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REFORMULATION OF STATISTICAL DATA SOURCES: BIG DATA
NEW DATA SOURCES SUPPORTING FUTURE OFFICIAL
STATISTICS ?
Ari Ardiansyah, Amir Ilyas, Haeranah
Universitas Hasanuddin Makassar, Indonesia
Email: ariabp[email protected]
Abstract
Big Data is the new oil. The real issue of the development of information technology is the existence of Big
Data. Big Data is very important, talking about Big Data not only touches the issue of how big data you have,
but what can be done with the data. The high cost of Big Data access is inversely proportional to the collection
of data through surveys or censuses for free. analysis of the application of efficiency theory in an economic
approach in imposing access costs to statistical activities, knowing the impact of imposing Big Data access
fee tariffs, knowing the potential of Big Data as a supporter of official statistics in the future, what are the
obstacles and solutions in implementing it. The preparation of this paper is juridical-empirical research with
the nature of descriptive research with an economic law approach. Big Data is managed for different purposes
using different systems and methods and not necessarily using statistical rules. The implementation of Big
Data as a new data source is carried out through a combination of data sources. The imposition of Big Data
access fees as a source of data supporting official statistics causes low utilization of Big Data as a new data
source. The information technology revolution makes Big Data has the potential to complement, replace,
improve, add, and improve the composition of existing statistical data sources, and produce more timely
outputs. Difficult access and high costs for data collection are major obstacles. Therefore, it needs to be
supported by legal instruments that facilitate its implementation in Indonesia. One is the reformulation of
existing regulations that make it easier for basic statistical organizations to obtain such data sources for free
and use only in the national interest.
Keywords
: reformulation; statistics; big data; official statistics
INTRODUCTION
Today, millions or even billions of bytes of data are generated at unprecedented speeds
from heterogeneous sources every day. This is due to the trend of technological development,
including the Internet of Things and cloud computing (Botta et al., 2016). This system forms a
distributed and resilient system that supports several interconnected systems such as the smart
grip system (M. Chen et al., 2014). Health care systems Kankanhalli et al., (2016), retail
systems such as Walmart Schmarzo, (2013), and Government systems Sirait, (2016), such as
law enforcement security infrastructure (Stoianov et al., 2015).
Technological advances have created a digital revolution in the form of new innovations
in the collection, storage, processing, and transmission of large, complex amounts of data in
real time (Djafar, 2019). Therefore, the digital revolution is often considered synonymous with
the data revolution (Djafar, 2019). These developments have encouraged the collection of a
wide variety of data, no longer relying on consideration of what data might be useful in the
Injuruty: Interdiciplinary Journal and Humanity
Volume 2, Number 5, Mei 2023
e-ISSN: 2963-4113 and p-ISSN: 2963-3397
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future (Djafar, 2019). Data is treated like a tangible asset. This new era of data management is
commonly referred to as Big Data (Malik, 2013).
Big Data, a term typically associated with very large and rapidly growing, complex and
complex data sets, as well as the diversity of data types collected, processed, and analyzed by
organizations (Malik, 2013). Before the Big Data revolution, conventional data collection
through surveys or censuses to produce Official Statistics was considered less efficient in terms
of time. Conventional data collection takes a long time starting from data collection, data
processing, data dissemination to publication. However, with the advent of Big Data
technology, there has been a paradigm shift in terms of fast and accurate data availability. Big
Data is one of the new data sources that allows quick and accurate decision making.
Big Data emerged from the need for large companies like Yahoo, Google, and Facebook
to analyze large amounts of data (Garlasu et al., 2013). Doug Laney describes Big Data in terms
of volume, velocity and variety or often referred to as 3V (Kitchin & McArdle, 2016). The
development of Big Data technology in Indonesia is increasingly attracting interest not only
from the corporate sector but also from government agencies.
The trend of Big Data utilization in government agencies is increasing along with the
increasing need for real-time updated data for timely and accurate public policies. Big Data
technology is of course used by many organizations, including large, small and medium-sized
companies, governments, and statistical organizations (Agusta, 2021).
The Big Data revolution has succeeded in changing the paradigm of data. Big Data as
new oil, is now the most valuable source of wealth besides oil. The Economist notes that "A
century ago the resource in question was oil. Today, in the digital age, similar concerns are
raised by giants dealing with data, oil in the digital age" (Www.economist.com, 2017).
One of the keys to winning the competition between countries is mastering data. There
is a strong correlation between a country's development capacity and the availability of quality
data. Quality data definitely meets the Quality Assurance Framework (QAF) (Nurseto
Wisnumurti et al., 2022).
The real issue of the development of information technology is the existence of Big Data.
This factual condition is a challenge in the implementation of statistics. Big Data is very
important, because talking about Big Data not only touches the issue of how much data you
have, but what can be done with the data. Big data is a reality and has the potential to be used
as a source of data in the implementation of statistics in Indonesia. Kitchin, (2015)identified
that Big Data can be used to: replace all existing data sources, replace some existing data
sources, generate complementary data with different perspectives to complement existing data,
increase estimates from other data sources, generate new data (Kitchin, 2015).
Table.1 Mobile network operator (Big Data) data access costs for 2021-2022
Big Data
Cost
Information
Year
Mobile Network
Operator Data
30.650.000.000
Nusantara Tourism and Mobility
Statistics
2021-
2022
Source: DIPA BPS for 2021-2022
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BPS asa provider of basic statistics can obtain data in other ways in accordance with the
development of science and technology. Setting the phrase is still general in nature, causing
BPS limitations to access data sourced from Big Data and the high cost of access to these data
sources. During 2021-2022, Rp. 30,650,000,000 is allocated to get mobile network operator
data access for statistical activities, the greater the data needed, the higher the costs that must
be incurred by BPS. This is inversely proportional to data collection through surveys or
censuses carried out free of charge. Where the two data produced are used as Official Statistics
for national interests (Sabtiana et al., 2018).
The ideal theory of harmonizing economic and legal aspects is the theory of economic
efficiency. The use of efficiency theory is in line with the goal of national economic efficiency
as an effort to improve people's welfare through statistical activities. The author is interested
in discussing the application of efficiency theory in legal and economic approaches in the
imposition of access costs to statistical activities, how is the impact of the imposition of access
costs and the difficulty of access to the use of Big Data, what is the potential of Big Data as a
supporter of official statistics in the future, what are the obstacles and solutions in
implementing Big Data as a source of data supporting official statistics and what is the
imposition The cost of access to statistical activities is appropriate in terms of the Economic
Analysis of Law approach.
METHOD RESEARCH
The type of research used in the preparation of this paper is juridical-empirical research
with the nature of descriptive research. Juridical-empirical research with a descriptive nature
is a literature research conducted by examining secondary data. The research was conducted
by examining the provisions in laws and regulations and literature related to law formation and
examining the socio-legal symptoms with an economic approach using the theory of economic
analysis of law (Irwansyah, 2017).
RESULT AND DISCUSSION
Reformulation of Statistical Data Sources: Big Data New Data Sources supporting Future
Official Statistics.
The concept of big data focuses more on the characteristics of big data. Doug Laney with
his concept of 3V whichis Volume, Velocity, Variety, and there are also those who add other
V characteristics, namely exhaustive. Volume relates to the size of very large data storage
media; Velocity relates to the Kaur et al., (2018) speed at which data is created, reprocessed,
and generalized; variety relates to the type or types of data that can be processed ranging from
structured or unstructured data; and exhaustive covers all populations.
Big data is different from conventional data collection (survey / census) or often called
Small Data. Based on a review of the definition of Big Data Kitchin, (2015) argues that Big
Data differs qualitatively from Small Data in seven characteristics, Lovelace, (2016)see in table
2. While MacFeely, in contrast to Kitchin, claims that "there are 6 characteristics of Big Data
or often referred to as 6V, see tabel 3.
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Basically, Kitchin and Macfeely have a lot in common, but in current conditions,
Macfeely's 6V concept is more relevant to Official Statistics' data business processes. The
characteristics of Big Data Value are termed new oil. In addition, the characteristics of
Volatility allow data to be collected faster than conventional data sources (census/survey)
which allows quick and precise decision making.
Table 2. Conventional Characteristics Vs Big Data
Small Data
Big Data
Limited to large
Very large
freeze-framed/bundled
Fast,
continuous
Limited to wide
Wide
Sample
Entire populations
Course and weakto tight and strong
Tight
and strong
Weak to strong
Strong
Low
to middling
High
Source: (Kitchin, 2015)
Table 3.Conventional VS Big Data Characteristics
Characteristics
Conventional (survey/Census)
Big
Data
Volume
Limited to very large
Very large
Velocity
freeze-framed/bundled
Fast,
continuous
Variety
Limited to wide
Wide
Veracity
known to be true
Unknown
Volatility
weakbecome fast
Very
fast
Value
very
valuable
very
valuable
Source: adjustment of the Mac Feely 6v (2018).
Big Data is often associated with data science, data mining, or data processing. However,
Big Data involves a larger infrastructure than ever before. There are 4 important elements in
implementing Big Data technology in Rumata, (2015) organizations related to statistics ,
including:
a. Data
The basic description of data refers to objects, events, activities, and transactions that
are documented, classified, and stored to give it a specific meaning. Data that has been
processed so that it can provide meaning is called information. Data availability is the initial
key of Big Data technology. There are some organizations that have a lot of data from their
business processes, both structured and unstructured data.
b. Technology
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It deals with the tools and infrastructure used to run Big Data, such as computational
specialists, mathematicians, and statisticians. BPS will usually not face major technology
limitations because BPS can buy technology or work with third parties to obtain the
technology.
c. Process
The process of adoption of Big Data technology requires a change in organizational
culture. For example, before Big Data, BPS only collected data from Surveys and Censuses
as Official Statistics in policy making. But after the existence of Big Data technology, there
has been a paradigm shift in the importance of the availability of fast and reliable data to
take fast and appropriate policies. One of the new data sources that enable such policy
making is through Big Data.
d. Human Resources
The implementation of Big Data technology requires human resources who have
analytical and creative abilities, namely the ability to determine new methods that can be
used to collect, interpret and analyze data, and computational capabilities (computer
programs).
Definition of Official Statistics
Official statistics are statistics produced in the national statistical system. The data are
collected within a legal framework, and conform to statistical principles such as independence
and objectivity. Statistical concepts are standardized and output requirements are
internationally harmonized, and often governed by binding regulations (Braaksma et al., 2018.)
The use of Official statistics is intended to support national development. Meanwhile,
according to MacFeely, (2020), the purpose of official statistics is to provide statistical data
needed by the government, private sector and society related to economic, demographic, social
and environmental conditions (MacFeely, 2020)
Official statistics are based on strong principles. The main foundation of this principle is
the protection of data confidentiality. Data collected for statistical purposes (in particular in
relation to personally identifiable or corporate data) may not be disclosed and disseminatedin
aggregate data form and may only be used for statistical purposes. The principle of maintaining
the confidentiality of data provided by respondents is very important in the implementation of
statistics. The existence of confidentiality guarantees will encourage respondents' trust in BPS,
which will affect the way basic statistics are administered.
Economic Analysis Of Law
Legalquestions can be analyzed using rational choices byapplying economic principles
or often called Economic analysis of law
(LAKO, 2016). The Economic Approach to Law is a
branch that is beginning to grow and is increasingly in demand among scholars. One of these
can be found in the legal literature entitled Economic Analysis of Law by Richard A. Posner.
Like economics, the legal system is concerned with rational behavior. Standard analysis
begins with the assumption that in deciding to carry out statistical activities, BPS has carried
out a rational assessment by calculating the benefits and costs of these statistical activities to
maximize the provision of statistical data to the wider community. Thus, when BPS assumes
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that the benefits of Big Data sources outweigh the costs of Big Data access, BPS will be more
likely to utilize these Big Data sources, but if the costs incurred are greater than the benefits,
then BPS will tend not to do so.
In addition, how these microeconomic concepts are applied to legal issues. The concepts
of biaya, harga, nilai, utility are very important, including in evaluating a rule.
The impact of the imposition of Big Data access fees and the difficulty of Big Data access
on the use of Big Data as a source of data supporting Official Statistics
The presence of Big Data is expected to have a major impact on institutions that produce,
process and analyze data and information
(De Broe et al., 2021). Band the Statistics Center or
often abbreviated as BPS is one of these organizations. The Central Bureau of Statistics in
charge of official statistics uses it for various purposes, both for state and private and public
interests. Arguably, the way BPS takes data from Big Data will ultimately have an impact on
the entire community.
Data plays an important role in strategic decision making, especially in the era of B ig
Bata where official statistics are not only derived from surveys and censuses but are required
to handle a number of Big Data in various sectors. In fact, it not only provides valuable
insights, but also a competitive advantage when supported by technology and organizational
resources
(Mahrani et al., 2021)
Big Data is generally defined as 3V, i.e. Volume, Velocity and Variety. The volume
associated with data size increases as data storage capacity increases. Velocity refers
(C. L. P.
Chen & Zhang, (2014) to how quickly data can be transmitted in real-time, streamlined, or in
batches Assunção et al., (2015) and Variety refers to data mining that can be obtained from
various data sources such as social media, the Internet of Things (IoT), or sensors in a
structured, unstructured data format (Kitchin & McArdle, 2016).
Along with the development of science and technology, these three characteristics are
transformed into: Volume (amount of data), Velocity (data processing speed), Variety (data
diversity), Variability (data variability), Veracity (data quality), Validity (data validity),
Viscosity (data complexity), Volatility (data volatility), Visualisation (data visualization), and
Value (data / utility) or often called 10V (Khan et al., 2018).
Meanwhile, in relation to official statistics, Mac Feely stated that the 6V characteristics
of Big Data are very relevant to the business process of official statistical data (MacFeely,
2020)v. In addition to the 3V outlined above, Mac Feely adds three other characteristics:
Veracity, Volatility, and Value. The characteristic "Veracity" refers to errors and biases in Big
Data. The characteristic of "Volatility" MacFeely, (2020) refers to the rapid changes in
technology and business processes that generate Big Data. The "Value" characteristic contains
a very valuable value, see chart 2.
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Chart 2
Figure 1 : 6V Characteristics of Big Data Relevant to Official Statistical Data
The existence of big data can be both a challenge and an opportunity for national
statistical agencies to produce official statistics. Official statistics abbreviated as NSO as a
source of government decision-making and key public information have undergone changes
(De Broe et al., 2021). In facing these challenges, NSO must be able to utilize big data and
manage it according to procedures. Big Data is expected to be a source of data other than
censuses and surveys. if managed properly, Big Data has the potential toreplace official
statistics or official statistics that are completely new or as a complement to official statistics
Florescu et al., 2014; Piwowar-Sulej, (2021)
Big Data technology has only begun to be known in statistical activities since 2016. BPS
utilizes Big Data through web crawling, Google and Facebook mobility, satellite imagery, and
mobile phone data in data collection. So far, one of the difficulties in using Big Data is one of
the characteristics of Big Data, namely Variety refers to structured and unstructured data
formats. Big Data is managed for different purposes, different systems and methods and does
not always conform to statistical principles. No Big Data has complete data, so to make it an
official statistic requires a combination of various data sources such as censuses or surveys.
Currently, the role of Big Data in the Implementation of Statistics as a source of data supporting
official Statistics.
Implementation of the 2016 Economic census, a combination method between the use of
Statistical Business Register (SBR) as initial data with census data sources. In addition, the
implementation of the 2020 census will be combined with census data sources that use the Big
Data Population Administration dataset as initial data combined with census data sources.
Implementation of Area Sample Framework (KSA) to calculate rice production by combination
method using satellite imagery as rice field area data and surveys. The combination method
uses Big Data from Mobile Positioning Data (MPD) to collect the number of domestic tourists
and length of stay with survey data sources. Data in the form of Google and Facebook Mobility
Index to compare people's mobility in various places, such as offices, homes, and grocery
stores, to meet daily needs before and after the pandemic to make mobility statistics.
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Arrangements related to basic statistical operations can obtain data in other ways in
accordance with the development of science and technology. Setting phrases in other ways in
accordance with the development of science and technology is still general in nature, causing
low utilization of these data sources.
Interms of the aspect of Big Data Utilization Access Costs in statistics is still very limited
due to the high cost of Big Data access. In 2021-2022, a budget of Rp. 30,650,000,000 is
needed to accessmobile positioning data (MPD), see table 1. In addition, the high cost of
accessing these data sources causes the use of paid Big Data in basic statistical activities is
still very limited, there is only 1 routine data collection activity each year see in table 4.
Judging from the aspect of the difficulty of accessing Big Data sources, there are only 2
new data sources that can be utilized every year. Only 2 new data sources can be used each
year. These data sources are utilized through inter-ministerial cooperation or through free and
open access. It is difficult to find open sources of Big Data (0pen access) and of course free
for statistical purposes today, one of which is due to the high sectoral egos among data owners.
Table 4. Official Statistics that use Big Data as a Source of Supporting Data
Official Statistics
Data Sources
Year
Economic Census 2016
(SE2016)
Combination Method (use of Statistical
Business Register (SBR) and field census)
2016
Tourism statistics to
calculate foreign
tourists*
Combination Method (Big Data (use of
mobile positioning data (MPD) and
surveys)
Every Year
Mobility statistics
Combination Method (Big Data (google
and facebook mobility index and survey)
Every Year
Food Survey Area
Sample Framework
Combination Method (survey and
utilization of Big Data from satellite
imagery (GIS)
Every Year
Population Census 2020
combination method (Big Data (population
administration records) and Census)
2020
When viewed from the utilization of Big Data on an annual basis, the utilization of new
data sources is still very low. Thehigh cost of Big Data access and the difficulty of accessing
these data sources are the main reasons. In 2021, out of 65 survey/census activities produced
only 3 data sources using Big Data, then in 2022 out of 66 publications produced only 3 data
came from Big Data, see Table 5. P in 2021-2022 there are only 6 publications or 4.6% sourced
from Big Data, the remaining 95.4% comes from censuses/surveys, see in Graph.1. Effective
utilization of data is important because it is considered the foundation for an organization (Kaur
et al., 2018).
Table.5 Comparison of official statistics derived from Big Data vs Conventional
Publications
2021
2022
amount
Average
Publication of survey/census data
62
63
125
95,4
Publications from Big Data sources
3
3
6
4,6
number of publications
65
66
131
100
Source: the central statistics agency is taken from the output achievements of each surve
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Graphs. 1. Conventional VS Combination of Conventional Statistics with Big Data
Source: BPS Pusat Statistik, BPS census/survey calendar for 2021-2022
Statistical data provide an objective basis for the true picture of an event. By knowing
the situation correctly and as it is, individuals, society and society, as well as state
administration can easily determine appropriate, effective and efficient measures to solve
various problems. A clear and well-structured picture of reality will provide benefits for the
enhancement of intelligent knowledge (Hasbullah, 2023).
Statistical data also helps the government in monitoring and evaluating national
development performance. By monitoring statistical data, the government can find out the
extent to which development programs have succeeded in achieving their goals and improve
programs that have not succeeded in achieving targets.
In addition, statistical data is also useful for the private sector to develop relevant
products and services and to support business planning. Companies can use statistical data
related to public consumption to understand consumer behavior
(Hasbullah, 2023). What
commodities are favored by the people. What goods are not yet known and are now consumed
on a wide scale. Companies can develop business strategies to develop market potential and
prepare strategies to win market competition.
Big Data Opportunities as a Source of New Bricks support the Official Statistics of the
future.
The information technology revolution causes the need for fast and reliable data is a
necessity. One of the new data sources for policy making is Big Data. Big Data as new oil.
Data is a new kind of wealth, now more valuable than oil.
The era of digital society in Indonesia is marked by the rapid development of information
technology (ICT), especially the increasing use of the internet through cellular phones. In
2021, citing data from Indonesia.Id, it was reported that internet users in Indonesia had reached
203 million and became 212.9 million in 2023.Interestingly, the average internet user spends
- 20 40 60 80
Publikasi data survei/Sensus
Big Data
Total
62
3
65
63
3
66
2022 2021
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8 hours 36 minutes a day surfing in cyberspace. Development of internet users in Indonesia
January 2012 to January 2023), see graph.2.
Source : DataIndonesia.Id.
Graphs. 2 Growth of internet usage in Indonesia (January 2012-January 2023).
Every day without realizing it, our activities generate a lot of data. We use various online
applications toreveal our work. Financial apps, vehicle booking apps, food and beverage
booking apps, online meeting apps, online shopping and a variety of other apps all require
internet and access to personal data. These activities are then stored as data, and will continue
to grow to form Big Data. Big Data can be turned into information that can help users to get a
lot of input in determining strategic policies in a government agency or company.
The fundamental difference between the application of statistics in Big Data and
conventional data systems (surveys and censuses) is that in conventional systems, data will be
published through planning from sampling, data collection, analysis, to dissemination.
Whereas in Big Data, data is available, created, and continues to grow all the time so that what
is needed is the ability for what the data is used for. Differences in the analytical capabilities
of conventional data collection (survey/census) with Big Data, see table 6.
Table 6. Differencesbetween conventional data analysis and Big Data analysis data
Conventional Statistical Analysis
Data Analytics
Confirmative
Explorative (predictive)
Small data set
Large data set
Small number of variables
Large number of variables
Deductive (no predictions)
Inductive
Numeric data
Numeric and non-numeric data
Clean data
Data cleaning
Source: Data Mining and Statistics: What are the Connections? (Jerome Friedman, 1997)
Due to the rapid development in the era of Big Data, the need for fast data and real-time
statistical information is increasingly needed, especially as a basis for public policy, or in other
words as a new source of supporting official statistics. Statistics Center often abbreviated as
BPS as one of the official statistical data providers has implemented big data in a number of
activities, including the use of satellite imagery for Area Sample Framework, commuter
mobility, tourism statistics, and other statistical activities. Utilization of Big Data through the
method of combining Big Data data sources with census or survey data sources to support
official statistics. Currently, the use of Big Data as a new data source is still limited and will
increase in the future. This is in line with the mission of the UN statistical committee Global
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Working Group (GWG) related to global programs including the use of Big Data for official
statistics.
Furthermore, the existence of statistical activities is influenced by BPS's ability to utilize
Big Data in the future. The impact of rapid technological progress will cause new problems,
therefore fast and appropriate action is needed. Policy making should be based on the timely
and accurate availability of data, one of the data sources is Big Data. Big D or a technology
that enables the processing, storage, and analysis of data in various forms or formats. In
addition, Big Data can be used to increase large volumes of data and transfer data quickly in a
relatively short time. Big Data has characteristics, namely: 1). A much larger number
(volume), 2). Data is transmitted at a very high velocity, 3). Data formats are variegated.
The rapid development of information technology makes big data a provider of dynamic
new data sources that can complement, replace, expand and complement and improve the
composition of existing statistics, as well as produce more timely output data (Kitchin, 2015).
Florescu et al. are cited by Kitcin in their journal, which clarifies that big data sources can be
used in five ways in current statistical systems:
a. To completely replace existing statistical sources (existing statistical output);
b. To partially replace existing statistical sources (existing statistical outputs);
c. To provide complementary statistical information in the same statistical activity but from
different perspectives(additional statistical output);
d. To improve estimates from statistical sources (including surveys) (better statistical
output);
e. To provide completely new statistical information in a particular statistical domain (new
alternative statistical output) (Kitchin, 2015).
The potential for the use of Big Data as a source of new data supporting Official Statistics
will be even greater in the future due to, among others:
1. The utilization of Big Data in the implementation of new statistics is around 4.6%,
indicating that the potential for the use of Big Data as a new data source is still large and
wide open. Of course, this must be supported by all parties, especially from the legal aspect
which makes it easy for statistics organizers to get access and of course for free to Big Data
sources in the government and private and used only for official statistics for national
interests.
2. The United Nations Economic Commission for Europe (UNECE) classifies three main
sources of Big Data: social networks, traditional business systems, and the Internet of
Things. Government business processes and private institutions can also generate data. This
type of Big Data group can be utilized optimally in the future as a new source of data.
3. The mission of the UN statistical committee is the Global Working Group (GWG), which
provides direction, analysis, and strategic coordination for global programs including the
use of Big Data for official statistics.
4. The single data system in Indonesia, which is gaining popularity in government data
governance, is predicted to improve government data governance. One Data aims to
overcome the occurrence of data differences between data guardians. The One Data concept
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is a Government initiative to accelerate the process of data-based policy making. Data
obtained by a government or organization must be accurate, complete, and interoperable in
order to be easily distributed to data users. With this system, it is expected that data
collection will be easier, especially data related to Big Data.
5. The rise of data leakage cases that occur, some members of the public are worried about the
security and confidentiality of data collected by BPS, especially those related to respondents'
personal data. If this concern continues in the long run, respondents will feel hesitant to
provide actual data. Thedevelopment of technology has caused changes in people's
behavior, especially urban communities, towards privacy. The protection of privacy causes
people to be closed to social interactions, including sharing personal data directly with data
collection officers. Thecommunity will be very active in cyberspace. Big Data is one of
the new alternative data sources related to population data because applications that use the
internet will access personal data.
6. The implementation of the Electronic-Based Government System (SPBE) or better known
as e-government, has an impact on system integration. These activities are then stored in the
form of data, and will continue to grow to form Big Data. Big Data can be converted into
information that can help users to get a lot of input in determining strategic policies in a
government agency or company
7. Kennedy et al., (2007) Technology disruption changes BPS business processes in termsof
data collection mode. Data collection through PAPI is time-consuming, and expensive. The
eraof information technology requires fast, precise, cheap and technology-based data such
as CAPI and CAWI. The implementation of both modes requires a combination of methods
between data sources, one of which is through Big Data. For example, the implementation
of the 2020 population census using CAPI and CAWI modes by utilizing Big Data can
shorten the population census data collection time by 10-15 minutescompared to using
conventional PAPI modes and at a lower cost, no printing costs, and shipping uesionary as
well as data entry fees.
Currently, the use of Big Data in statistical activities is only at the stage of additional
statistical output and as a source of new data supporting official statistics has not yet reached
the stage of replacing statistical data sources. This is because its implementation has
encountered obstacles. Some obstacles in the adoption of Big Data technology, especially in
the implementation of statistics, include:
1. Data availability
One of the keys to making Big Data a new data source is data availability. Currently,
Big Data owners and managers are spread across various sources ranging from the
government to private parties. Access to data sources requires a lot of effort and cost as
permits and licenses are required to legally access non-public data. This is especially true
because there are still sectoral egos between agencies as data owners. The data collection
phase is the main bottleneck phase.
2. Data standardization
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Big Data is managed for different purposes using different systems and methods that
are not necessarily in accordance with statistical standards. No Big Data has complete data,
to make it an official statistic requires a combination of data from various data sources.
currently the use of new Big Data as supporting data for Official Statistics.
3. Infrastructure
a. A very large volume of Big Data requires a very large data storage infrastructure.
b. The speed of Big Data is very fast requires the speed of data collection, storage, and
processing so that the resulting analysis results do not lose momentum.
c. Diverse Big Data requires specialized expertise to benefit from a wide variety of highly
diverse content.
4. Study on the Utilization of Big Data as a new data source
There is no comprehensive and continuous study of ideal statistical methods related
to Big Data. This was seen during the Covid-19 outbreak. When there are social restrictions
that force data collection without face-to-face. To ensure that official statistics can continue
to be produced on schedule, there has been no review of the best statistical methods that can
be used when comparing data collection with face-to-face.
5. Data security and confidentiality levels
According to the Microsoft Digital Defense Report 2021, government data is one that
is at risk of leakage and misuse. The case of leakage of 1.3 billion card registration data.
Then, in 2021, the case of leaking 279 million BPJS Health user data in May 2021. Leak of
1.3 million e-HAC application user data in August 2021. BPS as a government agency that
provides statistical data in Indonesia, is very risky to be the target of hacking because it acts
as a data center in charge of collecting and processing national data.
Big Data Solutions as Official Statistics
1. Reformulation of regulations that make it easier for official statistical institutions to
manage and access new data sources, namely Big Data free of charge (free). The Personal
Data Protection Law that has been passed already regulates the transfer of personal data
but is still general, regulations are needed that specifically regulate the transfer of Big
Data for the benefit of government policies, especially statistical institutions.
2. Optimizing the role of BPS as fostering sectoral and special statistics organizers,
especially Big Data managers to improve the quality of the data produced. Development
starts from government agencies that manage Big Data such as the Director General of
Population and Civil Registration, BPJS Health, BPJS Employment, Directorate General
of Taxes, Education Office, Government Hospitals.
3. Infrastructure development that supports Big Data Implementation, provision of large
storage capacity, data processing tools and building human resource capacity that has
data science capabilities.
4. Internally, BPS needs to be encouraged to review statistical methods that utilize Big Data
for official statistics on an ongoing basis.
5. Increased Security and confidentiality of data.
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Strengthening the protection of confidentiality and security of respondents' personal data
starting from the stages of data collection, data processing and data dissemination. Formation
of a work team tasked with auditing data security systems and collaborating between
institutions, especially with the National Cyber Code.
The imposition of access fees to statistical activities is viewed from the approach of
the Economic Analysis Of Law Theory
Nikolas Anova in Statistical Thinking in the Era of Big Data states that the new era of
information technology changes everything for statistics. An era where the flow of various
types of data (velocity) is very high and fast, resulting in very large data (volume) with high
variety or what is called the era of Big Data.
The United Nations Economic Commissions for Europe (UNECE) classifies three main
sources of Big Data: social networks, business systems, and the internet of things. Government
business processes and private institutions can also generate data. This type of Big Data group
can be utilized optimally in the future as a new source of data.
First, a social network is a social structure formed of nodes intertwined with one or more
specific types of relationships such as values, visions, ideas, friends, offspring, and so on. the
number of active social media users in Indonesia reached 191 million people in January 2022,
becoming a potential source of data for statistical activities.
Second, a study conducted by Frost & Sullivan states that online businesses in Indonesia
are growing by 17% per year. This growth is due to: No need for large capital, flexible sales
time, wider market reach, and easier service. The data can be used to support economic
statistics.
Third, the Internet of Things was first proposed by Kevin Ashton in 1999 and first
recognized through the MIT Auto-ID Center. Currently being hotly discussed by several
parties. The Internet of Things is also starting to be known in Indonesia and began to be used
to support various daily activities to connect many objects both physically and virtually through
the internet. The Internet of things can also be used by governments through e-Government to
manage government and public service delivery more effectively.
Rapid technological development requires the availability of up-to-date, accurate and fast
data. One such source comes from Big Data. Here's a comparison between survey, census and
Big Data data, see table 7.
Tabel 7 Comparison of Surveys, Census Data and Big Data
Indicators
Survey Data
Census Data
Big Data
Specificatio
ns
Statistical Products specified Ex-
aunt
Statistical Products
specified Ex-aunt
Specified statistical products
ex-post
Purpose
Designed for statistical purposes
Designed for statistical
purposes
Organic or designed for other
purposes
By-products
lower potential by-products
lower potential by-
products
higher potential by-products
Method
Statistical methods
Statistical methods
Haven't used the static method
yet
Data
structure
Structured
Structured
structured and unstructured
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Representat
ion
representativeness and coverage
according to design
All representation and
scope
Representation and coverage
are difficult to assess
Data bias
Possible bias (sampling and non-
sampling bias)
possible bias, non
sampling bias
Unknown
Data errors
Common errors, sampling and
non-sampling
Non sampling errors
Non sample error (error
reporting)
Timeliness
medium depending on the number
of samples
slow
Potentially faster
Cost
medium depending on the number
of samples
Expensive
can be more expensive or
cheaper even free
Demogfrafi
Featured hotels
all
Service users
Intellectual
property
Government/BPS
Government/BPS
Government/private/copyright
users
Source : Comparison: survey, census and Big Data.
The data revolution can be an input for strategic policy decisions in looking at a problem.
The size and progress towards the sustainable development agenda, in today's digitally
connected world, will depend on our ability to look at new sources of data in terms of time and
innovative technologies to inform policy formulation. The data revolution leverages existing
and new data sources to fully integrate statistics into decision-making, encourage open access
to data use, and ensure increased support for statistical systems.
The Center for Statistics is a non-departmental institution that is obliged to provide basic
statistical data used for its use intended for broad interests, both for the government, private
sector, and society, characterized by cross-sectoral, national-scale, and macro characteristics.
As a government agency that provides official statistical data in Indonesia. BPS Pusat Statistik
or often abbreviated as BPS conducts data collection activities, both in the form of censuses
and surveys. Furthermore, technological developments have an impact on the development of
new data sources which are often called Big Data.
Big Data is termed as a kind of "oil mine" for the future that will affect many people. Big
Data analytics does not always rely on internal data. Big Data adopts different infrastructure
methods and systems. Philosophically, the use of Big Data reduces costs to be cheaper, but in
its development, the use of Big Data is actually expensive because most of these new data
sources are paid.
The Law on Statistics regulates the implementation of basic statistics, in this case BPS
can obtain data in other ways in accordance with the development of science and technology.
Setting the phrase is still general in nature, causing BPS limitations to access data sourced from
Big Data and the high cost of access to these data sources. The use of Big Data as a new source
of data in statistics is limited.
The purpose of law is also expediency in addition to legal certainty and justice. Law has
many aspects, one of which is the Economic aspect. Legal weaknesses in the development
process only look at the present with the approach of the past and do not see the future. Instead,
economics sees the future of legal policy.
Economic law analysis is important because law is a dynamic process or is Law in the
making (Satjipto Raharjo). Law is not just about maintaining the existing conditions of society
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but continuing to seek and discover about the efficiency and effectiveness of laws that work in
society.
Legal issues continue to be objects constellated (organized, created, linked) with
fundamental economic ideas. The idea is to be able to place legal issues so that the flexibility
of legal analysis (rather than economic analysis) can be developed more thoroughly. An area
of study known as "legal economic analysis" focuses on how economic principles can be used
to address real-world legal problems.
Judging from the cost of respondents percomparison of conventional data collection
(census or survey) with Big Data, the use of mobile positioning data (MPD). The cost of Big
Data access to the use of mobile positioning data (MPD) is very expensive where the collection
of survey / census data for official statistical purposes by BPS is carried out free of charge. See
table 8
Table 8.
Comparison of Costs and Books Publication of Big Data &; Survey/Census sources
Years
Big Data Respondents
Respondents from Surveys/census
Cost (Rp)
Publication Price
(Rp)
Cost
(Rp)
Publication Price(Rp)
202
15.400.000.000
30.000
0
30,000 to 300,000
2022
15.250.000.000
30.000
0
30,000 to 300,000
Source: DIPA BPS for 2021-2022 & PerBPS Head Regulation No.7 of 2015.
Judging from the price of statistical publication books sourced from paid and free Big
Data. Theamount of both publications is only Rp. 30,000.-. and both are grouped as non-tax
state income (PNPB).
Furthermore, in terms of state revenue, in 2021, the total state revenue for publication
sales of Rp. 6,568,494,459 is not comparable to the cost of accessing Big Data sources, the
use of mobile positioning data (MPD) required of Rp. 15,400,000,000 in 2021.
Judging from the utility (usefulness) of data collection sourced from censuses / surveys
and Big Data both the use of data is used for statistical activities. The use of this data is for
broad purposes, both for the government, the community, and the private sector in supporting
national development. See tables 9 & 10.
Tabel 9.Comparison of Big Data Access Costs Vs Publication Prices for 2021-2022
Year
Publication of Foreign Tourism Statistics
Sum
Big Data access fees*
Softcopy (Rp)
Hardcopy (IDR)
2021
1
15.400.000.000
30.000
30.000
2022
1
15.250.000.000
30.000
30.000
Source: Band Statistics Center, DIPA BPS &; Perka No.7 Year 2015 Based on DIPA
Ceiling
Table 10. Cost and price comparison of Paid and Free Big Data publications 2021-2022
Years
Big Data paid
Big Data for Free
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Cost (Rp)
Publication Price (Rp)
Cost (Rp)
Publication Price(Rp)
2021
15.400.000.000
30.000
0
30.000
2022
15.250.000.000
30.000
0
30.000
Source: Badan Pusat Statistik&; Perka BPS No.7 Year 2015
In economics, the level of demand can be influenced by prices, where if prices are high
then demand will decrease, and vice versa. When economic principles are used to analyze law,
demand is used and price is access to data. So if the price of a data source is high, the utilization
rate of the data will decrease. The imposition of data access fees on official statistics is
inefficient and reduces the usefulness of the statistical data. BPS as the sole authority in the
administration of statistics must be given the convenience to access new data sources free of
charge for official statistics purposes only for national purposes. In 2021-2022, data utilization
from Big Data averaged 4.6% or only 6 official statistics publications whose data was sourced
from Big Data, see table 5.
Rationally, the imposition of fees for official statistics is not appropriate because basic
statistics as official statistics concern the public interest and national interest.
When viewed from an economic approach in terms of efficiency, the imposition of costs
to utilize Big Data data sources causes inefficiencies where in the implementation of statistics
so far free of charge or free resulting in limited use of these new data sources in the
implementation ofbasic statistics.
In the era of the industrial revolution 4.0, the provision of statistical information based
on economic, social, political, cultural, and natural resources is increasingly needed, because
development planning is increasingly widespread, diverse, and becomes the basis for
determining the direction of national development policies. Statistical data is essential for
decision-making and policy-making. Decision making and policy making can be done more
simply, quickly, and accurately with statistical data.
The current data revolution has fundamentally affected the stages of the public policy
decision-making process. Data related to statistics play a role in almost all stages of the public
policy decision-making process. In the early stages of initiation and formulation of public
policy, the role of statistics is very high as inputs which can be in the form of statistical
information about socio-economic conditions or through research. The role of statistics is also
very high at the stage of monitoring and evaluating the implementation of public policies that
have been established at the policy selection stage. Even at the policy selection stage, not only
statistical information is required, but statistics can also be utilized
It is necessary to reformulate regulations on statistics to accommodate the use of Big
Data into statistical activities. Reformulation is needed for more specific (special)
arrangements for the explanation of phrases in other ways in accordance with the development
of science and technology in the Law on Statistics which regulates the use of Big Data as a
new source of data in the implementation of statistics. The principle of legality is needed to
provide legal certainty in statistical providers that make it easier for BPS to be able to access
all new data sources in the form of Big Data in the government and private sector for free, so
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that the implementation of statistics runs more efficiently and maximum utilization of the data
for offical statistics purposes.
CONCLUSION
MacFeely's 6V characteristic concept is relevant to the business process of official
statistical data. Big Data is managed for different purposes using different systems and methods
that do not necessarily use statistical rules. The absence of Big Data that has complete data to
make it an official statistic requires a combination of data from various data sources.
Implementation of the 2016 Economic census, a combination of data between the use of the
Statistical Business Register (SBR) and the field census. The use of Big Data in statistics is
still very limited due to the high cost of access to obtain the data.
The information technology revolution causes the need for updated, fast and reliable data
is a necessity. One of the new data sources that enable such policy making is through Big Data.
Big Data as a provider of new dynamic data sources, has the potential to complement, replace,
improve, and augment and improve the composition of existing statistics, as well as produce
more timely outputs.
Access to data sources requires considerable effort and cost due to the need for permits
and licenses to legally access non-public data. This is especially true because there are still
sectoral egos between agencies as data owners. The data collection phase is the main bottleneck
phase. Reformulation of regulations related to new data sources in statistical providers urgently
needs to be carried out immediately to support new data sources in statistical operators. The
technological revolution has changed the paradigm about data. The availability of fast, accurate
data is needed for policy making, one of which is Big Data. Regulatory support needs to be
encouraged for the implementation of statistics to obtain these new data sources with access to
Big Data in the government and private sectors with young and free costs and only for official
statistics to be used for national interests.
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Copyright holders:
Ari Ardiansyah, Amir Ilyas, Haeranah (2023)
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Injurity - Interdiciplinary Journal and Humanity
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