4 Data Fusion and Analytics for Chief Investigators, 2022 Survey Report Introduction & Methodology It’s becoming more complex than ever for government organizations to investigate and assess potential risks, due to the growing volume of structured and unstructured data sources. In our last survey, the 2022 IT leaders in the Data Fusion/Analytics Domain, we spoke to CIOs and IT executives about their challenges and investment priorities for data fusion. This time, we wanted to dive into the perspectives of investigation and intelligence directors in the trenches, who are tasked with managing a growing volume of diverse data and making smarter, faster and more accurate decisions. We wanted to understand the maturity of the market for data fusion and ML-driven analytics for investigation purposes. What exactly are today’s decision-makers looking for? Are they building in-house solutions to meet their needs, or relying on third-party solutions? The results shine a light on the priorities and strategies of investigation executives across multiple domains. The data indicates three major goals among respondents: Openness, automation, and flexibility to suit the needs of their specific units. Methodology To understand the direction of data fusion and analytics for investigation purposes in government organizations, we commissioned a survey of 200 senior decision makers from 29 countries in Europe, North America, LATAM and APAC. The survey was completed by Global Surveyz, an independent survey company, and took place during May-July 2022. The survey is based on responses from employees in positions from Director to C-level executives from Investigations, Intelligence and Data Analysis, in domains including Law enforcement, Financial Crime Units, Immigration, Border control, Tax authority, National Security, Port authority and Airport authority. The respondents were recruited through a global B2B research panel and invited via email to complete the survey. The average time spent on the survey was 8 minutes and 52 seconds. The answers to the majority of non-numerical questions were randomized, in order to prevent order bias in the answers.