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 Ai–Hua 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.