Looking into Big Data: The Case of the U. S. Federal Government

Sherry L. Xie

School of Information Resource Management, Renmin University of China, 59 Zhongguancun, Beijing, China

Keywords: Big Data, Data Feature, Existing Data, To-be-collected Data, Data Science, Data Analytics.

Abstract: This paper reports on a study that aimed to examine the term big data for its meaning in a particular setting.

The study chose the U.S. Federal Government as its case and analysed all the big data projects and programs

identified as representative of the U.S. Big Data Initiative. It constructed an analytical framework and

generated findings in forms of statistic descriptions and narrative discussions. The study discovered that 1)

not all the big data projects and programs possess in a collective manner the typical 3 Vs (i.e., volume, variety,

and velocity), 2) variety appears to be the most valued characteristic, and 3) to-be-collected data lags largely

behind existing data, indicating that technologies such as the Internet of Things are still at the stage of being

developed. It also unrevealed that the U.S. Federal Government’s current big data focus is heavily placed on

IT and the term big data has made that focus hidden. It then suggests to sufficiently distinguish data and the

technologies underlying the various features of data so that collaborations between the owners of data and

technologies can be forged with easiness and big data benefits can be realized with efficiency and

effectiveness.

1 INTRODUCTION

Big data has since its inception continued to receive

focused attention. To use Web of Science Core

Collection (WoS) as reference, more than seven

thousand hits returned for the query of “big data” in

Title, with the earliest one in 2004 as a conference

paper and dramatic increases starting in 2013.

Serving also as a strong indicator is the publishing of

big data special issues in diverse fields such as Big

Data by Nature (2008), Big Data by Significance

(2012), Big Data in Political Science by Political

Analysis (2013), Big Data and Organization Design,

Journal of organization design (2014), Journalism in

an Era of Big Data by Digital Journalism (2015), Big

Social Data by Journal of Information Science

(2015), The Value of Big Data in Agriculture by

Journal of Business & Economic Statistics (2016) and

Big Data in Psychology, Psychological Methods

(2016).

Despite the rapidly increased volume of literature,

the meaning of big data remains clouded: definitions

abound, yet they do not necessarily agree with each

other (Floridi, 2012; TechAmerica Foundation, 2012;

Mayer-Schönberger, 2013; Ekbia, et al., 2014;

Kitchin, 2014; Baro et al., 2015; NIST, 2016; Pirc, et

al., 2016; Todman, 2016). Moreover, it is not just the

wordings of the definitions that vary, so do the

characteristics, i.e., the Vs. For example, Gartner

(2016) proposes 3Vs (i.e., volume, variety, and

velocity), IBM (2016) adds to the 3Vs with veracity,

SAS (2016) with variability and Oracle (2016) with

value. In addition, research dedicated to studying the

meanings of big data appears to be sparse and the

focuses of the handful articles (both journal and

conference papers included) are on “big data

analysis/processing” (e.g., Chebbi et al., 2015;

Gandomi and Haider, 2015; Pashayev and Sabziev,

2016; Bendre and Thool, 2016) or the application of

the concept to a specific area (e.g., Wielki, 2013;

Elarabi et al. 2016; Miloslavskaya and Tolstoy, 2016;

Drosio and Stanek, 2016). This situation continues to

date, after the analysis of the present research was

completed, as a search for dedicated research on big

data concept after 2016 has showed (e.g., Venkatram

and Geetha, 2017; Liu, et al., 2017; Gephart, et al.,

2018).

In this reality, how to quickly and adequately gain

understanding of big data has become a challenge for

professionals whose fields are not in direct contact

with the emergence of big data (e.g., computer

science, data management, statistics etc.), yet, at the

same time, are being constantly reminded that big

data will ultimately impact their fields (e.g., Wong et

144

Xie, S.

Looking into Big Data: The Case of the U. S. Federal Government.

DOI: 10.5220/0006919001440152

In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 3: KMIS, pages 144-152

ISBN: 978-989-758-330-8

al, 2016 and the above listing of big data special

issues). To examine existing big data definitions or to

synthesize the varied numbers of Vs may be one way

of gaining understanding (e.g. Baro et al, 2015; De

Mauro et al, 2016), to examine big data projects in

operation may be the other. This paper reports on a

study that took the later method, which analysed

operating big data projects and programs considered

by the United States (U.S.) Federal Government as

the highlights of its Big Data Initiative. This gives

support to the selection of these projects and

programs as a representative sample, which were

coded against an analytical framework constructed by

the study. The rest of this paper presents the sections

of the U.S. Federal Government Big Data Context,

the Analytical Framework, Analyses and Findings,

Discussions, and Conclusion.

2 THE U.S. FEDERAL

GOVERNMENT BIG DATA

CONTEXT

2.1 Usage of the Term

The term big data (hereafter Big Data, or BD)

appeared first in the U.S. Federal Government in

December 2010, in a document entitled Report to the

President and Congress Design a Digital Future:

Federally Funded Research and Development in

Networking and Information Technology. This report

was produced by the President’s Council of Advisors

on Science and Technology (PCAST), an advisory

group of leading scientists and engineers in the U.S.

appointed by the White House for policy

consultations on issues of science, technology, and

innovation. The main purpose of this report is to

present its assessment of the Federal Networking and

Information Technology Research and Development

(NITRD) Program, the primary source of advanced

information technologies funded by the Federal

Government in the contexts of the U.S. High-

Performance Computing Act of 1991, the Next

Generation Internet Research Act of 1998, and the

America COMPETES (Creating Opportunities to

Meaningfully Promote Excellence in Technology,

Education, and Science) Act of 2007 (NITRD, 2016).

For the 2010 assessment, the report focused on two

aspects: the NITRD Program as ‘the mechanism by

which the Federal Government coordinates its

unclassified research and development (R&D)

investments in Networking and Information

Technology (NIT)’ and as ‘the ensemble of

unclassified R&D efforts in NIT supported by the

Federal Government rather than the coordination

effort’ (PCAST, 2010, p. 1). Among the five major

‘crosscutting themes’ that the report identified, data

volume was listed as the first one because of its

exponential growth. A ‘big data’ strategy, therefore,

was recommended for ‘every agency’ to have

(PCAST, 2010, p. xvii). Since then, the term big data

has appeared in many important Federal documents,

first with quotation marks and all in lower letters

(such as the one in the PCAST 2010 report), then with

quotation marks being removed and first letters

capitalized (such as the one in the 2016 Federal Big

Data Research and Development Strategic Plan).

Figure 2 depicts the occurrence of the term big data

in some of the major documents of the U.S. Federal

Government. MGI in the figure stands for McKinsey

Global Institute, a research arm dedicated to business

and economics established by McKinsey & Company

in 1990. Although MGI is not a U.S. federal agency,

its 2011 report, Big data: The Next Frontier for

Innovation, Competition, and Productivity, is

included due to the fact that the first author of the

report, that is, James Manyika, was present at the

Federal Government Big Data Rollout held on March

29, 2012 in the AAAS Auditorium in Washington,

DC (NSF, 2012a). The Rollout consisted of agency

announcements of Big Data projects and a panel

discussion, of which Mr. Manyika was one of the

panellists. As the Rollout was the immediate follow-

up to the OSTP’s announcement of the 2012 U.S.

Federal Government Big Data Initiative, the influence

of the MGI report seemingly should not be

overlooked. OSTP in the figure refers to the Office of

Science and Technology Policy of the Executive

Office of the White House, which administers

PCAST. The OSTP announcement of the Big Data

Initiative and the companion Big Data Fact Sheet are

the focus of the present study. The Big Data IWG

refers to the Big Data Interagency Working Group,

formerly known as the Big Data Senior Steering

Group (BD SSG), formed in early 2011, with the

tasks of identifying Big Data research activities

across the Federal Government. In 2015, the NIST

Big Data Public Working Group (NBD-PWG)

published the first volume of its seven-volume series

Big Data Interoperability Framework, which defines

big data as “the inability of traditional data

architectures to efficiently handle the new datasets”

(NIST, 2016, p. 4). Same as the articles introduced

above, this way of defining big data does not help

professionals who traditionally do no possess

knowledge of “traditional data architectures”. The

Looking into Big Data: The Case of the U. S. Federal Government

145

most recent outcome of its work is the 2016 Big Data

Research and Development Strategic Plan.

2.2 Big Data Initiative

By the recommendation of the 2010 PCAST report,

the U.S. Federal Government formally announced its

‘Big Data Research and Development Initiative’ on

the day of March 29, 2012, committing a more than

200 million USD to Big Data R&D to ‘make the most

of the fast-growing volume of digital data’ and to

‘help solve some the Nation’s most pressing

challenges’(OSTP, 2012a). Six Federal departments

and agencies were identified in the announcement as

major representatives, including the National Science

Foundation (NSF), the National Institutes of Health

(NIH), the Department of Defense (DOD), the

Department of Energy (DOE), the Defense Advanced

Research Projects Agency, and the US Geological

Survey (USGS) of the Department of the Interior. The

six departments and agencies then provided in the

afternoon of the same day, more detailed information

regarding their commitments on their respective

websites. NSF (2012b), for example, listed 16 Big

Data programs on its website with descriptions only

for some projects and descriptions along with hotlinks

for others. It needs to be pointed out that the

announcing of the Big Data Initiative does not

indicate the actual beginning of the ‘big data action’

in the Federal Government and what it did is to make

the term ‘big data’ official. There were indeed many

Big Data programs and projects already going on at

the time of the announcement and the Big Data Fact

Sheet clearly indicates it. Published by OSTP (2012b)

on the same day of the announcement, the Fact Sheet

identified in total 89 ‘ongoing’ Big Data programs

and projects across the Federal Government,

including NASA's Earth Science Data and

Information System (ESDIS), which started in 1997,

and DOE’s The Next Generation Networking

program, which started in 2001.

A definition for big data was not provided by

either the OSTP 2012 announcement or the PCAST

2010 report, both, however, provided explanatory

information such as ‘large and complex collections of

digital data’ and ‘data volumes’ in exponential

growth. Definitions were found in less ‘authoritative’

documents such as the MGI 2011 big data report and

the NSF’s Big Data Solicitation. MGI considered big

data as ‘datasets whose size is beyond the ability of

typical database software tools to capture, store,

manage, and analyse) and the NSF Big Data

Solicitation defines Big Data as ‘large, diverse,

complex, longitudinal, and/or distributed data sets

generated from instruments, sensors, Internet

transactions, email, video, click streams, and/or all

other digital sources available today and in the future’

(NSF, 2012c). In addition, another definition

considered also relevant to the U.S. Federal

Government setting was found in the report produced

by the TechAmerica Foundation’s Federal Big Data

Commission, which read ‘Big Data is a term that

describes large volumes of high velocity, complex

and variable data that require advanced techniques

and technologies to enable the capture, storage,

distribution, management, and analysis of the

information’ (TechAmerica Foundation, 2012). Like

the situation outside, these definitions do not conform

to each other in their wordings or intentions even

though similarities do exist. As noted in the 2014 Big

Data review report, Big Data definitions abound, and

they vary depending on the party who defines it (The

White House, 2014, p.2).

The 89 Big Data programs and projects listed in

the OSPT Fact Sheet were considered ‘highlights’ of

the Federal Government’s Big Data action. They

therefore were chosen to be data for analysis for the

present study.

3 THE ANALYTIC

FRAMEWORK

The analytical framework for the study consists of

two parts, one addressing indicators of analysis and

the other addressing the approach of coding.

Analysis indicators were identified relying on the

various sources introduced above, including those in

the Introduction section. The discussions in these

sources collectively revealed the mostly recognized

or referenced features of big data, which enabled the

present study to form a set of indicators and weave

them into a coherent analytical framework. Nine

indicators were identified to form the framework,

including Volume, Variety, Velocity, Data, Existing

data, To-be-collected Data, NIT (Networking and

Information Technology), Data Processing, and Data

Collecting, assigned respectively to the categories of

Data Presence (Data), Data Characteristics (Volume,

Variety, Velocity; that is, the 3Vs), Data Type

(Existing data, To-be-collected Data), NIT Presence,

and NIT Function (Data Processing, Data Collecting).

The 3Vs in the present study has its study-specific

meanings despite the fact that they now have gained

a widespread familiarity with big data researchers and

practitioners. Like big data, the 3Vs have so far not

yet enjoyed the agreement of unambiguous meanings,

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and consequently, different definitions and/or

interpretations exist. For example, the 2010 PCAST

report focused exclusively on volume, and according-

ly, discussed variety and velocity as contributing

factors to volume. Many others, however, treat the Vs

as independent variables as in the cases of MGI and

Gartner. Logically speaking, the PCAST view holds

tight because it rightfully acknowledged the

intertwined relationships among the 3Vs as revealed

by existing narratives, and even if the 3Vs are defined

with clear-cut conceptual boundaries, their

connections to each other would still arise when big

data is the backdrop. This kind of view, however,

creates difficulties for studies that intend to

understand a concept by decomposing it, like the

present one. Volume in the present study, therefore,

is used to refer strictly to size, and size only.

For Variety, discussions on it are observable of

two types: one tends to be encompassing, referring to,

for example, not only data format and/or data type but

also data domain and/or data repository (NIST, 2016).

The other narrows down the scope to only data format

or data model and explains the concept by giving

specific examples (NSF, 2012c). Common to both

types is the understanding that variety implies the

presence of both structured and unstructured data,

which the present study adopts. The meaning of

Velocity in the present study comes from the basic

idea that was put forward in 2001 by now Gartner’s

Doug Laney, who was then with META Group.

According to Laney, velocity is relevant to the

‘increased point-of-interaction speed and,

consequently, the pace data used to support

interactions and generated by interactions’. While

framed in the context of e-commerce, this idea can be

easily transported into other settings including

Internet-enabled governments or digital

governments, where dynamic, real-time decision

making is as well much envisioned and may even be

more critical. Moreover, to understand Velocity as the

focus on the interplay of interaction and speed aids

technically the analysis as it excludes effectively the

application of speed in stand-alone data actions such

as collection, processing, or transmission.

Like big data, data too is a term that does not have

a universally accepted definition and too is used with

diversified meanings in different fields including

those of the information technology, information

management, and both the natural and social science.

For the purpose of this study, the definition of data

provided by the 2015 version of the ISO/IEC 2382

Information technology — Vocabulary was

employed. Data in this context refers to

‘reinterpretable representation of information in a

formalized manner suitable for communication,

interpretation, or processing’ (ISO, 2015).

Existing data refers to data that have participated,

or are participating, in the operations of the

departments and agencies of the Federal Government.

Such data provides ready access for Big Data

programs and project. To-be-collected Data, on the

contrary, is considered requiring efforts additional to

the activities that routinely generate Existing data in

departments and agencies for its access. In other

words, such data are determined to be collected

specifically for a certain Big Data program or project

despite the possibility that such collection may

become routine in the future. Data Collecting as one

of the two functions in the NIT Function category,

refers exactly to the additional efforts that produce the

data determined to be collected. Data processing, the

other NIT function, refers mainly to the activities that

yield results typically associated with Big Data

programs and projects such as knowledge, insights,

and/or actionable intelligence.

4 ANALYSES AND FINDINGS

Two rounds of coding were conducted, and

discrepancies were sorted out during the comparing

stage. Below are findings that emerged from the

coding.

4.1 Overarching Tendency

Twelve departments and agencies collectively

contributed to the 89 Big Data programs and projects,

with varying numbers individually. These numbers

were considered indicative of degree of focus, and

when linked to the names of the departments or

agencies, capable of revealing the overarching

tendency of the Big Data Initiative.

It is rather clear that health, natural science,

national security, and energy are the focused areas in

the Federal Big Data movement health. It should be

noted that given the fact that NITRD does not in its

scope cover classified networking and information

technology, the investment on national security Big

Data programs and projects cannot be inferred as less

than those on health and natural science. Also worth

noting is the complete absence of education as an

area. Education was pointed out in the OSTP 2012

announcement as one of the areas on which the Big

Data Initiative would have a major impact, none of

the 89 programs and projects, however, identified

itself with it.

Looking into Big Data: The Case of the U. S. Federal Government

147

4.2 Observations by Indicators

4.2.1 Data Presence

Data is present in all of the 89 programs and projects,

with 70 being explicit and 19 implicit. Examples of

being explicit include the DOD Data to Decision

project, the DOE High Performance Storage System

(indeed, all of the 12 DOE projects), the NIH Internet

Based Network for Patient-Controlled Medical Image

Sharing project, and the NSF Data Citation project.

Examples of being implicit include the DOD Cyber-

Insider Threat project, the NIH National Centers for

Biomedical Computing program, the NSF

Expeditions in Computing project, and the NSA

Combining Cybersecurity and Big Data program.

Among the 70 explicit presences, 5 appeared stronger

than the NIT presence in the same programs or

projects (see also 4.2.4 NIT Presence and NIT

Function) and they are the Cancer Imaging Archive

project and the WorldWide Protein Data Bank project

from NIH, and the Planetary Data System project, the

Multimission Archive at the Space Telescope Science

Institute program, and the Earth System Grid

Federation program from NASA.

4.2.2 Data Characteristics

Variety was present in almost all programs and

projects, with only one exception, the VA Informatics

and Computing Infrastructure project. Among the

3Vs, Variety appeared also as the one that was mostly

capable of being a stand-alone characteristic.

Examples of projects and programs that had only

Variety as their Big Data indicator include DOD’s

Machine Reading, Programming Computation on

Encrypted Data, VA’s Genomic Information System

for Integrated Science, and HHS’s Using

Administrative Claims Data (Medicare) to Improve

Decision-Making. For Volume, although it has a high

percentage, close to 90% indeed, there are still

programs and projects that did not consider it a

necessity for their Big Data tasks. Velocity appeared

to be unable to occur independently in any of the

programs or projects as it was only discernable when

Volume and Variety were both present. Moreover, its

explicit presence is less than its implicit presence,

making the certainty of its presence less than 20%

against all the programs and projects. From a

collective standpoint, the occurrence of the 3Vs all

together does not account for a majority, and even the

occurrence of 2Vs did not pass the 50% bar.

4.2.3 Data Type

Existing Data of the Data Type indicator appeared to

be present in all programs and project, with 69 being

explicit and 20 implicit. This 69 explicit: 20 implicit

ratio is almost identical to that of Data presence,

which is 70 explicit: 19 implicit. This suggests that

the programs and projects that included in their

descriptions a clear presence of data are those who

also had a focus on Existing Data. The one program

that is explicit in Data presence but implicit in

Existing Data is the Office of Advanced Scientific

Computing Research program at DOE, which focused

primarily on technologies. The To-be-collected Data

indicator has in total 36 presence, with 17 being

explicit and 19 being implicit. Making use of Existing

Data, therefore, clearly outweigh the collection of

new data (78% vs. 19% explicitly). In other words, to

collect new data was either not a priority or at most, a

co-focus with Existing Data.

4.2.4 NIT Presence and NIT Function

NIT was present in all programs and projects, with 88

explicit and 1 implicit. The only exception is the Data

and Communications in Basic Energy Sciences

Workshop program administered by the Office of

Basic Energy Sciences at DOE, which stated

explicitly about data but nothing about NIT. The

needing of NIT was thus inferred from the goal of the

program, which aimed at handling scientific

experimental data. When compared with Data

presence, 21 among all the programs and projects

(that is, 24%) had an equal focus on Data and NIT, 43

had a stronger focus on NIT (that is, 48%), and 20 had

a much stronger focus on NIT (that is, 22%).

Data Processing of the NIT Function occurred

alone in 53 programs and projects, and jointly

occurred with Data Collecting in the rest. With 83

being explicit and 6 being implicit, Data Processing

was thus at presence 100%. Not occurring as a stand-

alone indicator in any of the programs or projects,

Data Collecting occurred 36 times, with 31 being

explicit and 5 being implicit. This finding

corresponds to that on Data Type, where the type of

To-be-collected Data did not occur at all as a stand-

alone indicator. Among the 36 programs and projects

in which both Data Processing and Data Collecting

occurred, 29 treated these two equally, 6 focused

more on Data Processing, and 1 focused more on Data

Collecting (i.e., the VA’s Million Veteran Program).

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5 DISCUSSIONS

5.1 Data in Big Data

The above analyses suggest that BD can be

understood in two parts, ‘data’ in BD and ‘big’ in BD.

In the analytical framework, data is represented

by the categories of Data Presence (Data), Data

Characteristics (Volume, Variety, Velocity), and

Data Type (Existing data, To-be-collected Data).

Data presence was found in all of the BD programs

and projects, the commonly attributed BD 3V

characteristics, however, were not. Variety appeared

to be mostly visible, having an explicit presence of

64% (compared to Volume 52%, Velocity 17%), and

when implicit presence is considered, Variety has a

presence percentage close to 100%, higher than

Volume (89%). This deviates from PCAST’s original

assessment of the BD situation in the Federal

Government, which located data Volume as the focus.

It is however in agreement with the viewpoint of

Gartner. According to Gartner (Buytendijk, 2013),

among the 3Vs, ‘the new variety in the data’ is where

most of the value of the big data phenomenon lies.

Gartner thinks also getting the most out of variety is

more challenging than handling Volume and

Velocity. The latter point, however, differs from the

inference that the present study has drawn from its

findings. Velocity in this study has only 17% explicit

presence and this small percentage were only found

in departments and agencies that had big budgets such

as DOD and DOE or in research projects that were

equipped with large funds awarded by NSF or NIH.

Within these settings, the presence of Velocity did not

appear to dominate either. For example, there were

only 33% Velocity related projects and programs in

DOE among all of its Big Data projects and programs,

and similarly, only 30% in DOD, 17% in NIH, and

13% by NSF. These findings seem to suggest that

either Velocity is not a priority compared to Variety

or Volume, or it is challenging to many projects and

programs in particular those who had to be budget-

conscious. Nonetheless, the findings on the presence

of the 3Vs were largely in consistence with Gartner’s

characterization of big data. As discussed in the

Introduction section, Gartner’s big data definition

employs the expression ‘and/or’, indicating that any

single V or any combinations of the 3Vs can be

utilized to depict big data.

The difference between Existing Data and To-be-

collected Data (78% vs. 19% explicitly) confirms the

above finding that Velocity is not currently the

dominating feature of the U.S. Big Data Initiative.

While Existing Data does not constitute the entirety

of Data presence, the 100% presence of Data

Processing indicates sufficiently the reliance on

Existing Data of the BD projects and programs. This

focus on Existing Data reflects the reality that the

U.S. Federal Government had accumulated, at the

time of announcing its Big Data Initiative, a

substantial storage of data, rich in both Volume and

Variety. This reality gave rise also to the Obama

Administration’s Open Data initiative, which started

in 2013 and has continued on since then. On May 9,

2013, an Executive Order was signed to boost

‘openness in government’ (The White House, 2013)

and its companion government-wide policy – that is,

the Open Data Policy – made it clear that the goal was

to ‘ensure that the Federal Government is taking full

advantage of its information resources’ (OMB, 2013).

Building on the 2013 Open Data Policy, the OMB

(Office of Management and Budget) issued on

February 14 of 2014 its memo on administrative data,

providing guidance to departments and agencies on

leveraging ‘existing data’ for both the efficiency of

their programmatic work and the benefits of the

American public (OMB, 2014).

The much smaller presence of To-be-collected

Data, as well as Velocity, seems to be attributable to

the slow taking up of cloud computing and the

Internet of Things, two enablers for real-time data

collecting, processing, and presenting. The U.S.

Government’s Federal Cloud Computing Strategy,

also known as the ‘Cloud First Policy’ (Kundra,

2011) , was issued in 2011, however, due to strong

concerns with security (Corbin, 2015; Kapko, 2015),

it is not until 2016 when the U.S. Federal Government

as a whole ‘finally loves cloud computing’ (Darrow,

2016). The story with the Internet of Things is

different because it arrived later than cloud

computing and was found out with different causes

than security for its slow adoption. The Federal

Government was in 2016 still ‘examining

opportunities and challenges’ of the Internet of

Things (Bruce, Correa & Subramanyam, 2016), and a

list of challenges were identified as causes, including

‘a lack of leadership, skills, and funding, as well as

cumbersome procurement policies and a risk-averse

culture’ (Castro, et al, 2016). A greater presence of

Velocity, therefore, will have to come in the future.

For the time being, data in BD means much less on

the combined Vs, but more on 1) accumulated

information, 2) from diverse sources and in a variety

of formats, and 3) with the potential to be used as well

as to be continuously accumulated for the same

and/or a different use.

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149

5.2 Big in Big Data

Unlike data, ‘big’ in BD was not identified as in

association with any specific categories in the

analytical framework. If ‘data’ can be considered as

having a basic meaning in existing literature, yet the

situation is different with ‘big’. Its meaning,

therefore, was expected to emerge in the analysis

process of the study, which would also take into

consideration any accompanying findings that could

shed light on it. As it emerged, ‘big’ in BD appeared

to be all about NIT, which was explicitly present in

88 of the 89 BD projects and programs, and 19 of

them simply omitted mentioning data. Although the

study categorized NIT Function into only two types,

Data Processing and Data Collecting, the

technological abilities connected with these BD

projects and programs are evident in producing data,

storing data, transmitting data, processing data,

presenting data, and producing more data – the full

circle from initiating a BD project to delivering

outcomes. This finding largely corresponds to the

development history of the term Big Data, which

started as a ‘problem’ for computing capability

because of the huge size of datasets (Cox and

Ellsworth, 1997), then ‘the result of recent and

unprecedented advancements in data recording and

storage technology’ (Diebold, 2003), then ‘the ability

to gather information’ (NIST, 2016), and then the

‘information assets that demand … processing for

enhanced insight and decision making’ (Gartner,

2016).

This finding may appear insignificant because

today’s NIT, or information technology in general,

underlies almost every type of human endeavour. It is

this study’s contention that the understanding of this

underlying feature is not clear or intuitive to all the

fields that hope to understand big data: for many,

“big” describes the V-characterized features of data,

not the whole set of digital technologies and devices.

The implications may be instructive to organizations

who are contemplating on initiating big data projects

or academics who wish to join big data research. For

data users, especially the general ones, the

understanding of the Vs of big data is not important –

that of the technologies needed for tackling their data

analysis needs is.

6 CONCLUSIONS

Relying on the representative BD projects and

programs identified by the U.S. Federal Government,

the present study examined the meaning of the term

big data in this particular setting. Although the

examination relied primarily on descriptions of

projects and programs, not their internal operations,

the key features of these projects and programs were

sufficiently clear for the intended analysis. The term

big data started out in the U.S. Federal Government

BD Initiative without a formal definition，but a focus

on data volume. The subsequent NIST’s effort to

define big data struggled with bringing sufficient

clarity to its conceptual construction (NIST, 2016).

As the present study has demonstrated, the term is a

simple combination of ‘data’ and ‘big’, with ‘data’

mostly referring to existing data for the time being

and ‘big’ to digital technologies for now and for the

future. While claimed to be a “movement”,

“transformation” or “revolution”, big data is indeed

nothing more than a newer – or the most recent –

phase of digital data, exemplified by unstructured

data, which is a long existing concept. The Vs, no

matter how many, are unable to specialize or make

unique enough the term as data can always be

described by size, formats and/or forms, all existing

concepts. It may be argued that, as a term, big data is

convenient for usages in popular writings and news

reporting, it should not be ignored, however, to use

the term as a new, specialized one may create

confusions for the professions that have legitimate

interest in data. The conceptual identification of terms

such as data, information, content, records,

knowledge, intelligence, etc. do not always appear to

be clear-cut and they typically vary in distinct

contexts. For those professions, data and the

technologies underlying the various kinds of data

features are better to be distinguished. If data science

is used to describe the whole treatment of data,

including both the aspects of management and

technology, and data analytics is used for the

technological aspect, then, they would appear much

clearer as specialized domain knowledge to those

who owns certain type of data but may not own the

whole set of data analytical technologies. As such,

collaborations may be easier to be forged and the

promising benefits of big data may be realized with

both efficiency and effectiveness.

ACKNOWLEDGEMENT

This study is supported by the Fundamental Research

Funds for the Central Universities and the Research

Funds of Renmin University of China (15XNL032).

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150

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