complex analytics – Simple analytics may include joining 3 or more databases to answer databases all the way to data scientists who are doing predictive and prescriptive modelling. Business leaders are using Alteryx to change the culture of the organisation as they recognise the increased value and effectiveness of data driven decisions.

2.      Rise of the Citizen data scientist – There are many people in the organisation who may not have a PHD in statistics but are analysts that are truly solving the unanswerable questions for their organisation. They are willing to take on challenge, empowered by the technology and trusted by organisation to understand the value and complexity of data and answer tougher questions through higher order analytics.

3.      Rise of Alteryx in all areas including finance and audit– There is an explosion of analytics in the tax and audit space of companies of any size – finance teams have taken on Alteryx as excel is no longer sufficient due to the growth in the size and complexity of data. Alteryx is enabling finance teams to be true business partners and financial recommenders rather than data mungers.

4.      Enterprise adoption of analytics across the company- Every team in every department is trying to get to insight faster. Analytics is driving change in all areas of the company – from HR to sales operations, market operations, supply chain and transport. Companies using analytics are able to deliver the value from data and insight to drive their organisations forward.

5.      Analytics is a social experience – Alteryx enables people of all skill levels to speak the same language and solve business challenges together creating a comadre across multiple functional areas. Many teams within companies now run hackatons powered by Alteryx or run a certification program to see who can get to certification first.

I can identify with a lot of the themes and I am proud to have been a catalyst for change in the many organisations (Amaysim, Commonwealth Bank, consulting) that I have worked for.

Download the Alteryx product and see for yourself or reach out to me if you require any consulting assistance.

Come join me at the Alteryx’s first APAC Inspire customer conference in Sydney on Mar 20 – 21st to learn from the founders on how you can leverage analytics to deliver value in your organisation.

#Alteryx #CPAAustralia #datascience #analytics

Alteryx = Analytics – A Fireside chat with Dean Stoecker, Chairman, CEO & Co-founder Alteryx



Alteryx is a leader in the self-service data science and analytics market place, solving almost any analytical use case in companies of any size and industry vertical. I discovered Alteryx a few years ago whilst in my previous role at Amaysim through a fellow Alteryx user who presented at the Alteryx Inspire conference.

Come join me at Alteryx’s Inspire conference in Sydney on March 20-21st to learn how Alteryx can help you and your company Alter everything.

There will be keynotes from both Alteryx founders Dean Stoecker (Chairman, CEO Alteryx) and Olivia Duane Adams (Chief customer officer) and product management – Katie Haralson as well as numerous customer stories on how they have used Alteryx to drive improvements in their business.

Watch the full interview here

As an analytics leader, I have always been passionate about learning and helping businesses leverage technology to deliver financial value and am excited to share what has worked for me with everyone. Alteryx also works seamlessly both on premise and in cloud platforms like Amazon web services and Microsoft Azure. In fact I personally run the product including Alteryx server in all 3 enviroments (on premise and cloud) and it works beautifully.

Personally I am a big fan of the company and am also a Alteryx ACE. Most recently I had the opportunity to chat and learn from Dean Stoecker (CEO, Chairman and Co-founder of Alteryx) on the future of data science at the recent Global Alteryx kickoff.

Here are some of the key highlights

  •  Alteryx is a great general purpose platform for companies to monetise the data and intellectual property that they hold.
  • Increasing convergence between the PHD statistician and the citizen data scientist using the platform.
  • There are more data workers in the world than first thought – A guess that nearly 50% of users of Alteryx don’t even have analyst in their title.
  • More people are creating data and are curious about using data to understand things in their work world and personal world. This creates more questions across a broader audience of people.
  • Alteryx is a versatile platform with many use cases in many industries. Some examples used by accountants include Robotics process automation, Tax and audit, FX risk modelling and more.
  • Increasing verticalization is occurring across industries like Healthcare and Govt.
  • Alteryx is focused on designing and making the platform easier to use for more people whilst advancing higher order analytic outcomes (Assisted modelling, Auto modelling, Curating data assets, Deploying machine learning)

Download the product and try it out for yourself.

#Altereverything #Alteryx #analytics #datascience #cpaaustralia

MIP Titans roadshow

An interesting session hosted by MIP showcasing Alteryx, Alation and Wherescape Red.

Certainly a fantastic experience to see Dean Stoecker representing Alteryx and sharing experiences with the audience.

    Nearly 2/3 of the audience are new to the titans event

Most of an analysts time is wasted doing mundane processes.

  • Even worse is less than 11% of data science models make it into production .

Can your company afford not to transform digitally?

  • The half life of an enterprise has been dropping significantly
  • To not transform would be to disappear ! Just look at Kodak , blockbuster and other companies

Digital transformation and its challenges

Fragmentation of the technology stack has made it hard to transform

  • agile tools like Alteryx to enable us to acquire data from any data source whether on premise, in the cloud or anywhere.
    Only 6% of data required for advanced analytics is in your warehouse

Analytics is not just about KPIs and dashboards for the business. The future will be won and lost on productionalisation of data science models

  • Analytics is a social experience , power is not in what you know but what you share.
  • Share and scale across the business.
  • Analysts should become margin discovers of profitability for the enterprise

Personas generating value in the enterprise

    Data scientists – hard to find and need to focus on high value tasks
    Pilots – citizen analysts who can lead an apply themselves in the business context , asking and answering questions.

Stop wasting time , use the right tools and go home early!

Analytics is also a social experience-

Driving a data driven culture across the company is equally as important as having the right tools

Chief Data Officer Melbourne – Day 3

Today’s session opened with a fantastic presentation by Ravi Vijayaraghavan , VP at Flipkart.

It was amazing start to learn that Flipkart is “The Amazon of India”. It reminds me of the presentation made by at the  Databricks AI summit where there  analytics and artificial intelligence are used to solve real world business problems ( eg: supply / demand forecasting, fraud detection , optimise conversion via automated image recognition).

Presentation by Ravi Vijayaraghavan , VP at Flipkart

Key takeaways :

  • Flipkart is the “Amazon” of India
  • Flipkart has nearly 120m customers, with over 80m product reviews
  • They have a team of 200 analysts (comprising of data analysts, business analysts who build predictive models and a small core data scientists).
  •  Business analysts can build predictive models and have a good understanding of statistics.
  • Scale of data matters and can given a lot of insightsOver 120m users they really have a detailed understanding of customers
    • Mobile phones are transforming India (not laptops) – Penetration of mobile phones reigns supreme.

Scale of Flipart data platform

  • Leveraging the data platform and machine learning platform at scale
  • Distinguish between data assisted human decisions (eg: Operations planning/strategy) vs moData assisted machine decisions (listing quality, selection quality, fraud, personalisation)

A culture of continual experimentation : The need to continually experiment -> nearly 50+ launches a week which go through an A/B test.

Data translating to business decisions across nearly all areas including product insights, user insights, traffic, price elasticity modelling , supply chain optimisation etc,

Data science vs Measurement science

Measurement science equally as important as data science 

  • Understand the company strategy, identifying the areas of focus,  frame it into Level 0 metrics.
  • Articulate these L0 metrics then build out a statistical model for the underlying driver metrics
  • Assign each of drivers to the relevant business unit head to ensure accountability

Business problem:  Flipkart net promoter scores were dropping and they wanted to turn it around.

  • Flipkart felt that they lost ground in net promoter scores compared to a few years ago.
  • The execs and Ravi set an ambituous goal to improve net promoter scores by 10pts in a year.
  • Ravi’s team created a statistical model that looked at the relative importance of each driver, its sub drivers and assigned accountability where ownership is directly attributed to each of the sub drivers.
  • The key here is really assigning KPI accountabilities to the relevant business area and holding them to accountPricing  index assigned to Business category
    • Product category assigned to the consumer platform etc etc

Business problem  : How can we help customers purchase better through the use of online reviews?

  • Over 80m reviews so it cant be done manually.
  • Initial simplistic algorithm was based on ‘proportion of up-votes’ and recency of review however they found this was not working well

Applying machine learning to incorporate natural language ‘sentiment’ together with other metrics to inform a more balanced view for customers

Business problem solved by machine learning : “How much do inventory should Flipkart stock of each item? 

Leveraging deep learning and computer vision to Improve purchase conversion rates and reduce fraud  

  • Customers are sensitive to the quality of images and they dont seem to purchase/convert if its a poor quality image.  Leverage computer vision to identify and classify images by extracting key features
  • Auto categorisation of images that do not comply with standards (eg: images with a shadow)
  • Using image recognition to correctly scan product items to find their “Real retail price”

Presentation by Yellowfin – Future of analytics departments

  • As an industry, there is a need to shift to using BI to create value for the business
  • Majority of companies still struggling with preparing data
  • Focus on people with techniques and don’t hire for people with tools skills
  • We need to run analytics like a business

Round table Panel discussion: Aligning your data strategy to support AI

  • Focus on business value and not Hollywood AI.
  • Need to assign a business owner to each analytics initiative.
  • Focus on educating and applying the solution in the business.
  • Most companies still struggling with data prep and acquisition. AI not the real problem
  • Bring use cases, quick POCs , bring minimum viable product . Showcase to business and leadership teams

Presentation by Agustinus Nalwan, Head of AI at

Data quality is critical for AI to work effectively

  • Rubbish data will generate rubbish AI outcomes  eg: see example of generation of baby names

Cyclops 2.0 is car-sales image recognition software built in-house using the tensorflow framework

  • Incredible accuracy
  • 1st version was built in a few months and 2nd version built in 5 months (eg: Spent 1 day a week for next 3 months and buildout AI )
  • Key learnings:
    • AI enables competition advantage however
    • User interface design and the way customers use the product is critical for adoption to occur.
    • Use of transfer learning enabled the model to saved a lot of computation time( 4 weeks vs 8 months)

using AI to do comparisons on cars

leveraging AI to build features – Tensorflow

remember to incorporate customer behaviour

Roundtable : Future proofing for GDPR discussion

Key learnings include:

  • Impact of data breach can be significant eg: equifax., this can be used to educate your board to invest in data and analytics.
  • Run a scenario with your board for data breaches and the potential costs and impact.
  • Take the opportunity to re-architect IT to support the business outcomes.
  • Make the effort to identify data owners within the company.
  • Data Stewart/data owners can be segmented by:
    • geography
    • department :
    • data domain (can be multiple ) – Don’t look for perfection
  • Identify process owners, applications owners, data owners, and IT owners.
  • Data Stewart’s also funnel things upwards via management reports. Analytics can be used to influence data Stewards. eg: inconsistent management reports
  • Setup a support function- Leverage existing IT platform eg: servicenow to align tickets. Ticket can go to legal for GDPR deletion, only the end user can close the query.

Chief Data Officer Melbourne – Day 2

Another fantastic day in Melbourne for all analytic professionals attending the Chief data officer Melbourne event at the Park Hyatt.


Presentation by Anwar Mirza , Chief Data Officer TNT Global 

Anwar is an amazing speaker, sharing his 30 years of data governance expertise with the audience. After all its all about understanding and leveling the playing field with analytics.

Key takeaways are :

  • Shift analytics from compliance to enable digital transformation. We are here to not to just deliver data but instead to support business outcomes
  • Define what comprises data governance, master data management and business outcomes.
  • Data governance is the control and support of
    • Business definitions
    • Business rules
    • Master data management
  • All of us own the data and not just the chief data officer
    • There is an opportunity to setup a business analytics support desk whereby anyone in the business can call up just like a IT helpdesk.
  • Put a tangible value on the actual data itself
    • Data quality index
    • Completness
  • Typical cross functional processes may give
    • Incorrect or absent business rules which can then be used to derive a cost of data quality. A fantastic example was given on the unit of cost delivery



Quick poll of key challenges facing analytics professionals

There remains large challenges around data governance and modernizing legacy systems 


Presentation by Bala Ayyar – Chief data officer Société Générale

In any crisis lies an opportunity.. During the GFC period, there was need to run analytics in real-time and intraday especially in a crisis scenario eg Lehman brothers collapse


Building a strong defence is key to enabling the enterprise


Don’t be the next KODAK – Leverage your data to enable value creation such as improving the customer experience, driving operational efficiency and insights as well as monetise data.


Does your data strategy deliver a google like experience? Why do I need to get a data scientist to call a IT database guy.

  • Data is still viewed as a departmental process not an enterprise capability
  • Legacy processes and systems
  • Cultural willingness to change and drive decision making.

Data lineage

  • Starting and the end points appear to be more important than just the middleware
  • Experiment with different tools  to see what works

Presentation by Marklogic : Moving from just observing the business to running the business

Presentation by Mario Vinasco, Data science & Analytics at Uber

A few breakthroughs in AI came from image classification, availability of cheap computing resources to do scale and compute with a neural network.

Business question: how do we identify those drivers or customers who will churn

  • Most importantly is the way a classifier works eg: driver churn, riders who order Uber eats , people who unsubscribe
  • Similar to classifying cats and dogs, eg driver history , how far from home, collect many data points.
  • Tips of displaying churn outputs to the business
    • Setting a threshold
    • Evaluate ratio of true positives to the rest

Business problem: The business asks ‘how many emails should we send to our riders and driver’

Uber has a history of running other models to solve similar business problems.

  • No model is 100%
  • Split things into deciles and compare model output
  • Triage things into simple high medium and low (email open probability vs unsubscribe frequency)
  • Combine business intuition and model


Business model problem: how many u we customers will want to get an Uber eat account

  • Focus on people who may need help with converting ie: Middle of the pack
    • Don’t worry about those that fail to convert on the left,
    • Those on right will convert on right at high rates.

Example of optimisation of a key performance indicator eg: Marketing $

  • Marketing mix models
  • Top down attribution via mixed models
    • Smart marketers can just use a handful of curves until marginal return diminishes. Estimation of curve is hard

How much of extra marketing can lead to incremental trips?

Learns the parameters by iterating through

Roundtable discussion on centralised vs decentralised analytic models: 

Carsales operates a hybrid model ensuring skills and integrity of data and definitions are maintained

  • Centralised model:
    • A central team may be helpful to grow the capability given small pool of deep capability and expertise ? Like minded come together challenge each other and learn
    • Creating a community of practice is helpful.
    • Consistency in tools and skills will enable users to tap into various business data sources
  • De-centralised model:
    • Evaluate if there is enough data literacy in the business before embarking on a decentralised model
    • Focus on building reuseable data assets and reduce duplication of data.
    • Decentralised doesn’t mean that teams don’t talk to each other. How do we co-locate analysts to enable people of the same mindsets talking to each other
  • Hybrid model:
    • An alternative model is to have BI functions in the business , centralise of data science function to get scale.
    • Combine teams with diverse skill sets ( engineering, data science , IT) to get maximum business benefit.
  • Focus on developing productive relationships with IT, be the conduit between technology, business and other stakeholders

Chief data officer forum – Melbourne Focus day 1

Today I had the opportunity to attend the chief data officer conference in Melb on 3rd Sep 2018. It was the second time that I have attended the chief data officer summit and it was such a fantastic learning opportunity for analytic practitioners.

The day started with a fantastic keynote by Chris Butler, Chief data officer HSBC APAC.

Some of the key learning include :

  • Data governance comprises policy and standards , measurement , quality and change management.
  • Data is generated and dependent on processes and other parts of the organisation .Processes create data , fix the root cause instead of fixing data quality issues downstream. Take for example when a user interface is not properly designed Eg : include country for swift funds transfer. Address the root cause.
  • Measure the effectiveness of the process eg how many times does it take to get the right address
  • Talk in business language establish ownership
  • Dashboards must be intuitive, have a target and people must do something with it.
  • Embed data quality metrics in executive scorecards. CEOs should have a data quality measurement ( largely met , fully met, partial met. Don’t give just numbers )

This was followed by a Roundtable of CDOs which I hosted in data governance. Key learnings are

  • Data governance requires continual effort and focus to fix at the source.
  • Data governance is no longer a nice have but instead critical for the growth of both new and iconic companies
  • Developing a vision and align data governance to a business problem is a fantastic way to sell it into senior management

Presentation by ANZ bank – Paul Davies head of data governance

  • Distinguish data quality issues from source and target. Conscious decision around remediation and risk acceptance.
  • Data quality is an operational risk and it’s everyone’s problem
  • Align Data ownership assignment with process ownership
  • Win the hearts and minds – creators of data
  • Process is important as data quality as it links to data. Raise it as a data quality issue
  • Distinguish data quality issue vs system enhancements- people put system enhancements . Prexisting requirement Data quality vs data gap

Embedding data into the DNA of your company

Data governance by AiHua Kam, Standard chartered bank

Have a data requirements document , the standards and what it means. Data acceptance testing – profiling and testing.

Presentation from QBE head of data

Link business risks to data issues. One can inform the other.

An opportunity to align both offensive and defensive strategies together.

Realising the benefits of analytics requires a strong foundation. – data

It is important to include data ethics given the changing dynamics

  • build quality in and don’t just inspect quality coming out.

Data stewards – go beyond traditional KPIs and measurement

Recognise what people do. Creation of data ninjas – those that champion data in the business eg: governance

Presentation by GM information management Medibank

    opportunity to simplify and centralise 4 data warehouses into 1. Currently 2 in cloud ,2 on premise. 7 Bi tools looking to reduce to 2-3.

nearly 140 FTEs across analytics

Link a business glossary ( with appropriate business owners) to the physical data assets

Leverage the same metric from the global library of the metric owners.

Step by step guide to deploying alteryx server in complex enterprises

Alteryx server

  1. Service account Permissions – Critical to stable functioning of gallery/schedulingRun As setup 
    1. Logon as batch – setup this for all controller+workers+ active directory gps 
    1. Service account permission – reading from file servers
  2. Running In-database workflows:Windows auth vs embedding SQL credentials. Use a file DSN for max stability.
  3. Firewall relatedFirewall setup of proxy configuration to enable alteryx to work behind a corporate firewall – ProxyConfig
    1. Firewall/proxy settings – whitelisting *.alteryx to enable; Make sure auto proxy config via pac file is turned off.
  4. Setup of SSL – Gallery ;  
  5. Windows authentication errors for authentication across multiple domains– set browser prompting Authenticating across multiple domains in alteryx gallery
  6. Changing default ports for alteryx scheduling – you may need to do this if firewall zones are block common ports (eg: 80) and you need to redirect to port 8080 etc.
  7. Sending email via secure SMTPsendemail.exe -t (toemail) -f (fromemail) -u (subject) -m (body) -s (smtpsecuremailserver) -xu (username) -xp (password) . Install the sendemail.exe on each of workers e:\ to enable PDF documents to be sent out
  8. Shared common locations for controller and workers to allow macros to be put in common area accessible by serviceAcct
  9. ODBC Driver installation
    1. Auth mechanism – must be consistent(Windows Auth or SQL Auth)
    1. SQL native client 11 must be consistent across controller/worker and designer.
    1. System DSN name must be consistent as well
  10. TroubleshootingIntermittent workflow failure when scheduled or executed via gallery (restart alteryx service)
    1. Data connection fails using fileDSN – Ensure data source on server is configured to use the same version (eg: Sql server native client 11 and also ensure data source name is configured the same)
    1. Alteryx worker fails to connect to controller –Troubleshooting a server
      • Reset /regenerate token from controller;
      • Insert new token into all workers and also ensure right port number 8080;
      • Ensure controller and workers running under the right service account
      • test firewall ports with telnet;
      • check c:\programdata\alteryx\runtimeSettings.xml service port config on both controller and worker
      • Check if any security patches updated server
        • control panel-> programfiles-> programfiles and features->installed updates
    1. Setup of Logon as service account eg: acoe_alt_tri
    1. Telnet port; netstat to see where it is listening on
    1. Enabling alteryx prod API key – logon gallery as curator. Settings -> click keys tab; enable admin api; copy the key and secret.
    1. Server logging
  11. Security related:Reset controller token every [xx] days
    • Deletion script to flush temp files on gallery
  12. Backup and restoreMongoDB backup and restore 
  13. Performance optimisation on server

Alteryx designer

  1. Software Packaging – include crew pack macros, publish to tableau server 1.08, 2.0, Azure data lake, alteryx connect loaders 
  2. Testing / dev packages between versions eg: v11-11.7 vs 2018.2
  3. Licensing requirements – , firewall change for packet inspection

Alteryx connect

  1. Alteryx connect – reverse proxy to enable SSL support 

AWS Sydney summit 2018

I dropped by the Amazon summit on Apr 10th 2018 during lunchtime to learn on what is going on in the biggest cloud vendor in the world!

This raises an interesting question – can companies afford not to go to the cloud when competitors are achieving cost and customer experience advantages through the cloud ?

Key takeaways

1.What it means to be day 1 at AWS – innovate backwards starting from the customer.

Working backwards is a framework for how to think about product without lengthy roadmaps that end up being scrapped. It’s a way to short-circuit the traditional product development track, and make sure that you build something that your customers will actually care about.

For new initiatives a product manager typically starts by writing an internal press release announcing the finished product. The target audience for the press release is the new/updated product’s customers, which can be retail customers or internal users of a tool or technology. Internal press releases are centered around the customer problem, how current solutions (internal or external) fail, and how the new product will blow away existing solutions.

2. Serverless computing enables companies to focus on delivering better outcomes instead of worrying about capacity reducing operational expense costs – iRobot is a good example of this, switching to server less architecture delivering faster business benefits through continuous iteration without having to worry about scaling

3. A future where companies compete based on talent and agility – AWS cloud levels the playing field from an investment / capex / opex perspective.

Smaller companies can harness this to their advantage while larger corporates have the opportunity to use cloud to achieve more with less.

4. Partners are becoming specialised eg: Deloitte has an entire practice dedicated to GDPR practice to support customers

Welcome to AWS innovation day in Sydney !

An interesting demo of the connected factory

Customer experience demo of the futureGood to see the Tableau stand at AWS Summit

Gartner conference 2018

Fun pictures at Gartner conference with Alteryx, Datarobot, Tableau and MIP!

This way to Gartner….

Great to catch up with JJ, Christian& Kane at Alteryx! Also fantastic to meet Steve Walden all the way from California

Excited to use Alteryx with datarobot to support churn modelling

Great to see so many familiar faces at Tableau and MIP Australia