Journey into analytics


Thanks for stopping by! I am blogging to share my experiences with you on how i got started into analytics and hope it will give those inspire others to reach out and help others.

How did i get into analytics?

4 years ago while working as an senior analyst within a large insurer,  I was tasked with cutting SAS code to answer questions that business partners had. Why is my revenue ahead or behind budget, could it be due to the volume or price or mix of new business?

The trouble with this was that it took considerable effort and days to get answers to simple questions. I thought that there must be a better way so i went online and discovered that there was called Tableau.  For those new to Tableau, it is best described as a pivot table on steroids! Download it and give it a go. You can simply load data into the tool and then it draws fantastic looking graphs almost immediately.

Don’t take my word for it, go download the product and see how it works for yourself!

Having used Tableau with SAS for a few years, I came upon another tool called Alteryx.  One night , I came across an presentation by a BCG consultant at a Alteryx Inspire 2014 conference and it sparked my interest on how i could be using it within my company to help business users get to the answers they need faster.

For those new to Alteryx, It can be best described as a graphical drag and drop workflow that can be used to combine and pharse numerous data sources together in an easy to use way such that anyone can use it!

Alteryx is really a 3 in 1 product supporting data preparation, spatial analytics and predictive analytics and works fantastically well with Tableau.

Don’t try and do everything yourself!  Buy world class products that can make your analytics lives easier!


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

Welcome to 2018

A new year and new beginnings

As a new year starts , I have moved into a new role within the CommSec business leading the Alteryx deployment working for Vizchic!

It marks certainly a return to my passion as we will be deploying alteryx server, connect to enable self service analytics for the business.

Change and organisational benefits

One of the observations is that as a leader it is important to sell the benefits of deploying such a product to both business analysts and management.

Reduce the time to insight

From an analyst perspective using tools like Alteryx enable one to reduce the time taken to get business value. So many organisations I see hire so many analysts but do not give the the tools to enable them to be more productive. Many analysts will spend numerous days and hours cutting code instead of focusing on the business outcomes eg: delivering insight that increases revenue or cuts cost to the business.

Alteryx used to derisk legacy processes and SQL code

More serious than that often I have seen large blocks of SQL code left undocumented and not properly maintained often some of these being used for mission critical operational processes.

Alteryx on the other hand can be used as an easy way for teams to document their existing processes making it sustainable to run.

A story of upskilling and continual training

From a management perspective , it is important to practise what you preach. I continually upskill and learn about the various new elements of alteryx through continuous training and certification on top of a day job of managing 2 teams. This also helps me understand things from an analyst perspective and enables me to be a better leader.

Listen and learn from others – globall

I was excited to get to meet Stephen Junior who deployed alteryx at redbox in the US. We will be certainly sharing experiences and learning from each other.

A bit of a holiday before I start my new role

Lots of photos here from my recent trip to Mudgee. A heaven for wine and cheese tasting. A big shout out to Kane Glendenning for his hit list.

Funnily the day before I left for my weekend away at Mudgee , I did my alteryx certification for designer core and got a good result !

Some beautiful photos of the town

Alteryx certification – highly recommended for those wanting to invest in themselves

Reflections on 2017

As Christmas draws to a close, it’s often a good time to take stock of the year that had been and the year to come.

But first some nice photos of New Years at Freshwater beach at Manly – highly recommended way to spend New Years especially with children.

Personally for me it’s been a busy time for me. Having worked in both large and small companies it’s interesting to see both the opportunities and challenges facing both.

Agility in decision making and acting as 1

Larger corporates have the opportunity to improve speed to decision making. As senior leaders it is imperative that they collectively think wholistically ( sales , operational restocking, customer experience , IT application and infrastructure ) rather than their functional silos. This is not that say there can’t be disagreements about the direction. Instead it’s about disagreeing but deciding to act collectively as 1.

As a result , sales and operational teams can focus on delivering the work which will enable revenue growth.

Innovation is not a buzzword , instead this is critical for survival

The ability to constantly develop , iterate and deploy new products and services to customers is key in driving top line growth.

Take amaysim for example they are able to launch new products within a short 1-2 month window. That delivers incredible agility which allows them to test and iterate new offerings with the market.

Partner with suppliers

When faced with funding and resource constraints, one seeks out win-win partnerships with Vendors and suppliers to achieve more with less. Invest the time to visit suppliers and seek out ways they can benefit from having a relationship instead of just pitching a reduction in margin and price for their service offerings.

Self desruption is important

Being in an incumbent market leading position is important however a good understanding of startups and acting to self disrupt is more important.

Larger companies face the challenge of changing their business models to defend against smaller more nimble competitors. In such a scenario it is often critical to assemble a ‘hit squad’ of the best people to build, test and deliver those new products and services to the market without the bureaucracy of a larger machine.

Executive endorsement and sponsorship is critical to the successful launch of a project.

Certification and investment in people is critical

As part of ongoing skills improvement and development often managers need to reinforce the need for analysts to upgrade their skills and keep abreast with the latest changes in the market. Alteryx offers free certification for the fundamental designer course here. I highly recommend it to anyone who wants to improve their skills.

We should not look to certification as the end rather we should embrace it as a continual journey to learn.

That’s all for me for now!

Learnings from presenting at CPA Australia 

Last week I was invited by CPA Australia to a lunch presentation session to showcase analytics to  government organsiations. It was a fantastic session with many departments represented including Aust police, Health, Department of education and many more NSW government bodies.

The purpose was to facilitate discussion on how to best implement analytics to deliver business value.

My presentation can be found here  for anyone that wants to leverage it.

Some of the key challenges include the usual legacy systems but also most interestingly the challenge around people’s mind sets and defining the business problem. Often in analytics many BI managers are fixated on the latest and greatest technology that they forgot that the Tech is meant to serve a business purpose.

Framing the business problem and articulating the value of analytics can be one of the most important steps in leading an analytics team.

Another interesting point of discussion was the number of data scientists required in a team. A lot of people have over hired or under hired data scientists. To those new to data science it’s just a fancy title for applied statistics or maths or physics.

Ensure there is a good ratio of 3-4 business analysts to statisticians (data scientist)

The optimal ratio is really 3-4 business/ data analysts to 1 data scientist. Nearly 80% of a data scientist work is preparing a base table and not modelling and so it’s much more efficient to hire a cheaper resource to help with the task. More importantly than just hiring brute force numbers is to empower business analysts with the right tools like alteryx to help with not just the data portion but also the Spatial, predictive and optimisation parts.

Use best in class tools like Alteryx , Tableau and AWS to overcome technical challenges

Another common misnomer among people is that a data scientist needs to know R, python, Scala etc. in my view it’s more important to be able to work with the business and ask the right questions , framing the problem and then using alteryx to overcome the technical challenge.

This not only reduces time to deliver analytics but reduces key person risk when an analysts moves on the role. Recent developments in alteryx  including alteryx connect ( search and metadata repository) and alteryx promote ( productionising data science models ) have taken the product up a few levels to become an enterprise data science platform.

Start small iterative quickly

Another few interesting points included the need to start small iterate quickly and move away from writing those 100 page RFPs. After all if u really want to know how a product works u test drive it just like test driving a car. No one reads the technical specs and tries to compare each point, instead it’s better to actually try and product and form your own views.

Analytics can be game changing if leveraged correctly – Make sure Alteryx is pack of the technology stack

Organisations early on there analytics journey should also embrace visualisation and great to showcase analytics in this way to get  management  buyin. Later on in the analytic journey however we need to focus on use analytics to deliver game changing capabilities to the business. Just listening to the latest group of customers at Alteryx Inspire Europe reiterated this. Check it out below

Listen and learn to Alteryx customer presentations

My favourite customer presentations are by Shell, bookmyshow and Asahi. The keynotes by Dean stocker and the product demos were incredible. Didn’t realise u could use alteryx to read in photos and hook up with Microsoft API for face recognition. This is game changing stuff from a business perspective as it can be applied to recognise customers and improve customer experience.

That’s all for now.. Stay tuned for my next instalment..