5 Key Concerns of a Chief Data Officer (CDO)


Last week, I got an exciting opportunity to attend the Chief Data Officers (CDO) conference in Singapore. It was a gathering of who’s who from the financial world CDOs like DBS, Lazada, MySale, Singapore Exchange & others.

The key takeaway is that though it looks like that CDOs strategy revolves around algorithms, that is not the case. CDOs have to manage the data strategy with the help of Data Engineering & Data Science. Both are equally important for the success of the data strategy at any organization.Key concerns of the CDO can be categorized into five broad areas i.e.

1.Data Strategy & Direction:
In today’s world, CDO is no more a technical role, but it acts as the transformation agent for the organizations. CDOs like Paul Cobban of DBS Singapore are leading in defining the data strategy & direction for their respective organization.

CDOs should act the evangelist to promote & implement data driven decision making across the organization by alleviating the fear associated with it.

2. Data Governance:
Data governance (DG) refers to the overall management of the availability, usability, integrity and security of the data employed in an enterprise. Data based intelligence will be of no use if the data is not collected & prepared suitably. Data Governance becomes critical to the success of the CDO at the organization.

It is always suggested to have a data governance policy which should assign accountabilities for accuracy, accessibility, consistency, completeness & updating of the data.

There should be policy around data security, backup, disaster recovery & business continuity. It is always advisable to have a data governance vertical to help manage the policies & frameworks.

3. Data Integration
CDO can’t be successful if he doesn’t know where & how the data is being used across the organization. In today’s world, organizations have multiple data systems across the SCM, Marketing, HR & others. Last decade was a product’s market where organizations implemented various relevant products like SAP, Salesforce, Peoplesoft & others in respective departments. The wave of Mergers & acquisitions across the industries have deteriorated the situation.

Hence the significant amount of time needs to be spent on ETL ( Extract, Transform & Load) activities.

4. Data Value Realization
Post implementation of data governance policy & data engineering practices, Data Science arrives. Monetization of the data depends on the analysis of the data to provide significant insights. Companies like Dow Jones created an entirely new revenue stream via Factiva by enabling subscribers to search and query articles from over 36,000 licensed news sources. Retailers like Amazon, Walmart are using the same to improve customer loyalty & engagement. Companies like Uber are thriving on monetizing the data collected across various functions across the organization.

Generally, CDOs have a team of the data scientists to provide the intelligence. But the key lies in implementing those algorithms on the bigger & bigger data sets. Hence, a technology team which is proficient with scale up the algorithms to big data could be a great asset.

5. Data Security:
Last but not the least, Data Security !! Though data security has been a part of data governance, it has to see with an independent vision. CDOs must ensure that every piece of technology used within the organization is safe and enterprise ready. Recent incidents like data breaching of Zomato, Linkedin can impact the trust in a big way.

In the end, the conclusion was that CDOs role is not only to manage data but transform the organization into a data driven organization.