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
- 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.
- 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