Partnering with EMC Dell to Infuse Big Data Analysis into the Ministry of Public Administration of Republic of Slovenia

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This case was submitted as part of the Call for Innovations, an annual partnership initiative between OPSI and the UAE Mohammed Bin Rashid Center for Government Innovation (MBRCGI)

The pilot project “Big Data Analysis for HR efficiency improvement” has been established as part of the strategy supporting data-driven public administration in Slovenia. It ran within the Ministry of Public Administration in collaboration with the company EMC Dell to learn how big data tool could improve the efficiency of HR data.

Innovation Summary

Innovation Overview

The pilot project has been established two common objectives: to increase the efficiency of public administration and to create a favourable environment for economic development. It was executed in collaboration with international company EMC Dell which in 2015 signed a letter of intent for cooperation with Ministry of Public Administration (MPA) of the Government of Republic Slovenia.

The Big Data pilot project launched in April 2016, with the objective to learn what a big data tool installed on the Governmental State Cloud Infrastructure could enable in terms of the research of HR data in the Ministry of Public Administration to improve our efficiency, develop organisational capacity, improve effectiveness and efficiency and staff satisfaction.

The project started with several introductory workshops aiming to select adequate data resources for further analysis as follows: data on employee’s time management (Codeks), ISPAP – salaries data, HR data and finance data (MFERAC) and data on public procurement. Since some databases contained personal data, the Ministry of Public Administration contacted the Slovenian Informational Commissioner. Analysis was performed within MPA premises on MPA IT infrastructure under strict security rules. Masking and anonymization of personal data was conducted to disable any possible identification on individual level. Furthermore, several interviews with data owners were conducted to clarify the content and to get interpretation of intermediate results. Additional external data sources -open data were added, such as historical weather data and geographical distinct. During prioritization process, three business initiatives (out of 40) were selected for further exploring. They were about HR management, real estate management and public procurement. One of them was the development of Employee Profiles. The employee’s profiles were segmented into relevant groups (clusters) based on multiple internal datasets containing employee job grade or specific role. HR features associated with high performance and low performance employees were analysed. Based on over 250 employee’s characteristics, five different groups (clusters) were designed containing different average performance scores. Also, open data - external data sources such as weather and geographic distances were included to provide additional insights on the behaviours of employees.

One of the fruits of this pilot project was that we learned that combining different data sources can enrich information and give us new value and new perspective. Using big data tools, we found new characteristics in our databases. Second learning point was that we should collect more data related to performance management systematically to enable more accurate analysis in this field in the future. Third learning point was that big data tools could enable us performing predictions for better planning process. Another learning point was related to personal data security where we gained knowledge how to handle personal databases in sense of data protection according to GDPR. The main lesson learned was about importance of establishing trust, firstly among IT experts and data sources owners which had to learn to listen each other and to understand each other’s needs. Secondly it was of utmost importance that our data source owners realized that they are the only one who can properly interpret their data and gained results since they have the most valuable knowledge on it.

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