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Data in the Wild: A KM Approach to Doing a Census Without Asking Anyone and the Issue of Privacy


Abstract and Figures

Knowledge Societies strive to better their citizens by maximizing services while minimizing costs. One of the more expensive activities is conducting a census. This paper explores the feasibility of conducting a smart census by using a knowledge management strategy of focusing on actionable intelligence and the use of open source data sources to conduct a national census. Both technical and data privacy feasibility is discussed.
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Data in the Wild: A KM Approach to doing a Census Without Asking Anyone
and the Issue of Privacy
James L. Kelly
San Diego State University
Alexandra Durcikova
University of Oklahoma
Murray E. Jennex
San Diego State University
Kaveh Abhari
San Diego State University
Eric Frost
San Diego State University
Knowledge Societies strive to better their citizens
by maximizing services while minimizing costs. One of
the more expensive activities is conducting a census.
This paper explores the feasibility of conducting a
smart census by using a knowledge management
strategy of focusing on actionable intelligence and the
use of open source data sources to conduct a national
census. Both technical and data privacy feasibility is
1. Introduction
The federal government collects data on the
population of the United States to fulfill its
constitutional requirement to count and analyze the
population every ten years. As the amount of data
collected grows it fuels advances in technology with
respect to counting and processing the census data.
Unfortunately, new technology for identifying,
collecting, and then sharing the census data has yet to
be adopted [29], forcing some data to be collected
numerous times at varying levels of accuracy. As a
solution this study proposes using existing web based
sources instead of paper surveys. To show that this is a
feasible approach, we analyze current government data
collection and analysis efforts, suggest alternate data
sources and propose a strategy for an open source
system of population statistics. Our study indicates that
a strategy that is based on open source data could
generate better focused actionable intelligence as well
as improve the cost, accuracy, efficiency, timeliness,
and collaborative efforts of the census. Specifically, we
show how an actionable intelligence strategy is created
using current and proposed data sources that can help
answer complex questions such what is poverty and a
new way to analyze income by using take home pay
instead of gross dollar amounts. Lastly, this study
proposes a set of data standards for supporting
development of an open source census system that also
addresses privacy.
2. Research Motivation
Why do we need a new approach to census data
collection? The census is expensive. The reported cost
of the 2010 census was approximately $13 Billion [37].
The 2020 Census if administered the same way will
cost approximately $17.5 Billion [11]. Along with the
census costs, the American Community Survey (a
newer development discussed later) costs as much as
$204 million per year to administer [10]. The proposed
strategy would drastically cut costs by using existing
open source data sets as well as existing government
raw data to reduce or eliminate collecting similar or
identical information through Census surveys or the
American Community Survey. The US Census Bureau
would also begin to utilize state information databases
to cut down on its data collection efforts. California
and Hawaii have robust websites with information on
their specific states already in use, making some of the
data the census and American Community Survey
currently collects redundant for those states.
Accuracy is crucial for decision making.
Unfortunately, the census may be inaccurate as most of
the questions on the American Community Survey
have the potential to produce inaccurate information
because of the design of the questions and the way
answers are provided. Most questions about annual
income and monthly and annual expenses are answered
by providing write in totals. This has the potential of
producing data that is not accurate. People that fill out
the American Community Survey have no incentive to
be exact when answering the questions. Not only is the
survey voluntary but it is not checked against any other
systems to determine the data’s accuracy. The survey is
long, consisting of a minimum of 11 pages and 48
Proceedings of the 52nd Hawaii International Conference on System Sciences | 2019
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(CC BY-NC-ND 4.0) Page 5608
questions per member of a household. Each question
generally has multiple parts to answer. Logically if
options exist for data collection, self-reporting and
long surveys should not be the first choice. Open
source or other data sources may be more accurate.
Utility companies have a vested interest in keeping
accurate information for billing purposes and banks
have a vested interest in keeping track of how much
money their clients have on account.
The census is inefficient as the same data is
collected more than once. The American Community
Survey attempts to collect information that has already
been obtained by other means and other departments of
the federal government. The best example of this is the
IRS not sharing data with the Census Bureau, thus
forcing the Census Bureau to collect less than accurate
information that the IRS already has in its possession
[5]. The strategy for data collection calls for collecting
data once by allowing government agencies and other
entities to share data in a centralized location.
The Census in not timely as it took roughly 9
months for the census bureau to release its first data
files for the 2010 census and the census only occurs
every ten years. The American Community Survey is
timelier than the decennial reports, since it is for one
year of data, and is released in total in roughly 9
months. Despite how quickly the results can be
tabulated the issue is that the time periods between the
surveys are too long. There are important
developments that are missed during a 1 or 10-year
survey period that could help in creating actionable
intelligence. A better strategy is to collect as often as
feasible. A perpetually updated, dashboard style
reporting system would create timely data for
development of actionable intelligence.
There is little collaboration between data owners
and census takers. Collaboration with groups that
specialize in specific types of data collections or with
different government agencies that maintain data sets
on specific types of data, like the IRS with detailed
data on gross income, income sources, tax credits and
tax liability, has the potential to create powerful
analysis about population statistics that can be turned
into actionable intelligence. Currently raw IRS income
data is restricted and not easily obtained [22]. This
strategy calls for multiple government agencies, local
governments, private and public companies to share
information in an open source format to share with
each other and the public to perform analysis and
create actionable data. The Digital Accountability and
Transparency Act of 2014 [8] requires the issuance of
guidance to federal agencies on such data standards
[8]. The federal government should engage with public
and private institutions on not just establishment of
standards, but also in collecting data and developing
more collaborative strategies for collecting data that
could help improve the quality and timeliness of data.
Should census be fully automated? There are cases
where data was easily obtained from existing datasets
for a secondary purpose. Edwin Black in “IBM and the
Holocaust” explored the tremendous impact on the
ability of the Nazi regime to identify and exterminate
persons of Jewish origin or heritage through the
application of IBM’s Hollerith Card technology
originally developed and applied to the United States
Census [4]. Additionally, it has been shown how the
Obama campaign used Facebook data to influence
voters in the 2012 presidential election. Finally, recent
revelations in testimony to the United States Congress
by Mark Zuckerberg, Chief Executive Officer of
Facebook, showed how Facebook data was obtained by
the Trump campaign for potential use in targeted
marketing of voters [18]. These examples raise privacy
issues as it may be possible to use privately owned data
without approval to influence elections, and in the case
of Nazi Germany, to actually try to exterminate a
targeted group.
This paper proposes a knowledge management,
KM, strategy approach to create actionable intelligence
for guiding a census using open source data.
Additionally, the paper will begin the discussion on the
impact on privacy that such an approach may create.
Readers are reminded that the technical demands of
doing a census have historically driven the
development of data analysis technology and that the
proposed process is likely to be developed for real
world applications. This makes the discussion of
privacy issues important as it is better to develop
technology with eyes wide open as to the technological
impact rather than to develop the technology and then
be surprised by the privacy consequences.
3. The United States Census
The US census has one constitutional purpose,
which is to count the population of the United States
every ten years, according to Article 1, Section 2 of the
Constitution [35]. This number is used for
congressional apportionment to each state based on
population. The census Bureau has also taken on the
task, though not required by the Constitution, of
collecting data other than a straight population count
which is apparent by the questions that are asked in
historical census and the new American Community
Survey. Before the invention of the internet, personal
computers, and other more recent advances in
computing power and technology, the census survey
was one of the only ways of collecting any sort of data
on the population of the United States. In today’s high-
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tech world there have been numerous technological
advances that would allow more robust data collection
at a faster rate with more accuracy, than the methods
that have been and are currently being used by the
Census bureau. The population of the United States has
grown from 3,929,326 in 1790 [30] when the first
census was completed to 308,745,538 as of 2010
census, the most recent completed census [31]. The
United States of America is simply too large of a
population to not be using more advanced data
analytics, systems design and KM techniques. Not only
does the census bureau have a difficult mission in
collecting accurate data on the entire population in its
current form, the data it is able to collect is less useful
in making decisions, creating policy and helping to
manage the huge amounts of resources under the
control of the federal government which are estimated
at an annual budget of 4.4 trillion and assets of 3.5
Trillion [19].
Additionally, federal data requirements have
changed with the addition of an annual survey in 2005.
This survey, the American Community Survey, uses
statistical modeling to estimate the data that was
normally collected on the long form census and goes to
an estimated 1 in 6 families each year without
repetition for those families in a 5-year period [32].
This was a step in the right direction with respect to
timeliness, the problem remains that the American
Community Survey still asks questions with little or no
analytical value in helping to produce valuable
actionable intelligence. The questions analytical value
has changed very little over the past 100 years.
Moreover, the method of collection, a paper survey,
has remained mostly unchanged throughout the history
of the census. This survey is mailed to all households
in the United States. Follow up is done on people that
do not answer the survey and census workers go door
to door if necessary to get the survey completed [34].
This is a very inefficient and expensive way to collect
data with costs as stated in section 2.
The 2000 census saw a technological advance with
the census bureau adding ocular character recognition
to help cut down on data entry errors and personnel
[33]. The Census Bureau has used many new
technologies to help tabulate the results in past census
counts. Most of the technological advances are used to
count more efficiently, not to get data from a different
source which is what this study is proposing [33].
Reducing the time, it takes to count the results as well
as the staff necessary to count is a worthwhile
endeavor, though it would not be necessary if the
census bureau took steps to collect the data in a whole
different manner rather than finding solutions to be
able to use the current survey method for a longer
period of time.
Private companies have embraced advanced data
analytics and big data solutions while the government
has not yet done so. The Federal government has more
opportunity to effect change in this area of
technological advances than any other company or
group in the United States. Many industries like health
care, marketing and finance have taken data analytics
to a whole new level in just the last 5 years. Big data is
trending, schools are creating programs based on
analytics and thousands of books have been written on
the subject, big data in its current form started in 2007
[20]. The federal government can collect more “good
quality” data than any other organization, and now
with technological advances can do it at a lower cost
than what is being spent on data with questionable
value by following the lead of organizations in for
profit and nonprofit industries.
4. Actionable Intelligence
This paper uses a KM strategy approach to determine
data sources useful for generating the census. Jennex
[15] defined the knowledge content process of KM
strategy as the identification of actionable intelligence
needed to make a specific decision and then
determining what knowledge, information, and data is
needed to create that actionable intelligence. For this
paper, actionable intelligence is the exact knowledge,
information, and data needed to support a specific
census question/decision and includes the specific
knowledge, information, and data needed to create the
actionable intelligence [15] [16]. In general, actionable
intelligence is similar to wisdom in the traditional
hierarchy of information first presented by Ackoff [1]
as shown in the final revised knowledge pyramid, see
Figure 1 [16]. This model establishes a top down
strategy approach based on the decisions to be made
and identifying the technologies and decision support
components needed. Creating actionable intelligence
starts at the top and emphasizes the question that is
being asked before deciding which data to collect and
from where [16]. This not only focuses the data
collection on the problem to be solved but it also
eliminates unnecessary data collection, saving time,
money and bolstering data privacy strategies. To apply
this to the census we first determined what each census
question was trying to answer and then the actionable
intelligence needed to generate the answer. Analysis
then continued to determine what knowledge,
information, and data was needed to create this
actionable intelligence.
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IoT and
Other Sensors
KM Pyramid
Social Networks, analytics,
and weak filters
Insight, Analysis,
Sense Making
Machine Networks
Automated Analysis
Tools and Systems
Big Data
Big Data
IoT and Other
Figure 1, The Revised Knowledge Pyramid [16]
KM strategy is being used because what is provided
by the American Community Survey, the short form
census and the Statistics of Income, SOI, annual
reports is best labeled as information or data. This is
useful for trend analysis and asking question like what
is the average income tax expense on a tax return for
reporting income between $40,000.00 and $50,000.00
per year? Or what percentage of the US population has
a sink with a faucet in their home in 2010? The
answers to both questions are simple and easy to find
with the current set of data. What someone can do with
those answers is what sets wisdom or actionable
intelligence and information/data apart. A question that
could only be answered with more advanced data
collections and the creation of actionable intelligence
would be; what is the solution for poverty in a specific
geographical area or demographic? This question
cannot be answered with a set of trend data. The
answer would require the collection of advanced data
and further study and application of knowledge about
social programs, government resource limitations, past
success in raising families out of poverty as well as
privacy policies for data and legal restriction. Another
issue is that questions were not specifically linked to
the problems needing to be solved when developing
the American Community Survey, the short form
census or the SOI annual reports. The old approach is
inconsistent because the government agencies in
charge were collecting data to report to the public as
opposed to solving problems.
5. Data Privacy Implications
The increased use of connected devices utilizing IoT
(Internet of Things), and associated data collection and
data usage have generated data privacy concerns with
74% of Americans saying it is very important for them
to be in control of who can get information about them
[24] and thus have access to Personally Identifiable
Information (PII). What is PII? According to the
department of labor “Any representation of
information that permits the identity of an individual to
whom the information applies to be reasonably inferred
by either direct or indirect means. Further, PII is
defined as information: (i) that directly identifies an
individual (e.g., name, address, social security number
or other identifying number or code, telephone number,
email address, etc.) or (ii) by which an agency intends
to identify specific individuals in conjunction with
other data elements, so-called indirect identification
(these data elements may include a combination of
gender, race, birth date, geographic indicator, and other
descriptors). Additionally, information permitting the
physical or online contacting of a specific individual is
the same as PII. This information can be maintained in
either paper, electronic or other media” [6].
Major data privacy breaches since 2013 including
Target, Uber, and Equifax have further increased
public awareness of privacy issues [2] with data
collected by many organizations in the United States,
including but not limited to websites like Facebook and
Amazon, government agencies, credit bureaus and
telecommunications companies. An open source data
system could create major problems with data
collections of personal information or information that
is able to be traced back to the original subject even
after personal data is stripped from the published data.
Hackers and criminals have grown sophisticated
enough to bypass even some of the best secure systems
including government agencies like the IRS in 2015
[21]. Privacy is a big issue that needs to be considered
when attempting to create any large data system that
includes potentially private data. This open source data
model will address the issue of data protection and
privacy in the three following ways.
First, all data submitted and published for public
consumption will have to have all PII stripped from the
original data before it is accepted and published. Only
meta data will be allowed to be included in the system.
For example, IRS submits raw tax return information
for the tax year 2015. The data including names,
address, social security numbers, business names and
any other information included on the return will need
to be taken off the data set (it is okay to insert a neutral
unique identifier for record tracking). The remaining
data should be line by line items or aggregated items to
make tracing the data back almost impossible.
Second, privacy can be protected by not giving any
original data with PII to the agency or organization that
controls the meta data. In the example above the
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agency maintaining the open source system would not
have any risk of a breach effecting the personal
information of the data since they didn’t have it in the
first place. The open source data system is simply a roll
up of all the summary data from various sources. This
is not as important for data that is already public but
would be crucial for data that includes PII in the
original data sets like banking records, tax returns etc.
It would be crucial when combining several data sets
that have a public and private mix. Surprisingly, very
few pieces of information are needed to identify
individuals in the United States. For example, 5-digit
zip, data of birth and gender could be used to identify
87% of the population. Place, gender and date of birth
could be used to identify 53% of the population and
county, gender and date of birth could be used to
identify roughly 18% of the population [28].
Third, only knowledge, data, and information
necessary to create actionable intelligence should be
included in any meta data to avoid possible reverse
engineering of original data from meta data sets. The
General Data Protection Regulation (GDPR) can be
used as a guide for keeping personal data private and
untraceable. GDPR is a European Union regulation
that helps to protect private data held by companies
that operate in the European Union. GDPR became law
on May 25, 2018. It is designed so that data cannot be
traced back to the original person that provided the
data or the subject of the data.
Debate on data privacy escalated on April 10 and 11,
2018, when Mark Zuckerberg, Chief Executive Officer
of Facebook, testified before the United States
Congress on data privacy and other issues raised
following the disclosure that Cambridge Analytica
obtained Facebook member data and used it to aid in
election advertising for conservative candidates in the
2016 US presidential elections. [18]. While congress
and the public gave the appearance that this was an
unethical use of personal data there is evidence to
support that the public did not care. There was much
celebration in the data analytics community following
the 2012 presidential election when data analysts
working with the Obama campaign made that
campaign the first to use data analytics to drive
campaign strategy and marketing [23]. While the
resulting Obama victory was wildly hailed as a victory
for data analytics which the data scientists celebrated,
the announcement that the Trump campaign had done
essentially the same thing in 2016 was roundly
criticized [9]. Was this a real change in public opinion
on data privacy? Evidence may suggest not, as people
increased their Facebook usage following Zuckerberg’s
congressional testimony [17]. Our conclusion is that
while the privacy debate is raging, citizens of the
United States are not so enamored with data privacy to
prevent an open source, actionable intelligence based
approach to a census as a viable alternative.
6. Possible Direction for the Creation of
Actionable Intelligence
The United States government does not have a
central statistics agency, each department collects and
analyzes its own data and does not necessarily share
that data with other departments or agencies. This sort
of lose knit or decentralized data collections and
analysis process lacks an overriding strategy and a set
of goals for what they hope to achieve with data
collection and analysis. Most of the data collected is
centered around trend analysis which does not create
the kind of actionable intelligence that can be used to
create solutions based on fact. Trend data can be taken
out of context easily, or misinterpreted. The proposed
strategy advocates collecting data with the intention of
providing clear pictures of reality, using multiple
sources of differing information and painting a broad
picture with context and facts of the overall population
of the United States. Below is an example of why trend
analysis and the current government data collection
process can easily be misleading and misinterpreted.
Census data about income is incomplete, the American
Community Survey asks about gross income
information in the income section of the survey. This
does not produce take home pay which can have a
wide variance depending on the make-up of a
household. Note the following examples of the same
size household with different marital status’.
Example 1: Two adults unmarried each with one
child making $50,000 per year each. In this
situation let us assume that the adults do not
comingle funds, share a bedroom and are not in a
relationship and both are able to file head of
Example 2: Two adults married with 2 children
making $50,000 per year each.
Example 3: Two adults unmarried each with one
child making $50,000 per year each. In this
situation let us assume that the adults are in a
relationship comingle funds and only one can file
Head of Household filing status.
Table 1 summarizes the federal tax calculations.
Note that had the American Community survey
gathered the data in Table 1, the incomes would have
an average of $100,000.00 per year per household
without considering the bottom line number of what
the “household” takes home in net pay.
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Table 1. Federal Income Tax Calculation
* taxable income for example 1 and 3 is calculated in 2
separate returns using combined totals.
**for purposes of this example net pay is before state and
local tax, SSI and Medicare and ignores tax credits.
7. Recommendations
7.1. Proposed Actionable Intelligence Strategy
To create a system that fulfills the needs of multiple
independent parties, government and the public the
following broad strategic goals are proposed.
Goal 1: The system is available to anyone to create
reports, aid in research and help to develop actionable
intelligence for the development of solutions to the
problems associated with population statistics.
Goal 2: The system allows for working with raw
data from multiple sources which can be updated on a
continuous basis as well as other increments of time
depending on the data. Discrete historical data could
also be included for context creation and comparisons.
Goal 3: The system allows for maximum flexibility
and transparency while maintaining the privacy of PII.
Goal 4: The system will maintain privacy by
providing raw data that cannot be traced back to
individuals within the data sets using either sources in
this system or combining other data sets.
Goal 5: System transparency is on a scale with
privacy, the more transparency provided the more
likely privacy can be compromised. This scale should
be weighted heavier towards privacy than transparency
to ensure that privacy is protected.
Goal 6: Data warehouse design should be used to
store all data in one place and following industry best
practices for data governance, access control, data
security and privacy protection.
7.2. Proposed General Data Standards
The proposal of collecting census type data, be it
socio economic, housing or financial data from
multiple sources brings up the question of the
consequences of collecting data that is in different
formats and asks questions differently. Each data set
uploaded, or data source provided should be graded
from 1 being the lowest quality in each category and 5
being the best quality in each category. Proposed
standards for this strategy are as follows.
Standard 1: All data sets must include age (not date
of birth), gender, race and location for segmentation
purposes as a minimum. Possible other information to
include would be education level or political ideology.
Standard 2: No ranged data. Financial information
in census products includes data sets in ranges. Raw
data should be provided with no ranges for the end user
to be able to segment properly into whatever data
ranges they see fit.
Standard 3: Use the most accurate source of
information. Any time a collector of data has a vested
interest in keeping accurate records the data is more
likely to be accurate. For example, utility companies
need to keep accurate records of energy use because
they must bill for their services. Data sources that are
the most likely to provide the most accurate sources of
information should always be used. A rating system for
data accuracy will be used to score each data set.
Standard 4. Ask questions in a formatted way.
Having a no write in option for any answer will not
only standardize the answers and data but it will make
aggregating the data quicker and more accurate.
Keeping the options down to a minimum amount to
answer the question in a useful way helps to bring
more value to the data collected.
Standard 5: All data must be raw data without PII.
Utility records for example would need to be stripped
of the specific address of the subject and only include
information pertinent to that data set. Data is
segmented at the raw level and then stripped of its
personal data to avoid PII at the source of the data set
as opposed to at the location of the data. Query level
data, as well as summary data could also be used. A
rating system for quality of data or flexibility of data
could be established.
Standard 6: Reports and analysis must create
context rich reports. As an example, the current trend
analysis style of government data collections and
analysis creates opportunities to present data that is out
of context. The annual average wage index, produced
by the social security administration has increased in
all but one year since 1951 [27]. Out of context this
could be used as a way to portray that annual wages are
on the rise throughout the United States for all but one
year in the last 65. To provide context for this dollar
amount other data points would need to be collected
Example 1
Example 2
Example 3
Net Pay **
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and used alongside this point. These other data points
might include: (a) Inflation index to show how much of
the increase is simply due to inflation or a normalized
table that takes inflation out of the annual comparison;
(b) Spending power which would show how much
those dollars are worth in comparison to prior periods;
and (c) Average increase in different professions,
levels of education, geographic zones, age groups etc.
To show how much of the annual increase can be
allocated to outlier increases in different segments.
Perhaps in this scenario Chief Executive Officer, CEO,
pay has increased enough to skew the overall data, or
annual salary in a specific profession or location.
7.3. Proposed Open Source Process
The process for obtaining data for the below
examples would be dependent on the agency that the
data would be coming from. Using the precedent set by
Chetty and Saez [22] would be how any IRS raw data
would be obtained. The Same precedent could be used
for obtaining social security information. SSN’s would
not be kept in the final data tables to avoid additional
security risk. Data that is already public would be
scraped from the public source and entered into a
database, for example, property tax information county
by county is public information. Some of the proposed
data sources do not currently exist in an open and
available environment. Categorized banking and credit
card information does exist but is likely unshared,
proprietary information. The agency that created the
actual system for this strategy would be responsible for
developing relationships and possibly influencing
legislation that would make a full open source system
8. Examples
Example 1: What is the Average Take Home Pay
for The Population of the United States? Most
measures of income including average salaries and
average household income concentrate on gross pay,
for example the American Community Survey asks
about income in its income section and refers to gross
income not net. The calculation for this number is
much easier to obtain that any other income trend
figure. In section 6, we provided an example of three
different tax scenarios that started with the same
number of household members and the same income
and with a simplified tax calculation produced three
different take home pay numbers. After adding in
credits, other deductions and state and local tax items
these take-home pay numbers could be drastically
different. That example shows, even with a simplified
calculation method, that take-home pay will have a
much different outcome than looking at trends in gross
pay which is simply the amount of money that a person
earns before any taxes, including income tax, social
security and local and state tax, are deducted. Take
home pay is really the only number that matters.
Wages could go up every year by 10% for everyone on
average and if it is being outpaced by an increase in
deductions from pay or a decrease in credits that a
person received the previous year the inference from
the data would be very different when looking at take
home versus gross pay. This could paint an unrealistic
picture of the economy and the populations spending
power in any given year or other period. Using a
simplified method can hide things that are happening
between gross pay and take home pay.
It is important to create content rich actionable
intelligence with data projects. Example 1 is an input
to Example 2 about poverty. Data segmentation is very
important in this data set. The population of the data
set is large enough that multiple data segments would
be possible without creating groups of data that do not
have a large enough sample size to make good data
inferences [7]. According to the IRS, approximately
145,329,000 tax returns where filed during the 2017
filing season for tax year ending 12/31/16 [14]. The
following data would need to be collected to provide
enough information to create actionable intelligence in
accordance with this proposed strategy in section 7.1
for population statistical analysis. We propose that, to
provide trend data as well as context that at least 50
years of data is taken. Adjustments would need to be
made for inflation on an annual basis. Data needed
would be as follows; SSN, zip, age, gender race, filing
status, gross income, deductions tax due and credits
Changes in take home pay can be assigned to actual
changes in gross pay versus take home pay. For
example, from one year to the next changes in the tax
code could increase or decrease take home pay and
produce results that were hidden when only looking at
trend data for gross pay. The effectiveness of certain
tax credits could be measured as well by looking at the
before and after trends of take home pay. The EITC
(Earned Income Tax Credit) and child tax credit
created in 1975 and 1997 respectivelywould create
good breaking points for detailed take home pay
analysis [12]. A measure of effectiveness for a tax
credit would be valuable in creating future tax credits
or other tax policy.
Example 2: What is Poverty? Poverty is defined by
two widely used formulas, the official poverty measure
and the supplemental poverty measure [26]. The
formula does not go into what poverty means beyond
the amount a household must make in annual gross
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income to be “under the poverty line” nor does it adjust
fully for pricing differences across the United States in
either formula, though the supplemental formula does
adjust for some pricing differences in housing. Using
this system, the following data would be needed to
develop a comprehensive definition of what amount
the poverty thresholds should be set at as well as what
poverty is, beyond the simple dollar amount. The main
features of the actionable intelligence that this data
would create is an ability to subset data and rapidly
determine poverty levels using multiple different
criteria. As well as to make decisions about which anti-
poverty programs should have high priority and which
ones should not or to design new programs and
solutions based on the results. The following chart
provides the data that would be needed and a prime
source for it along with an alternative.
All data would need to include common population
descriptions including, age, gender, race, marital status,
education level and geographic tags like county, city of
zip code. The preference for geographic tags should be
what most closely matches the reality of price
differences from one geographic code to the next. For
example, there are 43,000 zip codes in the united states
[38], it is unlikely that zip codes that are right next to
each other produce a statistically significant price or
wage difference. Conversely, there are 3,141 county or
county equivalents in the United States [36] counties
are more likely to have a statistically significant
difference in prices and wages than zip codes. We
suggest that future research determine significant
geographic boundaries to base population statistical
analysis on, for now the tables include zip code as the
geographic tag.
Non-data set related information to create an
overall context would need to include proposed budget
levels for each item. Definitions for a needed item
versus a non-need item for each geographic region
must be included. For example, transportation could be
considered a need though public versus private would
have to be taken into consideration depending on the
geographic attributes, New York City where owning a
car is not a need versus San Diego, where public
transportation is not as robust and a car is a need.
The results could be used to aid in the creation of
anti-poverty programs targeted by geographic regions,
household sizes, and other population segments. It
could also be used to develop personal benchmarks to
help the public develop their own personal healthy
spending habits as well as answer questions about
systematic poverty versus families that are simply
living beyond their means. The information could also
help determine clearly stated goals for anti-poverty
programs and tracking their effectiveness. (a) What
are needs per person segmented by geographic
location? (b) What is a needs budget for a household of
4 (2 children,2 adults)? (c) What is the delta between
the needs budget and the current average budget? and
(d) Do spending patterns show that part of the problem
with poverty is overspending?
Table 2. Needed Data Set’s and Sources for Poverty
Data Example
Data set
of data
Proposed source of data
Take home pay
output example 1 N/A IRS Raw data
Household size ACS IRS Raw data
Rent/Mortgage ACS Mortgage holder raw data
Monthly debt
Debt holder raw data
Vehicle expenses ACS Categorized bank/Credit card raw
Gas ACS Categorized bank/Credit card raw
Groceries BLS (1) Categorized bank/Credit card raw
Telecommunications BLS (1) Categorized bank/Credit card raw
Health Insurance ACS Categorized bank/Credit card raw
Medical Expenses BLS (1)
Insurance company raw data
Property Taxes BLS (1) Hospital and bank Credit card raw
Other expenses BLS (1) Local public property tax raw data
9. Conclusions
This study has explored the feasibility and need for
radically changing the way the United States census is
performed to that of using an actionable intelligence
approach to generating a data strategy and then using
that strategy to identify open sources of data. We have
explored the issue of data privacy and while much was
made of data privacy in 2018 due to Facebook and the
GDPR, we conclude that the public is willing to utilize
this approach as long as data privacy is addressed and
there is a large cost reduction in the census process.
Our recommendations to ensure data privacy
include stripping out personally identified information
from the data and metadata and replace with non-
Page 5615
traceable identifiers. Control access to the source data
to only those with a need to know while allowing
general access to the stripped data. Store data in secure
data storage at the source. Not storing multiple copies
of the data. Transmit data using secure connections.
Use the most accurate data available. Do not use paper
This study is limited to the role of the federal
government in data collection activities and designing
a strategy that can be used for other groups as well. An
open source system for government data collections
means that it will be more accessible and not fall under
the direct management of the federal government
though it will be able to be accessed by agencies within
the government. This study does not go into the
absolute effectiveness of possible data that will be
collected, it is simply a proposal for a strategy that can
offer more agility, transparency and actionable
intelligence for decision making to be used on by many
different groups. The study is also limited to two
specific government agencies the IRS and the US
Census bureau in analyzing what information is
currently collected, their methods and purpose of the
data that is collected. Many other departments of the
federal government collect data on the US population
and publishes multiple reports ranging from weekly
reports to reports published every ten years, like the
census short form results. Other major agencies that
participate in data collections include the Department
of labor, agriculture, education and energy.
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... Licensing provides the right to others to use and modify the software's assets. [63]. These software need to preserve privacy and integrity of user data. ...
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Software Engineering is a constantly evolving subject area that faces new challenges every day as it tries to automate newer business processes. One of the key challenges to the success of a software solution is attaining sustainability. The inability of numerous software to sustain for the desired time-length is caused by limited consideration given towards sustainability during the stages of software development. This review aims to present a detailed and inclusive study covering both the technical and non-technical challenges and approaches of software sustainability. A systematic and comprehensive literature review was conducted based on 107 relevant studies that were selected using the Evidence-Based Software Engineering (EBSE) technique. The study showed that sustainability can be achieved by conducting specific activities at the technical and non-technical levels. The technical level consists of software design, coding, and user experience attributes. The non-technical level consists of documentation, sustainability manifestos, training of software engineers, funding software projects, and leadership skills of project managers to achieve sustainability. This paper groups the existing research efforts based on the above aspects. Next, how those aspects affect open and closed source software is tabulated. Based on the findings of this review, it is seen that both technical and non-technical sustainability aspects are equally important, taking one into contention and ignoring the other will threaten the sustenance of software products.
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The knowledge pyramid has been used for several years to illustrate the hierarchical relationships between data, information, knowledge, and wisdom. An earlier version of this paper presented a revised knowledge-KM pyramid that included processes such as filtering and sense making, reversed the pyramid by positing there was more knowledge than data, and showed knowledge management as an extraction of the pyramid. This paper expands the revised knowledge pyramid to include the Internet of Things and Big Data. The result is a revision of the data aspect of the knowledge pyramid. Previous thought was of data as reflections of reality as recorded by sensors. Big Data and the Internet of Things expand sensors and readings to create two layers of data. The top layer of data is the traditional transaction / operational data and the bottom layer of data is an expanded set of data reflecting massive data sets and sensors that are near mirrors of reality. The result is a knowledge pyramid that appears as an hourglass.
We argue that the development and expansion of direct, secure access to administrative micro-data should be a top priority for the NSF. Administrative data offer much larger sample sizes and have far fewer problems with attrition, non-response, and measurement error than traditional survey data sources. Administrative data are therefore critical for cutting-edge empirical research, and particularly for credible public policy evaluation. Although a number of agencies have successful programs to provide access to administrative data - most notably the Centers for Medicare and Medicaid Services - the United States generally lags far behind other countries in making data available to researchers. We discuss the value of administrative data using examples from recent research in the United States and abroad. We then outline a plan to develop incentives for agencies to broaden data access for scientific research based on competition, transparency, and rewards for producing socially valuable scientific output.
The 16 biggest data breaches of the 21st century
  • T Armerding
Armerding, T. (2017). The 16 biggest data breaches of the 21st century. CSO. Retrieved December 1, 2017 from
The cost of the U.S. census
  • J Beine
Beine, J. (n.d.). The cost of the U.S. census. Retrieved June 13, 2018 from
The effects if a small sample size limitation
  • C Deziel
Deziel, C. (2017). The effects if a small sample size limitation. Retrieved June 10, 2018 from