AbstractBehavior genetics has dem-onstrated that genetic.docx

Abstract
Behavior genetics has dem-
onstrated that genetic variance
is an important component of
variation for all behavioral out-
comes, but variation among
families is not. These results
have led some critics of behav-
ior genetics to conclude that
heritability is so ubiquitous as
to have few consequences for
scientific understanding of de-
velopment, while some be-
havior genetic partisans have
concluded that family environ-
ment is not an important cause
of developmental outcomes.
Both views are incorrect. Geno-
type is in fact a more system-
atic source of variability than
environment, but for reasons
that are methodological rather
than substantive. Development
is fundamentally nonlinear,
interactive, and difficult to con-
trol experimentally. Twin stud-
ies offer a useful methodologi-
cal shortcut, but do not show
that genes are more fundamen-
tal than environments.

Keywords
genes; environment; develop-
ment; behavior genetics
The nature-nurture debate is
over. The bottom line is that every-
thing is heritable, an outcome that
has taken all sides of the nature-
nurture debate by surprise. Irving
Gottesman and I have suggested
that the universal influence of
genes on behavior be enshrined as
the first law of behavior genetics
(Turkheimer & Gottesman, 1991),
and at the risk of naming laws that
I can take no credit for discovering,
it is worth stating the nearly unani-
mous results of behavior genetics
in a more formal manner.
c First Law. All human behavioral
traits are heritable.
c Second Law. The effect of being
raised in the same family is
smaller than the effect of genes.
c Third Law. A substantial portion
of the variation in complex hu-
man behavioral traits is not ac-
counted for by the effects of
genes or families.
It is not my purpose in this brief
article to defend these three laws
against the many exceptions that

might be claimed. The point is that
now that the empirical facts are in
and no longer a matter of serious
controversy, it is time to turn atten-
tion to what the three laws mean,
to the implications of the genetics
of behavior for an understanding
of complex human behavior and its
development.
VARIANCE AND
CAUSATION IN
BEHAVIORAL
DEVELOPMENT
If the first two laws are taken lit-
erally, they seem to herald a great
victory for the nature side of the
old debate: Genes matter, families
do not. To understand why such
views are at best an oversimplifica-
tion of a complex reality, it is nec-
essary to consider the newest wave
of opposition that behavior genet-
ics has generated. These new crit-
ics, whose most articulate spokes-
man is Gilbert Gottlieb (1991, 1992,
1995), claim that the goal of devel-
opmental psychology is to specify
the actual developmental processes
that lead to complex outcomes. In
lower animals, whose breeding
and environment can be brought
under the control of the scientist, it

is possible to document such devel-
opmental processes in exquisite de-
tail. The critics draw an unfavor-
able comparison between these
detailed animal studies and twin
studies of behavior genetics, which
produce only statistical conclu-
sions about the relative importance
of genes and environment in devel-
opment.
The greatest virtue of the new
challenge is that it abandons the
Three Laws of Behavior Genetics and
What They Mean
Eric Turkheimer1
Department of Psychology, University of Virginia,
Charlottesville, Virginia
160 VOLUME 9, NUMBER 5, OCTOBER 2000
Published by Blackwell Publishers Inc.
implausible environmentalist con-
tention that important aspects of
behavior will be without genetic
influence. Gottlieb (1992) stated,
“The present . . . viewpoint holds
that genes are an inextricable com-
ponent of any developmental sys-
tem, and thus genes are involved in
all traits” (p. 147). Unlike earlier
critics who deplored the reduction-

ism they attributed to behavior
genetic theories of behavior, the
developmental biologists take be-
havior genetics to task for not be-
ing mechanistic enough. Once vili-
fied as the paragon of determinist
accounts of human behavior, be-
havior genetics is now chastised for
offering vague and inconclusive
models of development (Gottlieb,
1995; Turkheimer, Goldsmith, &
Gottesman, 1995), and judged by
the standards of developmental
psychobiology in lower animals, it
is true enough that behavior ge-
netic theories of complex human
behavior seem woefully poorly
specified. But ultimately the charge
is unfair, because there is no
equivalent in developmental psy-
chobiology to the behavior genetic
study of marital status or school
performance. The great preponder-
ance of the exquisite experimental
science that goes into animal psy-
chobiology is quite simply impos-
sible to conduct in humans.
Human developmental social
science is difficult—equally so for
the genetically and environmen-
tally inclined—because of the
(methodologically vexing, human-
istically pleasing) confluence of
t w o c o n d i t i o n s : ( a ) B e h a v i o r

emerges out of complex, nonlinear
developmental processes, and (b)
ethical considerations prevent us
from bringing most human de-
velopmental processes under effec-
tive experimental control. Figure 1
is a schematic illustration of the
problem. Individual genes (Genes
1, 2, and 3) and their environments
(which include other genes) inter-
act to initiate a complex develop-
mental process that determines
adult personality. Most characteris-
tic of this process is its interactivity:
Subsequent environments to which
the organism is exposed depend on
its earlier states, and each new en-
vironment changes the develop-
mental trajectory, which affects fu-
ture expression of genes, and so
forth. Everything is interactive, in
the sense that no arrows proceed
uninterrupted from cause to effect;
any individual gene or environ-
mental event produces an effect
only by interacting with other
genes and environments.
For the behavior geneticist,
Fig. 1. Schematic diagram of contrasting roles of genes and
environment in development of personality. One-headed arrows
link
causes to effects; two-headed arrows indicate correlations.
Genes and environments are both causal inputs into an

interactive
developmental system (represented by the network of arrows in
the center of the figure), but because people select and shape
their
own environments (as represented by lighter one-headed arrows
from personality to environments), correlations across the
developmental system (dotted two-headed arrows) are easier to
detect for genes than for environments.
161CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE
Copyright © 2000 American Psychological Society
however, the quasi-experimental
gift of genetically identical and
nonidentical twins offers a remark-
able, if deceptively simple, method
to span this daunting interactive
complexity. Thanks to the fact that
identical twins are on average ex-
actly twice as similar genetically as
nonidentical twins, one can use
straightforward statistical proce-
dures to estimate the proportion of
variability in complex outcomes
that is associated with causally dis-
tant genes, all the while maintain-
ing a state of near-perfect igno-
rance about the actual causal
processes that connect genes to be-
havior. This methodological short-
cut is not available to rivals of be-
h a v i o r g e n e t i c s w h o s e e k t o
measure the effects of families on

behavior. How similar was my
rearing environment to that of my
siblings? And how similar was it to
the environment of my adopted
sibling, if I have one, or to the en-
vironment of my biological sibling
who was raised by someone else?
The apparent victory of nature
over nurture suggested by the first
two laws is thus seen to be more
methodological than substantive.
In a world in which there were oc-
casional occurrences of “identical
environmental twins,” whose ex-
periences were exactly the same,
moment by moment, and another
variety who shared exactly (but
randomly) 50% of their experi-
ences, environmentalists could re-
produce the precision of their ri-
v a l s , a n d l i k e t h e b e h a v i o r
geneticists could measure with
great precision the total contribu-
tion of the environment while
knowing almost nothing about the
developmental processes that un-
derlie it.
The old-fashioned nature-nur-
ture debate was about whether or
not genes influence complex be-
havioral outcomes, and that ques-
tion has been decisively answered
in the affirmative. The new ques-
tion is how we can proceed from

partitioning sources of variance to
specifying concrete developmental
processes (Turkheimer, 1998), and
although critics like Gottlieb are
correct that heritability per se has
few implications for a scientific un-
derstanding of development, they
have failed to emphasize two cru-
cial points. First, heritability does
have one certain consequence: It is
no longer possible to interpret cor-
relations among biologically re-
lated family members as prima fa-
cie evidence of sociocultural causal
mechanisms. If the children of de-
pressed mothers grow up to be de-
pressed themselves, it does not
necessarily demonstrate that being
raised by a depressed mother is it-
self depressing. The children might
have grown up equally depressed
if they had been adopted and
raised by different mothers, under
the influence of their biological
mother’s genes. For every behavior
geneticist who continues to report
moderate heritabilities as though
they were news, there is an envi-
ronmentalist who reports causally
ambiguous correlations between
genetically related parents and
children. Second, the problem the
critics have uncovered extends
well beyond behavior genetics: It is
a rare environmentalist who has
never used statistical methods to

predict behavioral outcomes from
earlier events, in the hope that the
specific developmental mecha-
nisms can be filled in later. The dis-
connect between the analysis of
variance and the analysis of causes,
to use Lewontin’s (1974) phrase, is
not a proprietary flaw in behavior
genetic methodology; in fact, it is
the bedrock methodological prob-
lem of contemporary social science.
NONSHARED
ENVIRONMENT AND THE
GLOOMY PROSPECT
Even after the effects of genes
and the shared effects of families
have been accounted for, around
50% of the differences among sib-
lings is left unexplained. In recent
years, scientists interested in the
genetics of behavior have come to
call this unexplained portion the
“nonshared environment.” Al-
though according to the second
law shared environment accounts
for a small proportion of the vari-
ability in behavioral outcomes, ac-
cording to the third law, nonshared
environment usually accounts for a
substantial portion. So perhaps the
appropriate conclusion is not so
much that the family environment

does not matter for development,
but rather that the part of the fam-
ily environment that is shared by
siblings does not matter. What
does matter is the individual envi-
ronments of children, their peers,
and the aspects of their parenting
that they do not share. Plomin and
Daniels (1987) reviewed evidence
of the predominance of nonshared
environmental variance and posed
a seminal question: Why are chil-
dren in the same family so differ-
ent? They proposed that siblings
are different because nonshared
environmental events are more
potent causes of developmental
outcomes than the shared environ-
mental variables, like socioeco-
nomic status, that have formed the
traditional basis of sociocultural
developmental psychology.
Plomin and Daniels’s explana-
tion involves a subtle conceptual
shift, best described in terms of a
distinction between the objective
and effective environment (Gold-
smith, 1993; Turkheimer & Wal-
dron, 2000). What qualifies an en-
vironmental event as nonshared?
There are two possibilities. The
first is objective: An event is non-
shared if it is experienced by only
one sibling in a family, regardless
of the consequences it produces.

The other possibility is effective:
An environmental event is non-
shared if it makes siblings different
rather than similar, regardless of
whether it was experienced by one
or both of them. Plomin and
Daniels’s proposal, then, is that the
nonshared environment as an ef-
fectively defined variance compo-
nent can be explained by objec-
tively nonshared environmental
events. The question, “Why are
children in the same family so dif-
ferent?” is answered, “Because
measurable differences in their en-
vironments make them that way.”
This proposal has been enor-
mously influential, spawning an
entire area of empirical inquiry into
the consequences of measured en-
vironmental differences among
siblings. Ironically, that same lit-
erature has quite decisively dem-
onstrated that the conjecture is
false. A review of 43 studies that
measured differences in the envi-
ronments of siblings and related
them to differences in the siblings’

developmental outcomes (Turk-
heimer & Waldron, 2000) has
shown that although upwards of
50% of the variance in behavioral
outcomes is accounted for by the
effectively defined variance com-
ponent called nonshared environ-
ment, the median percentage ac-
counted for by objectively defined
nonshared events is less than 2%.
What could be going on?
Plomin and Daniels (1987) al-
most identified the answer to this
question, but dismissed it as too
pessimistic:
One gloomy prospect is that the salient
environment might be unsystematic,
idiosyncratic, or serendipitous events
such as accidents, illnesses, or other
traumas . . . . Such capricious events,
however, are likely to prove a dead end
for research. More interesting heuristi-
cally are possible systematic sources of
differences between families. (p. 8)
The gloomy prospect is true. Non-
shared environmental variability
predominates not because of the
systematic effects of environmental
events that are not shared among
siblings, but rather because of the
unsystematic effects of all environ-
mental events, compounded by the

equally unsystematic processes
that expose us to environmental
events in the first place (Turk-
heimer & Gottesman, 1996).
A model of nonshared variabil-
ity based on the gloomy prospect is
radically different from the Plomin
model based on systematic conse-
quences of environmental differ-
ences among siblings. Most impor-
tant, the two models suggest very
different prospects for a genetically
informed developmental psychol-
ogy. Again and again, Plomin and
his colleagues have emphasized
that the importance of nonshared
environment implies that it is time
to abandon shared environmental
variables as possible explanations
of developmental outcomes. And
although modern environmental-
ists might not miss coarse mea-
sures like socioeconomic status, it
is quite another thing to give up on
the causal efficaciousness of nor-
mal families, as Scarr (1992), Rowe
(1994), and Harris (1998) have
urged. If, however, nonshared en-
vironmental variability in outcome
is the result of the unsystematic
consequences of both shared and
nonshared environmental events,
the field faces formidable method-
ological problems—Plomin and
Daniels’s gloomy prospect—but

need not conclude that aspects of
families children share with sib-
lings are of no causal importance.
CONCLUSION:
ANTICIPATING THE
GENOME PROJECT
It is now possible for behavior
genetics to move beyond statistical
analyses of differences between
identical and nonidentical twins
and identify individual genes that
are related to behavioral outcomes.
What should we expect from this
endeavor? Behavior geneticists an-
ticipate vindication: At long last,
statistical variance components
will be rooted in the actual causal
consequences of actual genes. Crit-
ics of behavior genetics expect the
opposite, pointing to the repeated
failures to replicate associations be-
tween genes and behavior as evi-
dence of the shaky theoretical un-
derpinnings of which they have so
long complained.
There is an interesting parallel
between the search for individual
genes that influence behavior and
the failed attempt to specify the
nonshared environment in terms of
measured environmental variables.
In each case, investigators began

with statistically reliable but caus-
ally vague sources of variance, and
set out to discover the actual causal
processes that produced them. The
quest for the nonshared environ-
ment, as we have seen, got stuck in
the gloomy prospect. Although in-
dividual environmental events in-
fluence outcomes in the most gen-
eral sense, they do not do so in a
systematic way. One can detect
their effects only by accumulating
them statistically, using twins or
adoptees.
If the underlying causal struc-
ture of human development is
highly complex, as illustrated in
Figure 1, the relatively simple sta-
tistical procedures employed by
developmental psychologists, ge-
neticists, and environmentalists
alike are being badly misapplied.
But misapplied statistical proce-
dures still produce what appear to
be results. Small relations would
still be found between predictors
and outcomes, but the underlying
complex causal processes would
cause the apparent results to be
small, and to change unpredictably
from one experiment to the next. So
individual investigators would ob-
tain “results,” which would then
fail to replicate and accumulate
into a coherent theory because the

163CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE
Copyright © 2000 American Psychological Society
simple statistical model did not fit
the complex developmental pro-
cess to which it was being applied.
Much social science conducted in
the shadow of the gloomy prospect
has exactly this flavor (e.g., Meehl,
1978).
The gloomy prospect looms
larger for the genome project than
is generally acknowledged. The
question is not whether there are
correlations to be found between
individual genes and complex be-
havior—of course there are—but
instead whether there are domains
of genetic causation in which the
gloomy prospect does not prevail,
allowing the little bits of correla-
tional evidence to cohere into rep-
licable and cumulative genetic
models of development. My own
prediction is that such domains
will prove rare indeed, and that the
likelihood of discovering them will
be inversely related to the com-
plexity of the behavior under
study.

Finally, it must be remembered
that the gloomy prospect is gloomy
only from the point of view of the
working social scientist. Although
frustrated developmental psy-
chologists may be tempted to favor
methodologically tractable heuris-
tics over chaotic psychological re-
ality, it is a devil’s choice: In the
long run, the gloomy prospect al-
ways wins, and no one would want
to live in a world where it did not.
Psychology is at least one good
paradigm shift away from an em-
pirical answer to the gloomy pros-
pect, but the philosophical re-
sponse is becoming clear: The
additive effect of genes may con-
stitute what is predictable about
human development, but what is
predictable about human develop-
ment is also what is least interest-
ing about it. The gloomy prospect
isn’t.
Recommended Reading
Gottlieb, G. (1992). (See References)
Lewontin, R.C. (1974). (See Refer-
ences)
Meehl, P.E. (1978). (See References)
Plomin, R., & Daniels, D. (1987). (See
References)

Note
1. Address correspondence to Eric
Turkheimer, Department of Psychol-
ogy, 102 Gilmer Hall, P.O. Box 400400,
University of Virginia, Charlottesville,
VA 22904-4400; e-mail: [email protected]
virginia.edu.
References
Goldsmith, H. (1993). Nature-nurture issues in the
behavioral genetic context: Overcoming barri-
ers to communication. In R. Plomin & G. Mc-
Clearn (Eds.), Nature, nurture and psychology
(pp. 325–339). Washington, DC: American
Psychological Association.
Gottlieb, G. (1991). Experiential canalization of be-
havioral development: Theory. Developmental
Psychology, 27, 4–13.
Gottlieb, G. (1992). Individual development and evo-
lution. New York: Oxford University Press.
Gottlieb, G. (1995). Some conceptual deficiencies
in “developmental” behavior genetics. Human
Development, 38, 131–141.
Harris, J.R. (1998). The nurture assumption: Why
children turn out the way they do. New York:
Free Press.
Lewontin, R.C. (1974). The analysis of variance
and the analysis of causes. American Journal of

Human Genetics, 26, 400–411.
Meehl, P.E. (1978). Theoretical risks and tabular
asterisks: Sir Karl, Sir Ronald, and the slow
progress of soft psychology. Journal of Consult-
ing and Clinical Psychology, 46, 806–834.
Plomin, R., & Daniels, D. (1987). Why are children
in the same family so different from one an-
other? Behavioral and Brain Sciences, 10, 1–60.
Rowe, D.C. (1994). The limits of family influence:
Genes, experience, and behavior. New York: Guil-
ford Press.
Scarr, S. (1992). Developmental theories for the
1990s: Development and individual differ-
ences. Child Development, 63, 1–19.
Turkheimer, E. (1998). Heritability and biological
explanation. Psychological Review, 105, 782–791.
Turkheimer, E., Goldsmith, H.H., & Gottesman,
I.I. (1995). Commentary. Human Development,
38, 142–153.
Turkheimer, E., & Gottesman, I.I. (1991). Is H2 = 0
a null hypothesis anymore? Behavioral and
Brain Sciences, 14, 410–411.
Turkheimer, E., & Gottesman, I.I. (1996). Simulat-
ing the dynamics of genes and environment in
development. Development and Psychopathol-
ogy, 8, 667–677.
Turkheimer, E., & Waldron, M.C. (2000). Non-

shared environment: A theoretical, method-
ological, and quantitative review. Psychological
Bulletin, 126, 78–108.
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FINAL PORTFOLIO PROJECT
Rupesh Pasam
ITS-831-53 InfoTech Importance in Strategic Planning
University of the Cumberlands
Dr. Eric Hollis
April 18, 2020
Abstract
Most organizations today rely on knowledge-based management
systems. Nevertheless, these systems derive knowledge from big
data analysis. Data
warehouses are the core components of knowledge-management
systems. The primary purpose of building a data warehouse is to
integrate multiple, independent,
and distributed data sources within an organization. The
historical data is used for analysis to support business decisions
at all levels ranging from strategic planning
to performance evaluation of a discrete organizational unit. All
these components are characterized by high volumes of data and

data flows that require continuous
analysis and mining. These applications, therefore, require data
warehousing and analysis. It also provides a platform for
advance and sophisticated data analysis. The
fact that big data deals with large amounts of the data, it is still
able to manage and uncover hidden patterns and correlations
that may be present. With big data, it is
possible to analyze enormous amounts of data and get the
outcome from it instantly –something that was not possible with
the traditional data handling methods.
Final Portfolio Project
We live in a contemporary world where technology is fast
outpacing our ideologies. Today, we see business intelligence
applications in electronic commerce,
telecommunications, and other industries. These applications,
therefore, require data warehousing and analysis. As such, this
paper provides a detailed analysis of
data warehousing and its main components, not forgetting its
modern trends. All these components are characterized by high
volumes of data and data flows that
require continuous analysis and mining. It also provides a
summary analysis of big data and, lastly, discusses the green
computing technology. Data Warehouse
Database
Data warehouse alludes to a data framework that involves
recorded and commutative information from single or various
sources. In simplifying, it plays a vital
part in the reporting and analysis processes of an organization
In other words it is a database containing data that usually
represent the business history of an
1

2
3
1
1
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part in the reporting and analysis processes of an organization.
In other words, it is a database containing data that usually
organization. The data warehouse depends on some key
components that make a professional workplace to be
utilitarian, reasonable, and available. In online
analytical processing, to analyze the data in the data warehouses
complex queries are utilized as opposed to handling the
exchanges. In the data warehouse,
there are five main components, and they include; Acquisition,
Sourcing, Clean-up, Metadata, Transformational Tools (ETL),
Query Tools, Data Warehouse Database,
and Data warehouse Bus Architecture (Santoso, 2017). This is

the foundation of the data warehousing environment and is
implemented on the RDBMS technology.
For consideration of scalability, it is deployed in corresponding.
The database additionally accommodates shared memory on
different multiprocessor designs. The
reports produced using complex queries inside an information
dissemination center are used to shape business decisions.
Sourcing, Acquisition, Clean-up and
Transformational Tools (ETL) ETL refers to a data integration
function that involves the extraction of data from the source,
assuring quality, and data cleaning to
deliver data in a physical format that can be useful for further
reference. Elimination of outdated or unwanted data in
operational databases from stacking into the
data warehouse is the essential function of the ETL. According
to Santoso (2017), the organization uses the ETL technology to
read data from the outside source, clean
it up, as well as format it uniformly to load it into a target data
warehouse. Metadata
Metadata, as the name suggests, refers to data that defines the
data warehouse. The data is utilized to maintain, build just as
deal with the data warehouse.
Metadata assumes a vital role in the Data Warehouse
architecture as it indicates the features of data warehouse data,
usage values, and source. Santos,
Martinho, and Costa (2017) also tell us that Metadata is closely
linked to the data warehouse and hence facilitates how data is
changed and processed. Therefore,
Metadata can be said to be a vital component in the
transformation of data into knowledge. Query Tools
As communicated previously, one of the principal goals of data

warehousing is to give valuable data to associations to make
strategic decisions. To interact with
the data warehouse system, query tools in this way gives an
effective platform for the users. The devices referenced here are
additionally isolated into four classes, to
be specific, data mining tools, Query and reporting tools,
application and development tools, as well as OLAP tools.
Every one of these tools is essential in permitting
clients to connect with the information stockroom framework.
To interact with the data warehouse system, every one of these
tools is essential in allowing the
users.
1
1
1
1
1
1
Data Warehouse Bus Architecture How data streams into a data
warehouse is decided by this component. The stream can be
classified as either inflow, outflow,
Meta flow, or upstream. In data marts, an IT manager must
think about all facts as well as shared dimensions to design a
data warehouse bus. Data marts refer to
access layers that are typically used to process user data to the
users (Santos et al., 2017). Current Key trends in data

warehousing
The modern world generates, uses, as well as retains useful data
for future usage. Since the global world is projected to continue
to grow for the foreseeable
future, it is approximated by 2026 that the world will generate
and replicate 165ZB of data. This will arise as a result of
increased use of computers in doing business;
hence data will need to be instantly available whenever
required. Since Data warehousing solutions came into play,
most big companies such as Google BigQuery,
Amazon, Panoply, and Redshift have all adopted the use of this
tool to manage their data. These organizations manage
partitioning as well as the scalability of a data
warehouse in a transparent manner. Data warehousing has made
it possible for enterprises to set up a petabyte-scale to hold up
all data safely without any
complexity. Nevertheless, the future looks a lot smarter because
working with a suitable data warehousing system has shown to
enhance efficiency and effectiveness.
Big Data
Big data refers to the massive collection of data that can be
analyzed computationally to extract useful information (Santos
et al., 2017). The fact that big data
deals with large amounts of the data, it is still able to manage
and uncover hidden patterns and correlations that may be
present. With big data, it is possible to
analyze enormous amounts of data and get the outcome from it
instantly –something that was not possible with the traditional
data handling methods. Big data
assists companies to harness their data more efficiently and use
it to identify new opportunities. The ability to work faster and
promptly has given these organizations

a competitive age they never had before. Moreover, as a result,
smarter business moves have been implemented that lead to
higher revenues as well as increased
levels of customer satisfaction. There are several significant
areas that I have detailed below where big data is currently
being applied to excel advantage in practice.
One of the most common areas where big data is used today is
understanding and targeting consumers. Organizations use big
data to understand consumer's
behavior and preferences. This is achieved through performing
data analysis to get a complete picture of their customers, and
after that, create predictive models. In
the United States, many companies have adopted the use of big
data to predict their clients' needs accurately. For instance,
Wal-Mart can predict what products
to sell, Telecom companies can predict their customer churn,
and car insurance companies understand perfectly how well
their clients drive their vehicles. Big data I
used in not only the business environment but also other
platforms. For example, in government elections where it is
widely believed that Obama's Presidential
victory in 2012 was primarily due to his campaign team's
superior ability in the use of big data. Big data is not only for
organizations use alone but for us to use as
well, and I am an example of myself as one of the beneficiaries
of big data. Through the help of smartwatches, I can collect data
activity levels, calorie consumption, as
well as sleep patterns, but the actual value is in the analysis of
the collected data. Through the study of the gathered data, I can
create entirely new ideas and develop
a healthy lifestyle. Green Computing
According to Sreenandana, Nair, and Aneesh (2020), the global
green IT services market is projected to reach more than 7
billion by 2025, to reflect an annual

growth rate of nearly 7 percent. The growth trend is primarily
attributed to green data center initiatives that are not only aimed
at reducing environmental pollution
but also in managing the ever-increasing energy costs. Several
factors play a significant role in loss and carbon footprint
reduction, and the major one includes
alternative green energy technologies. Assert that there are
several ways in which organizations can build and implement
green data center initiatives to maximize
efficiency and profits (Airehrour, Cherrington, Madanian &
Singh, 2019). The first step to achieving this include,
conducting a baseline energy audit to provide a real-
time assessment of usage and efficiency, and it will also be used
as a benchmark for evaluation to guide long term planning. This
is significant since data centers are
typically comprised of a variety of diverse systems. After the
full audit is accomplished, the next step would be to select
green friendly and environmental materials
such as renewable sources. The third way would be prioritizing
the reduction of data center power usage as this is critical in
lowering the amount of energy needed to
power the IT equipment.
1
4
1
1
1
1

The last step would be to build the green data center
infrastructure, and this would include eliminating all the
inefficiencies. Microsoft Corporation is an example
of an organization that has already implemented IT green
computing successfully. The company has tested the undersea
data center through its new research
initiative, known as Project Natick. The project has supposedly
reduced costs, enhanced environmental sustainability, as well as
accelerated deployment. The data
center is environment friendly because it does not consume
ocean water and runs on energy produced by the water’s
movement.
Conclusion
1
-db07-4fb3-8984-c92a4e917c… 3/6
Source Matches (38)
Student paper 94%
Student paper 100%
Student paper 65%
Student paper 96%

Student paper 97%
Student paper 100%
Student paper 100%
Student paper 85%
Student paper 99%
Data Warehouse, Big data, and Green computing, once defined,
show how they relate with each other. While Big data is a
collection of information, the data
warehouse is where all the collected data are stored to help in
decision making as well as support the organization’s needs.
Business trends are ever-changing, and
environmental needs must enhance the system that supports
them. Data is vital for organizations, and that is why managers
keep this critical resource effectively in
the data warehouse to make better decisions and gain
competitive advantage. Managers also have to keep track that
their business operations are environmental-
friendly to improve and optimize business processes.
References
Airehrour, D., Cherrington, M., Madanian, S., & Singh, J.
(2019). Reducing ICT carbon footprints through adoption of
green computing. In 10.12948/ie2019.
04.17. Academy of Economic Studies in Bucharest. Department
of Economic Informatics and Cybernetics. Santoso, L. W.
(2017). Data warehouse with big
data technology for higher education. Procedia Computer

Science, 124, 93-99. Santos, M. Y., Martinho, B., & Costa, C.
(2017). Modelling and implementing
big data warehouses for decision support. Journal of
Management Analytics, 4(2), 111-129. Sreenandana, M. V.,
Nair, G. B., & Aneesh, A. S. (2020). GREEN
COMPUTING: TECHNOLOGY AS GREEN ENABLERS.
SUSTAINABILITY, TRANSFORMATION, DEVELOPMENT
IN BUSINESS AND MANAGEMENT, 206.
1
1 1
1 1
1 1
1
1
1
Student paper
FINAL PORTFOLIO PROJECT 1 FINAL
PORTFOLIO PROJECT 5 FINAL PORTFOLIO
PROJECT
Original source
FINAL PORTFOLIO PROJECT 1 FINAL
PORTFOLIO PROJECT FINAL PORTFOLIO
PROJECT

2
Student paper
Original source
3
Student paper
April 18, 2020
Original source
04/18/2020
1
Student paper
Most organizations today rely on
knowledge-based management systems.
Nevertheless, these systems derive
knowledge from big data analysis. Data
warehouses are the core components of
knowledge-management systems. The
primary purpose of building a data
warehouse is to integrate multiple,
independent, and distributed data
sources within an organization.

Original source
The Most organization today rely on
knowledge-based management systems
Nevertheless, these systems derive
knowledge from big data analysis Data
warehouses are the core components of
knowledge-management systems The
primary purpose of the building data
warehouse is to integrate multiple,
independent, and distributed data
sources within an organization
1
Student paper
The historical data is used for analysis to
support business decisions at all levels
ranging from strategic planning to
performance evaluation of a discrete
organizational unit. All these
components are characterized by high
volumes of data and data flows that
require continuous analysis and mining.
These applications, therefore, require
data warehousing and analysis. It also
provides a platform for advance and
sophisticated data analysis.
Original source
The historical data is used for analysis to
support business decisions at all levels
ranging from strategic planning to
performance evaluation of a discrete

organizational unit All these components
are characterized by high volumes of
data and data flows, that require
continuous analysis and mining These
applications, therefore require data
warehousing and analysis It also
provides a platform for advance and
complex data analysis
1
Student paper
The fact that big data deals with large
amounts of the data, it is still able to
manage and uncover hidden patterns
and correlations that may be present.
With big data, it is possible to analyze
enormous amounts of data and get the
outcome from it instantly –something
that was not possible with the traditional
data handling methods. Final Portfolio
Project We live in a contemporary world
where technology is fast outpacing our
ideologies.
Original source
The fact that big data deals with large
amounts of the data, it is still able to
manage and uncover hidden patterns
and correlations that may be present
outcome from it instantly something that
was not possible with the traditional data

handling methods FINAL PORTFOLIO
PROJECT We live in a contemporary
world where technology is fast outpacing
our ideologies
1
Student paper
Today, we see business intelligence
telecommunications, and other
industries. These applications, therefore,
require data warehousing and analysis.
As such, this paper provides a detailed
analysis of data warehousing and its
main components, not forgetting its
modern trends. All these components
are characterized by high volumes of
data and data flows that require
continuous analysis and mining.
Original source
Today, we see business intelligence
telecommunications, and other
industries These applications, therefore
require data warehousing and analysis
As such, this paper provides a detailed
analysis of data warehousing and its
main components, not forgetting its
modern trends All these components are
characterized by high volumes of data
and data flows, that require continuous
analysis and mining

1
Student paper
It also provides a summary analysis of
big data and, lastly, discusses the green
computing technology. Data Warehouse
Database
Original source
It also provides a summary analysis of
big data Data Warehouse Database
1
Student paper
In simplifying, it plays a vital part in the
reporting and analysis processes of an
organization. In other words, it is a
database containing data that usually
organization.
Original source
It plays a vital part in simplifying the
reporting and analysis processes of an
organization In other words, it is a
database containing data that usually
organization

-db07-4fb3-8984-c92a4e917c… 4/6
Student paper 91%
Student paper 94%
Student paper 100%
Student paper 96%
Student paper 79%
Student paper 86%
Student paper 85%
Student paper 100%
Student paper 95%
Student paper 100%
1
Student paper
In the data warehouse, there are five
main components, and they include;
Acquisition, Sourcing, Clean-up,
Metadata, Transformational Tools (ETL),
Query Tools, Data Warehouse Database,

and Data warehouse Bus Architecture
(Santoso, 2017). This is the foundation of
the data warehousing environment and
is implemented on the RDBMS
technology.
Original source
According to Santoso (2017), there are
five main components of a data
warehouse, and they include Data
Warehouse Database, Sourcing,
Acquisition, Clean-up, and
Transformational Tools (ETL), Metadata,
Query Tools, Data warehouse Bus
Architecture This is the foundation of the
data warehousing environment and is
implemented on the RDBMS technology
1
Student paper
Transformational Tools (ETL) ETL refers
to a data integration function that
involves the extraction of data from the
source, assuring quality, and data
cleaning to deliver data in a physical
format that can be useful for further
reference. Elimination of outdated or
unwanted data in operational databases
from stacking into the data warehouse is
the essential function of the ETL.
According to Santoso (2017), the
organization uses the ETL technology to

it up, as well as format it uniformly to
load it into a target data warehouse.
Original source
Transformational Tools (ETL) ETL refers
to a data integration function that
involves the extraction of data from the
source, assuring quality, and data
cleaning to deliver data in a physical
format that can be useful for further
reference The primary function of ETL
includes the elimination of outdated or
unwanted data in operational databases
from loading into the data warehouse
According to Santoso (2017), the
organization uses the ETL technology to
it up, as well as format it uniformly to
load it into a target data warehouse
1
Student paper
Metadata, as the name suggests, refers
to data that defines the data warehouse.
Original source
Metadata, as the name suggests, refers
to data that defines the data warehouse
1

Student paper
Metadata assumes a vital role in the Data
Warehouse architecture as it indicates
the features of data warehouse data,
usage values, and source. Santos,
Martinho, and Costa (2017) also tell us
that Metadata is closely linked to the
data warehouse and hence facilitates
how data is changed and processed.
Therefore, Metadata can be said to be a
vital component in the transformation of
data into knowledge.
Original source
Metadata plays a vital role in the Data
Warehouse architecture as it specifies
the source, usage values, and features of
data warehouse data Santos, Martinho &
Costa (2017) also tell us that Metadata is
closely linked to the data warehouse and
hence facilitates how data is changed
and processed Therefore, Metadata can
be said to be a vital component in the
transformation of data into knowledge
1
Student paper
To interact with the data warehouse
system, query tools in this way gives an
effective platform for the users. The
devices referenced here are additionally

isolated into four classes, to be specific,
data mining tools, Query and reporting
tools, application and development tools,
as well as OLAP tools.
Original source
Query tools, therefore, provides an
effective platform for users to interact
with the data warehouse system The
tools mentioned here are further divided
into four categories, namely, Query and
reporting tools, application and
development tools, data mining tools, as
well as OLAP tools
1
Student paper
To interact with the data warehouse
system, every one of these tools is
essential in allowing the users.
Original source
All these tools are essential in allowing
users to interact with the data
warehouse system
1
Student paper
The stream can be classified as either
inflow, outflow, Meta flow, or upstream.

In data marts, an IT manager must think
about all facts as well as shared
dimensions to design a data warehouse
bus. Data marts refer to access layers
that are typically used to process user
data to the users (Santos et al., 2017).
Original source
The flow can be categorized as either
outflow, inflow, upstream, or Meta flow
To design a Data Warehouse Bus, an IT
manager must consider all the shared
dimensions as well as facts in data marts
Data marts refer to access layers that are
typically used to process useful data to
the users Santos, Martinho & Costa
(2017)
4
Student paper
Current Key trends in data warehousing
Original source
Current key trends in data warehousing
1
Student paper
The modern world generates, uses, as
well as retains useful data for future
usage. Since the global world is projected

to continue to grow for the foreseeable
future, it is approximated by 2026 that
the world will generate and replicate
165ZB of data. This will arise as a result
of increased use of computers in doing
business; hence data will need to be
instantly available whenever required.
Original source
Current Key trends in data warehousing
The modern world generates, uses, as
well as retains useful data for future
usage Since the global world is projected
to continue to grow for the foreseeable
future, it is approximated by 2026 that
the world will generate and replicate
165ZB of data This will arise as a result of
increased use of computers in doing
business hence data will need to be
instantly available whenever required
1
Student paper
Since Data warehousing solutions came
into play, most big companies such as
Google BigQuery, Amazon, Panoply, and
Redshift have all adopted the use of this
tool to manage their data. These
organizations manage partitioning as
well as the scalability of a data
warehouse in a transparent manner.
Data warehousing has made it possible
for enterprises to set up a petabyte-scale

to hold up all data safely without any
complexity. Nevertheless, the future
looks a lot smarter because working with
a suitable data warehousing system has
shown to enhance efficiency and
effectiveness.
Original source
Since Data warehousing solutions came
into play, most big companies such as
Google BigQuery, Amazon, Panoply, and
Redshift have all adopted the use of this
tool to manage their data These
organizations manage partitioning as
well as the scalability of a data
warehouse in a transparent manner Data
warehousing has made it possible for
enterprises to set up a petabyte-scale to
hold up all data safely without any
complexity Nevertheless, the future
looks a lot smarter because working with
a suitable data warehousing system has
shown to enhance efficiency and
effectiveness
-db07-4fb3-8984-c92a4e917c… 5/6
Student paper 97%

Student paper 99%
Student paper 96%
Student paper 93%
Student paper 91%
Student paper 100%
Student paper 98%
1
Student paper
Big data refers to the massive collection
of data that can be analyzed
computationally to extract useful
information (Santos et al., 2017). The fact
that big data deals with large amounts of
the data, it is still able to manage and
uncover hidden patterns and
correlations that may be present. With
big data, it is possible to analyze
outcome from it instantly –something
that was not possible with the traditional
data handling methods. Big data assists
companies to harness their data more
efficiently and use it to identify new
opportunities.
Original source
Big data refers to the massive collection

of data that can be analyzed
computationally to extract useful
information (Santos, Martinho & Costa,
2017) The fact that big data deals with
large amounts of the data, it is still able
to manage and uncover hidden patterns
and correlations that may be present
outcome from it instantly something that
was not possible with the traditional data
handling methods Big data assists
companies to harness their data more
efficiently and use it to identify new
opportunities
1
Student paper
The ability to work faster and promptly
has given these organizations a
competitive age they never had before.
Moreover, as a result, smarter business
moves have been implemented that lead
to higher revenues as well as increased
levels of customer satisfaction. There are
several significant areas that I have
detailed below where big data is
currently being applied to excel
advantage in practice. One of the most
common areas where big data is used
today is understanding and targeting
consumers.
Original source

The ability to work faster and promptly
has given these organizations a
competitive age they never had before
And as a result, smarter business moves
have been implemented that lead to
higher revenues as well as increased
levels of customer satisfaction There are
several significant areas that I have
detailed below where big data is
currently being applied to excel
advantage in practice One of the most
common areas where big data is used
today is understanding and targeting
consumers
1
Student paper
Organizations use big data to understand
consumer's behavior and preferences.
This is achieved through performing data
analysis to get a complete picture of their
customers, and after that, create
predictive models. In the United States,
many companies have adopted the use
of big data to predict their clients'
Original source
Organizations use big data to understand
consumer's behavior and preferences
This is achieved through performing data
analysis to get a complete picture of their
customers, and after that, create

predictive models Many companies in
the U.S have adopted the use of big data
to predict their clients'
1
Student paper
For instance, Wal-Mart can predict what
products to sell, Telecom companies can
predict their customer churn, and car
insurance companies understand
perfectly how well their clients drive their
vehicles. Big data I used in not only the
business environment but also other
platforms. For example, in government
elections where it is widely believed that
Obama's Presidential victory in 2012 was
primarily due to his campaign team's
superior ability in the use of big data. Big
data is not only for organizations use
alone but for us to use as well, and I am
an example of myself as one of the
beneficiaries of big data.
Original source
For instance, Wal-Mart can predict what
products to sell, Telecom companies can
predict their customer churn, and car
insurance companies understand
perfectly how well their clients drive their
vehicles Big data I used in not only the
business environment but also other
platforms For example, in government
elections where it is widely believed that

Obama's Presidential victory in 2012 was
primarily due to his campaign team's
superior ability in the use of big data Big
data is not only for organizations use
alone but for us to use as well
1
Student paper
Through the help of smartwatches, I can
collect data activity levels, calorie
consumption, as well as sleep patterns,
but the actual value is in the analysis of
the collected data. Through the study of
the gathered data, I can create entirely
new ideas and develop a healthy lifestyle.
Original source
Through the help of smartwatches, I can
collect data activity levels, calorie
consumption, as well as sleep patterns
Through the study of the gathered data, I
can create entirely new ideas and
develop a healthy lifestyle
1
Student paper
According to Sreenandana, Nair, and
Aneesh (2020), the global green IT
services market is projected to reach
more than 7 billion by 2025, to reflect an
annual growth rate of nearly 7 percent.

The growth trend is primarily attributed
to green data center initiatives that are
not only aimed at reducing
environmental pollution but also in
managing the ever-increasing energy
costs. Several factors play a significant
role in loss and carbon footprint
alternative green energy technologies.
Assert that there are several ways in
which organizations can build and
implement green data center initiatives
to maximize efficiency and profits
(Airehrour, Cherrington, Madanian &
Singh, 2019).
Original source
According to Sreenandana, Nair &
Aneesh (2020), the global green IT
services market is projected to reach
more than 7 billion by 2025, to reflect an
annual growth rate of nearly 7 percent
The growth trend is primarily attributed
to green data center initiatives that are
not only aimed at reducing
environmental pollution but also in
managing the ever-increasing energy
costs Several factors play a significant
role in loss and carbon footprint
alternative green energy technologies
Airehrour, Cherrington, Madanian &
Singh (2019) assert that there are several
ways in which organizations can build
and implement green data center

initiatives to maximize efficiency and
profits
1
Student paper
The first step to achieving this include,
conducting a baseline energy audit to
provide a real-time assessment of usage
and efficiency, and it will also be used as
a benchmark for evaluation to guide long
term planning. This is significant since
data centers are typically comprised of a
variety of diverse systems. After the full
audit is accomplished, the next step
would be to select green friendly and
environmental materials such as
renewable sources. The third way would
be prioritizing the reduction of data
center power usage as this is critical in
lowering the amount of energy needed
to power the IT equipment.
Original source
The first step to achieving this include,
conducting a baseline energy audit to
provide a real-time assessment of usage
and efficiency, as well as a benchmark
for evaluation to guide long term
planning This is significant since data
centers are typically comprised of a
variety of diverse systems After the full
audit is accomplished, the next step
would be to select green friendly and

environmental materials such as
renewable sources The third way would
be prioritizing the reduction of data
center power usage as this is critical in
lowering the amount of energy needed
to power the IT equipment
-db07-4fb3-8984-c92a4e917c… 6/6
Student paper 96%
Student paper 100%
Student paper 94%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%
Student paper 100%

Student paper 100%
1
Student paper
The last step would be to build the green
data center infrastructure, and this
would include eliminating all the
inefficiencies. Microsoft Corporation is an
example of an organization that has
already implemented IT green computing
successfully. The company has tested the
undersea data center through its new
research initiative, known as Project
Natick. The project has supposedly
reduced costs, enhanced environmental
sustainability, as well as accelerated
deployment.
Original source
The last step would be to build the green
data center infrastructure, and this
would include eliminating all the
inefficiencies Microsoft Corporation is an
example of an organization that has
already implemented IT green computing
successfully The company has tested the
undersea data center through its new
research initiative, known as Project
Natick (www.informationweek.com/data-
centers) The project has supposedly
reduced costs, enhanced environmental
sustainability, as well as accelerated

deployment
1
Student paper
The data center is environment friendly
because it does not consume ocean
water and runs on energy produced by
the water’s movement.
Original source
The data center is environment friendly
because it does not consume ocean
water, and runs on energy produced by
the water’s movement
1
Student paper
Data Warehouse, Big data, and Green
computing, once defined, show how they
relate with each other. While Big data is a
warehouse is where all the collected data
are stored to help in decision making as
well as support the organization’s needs.
Business trends are ever-changing, and
environmental needs must enhance the
system that supports them. Data is vital
for organizations, and that is why
managers keep this critical resource
effectively in the data warehouse to
make better decisions and gain

competitive advantage.
Original source
Data Warehouse, Big data, and Green
computing, once defined show how they
relate with each other While Big data is a
warehouse is where all the collected data
are stored to help in decision making as
well as support the organization’s needs
Business trends are ever changing, and
the system that supports them must be
enhanced in accordance with
environmental needs Data is vital for
organizations and that is why managers
keep this important resource effectively
in the data warehouse to make better
decisions and gain competitive
advantage
1
Student paper
Managers also have to keep track that
their business operations are
environmental-friendly to improve and
optimize business processes.
Original source
Managers also have to keep track that
their business operations are
environmental-friendly to improve and
optimize business processes

1
Student paper
Airehrour, D., Cherrington, M., Madanian,
S., & Singh, J.
Original source
Airehrour, D., Cherrington, M., Madanian,
S., & Singh, J
1
Student paper
Reducing ICT carbon footprints through
adoption of green computing. In
10.12948/ie2019.
Original source
Reducing ICT carbon footprints through
adoption of green computing In
10.12948/ie2019
1
Student paper
Academy of Economic Studies in
Bucharest. Department of Economic
Informatics and Cybernetics.
Original source

Academy of Economic Studies in
Bucharest Department of Economic
Informatics and Cybernetics
1
Student paper
Data warehouse with big data technology
for higher education. Procedia Computer
Science, 124, 93-99.
Original source
Data warehouse with big data technology
for higher education Procedia Computer
Science, 124, 93-99
1
Student paper
Y., Martinho, B., & Costa, C.
Original source
Y., Martinho, B., & Costa, C
1
Student paper
Modelling and implementing big data
warehouses for decision support. Journal
of Management Analytics, 4(2), 111-129.

Original source
Modelling and implementing big data
warehouses for decision support Journal
of Management Analytics, 4(2), 111-129
1
Student paper
V., Nair, G. B., & Aneesh, A.
Original source
V., Nair, G B., & Aneesh, A
1
Student paper
TECHNOLOGY AS GREEN ENABLERS.
SUSTAINABILITY, TRANSFORMATION,
DEVELOPMENT IN BUSINESS AND
MANAGEMENT, 206.
Original source
TECHNOLOGY AS GREEN ENABLERS
SUSTAINABILITY, TRANSFORMATION,
DEVELOPMENT IN BUSINESS AND
MANAGEMENT, 206
Watch the videos by clicking the hyperlinks, then write one
paragraph for each topic addressing the key points, and

takeaways that were interesting to you.
Literature Review
1https://www.youtube.com/watch?v=5W_x6opCvpQ&feature=y
outu.be
Literature Review
2https://www.youtube.com/watch?v=QrmI84dokgs&feature=you
tu.be
Paragraph
Writing https://www.youtube.com/watch?v=he_rpSNhVZA&feat
ure=youtu.be
Synthesizehttps://www.youtube.com/watch?v=33XAlVFnhlM&f
eature=youtu.be
Response Guidelines
Participants must create a thread in order to view other threads
in this forum.
Main Post is due by the end of Wednesday (250 words).
2 Responses (100 words) using at least one of the following:
· Ask a probing question.
· Offer a suggestion.
· Elaborate on a particular point.
· Provide an alternative opinion.
Research the below sections of a dissertation and write a few
sentences describing "Why they are needed in a dissertation and
the intent of that particular section".
-
Assumptions/Biaseshttps://www.youtube.com/watch?v=6loXKZ
rKqJA&feature=youtu.be
- Significance of the Study
- Delimitations
- Limitations
- Definitions of Termshttps://www.youtube.com/watch?v=-
Q6fM2Tg-X0&feature=youtu.be
- General Overview of the Research design

- Summary of Chapter One
- Organization of Dissertation (Proposal)
Response Guidelines
Participants must create a thread in order to view other threads
in this forum.
Main Post is due by the end of Wednesday (250 words).
2 Responses (100 words) using at least one of the following:
· Ask a probing question.
· Offer a suggestion.
· Elaborate on a particular point.
· Provide an alternative opinion.

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