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1. Establishment of Central Data Lake
Repository and Business Analytics
System

2. Why Government
Data Lake?
SREDIŠNJI DRŽAVNI URED ZA RAZVOJ DIGITALNOG DRUŠTVA 2

3. Why Government Data Lake?
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1.Enhanced data-drivendecisionmaking
2. Improvedtransparencyandaccountability
3. Cost-effective datamanagement
4. Cross-department collaborationandknowledge sharing
5.Real-timemonitoringandearlyintervention
6. Evidence-based policy-making
7.Predictiveanalyticsandforecasting

4. Introduction
• The project is funded by the National Resistance and Resilience Plan
• The investment is worth EUR 16 600 000
• Implementation period: 7/2021.-6/2026.

5. ETL pipeline
Dedicated ETL tools
Defined schema
Queries
Results
Relational
LOB Applications
Traditional business analytics process
1. Start with end-user requirements to identify desired reports
and analysis
2. Define corresponding database schema and queries
3. Identify the required data sources
4. Create a Extract-Transform-Load (ETL) pipeline to extract
required data (curation) and transform it to target schema (‘schema-on-write’)
5. Create reports, analyze data
All data not immediately required is discarded or archived

6. insight
noise

7. Store indefinitely Analyze See results
Gather data
from all sources
Iterate
New big data thinking: All data has value
• All data has potential value
• Data hoarding
• No defined schema—stored in native format
• Schema is imposed and transformations are done at query time (schema-on-read).
• Apps and users interpret the data as they see fit

8. Data Warehousing vs Data Lakes
• Data Warehousing
•Structured data
•Defined set of schemas
•Requires Extract-Transform Load
(ETL) before storing
•Exploratory analysis is hard
because of transforming the data
•…
• Data Lake
•Raw data (unstructured/semi-
structured/structured)
•“Dump” all your data in the lake
•Data scientists will interpret data
from the lake
•Without metadata, turns in a data
swamp pretty fast
•…
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9. •Collaborative projects
•Data ingestion
•Data Asset catalog
•Dataset blend
•Data assets, tagging &
sharing
•Data discovery and
recommendations
•Usage, lineage & security
Data Lake – requirements

10. Scheme of the platform
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11. Data strategy
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1
Proposals for a cross-sectoral governance framework
for data access and use
MANAGING THE CROSS-SECTORAL
USE OF DATA
2
Proposals for investments in data and strengthening
Europe’s capabilities and infrastructures for hosting,
processing and using data, interoperability
SCALABLE INFRASTRUCTURE
FOR VIABLE DATA MARKETS
4
Proposals for common European data spaces in
strategic sectors and domains of public interest
EUROPEAN SINGLE MARKETS NEED
CROSS-SECTORAL DATA FLOW
3
Proposals for empowering individuals, investing in skills
and in SMEs
EMPOWERING INDIVIDUALS AND
BUSINESS WITH THE RE-USE OF DATA

12. European digital strategy
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• The Data Strategy and the White Paper on Artificial Intelligence are the first
pillars of the new digital strategy of the Commission.
• They all focus on the need to put people first in developing technology, as
well as on the need to defend and promote European values and rights in
how we design, make and deploy technology in the real economy.
• Access to data and the ability to use it are essential for innovation and growth.
Data-driven innovation will bring major and concrete benefits, such as:
• personalised medicine
• improved mobility
• better policymaking
• upgrading public services

13. European data strategy – European Data Act
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14. Common European data spaces
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15. CENTRAL STATE OFFICE FOR THE DEVELOPMENT OF THE DIGITAL
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data.europa.eu
Data Governance
Act (DGA)
General Data
Protection
Regulation
(GDPR)
Directive on
open data and
the re-use of
public sector
information
Regulation on a
framework for
the free flow of
non-personal
data in the
European Union
Government
Data into Data Spaces

18. CENTRAL STATE OFFICE FOR THE DEVELOPMENT OF THE DIGITAL
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Thank you!