With half of the region’s public sector chief data officers (CDOs) unclear about their roles and responsibilities, what lies ahead?
Findings from a new research report revealed that Asia Pacific (APAC) public sector organizations have yet to develop a clear understanding of the importance of data and the emerging role of the Chief Data Officer (CDO) to the organization.
The study by Omdia and Qlik surveyed 103 senior public sector data executives across Singapore, Australia, New Zealand and India, unveiling the concerns, challenges, and priorities of these CDOs.
DigiconAsia discussed the findings of the report with Geoff Thomas, Senior Vice President of APAC, Qlik and Kevin Noonan, Emeritus Chief Analyst, Omdia, who authored the report:
What are some key lessons the COVID-19 pandemic surfaced on the use of data in Asia Pacific’s public sector?
Noonan: It was apt for Tedros Adhanom Ghebreyesus, the World Health Organization’s director-general, to say that we are not just fighting a pandemic. We are also fighting an infodemic – and the public sector needs a lot of data to build public trust, design the right policies and make better decisions for its citizens.
However, this sudden urgency to analyze and act on a vast amount of data revealed a large gap: many public sector organizations had shortfalls in their data strategies.
Qlik and Omdia’s study on public sector Chief Data Officers (CDOs) in Asia Pacific (APAC) found that 75% of them regretted not investing more in data-driven initiatives before the pandemic hit. In addition, 81% felt their organization needed more innovation, flexibility, and agility to handle the impact of the crisis.
Public sector CDOs need to reassess their priorities for the coming year as we look toward a post-COVID future. It is worth noting that half of the CDOs surveyed focused on foundational work in the past six months to turn things around. This involved conducting assessment plans around training needs, data sensitivity and data maturity.
In the next 12 months, these CDOs indicated that their top priorities are to improve data quality, introduce new technologies, and deliver a data strategy with a one-year action plan for their government agency.
What are the biggest challenges public sector CDOs face in their jobs?
Thomas: As public sector organizations focus more on data-enabled transformation, the underlying technology will also need to transform. This was strongly felt by the public sector CDOs in APAC who voiced technical and strategic concerns around data technology.
On the technical side, the CDOs struggle with data implementation involving data integration, handling large volumes of data from multiple sources, and analyzing both structured and unstructured data.
On the strategic side, the CDOs struggle with finding data literate staff and developing an organizational culture that understands the reasons for and benefits of changing technology.
How could these challenges be addressed? How does technology help?
Thomas: The rise of data volumes and complexity to accommodate for the non-stop creation of new technologies, and vice versa, is a never-ending virtuous loop. To keep up, CDOs must review the organization’s existing technology investment to ensure that it is suitable to meet the current and emerging requirements of today’s evolving digital climate.
This can be as simple as adopting data management, analytics software, and tools which can provide real-time insights from planning to execution – offering a complete picture of the business and situation. In doing so, public sector organizations can possess up-to-date information to trigger immediate action, accelerating the organization’s value to better serve the community.
CDOs can also keep a lookout for AI capabilities built into a modern analytics platform at a foundational level rather than those that adopt a bolt-on or one-size-fits-all approach, as this will offer a powerful potential: collaboration between humans and machines.
The analysis offered by AI can be paired with the intuition and knowledge of an employee to draw out new and smarter insights. This makes data analytics less complex and daunting, encouraging employees to leverage data as part of their day-to-day work. The more they practice with data, the more data literate they will become – enabling them to ask the right questions, generate insight, and make better decisions.
With the region greatly lagging the US and Western Europe in data governance frameworks, what steps do you suggest governments in Asia Pacific should take?
Noonan: To determine the appropriate steps to encourage data governance, we first need to identify the broader matter at hand. Government leaders in APAC still lack sufficient understanding of data’s value in driving policy development and decision-making.
Even if some APAC government agencies acknowledge data as a valuable core asset, our study found that two in five data executives (47%) lack clarity in their roles and responsibilities.
The little influence that data carries amongst government leaders in APAC and misalignment in job execution amongst data executives hinders the establishment of proper frameworks from driving data governance within government agencies.
To overcome this, it is vital to instill clear expectations for government agencies to strengthen data governance. Drawing key learnings from global counterparts, US government agencies have benefitted from formalizing the CDO role and standardizing their responsibilities.
Enshrining a CDO’s role into law can help ensure that data governance is not just a legal requirement but also embedded into agency cultures and practices. This is the foremost and most crucial step to address data governance deliverables.
Thomas: To add to that, in Asia Pacific specifically, we are also starting to see the trend of government agencies setting up data governance boards which will help with inter-agency data sharing and open data efforts down the line. This is definitely a step in the right direction for the future.
How can Asia Pacific public sector organizations develop a data-driven culture for better citizen/customer experience?
Thomas: Developing a data-driven culture is no easy feat and cannot be managed alone. Public sector organizations must appoint a data champion to advocate for the data-driven cause and equip the workforce – across functions and levels – with the necessary data skills to use data effectively. This role typically falls under a CDO.
For starters, CDOs can invest in talent that carry the necessary technology, business, and analytics skills to lead by example and empower every other level of the organization to work with data. This can be as simple as conducting regular town halls, formal and informal training sessions, and running mentorship programs to help employees understand the value that data can bring to their role and the overall business. CDOs can also consider implementing a rewards system to encourage wider data use across departments.
One good example is the yearly data arcade tournament organized by the Government Technology Agency of Singapore (GovTech). It aims to help public officers across different ministries and agencies to get familiarized and comfortable with data analytics using tools like Qlik Sense. To level the playing field, workshops are provided for those who were new to visual analytics. Last year’s event saw 400 teams competing against one another with two winning teams walking away with fantastic prizes.
On top of people and processes, CDOs must also invest in the right technology to ensure employees can easily access relevant, high quality, and secure data. Without it, any initial interest in data may lead to distrust or discouragement among employees, impacting an otherwise budding and healthy data culture.
Investing in an easy-to-use, scalable modern technology that takes advantage of AI, natural language processing and the latest in user experience will not only help encourage employees to experiment and explore data but foster an innovative culture where data is democratized. We also recommend agencies to bring the data to the users which can often be more efficient than trying to bring the users to the data.
