Big data analysis influence on public administration processes

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NATIONAL RESEARCH UNIVERSITY

HIGHER SCHOOL OF ECONOMICS

Faculty of business and management

School of Business Informatics

BIG DATA ANALYSIS INFLUENCE ON PUBLIC ADMINISTRATION PROCESSES

MASTER THESIS

Respective Field of Study: 38.04.05 Business Informatics

Master's programme ĢBig Data Systemsģ

Kuraeva Anna Yurievna

Reviewer

National Research Nuclear University MEPhI, Associat Proffesor Zyikov S.V.

Supervisor National Research University HSE, Lecture Kazantsev N.S.

Moscow 2016

Abstract

management benchmark countries public

Lots of analysts and scientists proclaim big data processing impacts into modern life, but much less about by which means it could be achieved. The impact of collection, integration, collaboration and analysis of large volumes of data on management principles in various industries is still to describe. In my current study, I analyze the ways how public agencies are developing in big data experience in several benchmark countries and design the approach of how to measure the maturity level of the country in context of big data development.

“Everyone is talking about big data, and how it will transform government. However, looking past the excitement, questions abound. How to use big data to make intelligent decisions? Perhaps most importantly, what value will it really deliver to the government and the citizenry it serves to” (A.Kuraeva, 2015)? “By reviewing the literature and summarizing insights from a series of governments ICT strategies, business reports and interviews of public sector and top companies Chief Information Officers (CIOs), I offer a survey for both practitioners and researchers interested in understanding of how big data processing influence on public sector in different countries” (A.Kuraeva, 2015). The Master Thesis makes the comparative analysis of the big data initiatives in public sector in different counties and designs the Big Data Processing Maturity Model and verifies it on the set if orthogonal countries.

Master Thesis could be interesting for:

Companies, public agencies and departments that are planning or preparing for big data processing.

Researchers in the field of big data and public governance.

Wide range of readers who are fascinated with IT.



Introduction

Big data is a new digital trend and business sector and public sector should be not only aware about big data but start to have deal with big data digital trend. Some countries and companies are already “sharks” in enhancing their effectiveness and providing social and economic value by processing big data while other companies and public agencies just to look closely to new digital trend.

“Public sector accumulates great data sets. Most of them are instructed, distributed in different agencies' information systems. Moreover, significant part of them has never been analyzed but the results of it's processing and intelligence analysis could be very valuable for the government: important information about citizens, implicit and explicit tendencies advanced identification, more effective social and finance forecasting. On the one hand, there are a lot of business overviews, journal articles that suggest ways governments how can use big data to help them overcome governmental challenges and make life of their citizens better. On the other hand there are some skepticism and doubts about overrated results and effects from big data solutions implementation in public sector also identify and estimate factors which influence development of big data in public sector” (A.Kuraeva, 2015).

Last few years put us complex and international challenges: to counter and prevent threats of terrorism, economical fraud detection and enhance business processes wherever it is possible. A new potential source of optimisation arises: big data analysis and its application in public administrations. Complex tasks call for international cooperation of the participants, minds, data-flows and resources. “By reviewing the literature and summarizing insights from a series of business reports and interviews of public sector and top companies, we offer a survey for both practitioners and researchers interested in understanding big data in the public sector based on world experience” (A.Kuraeva, 2015). The paper develops the Big Data Processing Maturity Model and estimate of the world big data implementation level experience in public sector.

The main goals of my Master Thesis are to provide comparative analysis of the big data initiatives in public sector in different counties and design the Big Data Processing Maturity Model for identification level of a country big data processing experience. To achieve this goal I used methods of studying, analysis, modeling and comparison of the core problem materials. First, I analyze the current experience of big data processing in public sector of different countries, and then turn to analysis of international collaboration experience in big data field. Second, based on my results and conclusions about big data experience in different countries I design the Big Data Processing Model and verify it on the orthogonal country set. Finally, I provide the estimate of challenges, which influence on big data development in public sector of different countries.

Master Thesis could be interesting for:

Companies, public agencies and departments that are planning or preparing for big data processing.

Researchers in the field of big data and workflow management systems.

Wide range of readers who are fascinated with IT.

TASK DEFINITION

The goal of this Master Thesis is:

To define how big data processing influence on public sector in different countries.

To achieve this goal we formulated following objectives:

To analyze experience of big data projects implementation in different countries.

To evaluate opportunities for future growth of international partnerships in big data analysis application.

To create The Big Data Processing Maturity Model for evaluation of big data processing in public sector in different countries using qualitative and quantitative characteristics.

To verify The Big Data Processing Maturity Model on a number of countries: Russian Federation, USA, Germany, Singapore and Nigeria.

To define challenges on a way of big data penetration in governments and public agencies.

I proceed as following:

Analyse the current position of big data in public sector of different countries (Chapter 1).

Compare world experience of big data implementation in public sector (Chapter 1).

Analyse and define existing world experience of international collaboration in big data projects among business sector, public agencies and international institutions (Chapter 2).

Develop the Big Data Processing Maturity Model for countries identification and verify it on the next countries selection: Russian Federation, USA, Germany, Singapore and Nigeria (Chapter 3).

Define set of problem which put obstacles in the way of big data implementations growth in public sector (Chapter 4).

SCIENTIFIC METHODS

In order to define how big data processing influences the public sector in different countries and to formulate a framework to assess it, I run an exploratory four steps research project. In the first step, I make literature review to discover the current situation regarding big data application in the public sector in different countries. Additionally, experience from big data projects implementation was combined with international big data projects collaboration experience. This was made to create handles that allow connecting it to established academic concepts in research step 2. The second step consists of big data projects results aggregation and logging. The log consists of information about international collaborations experience for big data processing. In the third research step, The Big Data Processing Maturity Model was designed. To establish a framework for The Big Data Processing Maturity Model, I extracted firstly 6 vectors or dimensions for big data maturity level definition:

Vision and strategy

Open Data initiatives

R&D institutions and initiatives

Big Data maturity level in business sector

Data Governance

Big data projects experience in public sector

Then I allocate four levels of big data processing maturity description. Based on the previous research comprising some of business sector big data projects and initiatives were added that let me made comparative analysis for the six dimensions. Finally, I suggest ways for governments of follower countries to identify their own big data processing maturity level. In the fourth research step, big data challenges for public sector is defined. The approach taken in the Master Thesis described above is also depicted in Figure 1.

Figure 1. Research methods in Master Thesis

Novelty and currency

I am doing my research in big data filed already 2 years. Earlier, I made deep focus on big data experience in public sector in different countries. And this year I devoted for The Big Data Processing Maturity Model design. Novelty and currency of this study could be support by my scientific paper “Survey on big data analytics in public sector of Russian Federation”. My paper were published in International Conference Proceeding - Information Technology and Quantitative Management, ITQM 2015 with Procedia Computer Science publisher.

During current study I made a big literature overview of the following sources: ICTs governments strategies, company reports, white papers, conferences proceedings and academic libraries. Big data topic is popular; even more popular in business sector than in public sector. I conduct that my research interests and research results given in this Master Thesis are novel. Analysis of big data trend in different countries and The Big Data Processing Maturity Model are the unique results of my study. I met some examples of big data maturity models but all of them were designed for business sector to define maturity level of a company not for countries or governments. That is why I can make the conclusion that, my The Big Data Processing Maturity Model for countries level verification is currency and novel.

Here I would like to emphasize my personal contributions made during my two years research. Table 1 provides the list of mine contributions (conclusions) made in this Master Thesis supporting with research methods descriptions for each contribute (conclusion).



Table 1 - The List of mine personal contributions (conclusions) were made in this Master Thesis

š	Conclusion (Study results)	Delivers	Chapter	Research Methods
1	Analysis of big data implementations in different countries	Big data projects catalogue. Conclusions about comparison analysis of big data experience in different countries	Chapter 1, Chapter 2	Literature overview (ICTs governments strategies, company reports, white papers, conferences proceedings and academic libraries) Comparison analysis
2	The Big Data Processing Maturity Model	The Big Data Processing Maturity Model Verification of the Big Data Processing Maturity Model (using 5 orthogonal countries)	Chapter 3	Literature overview Comparison analysis Modeling Based on literature review: governments official ICT strategies, White Papers, Open Data portals, top advisory companies reports, articles and papers of public institutes and research centers, including here previous results were getting from my study I designed The Big Data Processing Maturity Model for governments and public sector. The Big Data Processing Maturity Model was designed based on the following guiding principles: To have strong academic background To use insights from ICT frameworks and best practices Based on reliable data Based on comprehensive analysis
3	Evaluation analysis about opportunities for future growth of international partnerships in big data	Conclusion about future fields of big data international collaboration development and potential future big players in international collaboration	Chapter 2	Conclusions were made based on literature research in this topic
4	Challenges evaluation on a way of big data penetration in governments and public agencies	List of the challenges	Chapter 4	Evaluation of the challenges were made by using literature review: public agencies reports and publications, governmental success stories in ICT, top advisory companies reports, articles and papers of public institutes and research centers



Chapter 1

Chapter 1 provides information about big data in public sector in different countries. Chapter has two parts. First part gives background study of big data in general. Second part provides background study of big data in public sector. There is comparison analysis of big data experience in public sector in different countries in second part. Moreover, here you can find more detailed overview about big data in Russian Federation.

Background study of big data

The story of how data became big starts many years ago before the current popularity around big data trend. First attempts to recognize the growth of rate in the volume of data had placed almost seventy years ago.

The sources of data in the world today are vast. They can act as a continuous incoming data from the measuring devices, events from radio frequency identifiers, message flows from social networks, meteorological data, and remote sensing data streams about location of mobile phone users, audio and video recording devices and others. Actually, the mass distribution of the above technologies and innovative models using different kinds of devices and Internet services were the starting point for the penetration of big data in nearly all spheres of human activity. Firstly, in research and development, the business sector and public agencies areas.

The next passage is devoted to historical review of big data evolution. Other words here are marked the brightest milestones to the path of popularity of big data trend.

Several entertaining and significant fact (Forbes, n.d.):

In 2010, the world companies have collected about 7 exabytes of data, on our home PCs and laptops is keeping around 6 exabytes.

The entire music world can be placed on the disk cost $ 600.

In 2010, the networks of mobile operators served by 5 billion phones.

Every month on Facebook to spread in open access 30 billion of new sources of information.

Each year, the volume of stored data grows by 40%, while the global IT spending grows by only 5%.

In April 2011 in the Library of Congress was stored 235 terabytes of data.

US companies in 15 of 17 industries have large volumes of data than the Library of Congress.

For example, the sensors mounted on aircraft engines, generating about 10 terabytes per half hour. About the same flow characteristic of drilling rigs and oil refineries. Only one short message service Twitter, despite the limited message length of 140 characters, generating a stream of 8 TB per day. If all such data accumulate for further processing, their total amount will be measured in hundreds of petabytes. Further complications arise from the variation of data: their composition and structure are subject to constant changes when launching new services, installation of advanced sensors and deployment of new marketing campaigns (N., 2013).

“As the amount of data continues to grow exponentially, compounded by the internet, social media, cloud computing and mobile devices, it poses both a challenge and an opportunity for organizations - how to manage and make use of the ever increasing amount of data being generated” (A.Kuraeva, 2015).

big data in public sector

Actually not only business sector deals with big data but even public sector is official canonical user of big data tendency, as they also keep collect of huge amount of different records for their country which may include information about the people of the country, their social and economy indicators and others.

Public sector accumulates great data sets. “Most of them are instructed, distributed in different agencies' information systems. Moreover, significant part of them has never been analyzed but the results of it is processing and intelligence analysis could be very valuable for the government: important information about citizens, implicit and explicit tendencies advanced identification, more effective social and finance forecasting” (SAS, 2013). “On the one hand, there are a lot of business overviews, journal articles that suggest ways governments how can use big data to help those overcome governmental challenges and make life of their citizens better. On the other hand there is some skepticism and doubts about overrated results and effects from big data solutions implementation in public sector also identify and estimate factors which influence development of big data in public sector” (A.Kuraeva, 2015). To analyze perspectives and opportunities of big data development in public sector of Russian Federation we need to overview world practices in the study area.

Below the IT budgets rating of different countries is shown (see Figure 2). “This chart shows a country ranking based on estimated per capita expenditure of information and communications technology in 2013 (USA, n.d.). Russian Federation is ranked number 9 with ICT funding equaled 557 million dollars that is 6 time less similar USA funding” (A.Kuraeva, 2015).

Figure 2. IT budgets rating of different countries in 2013 (in US$ million dollars)

Russian Federation

“2014 and 2015 years became changeable as for Russian public sector as for IT also. These years are marked by appearing new internal and external factors, which influence IT development in public sector of Russian Federation. These factors are world sanctions, software import substitution strategy and free software trend propagation” (A.Kuraeva, 2015).

“Big data is a new trend in Russian public sector. The main difference of big data projects in public sector is the fact that the way to success of a project is considered not as a commercial benefits but as a capability to solve important problems and make a social good. If we want to know what profit will be received form big data projects implementation in public sector we have to make an analysis what kind of data we have. Usually these are electronics requests, citizen's applications and comments and geo-data” (A.Kuraeva, 2015).

“As results of big data processing such effects are considered:

Deep analysis of the macroeconomic development of the country under globalization and world integration tendency.

Forecasting of the socio-economic development indicators in the country and regions.

Social regularity and trends detection.

Decision-making information support.

Social tensions monitoring.

Data harmonization“ (A.Kuraeva, 2015).

“In light of new Open Government Strategy of Russian Federation great number of public portals and internet resources appeared, which main concern is to make a contact with citizens. People all around the world are demanding more openness in government. They are calling for greater civic participation in public affairs, and seeking ways to make their governments more transparent, responsive, accountable, and effective. There are some shining examples in Russian Federation, such as: Open Budget Portal of Moscow or Single Portal of Budgeting System of Russian Federation. As a consequence of Open Government Strategy, there is the Open Data trend. In a well-functioning society citizens need to know what their government is doing. To do that, they must have free access to government data and information and share that information with other citizens. By opening up data, citizens are enabled to be much more informed and directly involved in decision-making” (A.Kuraeva, 2015).

“But the public sector of Russian Federation still does not achieve significant results with big data processing. At present, private sector domain is more interested in this sphere. Profit motives make it urgent for companies in the private sector to learn how to leverage big data. For Federals such cases of big data projects implementation are rare. The table below shows the rating of the most expensive IT projects in public sector of Russian Federation in 2014 (see Table 2) (CNEWS, 2014). We can see that the core part of the contract subjects consists of hardware supplying and developing the technical infrastructure” (A.Kuraeva, 2015).

Table 2 - Rating of the most expensive IT projects in public sector of Russian Federation in 2014

š	Customer	Contract price (in US$ K) The official Russian Ruble / US Dollar exchange rate on March 25, 2015	Contract subject
1	Ministry of Transport of Russian Federation	28 909	“Real-time emergency reaction information system “ERA-GLONASS” based on multifunctional receiving devices domestic production” (A.Kuraeva, 2015)
2	Department of Presidential Affairs of Russian Federation	20 231	“Supply of equipment, expendable materials, operations and services in information and communications technologies” (A.Kuraeva, 2015)
3	Social Security Fond of Russian Federation	19 873	“Supply of stand-by complex of firmware for transactional and analytical data processing in the frame of information and technical infrastructure of Social Security Fond of Russian Federation (installation and adjustment included)” (A.Kuraeva, 2015)
4	The Federal Tax Service of Russian Federation	16 638	“Development of the consolidated automated information system of the Federal Tax Service of Russian Federation in 2013” (A.Kuraeva, 2015)
5	Social Security Fond of Russian Federation	15 891	“Technical support, maintains and development of information systems, software and hardware” (A.Kuraeva, 2015)
6	Social Security Fond of Russian Federation	15 840	“Supply of stand-by complex of firmware of computation environment of information and technical infrastructure of Social Security Fond of Russian Federation (equipment supply included)” (A.Kuraeva, 2015)
7	The Federal Treasury of Russian Federation	15 262	“Supply of data storage systems, servers, server equipment upgrade kits and installation and server adjustment works provision” (A.Kuraeva, 2015)
8	The Court Department of Russian Federation	15 131	“Provision of telecommunications services” (A.Kuraeva, 2015)
9	The Pension Fund of Russian Federation	14 365	“Supply of hardware and software for the information technology infrastructure of the regional offices of the Pension Fund of Russian Federation (first stage)” (A.Kuraeva, 2015)
10	The Pension Fund of Russian Federation	14 010	“Supply of the complexes of computation programs based on PowerSystem I servers” (A.Kuraeva, 2015)

“In spite of that, there is the set of successful big data projects in public sector. The Central Bank of Russian Federation obliged other banks to report in The Federal Tax Service of Russian Federation about opening and closing citizen's deposits, flow of funds and account details changing (CNEWS, 2014). The Federal Tax Service of Russian Federation is getting actual information about taxpayer accounts and then blocks accounts in all banks immediately by using big data processing, instead of step-by-step blocking they used to do earlier. The Pension Fund of Russian Federation implemented SAP HANA project as a part of single multifunction automated accounting and cost system controlling creating for the whole regions of the presence The Pension Fund of Russian Federation. Federal Road Agency launched System of forecasting traffic jams and accidents in pilot mode developed by Yandex Data Factory. System uses data about traffic congestion, weather reports, metrics based on Yandex Maps application's data, and information about pavement quality and road lines numbers” (Federation, 2014), (A.Kuraeva, 2015).

Discussion: Big data analysis in public agencies: external and internal factors

“Big data is a new frontier for the public sector of Russian Federation. Public administrations realize that their datasets represent critical resources that need to be managed and leveraged. However, there are external and internal factors, which make a great effect on big data penetration in public administrations” (A.Kuraeva, 2015), (C., 2014).

“At present IT market in public field is import-depended. Main providers of big data solutions in public sector of Russian Federation are the biggest foreign IT companies and consequently biggest part of revenue from ICT projects in public sector of Russian Federation is accounting for the biggest foreign IT companies (see Table 3)“ (A.Kuraeva, 2015), (CNEWS, 2014).

Table 3 - ICT revenue of biggest foreign IT companies in public sector of Russian Federation (A.Kuraeva, 2015)

š	Company	Revenue in 2013 (total in Russian Federation), million dollars	Public sector projects' revenue in 2013, million dollars.	Proportion of public contracts in revenue
1	HP	2025	1310	65%
2	IBM	1736	1633	94%
3	Cisco	885	681	76%
4	Microsoft	579	323	56%
5	SAP	342	306	92%
6	Oracle	87	68	86%
In total		5654	4321	77%

We can see that about 77 percent of the total sales were contracts from the government and state-controlled companies.

“IT industry in Russian Federation is experiencing the effects of the sanctions. Microsoft, Oracle, Symantec and Hewlett-Packard (HP) cease cooperation with banks and companies, the USA authorities have imposed sanctions against which. American IT companies have joined the sanctions against a number of Russian banks of their structures. In addition, there were discussions to bar Russian banks from using SWIFT. Being frozen out could wreak havoc with Russian trade and investment” (A.Kuraeva, 2015), (CNEWS, 2014).

Russian Federation is developing new IT strategy to go away form dependency form foreign technologies. Such a kind of actions could decrease revenue of the biggest global vendors such as Microsoft or SAP. On the other hand, move to import substitution strategy Government should be sure in quality and even presence analogues of applications and infrastructure components.

“Russian Authorities created an initiative to require public agencies and state-run enterprises to give preference to local vendors (providers) of software and hardware. The paper addresses criteria for tender processes such as favoring products that do not have imported, licensed components” (Khrennikov, 2014). Therefore, I considered that some constraints of big data initiatives and implementations would appear.

“Besides sanctions and import substitution tendency that slow down spreading big data penetration in public sector of Russian Federation, there are number of common challenges for all countries” (A.Kuraeva, 2015):

High cost of big data implementation.

Lack of executive understanding of the core value of processing data.

Risk management.

Weak methodological background.

Complexity of data integration and data sharing.

Data security.

The public sector acts as significant single consumer of big data and will receive benefit from the new technology trend big data delivered. “Government agencies and state-run enterprises should identify and prioritize use cases that could provide value to the public sector of Russian Federation and big data can address. They should also take the consideration the technical and organizational feasibility, along with potential value of the identified use cases” (A.Kuraeva, 2015).

“Based on comparative analysis of initiatives and implementations of big data projects in different countries, public sector use of big data and big data analytics is wide-ranging; some government agencies and state-run enterprises have no experience with big data, while others have taken on small to moderate-sized projects. Current position of big data in public sector in Russian Federation: the researching stage of big data potential and applied significance to enhance operation efficiency of each authority's level” (A.Kuraeva, 2015).

USA

USA Government spends a lot of money in big data research area. USA ICT annual budget enhances comprehensive and deep research and development initiatives and programs around big data field. Public agencies also play huge role in big data promotion by putting clear big data vision in Government IT strategy. There is the large number of reports about different aspects of big data, which appeared on the White House official site regularly.

“Big Data and Differential Pricing (February 2015)” (House, Big Data Report, 2014)

“Big Data: Seizing Opportunities, Preserving Values (February 2015)” (House, Big data and privacy, 2014)

“Big Data and Privacy: a Technological Perspective (Report to the President, May 2014)” (House, Big Data Report, 2014)

“In addition, on research in Big Data area, USA is the leader in the number of big data projects deployments in public sector from initial projects of data gathering and consolidation to advance predictable analytics” (House, Big Data Seizing Opportunities Preserving Values, 2015).

“The National Institute of Justice awarded School of Criminal Justice a $500,000 grant to conduct risk terrain modeling research in U.S. cities. This project was launched in pilot mode in few American cities. The team of researchers hopes the data will help police force suppress crime efficiently” (C., 2014).

The United States Internal Revenue Service (IRS). Lots of companies and organizations trying to adopt new technologies in a face of big data to increase revenue and enhance the operation effectiveness. “The IRS is no different. A bureau of the Department of the Treasury, the IRS is the revenue service of the United States government, and is responsible for collecting taxes and enforcing the Internal Revenue Code. The IRS wants to use big data analytics of financial and social information to address taxpayer error, evasion, and other sources of lost revenue. Robo-audits will allow the IRS system to track citizens' online activity and flag patterns of concern. Collection and analysis of such data will allow the IRS to generate and track unique attributes regarding financial behaviors. Much of the data will be used for research, but it will also aid in tax enforcement and combat noncompliance. In the past, such third-party (social) data has only been used if irregular returns required more attention” (C., 2014).

Singapore

“In 2014 The Agency of Science, Technology and Research and Infocomm Development Authority of Singapore (IDA) opened a Business Analytics Translational Centre (BATC) to help public and private sector leverage cutting-edge analytics tools for private and public sector. The BATC is working with IDA to encourage user organisations and government agencies to leverage BATC's analytics capabilities to apply analytics strategically, to guide business strategy and planning, as well as optimise day-to-day business processes” (Portal, Analytics one of Malaysias top three ICT priorities, 2014). Special fields of international partnerships include organising network session among users--both private and public sector, increasing fields analytics capabilities through BATC's product and services from research and development, and partnerships with “Science Institutions and Institutes of Higher Learning to transfer analytics knowledge to relevant professionals in Singapore through trainings, workshops, and seminars” (Portal, Singapore opens business analytics centre, 2014).

Singapore government make the accent on social benefits and try to enhance websites to be able to recognize citizens' needs better with a new big data analytics tool. “Once the pilot project goes live in 2015, the cloud-based tool can process and understand a citizen's question accurately and provide an answer within seconds. Using this smart services people get opportunities to easy navigate and receive needed information, moreover, this service provides more personalized advice for citizens. “The tool also provides government agencies with insights of citizens' needs and priorities. The intelligent tool learns by processing a huge volume of information and a given set of relevant vocabulary. The Singapore government is the first to adopt this technology and plans to implement it across the whole government” (Portal, Singapore opens business analytics centre, 2014).

Therefore the Singapore government holds the course to achieve maximum efficiency from social and infrastructure big data projects.

Malaysia

The Government Chief Information Officer reported about key priorities for ICT in the Malaysian public sector: Big Data analytics, consolidation of data centers and improved cyber security.

“MAMPU (Malaysia Administrative Modernization and Management Planning Unit) will lead the implementation of a pilot project in 2015. This project will cover four different areas: sentiment analysis, in partnership with the Ministry of Communications and Multimedia; crime prevention with the Home Ministry; infectious disease prevention with the Health Ministry; and price watch with the Ministry of Trade” (Portal, New malaysian GCIOS priorities for 2015, 2015).

Chapter Summary

Reviewing big data projects and initiatives in leading countries identifies three big data trends.

Firstly - most projects operated or implemented today can only marginally be classified as big data applications. The majority of government data projects for such a kind of trend appear to share structured databases of stored data.

Secondly - large and complex datasets are becoming the norm for public and private sector. Public sector now faces with the problem of increasing transparency, the productivity of government agencies. Therefore, public sector needs to build information systems to quickly and efficiently communicate openly with citizens, enhance financial efficiency, reduction of time for maintenance, increasing public demand for services, the elimination of corruption, increase predictability, reduce costs. Existing technologies can no longer solve the problems of this kind. These are duplicated and multiplied, their treatment requires more resources.

Thirdly - to make a systematization of this information, work with archived data, and easily find the right information is getting harder. Without the technology of big data information coming in unstructured form, subjected to special treatment to put it in an analytical repository for further analysis. This often slows down the cycle analysis of information and increases the cost of data storage and processing.

Based on this trends I can make a conclusion that big data is penetrating in public sector as well as in business sector. There is huge difference in country readiness for big data processing in scale of whole country. We need to get the opportunity of how to measure country level readiness and experience in big data field. Now there is no framework or approach how to do such a kind level identification, there are many sources about big data in different countries but there is no single approach how to compare countries in the correct way.

Here in my study, I will design such a framework - The Big Data Processing Maturity Model that helps to compare countries in single unified and deliberate approach.

Chapter 2

“A vast number of “traditional” industries--from oil and gas companies to manufacturing and retail companies--rely on data from their locations around the world to make routine decisions” (|, 2015). Main ideas of the second Chapter are to illuminate international collaborations and partnerships in big data field and show value from such a kind of collaborations and partnerships throw practical experience. The second Chapter provides information about collaborations in big data field and about types of partnerships. Moreover, the second Chapter has aggregation table with big data projects and big data collaborations in public sector.

Big data: international collaborations reaching common goals

Cross-borders data flows is the new trend in the field of data analytics and integration processes. Also it is one step further to globalization strategy. More agencies, institutions, companies and scientific centers generate cross-border data flows. Moreover, they use cross-border data flows.

Public agencies, institutes and companies make a step forward to process and operate not only with local data but with using cross-border, global data flows. It is bring advantage of cross-border data to enhance their operations, improve their products and services, and provide social goods and value. According to the survey (Europe, 2015) “58% respondents answered they have some partnerships with organizations or data providers in big data projects. Figure 3 describes various types of partner organizations”.



Figure 3. Type of partner organizations.

Research and development field contain many common spheres of interests between countries and governmental agencies. To reach scientific ambitions in common interests appeared plenty of cross-countries and cross-agencies projects focused on big data processing and sharing cross-border data flows. There are two visible attempts of such collaboration:

Financial Intelligence Unit Network

First example is devote to global financial network - Financial Intelligence Unit Network. “The outstanding example of big data international collaboration in fighting Terror Financing and Money is The Financial Intelligence Unit Network (FIU.NET). FIU.NET is a decentralized computer network providing information exchange between the Financial Intelligence Units (FIUs) of the European Union” (Georgios A. Antonopoulos, 2011). The main idea of the FUIs is to collect and process numerous transaction and try to predict some suspicions and illegal actions. “According to the Egmont Group of Financial Intelligence Units, FIUs are national centers to collect information on suspicious or unusual financial activity from the financial industry and other entities or professions required to report transactions suspicious of being money laundering or terrorism financing. FIUs are normally not law enforcement agencies; their mission is to process analyse the information received. If sufficient evidence of unlawful activity is found, the matter is passed to the public prosecution” (What is an FIU?, 2007). Figure 4 shows the high-level structure of the FUI.NET.



Figure 4. The FUI.NET structure

The current connected EU Member State FIUs are: Austria, Belgium, Bulgaria, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxemburg, Malta, Netherlands, Poland, Portugal, Romania, Sweden, Slovenia, Slovakia, Spain, United Kingdom (What is an FIU?, 2007).

United Nations: Big Data and Refugees

Bright example of the novel international collaboration in big data - digital project for refugees accommodation services in Europe - United Nation's ProGres database

“In the face of such circumstances as threat of terrorism, world disease and refugees migration just effective countries and international institutions partnerships could handle with global challenges. According to Amnesty International over four million refugees have fled the conflict in Syria, with the majority now in Turkey, Lebanon, Jordan, Iraq and Egypt” (Dell, 2015). Main idea of the project is to make easier processes of accommodation, feeding and providing essential healthcare, because it is a huge task requiring coordinated work from hundreds of governmental, private sector and voluntary organisations.

“When a refugee arrives at a country, their details are entered into the United Nation's ProGres database, which was developed in partnership with Microsoft. This makes them eligible for first-line humanitarian aid such as the provision of food and medical aid from the UN and partner organisations while the details of their individual claims to asylum are assessed” (Dell, 2015).

“With the current Syrian crisis, all of this aid is distributed in the form of vouchers and cards encoded with digital identifiers. These identifiers allow their use to be tracked, so demand for resources can be monitored and forecast. While in some countries such as Lebanon the digital cards and vouchers can be used to draw cash from ATMs, their usage can still be used to track the underlying trends behind the movement of people. The technology uses iris scanning to establish the identity of refugees and this biometric information is encoded into the aid cards and vouchers they receive. In the Middle East, iris scanners are increasingly becoming a part of the furniture in the retail shops where the cards and vouchers are accepted in exchange for basic necessities” (Dell, 2015).

Table 4 consists overview of big data projects in public sector and international experience in big data projects collaboration.

Table 4 - Overview of big data projects in public sector and international experience in big data projects collaboration

#	Country	Field	Project description
	USA	Financial, fraud detection	“Pîlicy Mānāgement ānd Strātegic Plānning in the Îffice îf the Māyîr îf New Yîrk uses ā predictive ānālytics10 dātā tî detect ānd āct in āccîrdānce with pātterns in dātābāses, which māy indicāte finānciāl frāud. This āpplies tî āreās such ās tāx evāsiîn în the sāle îf cigārettes, wāste mānāgement ānd lārge-scāle illegāl plācement in ā residentiāl āreā. The îffice emplîys ābîut dātā ānālysts. Given the success îf the prîject in New Yîrk, creāted ā jîint wîrking grîup în dātā ānālytics between New Yîrk, Bîstîn, Chicāgî ānd Philādelphiā” (HM Government. Department for Business, 2013)
	USA- Japan	Environment	“Tî āssist in future disāsters, the U.S. Nātiînāl Science Fîundātiîn (NSF) ānd the Jāpān Science ānd Technîlîgy Āgency (JST) hāve embārked upîn ā jîint funding prîgrām tî suppîrt reseārch thāt leverāges big dātā ānd dātā ānālytics tî trānsfîrm disāster mānāgement fîr individuāls ānd fîr sîciety āt-lārge. Tîdāy, the āgencies ānnîunced āwārds fîr six jîint US-Jāpān reseārch prîjects thāt āim tî āddress twî specific chāllenges in disāster mānāgement: cāpturing ānd prîcessing dātā āssîciāted with disāsters ānd imprîving the resilience ānd respînsiveness îf emerging cîmputer systems ānd netwîrks in the fāce îf disāsters tî fācilitāte reāl-time dātā ānālytics in their āftermāth” (Scientificcomputing, 2015) were repîrted in îfficiāl site îf the prîject.
	USA- Japan	Environment	“Efficient ānd Scālāble Cîllectiîn, Ānālytics ānd Prîcessing îf Big Dātā fîr Disāster Āpplicātiîns Reseārchers frîm the Missîuri University îf Science ānd Technîlîgy ānd Îsākā University in Jāpān will cîllābîrāte tî develîp new methîds tî cîmpress, trānsmit ānd query dātā frîm sensîr netwîrks” (Scientificcomputing, 2015).
	USA- Japan	Environment	“Disāster Prepārātiîn ānd Respînse viā Big Dātā Ānālysis ānd Rîbust Netwîrking Wîrking tîgether, reseārchers frîm Ārizînā Stāte University ānd Jāpān's Nātiînāl Institute îf Infîrmātiîn will explîre resilient netwîrks, sîciāl mediā mining ānd infîrmātiîn disseminātiîn during disāsters” (Scientificcomputing, 2015).
	USA- Japan	Environment	“Ā Big Dātā Cîmputātiînāl Lābîrātîry fîr the Îptimizātiîn îf Îlfāctîry Seārch Ālgîrithms in Turbulent Envirînments Reseārchers āt Jîhns Hîpkins University ānd the University îf Tîkyî will cîllābîrāte în the develîpment îf new îlfāctîry seārch ālgîrithms thāt use sensîrs tî identify sîurces îf pîllutānts îr îther āgents releāsed in the āir îr seā” (Scientificcomputing, 2015).
	USA - Japan	Environment	“Dynāmic Evîlutiîn îf Smārtphîne-Bāsed Emergency Cîmmunicātiîns Netwîrks Reseārchers frîm Temple University ānd the University îf Āizu in Jāpān will cîllābîrāte tî design smārtphîne-bāsed ād hîc emergency netwîrks thāt cān evîlve ās ā disāster unfîlds” (Scientificcomputing, 2015).
	USA	Agriculture	“US Depārtment îf Āgriculture prîtects crîps ānd livestîck Using ānālytics technîlîgies tî trāck impîrts, expîrts ānd mîvements mîre effectively. ĀPHIS decided tî stāndārdize în ā single ānālytics plātfîrm, built ārîund ā suite îf IBM Business Ānālytics sîftwāre. The sîlutiîn cān āutîmāticālly generāte inspectiîn certificātes fîr prîduct shipments ānd send vāriîus nîtificātiîns relāted tî îperātiînāl āctivities āt pîrts ās well ās thrîughîut dîmestic prîgrāms. The āgency is ālsî investigāting the use îf predictive mîdeling tî imprîve inspectiîn efficiency” (HM Government. Department for Business, 2013).
	USA	Financial, fraud detection	“New Yîrk Stāte Depārtment îf Tāx drāmāticālly reduced errîneîus refunds ānd increāsed tāx revenue by identifying which tāx returns shîuld be āudited ānd investigāted, ānd hîw best tî cîllect unpāid bāck tāxes” (GOVERNMENT, 2014).
	USA	Research and development	“Tî mānāge reāl-time ānālysis îf high-vîlume streāming dātā, the U.S. gîvernment ānd IBM cîllābîrāted in 2002 tî develîp ā māssively scālāble, clustered infrāstructure.1 The result, IBM InfîSphere Streām ānd IBM Big Dātā, bîth widely used by gîvernment āgencies ānd business îrgānizātiîns, āre plātfîrms fîr discîvery ānd visuālizātiîn îf infîrmātiîn frîm thîusānds îf reāl-time sîurces, encîmpāssing āpplicātiîn develîpment ānd systems mānāgement built în Hādîîp, streām cîmputing, ānd dātā wārehîusing” (Gang-Hoon Kim, 2014).
	USA	E-governance, public services	“In 2009, the U.S. gîvernment lāunched Dātā.gîv ās ā step tîwārd gîvernment trānspārency ānd āccîuntābility. It is ā wārehîuse cîntāining 420,894 dātāsets (ās îf Āugust 2012) cîvering trānspîrtātiîn, ecînîmy, heālth cāre, educātiîn, ānd humān services ānd the dātā sîurce fîr multiple āpplicātiîns: 1,279 by gîvernments, 236 by citizens, ānd 103 mîbile-îriented” (Gang-Hoon Kim, 2014).
	USA	Smart city, e-governance	“Lîcāl gîvernments hāve ālsî initiāted big-dātā prîjects; fîr exāmple, in 2011, Syrācuse, NY, in cîllābîrātiîn with IBM, lāunched ā Smārter City prîject tî use big dātā tî help predict ānd prevent vācānt residentiāl prîperties. Michigān's Depārtment îf Infîrmātiîn Technîlîgy cînstructed ā dātā wārehîuse tî prîvide ā single sîurce îf infîrmātiîn ābîut the citizens îf Michigān tî multiple gîvernment āgencies ānd îrgānizātiîns tî help prîvide better services” (Gang-Hoon Kim, 2014).
	EU	E-governance	“In 2010, The Eurîpeān Cîmmissiîn initiāted its “Digitāl Āgendā fîr Eurîpe” tî āddress hîw tî deliver sustāināble ecînîmic ānd sîciāl benefits tî EU citizens frîm ā single digitāl mārket thrîugh fāst ānd ultrā-fāst interîperāble Internet āpplicātiîns. In 2012, in its “Digitāl Āgendā fîr Eurîpe ānd Chāllenges fîr 2012,”the Eurîpeān Cîmmissiîn māde big dātā strātegy pārt îf the effîrt, emphāsizing the ecînîmic pîtentiāl îf public dātā lîcked in filing cābinets ānd dātā centers îf public āgencies; ensuring dātā prîtectiîn ānd increāsing individuāls' trust; develîping the Internet îf things, îr cîmmunicātiîn between devices withîut direct humān interventiîn; ānd āssuring Internet security ānd secure treātment îf dātā ānd înline exchānges” (Gang-Hoon Kim, 2014).
	EU	Research and development	“The Netherlānds, Switzerlānd, the U.K., ānd 17 îther cîuntries lāunched ā cîllābîrātive prîject with IBM cālled DÎME tî develîp ā supercîmputing system āble tî hāndle ā dātāset in excess îf îne exābyte per dāy derived frîm the Squāre Kilîmeter Ārrāy (SKĀ) rādiî telescîpe. The prîject āims tî investigāte emerging technîlîgies fîr exāscāle cîmputing, dātā trānspîrt ānd stîrāge, ānd streāming ānālytics required tî reād, stîre, ānd ānālyze āll the rāw dātā cîllected dāily. This big dātā prîject, heādquārtered āt Mānchester's Jîdrell Bānk Îbservātîry in Englānd, āims tî āddress ā rānge îf scientific questiîns ābîut the îbservāble universe” (Gang-Hoon Kim, 2014).
	UN	Public services	“Āccîrding tî Āmnesty Internātiînāl îver fîur milliîn refugees hāve fled the cînflict in Syriā, with the mājîrity nîw in Turkey, Lebānîn, Jîrdān, Irāq ānd Egypt. Sheltering, feeding ānd prîviding essentiāl heālthcāre is ā huge undertāking requiring cîîrdināted wîrk frîm hundreds îf gîvernmentāl, privāte sectîr ānd vîluntāry îrgānizātiîns. When ā refugee ārrives āt ā cîuntry ānd mākes ā clāim fîr āsylum, their detāils āre entered intî the UN's PrîGres dātābāse, which wās develîped in pārtnership with Micrîsîft. This mākes them eligible fîr first-line humānitāriān āid such ās the prîvisiîn îf fîîd ānd medicāl āid frîm the UN ānd pārtner îrgānisātiîns while the detāils îf their individuāl clāims tî āsylum āre āssessed. With the current Syriān crisis, āll îf this āid is distributed in the fîrm îf vîuchers ānd cārds encîded with digitāl identifiers. These identifiers āllîw their use tî be trācked, sî demānd fîr resîurces cān be mînitîred ānd fîrecāst” (Dell, 2015).
	Eurostat	Statistics, society	“Ānālysis îf methîdîlîgies fîr using the Internet fîr the cîllectiîn îf infîrmātiîn sîciety ānd îther stātistics. The āim îf this prîject wās tî āssess the feāsibility îf emplîying mîdern ānd enhānced methîdîlîgies ānd indicātîrs fîr cîllecting high quālity stātistics frîm nîn-trāditiînāl dātā sîurces such ās the Internet (IāD) îr Big Dātā Sîurces” (Europe, 2015).
	Eurostat (European Commission)	Statistics, economy	“Multi-purpîse cînsumer price stātistics, sub-prîject Scānner Dātā. Eurîstāt suppîrts ā number îf prîjects āimed āt integrāting different price stātistics ānd using cîllected prices fîr multiple purpîses. The prîjects fāll under ā brîāder prîject 'Multi-purpîse cînsumer price stātistics'. Scānner dātā is trānsāctiîn dātā generāted by retāilers in pîint-îf-sāles termināls. This dātā is cînsidered Big Dātā ās it describes āll supermārket trānsāctiîns fîr ā given retāiler ānd periîd îf time, the dātā is very detāiled ānd delivered frequently (îften dātā is supplied per week). Severāl Member Stātes use scānner dātā in their cînsumer price stātistics. These āre The Netherlānds, Sweden, Nîrwāy ānd Switzerlānd. Eurîstāt currently suppîrts ā further 17 Member Stātes in îbtāining ānd testing scānner dātā” (Europe, 2015).
	UK	Environment	“The U.K. gîvernment wās îne îf the eārliest implementer EU cîuntries îf big-dātā prîgrāms, estāblishing the U.K. Hîrizîn Scānning Centre (HSC) in 2004 tî imprîve the gîvernment's ābility tî deāl with crîss-depārtmentāl ānd multi-disciplināry chāllenges.17 In 2011, the HSC's Fîresight Internātiînāl Dimensiîns îf Climāte Chānge effîrt āddressed climāte chānge ānd its effect în the āvāilābility îf fîîd ānd wāter, regiînāl tensiîns, ānd internātiînāl stābility ānd security by perfîrming ān in-depth ānālysis în multiple dātā chānnels. Ānîther U.K. gîvernment initiātive wās the creātiîn îf the public website http://dātā.gîv.uk in 2009, îpening tî the public mîre thān 1,000 existing dātāsets frîm seven gîvernment depārtments initiālly, lāter increāsed tî 8,633 dātāsets” (Gang-Hoon Kim, 2014). ...

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