The public sector wants to tap into the efficiencies promised by AI. But what do they need to think about first? What are some of the challenges they face in getting ‘AI-ready’ when it comes to their data?
The public sector can benefit greatly from AI, but there are several factors that need to be considered before implementing it. Some of the challenges include:
Data quality and management: AI is only as good as the data fed into it. The public sector needs to ensure that its data is accurate, complete, and up-to-date. This involves cleaning and standardising data and implementing data governance policies.
Data privacy and security: The public sector handles sensitive information, and AI implementation must protect privacy and maintain data security. This involves anonymizing data, implementing role-based access controls, and ensuring compliance with data protection regulations.
Skills: Implementing AI requires specialised skills and knowledge. The public sector may need to invest in training existing resources or hire experts to execute AI vision.
Cost and Value: AI can be expensive to implement, and the public sector needs to consider the costs and benefits of implementing AI before investing.
Ethical considerations: AI must be implemented ethically, with consideration for issues such as hallucination, bias, fairness, and transparency.
By working on these challenges, the public sector can prepare itself to take advantage of the benefits of AI.
There are additional considerations when looking at using data from outside of the organisation – can you talk about those? Also how Snowflake can help in this regard?
Certainly. When using data from outside the organisation, there are additional considerations that need to be taken into account. Some of these include:
Data ownership and licensing: The public sector needs to ensure that they have the right to use the data they are accessing. This may involve obtaining licences or permissions from the data owner.
Data privacy and security: External data may be subject to different privacy and security standards than internal data. The public sector needs to ensure that they are complying with all relevant regulations when accessing and using external data.
Data integration: To be useful, external data may need to be integrated with internal data. This can be challenging, as external data may be in different formats or use different standards.
Data sharing and collaboration: Accessing external data may require collaboration with other organisations or data providers. The public sector needs to establish clear agreements and protocols for data sharing and collaboration.
Snowflake helps organisations share and consume data through its AI data cloud platform seamlessly. We have marketplace capabilities that help organisations to share data publicly and privately easily without the need to build any external integration like APIs, FTP, ETL, etc. Our Data Collaboration features allow organisations to join external data with their own data easily just like they are accessing their own data. This reduces the time to integrate and use external data significantly for organisations. We also take care of privacy and security using advanced technologies like data clean rooms. Data clean rooms offer a secure way to gain valuable insights while protecting sensitive or PII information
Do you have any examples?
Specifically for the UK, Snowflake has data providers like the Met Office and Facts and Dimensions Ltd. They host a wide range of data, such as weather forecast data, UK health statistics, and reference data. The NHS and many other organisations use these data sets.
What is your advice to organisations looking to utilise External/Open Data and AI?
I always like to ask technology heads of organisations what will bring value for them:
- Building great data integration pipelines and APIs or
- Releasing end data products and build AI solutions on top of data to leverage value out of data
All answer with point 2, but most let their teams spend a huge amount of time on point 1. My advice is to start focusing effort on things that will generate value, i.e., point 2, and not try to make technology and their tech stack complex by building complex integration and pipelines.
What are the low-hanging use cases for the public sector when it comes to generative AI (gen AI)?
There are several low-hanging use cases for the public sector when it comes to gen AI. These are areas where AI can be easily implemented and provide immediate benefits. Some of these areas include:
Automating routine tasks: gen AI can automate routine tasks such as data entry, document classification, and customer service inquiries. This can free up staff time for more complex tasks and improve efficiency.
Finding information faster: Provide civil service employees with internal chatbots to help them find information faster from their existing data sets. When powered by gen AI, these chatbots generate human-like responses, which can help civil service employees avoid spending time and effort looking for information.
Feedback analysis: gen AI can analyse feedback from citizens and social media data to understand public sentiment and identify emerging issues.
Improving citizen services: Gen AI can be used to develop chatbots and virtual assistants that can help citizens access information and services more easily. This can improve the overall quality of public services and make them more accessible to citizens.
By focusing on these low-hanging areas, the public sector can quickly realise the benefits of AI and gen AI, while also laying the groundwork for more advanced AI applications in the future.
