What will the public sector prioritise to achieve a successful 2018?

What will the public sector prioritise to achieve a successful 2018?

Analytics, security and a move from legacy systems will be the top focus, says Ovum's Kevin Noonan

Rebecca Gibson |

What three key trends do you think will emerge in the public sector in 2018?
The public sector is set to experience a more disruptive future. The main challenge facing organisations is to not only modernise legacy systems, but also to deal with the legacy thinking that is holding back change leadership, so this will be a key trend in 2018. Another trend will be that cybersecurity will move to the boardroom. Citizens will increasingly hold elected officials accountable for data breaches, so emerging security challenges will no longer be manageable from a purely technical perspective. Meanwhile, more public sector organisation will turn to advanced analytics solutions to drive their digital transformation and data exploration so they can support more innovative policy initiatives.

What will be the three top priorities for organisations when it comes to empowering employees, boosting efficiency and reducing costs in 2018?
Many government agencies still maintain hard boundaries between business and IT, and have an excessive number of tools, methodologies and internal processes that are now standing in the way of innovation. To better serve the public, organisations’ first priority will be to remove any remaining barriers between IT and business, and reduce operational complexity by discarding tools that have outlived their usefulness. Finally, leading change is about leading people. Many digital government initiatives are finding they have underestimated the challenges of driving cultural change, so this will be another key concern.

What new technologies do you think will have the most impact over the next year and why?
Artificial intelligence technologies have come a long way in the past 12 months, so it’s now time for the public sector to take a serious look at practical use cases. By starting small and experimenting with simple AI uses cases, the industry can take a careful, risk-managed approach to implementing the technology more widely in future.

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