Mad studies and Sansim are a form of Marxism that looks at power dynamics between those who are considered “normal” mentally and those who are not. As with all other forms of critical studies, Mad Studies wants to overturn the status quo for an untested utopia that has not been proven to solve any of the existing injustices.
Mad Studies
Mad studies is a field of inquiry that rejects how mental health has been medicalized and has become a tool of the pharmaceutical industry. Madness should not be pathologized as there should not be any norms for mental health. Proponents of this point of view call themselves “mad.”
ad
Mad proponents are convinced that there is a need to challenge the idea that people experiencing mental distress need to regulate or control their feelings. One of the reasons they make this claim is that people who do not fall within societal norms for acceptable mental well-being can be discriminated against because of these fall norms. For example, people may lose job opportunities, lose a job they already have, and face other obstacles to success because of the oppression that comes with having what others consider to be mental health issues. In addition, people who are considered mad are also often labeled incompetent and or dangerous.
Sanism
The act of discriminating against people because of their mental well-being is called sanism. When individuals are threatened with sanism they will try and hide their mental illness. Other coping mechanisms can include self-criticism in an attempt to place blame on one’s self. Mad studies are focused on challenging the status quo in regard to mental health.
Mad studies criticize several concepts that are a part of Western culture. Neoliberalism is one frequent target and neoliberalism is an economic and social focus on the the individual and the expectation of personal responsibility. As Mad studies is a child of Marxism means that it will have issues with economic policies focused on the individual. Another issue is with personal responsibility.
Mad studies do not support the idea that people should have to control their emotions and behavior at all times. If a person is having a meltdown at work the people around them should be patient and understanding of this challenge. To go one step further, some proponents of Mad studies believe that self-regulation is harmful.
Other supporters of Mad people have stated that Mad people should be in the workplace to provide mental diversity. In other words, the workplace should include a wide variety of people with different types of mental health from people who can control their emotions and reason to people who have no control of themselves and cannot reason. This is considered a balanced workplace for Mad people.
Conclusion
It is a trying situation for anybody who is experiencing mental health issues. However, how well such people can be included in society depends on the situation. It is unwise to bar Mad people from society completely as it is equally unwise to include them completely without examining the circumstances.
Homeschooling has grown tremendously over the years. From a fringe concept to over 6% of the school-aged children in the US. What makes this even more surprising is that home school was illegal in some parts of the US up until the late 1980s.
Homeschooling also takes many different forms. It can take the traditional form of a single family educating their own children. Other ways of providing one’s children with an education can include micro-schools where several families may come together to share resources in the education of their children. The benefit of micro-schools is that by sharing resources it is easier to ensure all academic subjects are covered adequately, which can be difficult to achieve when working alone as a single family. However it is done, homeschooling provides an alternative route to providing for the needs of children.
Motivations for Homeschooling
The motivation for homeschooling naturally varies from family to family. Some of the common reasons can include concerns with safety. The concern with safety is a legitimate worry with all of the school shootings that have happened in the past and the fact that bullying is still a problem in schools. Another reason is the poor academic quality of many schools. It is hard to label all schools as bad but it is reasonable to state that many schools do struggle with academic rigor.
ad
Another major reason for homeschooling for some families is the opportunity to pass on family and religious values. Schools tend to lean to the left when it comes to values and politics and often try to pull the students in this direction. For some families, this is not acceptable as the values of the schools and government conflict with traditional and or religious values. For this reason, some parents have decided it is better to educate their children at home in order to avoid the confusion of different messages from different sources reaching their children.
Money and the Government
With the growth of homeschooling has also seen a growth in school choice in general. This has led several states to develop some sort of education savings plan or account that can be used to support the education of a child regardless of how they are educated. Some examples of how this money is used are for tutoring, special needs, tuition for private schools, and materials for homeschooling.
The dilemma is that money from the government often comes with requirements and stipulations for the sake of accountability, which is frustrating for homeschool parents as they educate their children often to be free of government interference. This has led some families to reject this money in order to maintain independence.
Rejecting the money is within the rights of parents, however, the laws in some states did not originally allow for different types of accommodation for students who are eligible for this money. In other words, all students who could receive this money were classified as the same, which means that if there is any kind of reporting or government expectation then all students would have to comply whether they took the money or not.
To solve this problem, many states have enacted clarification to allow for families that do not take the money to not have to comply with any expectations that come with families that do take the money. In other words, there is now a clear distinction in the law between students who receive money from the government for educational purposes and those who do not.
Conclusion
The growth of homeschooling is an excellent form of alternative education for many students and families. However, sometimes the government means well but can potentially make life difficult for families. Therefore, parents must stay vigilante in order to prevent the loss of their freedom to educate their children
One of the first steps in reading comprehension is to know what the topic of the reading is. In other words, what is the author writing about is one of the first things a reader should try to uncover. It doesn’t matter if the reader understands they still need to know what subject the author is writing about. The video below provides tips on uncovering the topic of a reading.
In this post we will be using Python to import files. Importing a text file is rather easy into Python. We will look at several different examples and file types in this post.
Importing a Text File
Importing a text file is often done in Python. To do this see the code below.
In order to load the text file we used the open() function to open the file in the working directory. Next, we indicated the mode as ‘r’ which means ‘read’. Everything that was just mentioned was saved into an object called ‘file’. Then we use the read() function on the object called ‘file’ and save all this in a new object called ‘text’. The next step involves using the close() function in order to complete the process. The last step involves printing the content of the text object using print().
Using the ‘with’ approach is much faster and simpler. THe content of the open() function is the same while we save its as “file” by writing this after the open() function rather than before. Lastly, we print the file and use the read() function together.
Import with Numpy
Naturally, there is more than one way to import data. The example below involve the use of the NumPy library. This approach is used in particular for dealing with numerical data that might be saved as a texr file. Below is an example of how to do this.
We begin by importing the numpy librar as np. Next, we create an object called ‘text’ and use the loadtxt() function to load a text file called ‘sample’. The argument ‘delimiter’ is used to tell numpy how the numbers are separated in the file. Lastly, we print the ‘text’ object as an array.
Import with Pandas
Pandas is another way to import data. For our example, we will look at how to import csv files. Below is the code for how to complete this.
import pandas as pd
data=pd.read_csv('sample.csv')
data.head()
In line 1 of the code we load the pandas library as ‘pd’. In line 2, we use the read_csv() function to load our data into the ‘data’ object. Lastly, we use head() to take a peek at the first few lines of data.
Conclusion
With this information, you now possess some basic knowledge on how to get data into Python for the purpose of being able to manipulate it. In a future post, we will look at other ways and means of importing data into Python.
This post is a summary of the article “Toward a Theory of Culturally Relevant Pedagogy” by Gloria Ladson-Billings. One of the major contributions of this paper is attempting to define examples of culturally relevant pedagogy in the classroom of several teachers.
Problem
The problem addressed in this paper is multifaceted but can be summarized as follows. The author claims that success is defined in school for minorities by adapting their behavior to the expectations of the teacher and majority culture. For the author, this is poorly defined as meritocracy. The concern with adapting culturally for minority students is that they are not being affirmed in their own culture.
ad
Galdson-Billings continues by stating that schools are guilty of reproducing the current system and thus reproducing the inequities of the existing system. This is a common critical pedagogy critique of the existing educational system. Therefore, students need to develop an awareness of the inequity in school, which calls for the development of a critical perspective or critical consciousness as explained by Friere.
With this background, the author shares the following proposition when she states that pedagogical practice needs a theoretical model that deals with student achievement and the affirmation of minority students’ cultural identity while also developing a critical perspective for challenging inequities in school.
The proposition above addresses the following problems
Student achievement of minority students
Cultural affirmation of minority students
Development of a critical perspective
The need to challenge inequities in school
To put it simply, the other is looking for evidence of the propositions mentioned above in her research.
Questions
Galdson-BIllings lays out the following questions that she wants to answer in this paper.
What is student success?
How can academic success and culture complement each other in settings where student alienation characterizes the school experience?
How can pedagogy promote student success that critically engages larger social structural issues?
What are the implications for teacher preparation generated by this pedagogy?
Notice in question two the use of the word “alienation.” Alineation is a term commonly used in Marxist literature to mean a feeling of estrangement or being marginalized. There is an assumption in this question that students are feeling alienation even before any data is collected.
In question three, the word “critical” is used. Again, the word “critical” has a different meaning and it means to question and oppose power dynamics. In this question, the author is looking for a specific predetermined answer rather than exploring the data
Methodology
Gladson-Billings collected data from 8 teachers who were considered to be excellent teachers. The method of data collection was done through participant observation in which the author was actively involved in the classes that she visited.
There were four phases to the data collection.
Ethnographic interview of the 8 teachers.
 Participant observation of the teacher’s teachings 3 days a week for 2 years.
Videotaping of the teachers (this was in the 1990’s)
Teachers observing each other videotaped teaching.
There was no direct explanation of how the data was analyzed but it can be assumed that this was a qualitative study and the result section is focused on summaries and the occasional vignette to provide support for propositions the other shares.
Results
Among the findings, the author shares that students struggle to achieve academic excellence while showing cultural competence. This may be due in part to who has the power to define academic excellence, which the author thinks is unjustly given to the majority culture.
Another major finding is that teachers must help students to understand and critique social inequities. The need to challenge social inequities goes back to the critical pedagogy viewpoint of the paper. Students must become activists to challenge the existing system. The author then shares several examples of students becoming involved in community activism through the support of their teachers.
The author then shares three propositions that emerged from her research.
The culturally relevant teacher’s conception of self and others
How culturally relevant teachers structure social relations.
Culturally relevant teacher’s conceptions of knowledge.
Proposition one stresses the beliefs that the teachers of the study had about students. These excellent teachers believed that all students could succeed, that teaching is an art, that they were members of the community, and that teaching was a way to give back to the community.
Proposition two is focused on social relations. Excellent teachers believed that social relations should be fluid, show connectedness, develop a community of learners, and focus on collaboration and responsibility towards each other all for critical consciousness. On the last point, the relations are developed to be aware of injustices in terms of power dynamics another key part of Marxist thought.
Proposition three addresses conceptions of knowledge or epistemology among excellent teachers. For these teachers, knowledge is not static, which implies it is relative and not fixed, teachers need to be passionate about learning and knowledge, scaffolding is necessary, and there should be multiple forms of assessment.
According to the author, culturally relevant teachers are teachers who demonstrate these propositions in their classes. The goal is to be culturally relevant for developing a critical perspective in the students.
Conclusion
For someone who is looking to understand what critical relevant teaching is this article is an excellent source. Not only is it defined but the author can provide examples from actual teachers in the classroom. Therefore, anybody can implement these examples and be able to show that they are a culturally relevant teacher.
This post will provide a summary of the the article “Toward a Critical Race Theory of Education” written by Gloria Ladson-Billings and William F. Tate IV. This paper is significant in that it proposes the idea of considering critical race theory as a key component of education.
ad
The authors assume that anybody who reads this article is already aware of what critical race theory is. In short, critical race theory suggests that the world should be seen through a lens of power as it pertains to race. What this means is that those who have power use race to hold down and oppress those who are of a different race. This is most commonly used in the context of whites vs. blacks in the US.
In terms of the actual article itself, the authors clearly share the following propositions as the main ideas they will address in the paper.
Race continues to be significant in the US
US society is based on property rights rather than human rights
The intersection of race and property creates an analytical tool for understanding inequity
Proposition One
The first proposition is almost an axiom of someone who supports the Critical Race Theory view (CRT). Race will always be a major issue in the US because CRT believes that essentially everything begins and ends with a race dynamic. Almost all actions of injustice for blacks and justice for whites are connected with the dynamic of power differences based on race.
What is unique to the first proposition in comparison to the others is that they include several meta-propositions as listed below.
Race is un-theorized
Class and gender cannot explain all the differences in school experience and performance
What the authors mean by race being un-theorized is that much of the ideas of Marxism, which laid the foundation for CRT, were developed by White Marxists who were focused on class rather than race and that they oversimplified race. Therefore, theorizing race involves adding nuance to Marxist thought in relation to CRT. For the authors, race is the main construct to consider when explaining inequity. To simplify, theorizing race means explaining the role of race in education in relation to experiences and performance of minority students primarily black students.
The second meta-proposition of the authors is that class and gender cannot explain all the differences in the academic performance of students. The authors support this point with studies about differences in academic performance and behavior when controlling for class and gender. Race must be a part of the explanation in terms of inequity in the educational experiences of students.
Proposition Two
The second proposition the authors share is that the US is based on property rights rather than human rights. The authors support this point with the reality that in the days of the early republic, only land-owning capitalists had full participation in society. They follow this with the point that a government that is focused on property rights is not concerned with the human rights of individuals since not every singe person holds property.
Governments tend to be pragmatic in that the focus on property rights is because property can be taxed. It’s hard to hide land and other large resources so the government protects property in exchange for protection of the property. The rich have generally paid more in taxes because they have more wealth that they develop from their property (or capital if this term is preferred).
The poor are hard to track and often have a smaller obligation in terms of taxes. To tie property rights to education the authors make the next point that places where there is better property have better schools because these schools get more money to spend. For the authors, this is a form of inequity. In other words, the areas of town with more money should share this money with other areas of town that have less. Better property does not imply a better education because all people are equal.
Proposition Three
The third proposition is not stated directly but the thrust of this proposition is a conclusion based on the previous two. Proposition three uses the idea from proposition one about race not being theorized and proposition two about property rights to state that the cause of poverty (lack of property) combined with poor schooling (lack of nice property) among blacks is a form of structural racism. Structural racism is a system within society that fosters racial discrimination.
From here the authors present several interesting ideas. The first is that white privilege is a form of property that is transferable, can be enjoyed, affects reputation, and can be excluded. In terms of transfer it is meant that whiteness can be transferred by having people accept certain norms (acting white). For enjoyment, it is meant that get to enjoy certain advantages in their education (better teachers, etc.). For reputation, things identified as nonwhite are stigmatized. Lastly, for exclusion, resegregation through such things as tracking or gifted programs is also a form of whiteness as property because these programs generally exclude black students according-to the authors.
Multicultural Paradigm
The authors then pivot to a light criticism of multicultural education. Multicultural education is the idea of getting all cultures to mutually respect each other. No culture is superior to any other culture. The authors critique multicultural education for not ensuring justice and becoming part of the existing system. The authors call for a radical critique of the status quo and thus of multicultural education. An incremental approach is never going to work for bringing change. CRT supporters generally disdain the current system and prefer radical change to incremental approaches to solving what they consider are systemic problems.
The authors go on to state that multiculturalism is attempting to please everybody, which is a futile effort. They then state that they are believers in the philosophy of Marcus Garvey. Garvey is famous in part for attempting to move African Americans back to Africa unsuccessfully.
Conclusion
Ladsen-Billlings and Tate provide an excellent introduction to CRT with their emphasis on the theorizing race within the classroom, pointing their views on the focus on property in a capitalist society, and showing how property and race combined can lead to inequality. The authors also state the need for radical change and call on current efforts through multicultural education is be inadequate. Whether this is right or wrong is left to the reader.
A major challenge for a data analyst is working within an organization with people who do not have an analytical background. The reason this is difficult is that the data analyst has to try and determine what the audience of his analysis wants him to analyze. This is further complicated when the consumers of the results are not able to fully articulate what they want to know and lean on their intuition and understanding of the context. The analyst who lacks this shared experience is left to figure out what to do.
ad
The wording and language used by non-data and data people to develop questions are similar but different. To be fair, it is not the responsibility of the non-analyst to understand how to form analytical questions. However, it is the data analyst’s responsibility to speak the language of the non-data people and to translate the business questions that are provided into analytical questions.
In this post, we will look at business and analytical questions and how they are shaped and formed.
Definitions
Business questions are questions developed by non-technical individuals within an organization. These questions are derived from the business goals of the organization. The goals of the business or organization are often targets that need to be met over a period of time. An example of an organizational goal would be
To reduce turnover by 10% over a year
The business question that could be extracted from this goal could be
What programs should we use to reduce turnover?
The question above is a business or organizational question. However, this is not a question that a data analyst could answer in its current format. Whereas business questions are broad and open-ended analytical questions are specific. At times, it may be necessary to develop several analytical questions to answer one business question due to the need for specificity. One way to reword the business question above for data analysis is shown below.
Which if any of the turnover reduction programs implemented over the past year were able to reduce turnover by at least 10% in the sample population?
There are several assumptions in this question that were not built into the original business question
There are several turnover reduction programs
 The sample population was broken into various groups and each group experienced one type of turnover reduction program
Each program was given one year in length and was run concurrently
There was a way to collect data in a scientific manner
People were appropriately trained to implement the various turnover reduction programs
etc.
As you can see this can truly get complicated. The data analyst may not only focus on the analysis but may be called upon to shape how data will also be collected and or the research design. As mentioned in the introduction, business experts often know what they want but are not able to articulate it clearly and they struggle with developing the linear process that is needed to collect and analyze the data.
Often, the data analyst’s challenge may be a lack of experience in the field in which they have to analyze data. For example, an individual with a background in education may have to analyze health data. The analytical techniques are the same in terms of the data but a lack of knowledge in the context of health care can make it difficult to get things done. The real problem is that the business people know the context but not the data analysis while the data analysis knows the numbers but not the context.
Conclusion
The primary difference between a business and an analytical question is the amount of detail. A business question is a broad overview that may not articulate all that is needed to develop an answer. An analytical question is detail-oriented and captures all that is needed in terms of obtaining an answer using whatever statistical techniques are needed. Business experts have contextual knowledge but may not have analytical knowledge while the data analyst is burdened to have both contextual knowledge and analytical expertise to shape the questions so that they are answerable.
A generative theme is a pedagogical tool developed by Paulo Freire. Defining anything that Friere said or did is always tricky. For our purposes, a generative theme is an idea or concept that is relevant to the student’s life that evokes strong emotional responses. Generally, the emotional response is negative.
The reason for evoking a negative emotional response is to try and awaken the student to the injustice of whatever is being discussed. Friere was a huge proponent of awakening political literacy in students. By political literacy, we mean being aware of the oppression that the student is living through and realizing a need to resist those with power through revolution. If generative themes are used to evoke positive feelings it will probably not motivate the students to demand change.
In the Classroom
It is the teacher’s job to discover relevant generative themes for the students. There are several ways this can be achieved. Examples can include surveys, dialog, and exposure to provocative material. Focusing on proactive material, there has been a large amount of controversy about sexuality in schools and its appropriateness.
ad
One motivation for pushing the boundaries in this area is that exposure to controversial sexuality could trigger strong emotions within students about what is okay and acceptable. Teachers can use this to discuss questioning boundaries and fighting for people who are different. Without the shock of sexual material (the generative theme), there would be no emotional engagement of the student and thus less engagement in change.
Another way in which generative themes are inserted into a classroom is by removing Western-centric material for non-Western ideas. The process of doing this is known as decolonization. An example might be deemphasizing the work of European male scientists for female scientists and scientists from other parts of the world. The focus in doing this would often be how these other scientists had to fight discrimination to achieve what they did. Showing the students the injustice these scientists faced could serve as a generative theme to develop strong negative emotional responses, which lead to a desire for change.
Generative themes must always be taken from the students’ lives in order to engage them. This is one reason for the push for culturally relevant teaching. Taking examples from the students’ lives can serve as another mechanism for moving the students to push for change. For example, students might be asked how would it feel to be a slave before the Civil War. This might be followed by how would it feel to be a slave owner.
From slavery, the conversation might move to the present day where some of the descendants of slaves and slaveowners often live different lives because of their past. A final question would ask the students if they think it is fair and just that the system treats people differently based on their past. In all of these questions, the focus was always on feelings.
Why Feelings
The reason for the focus on feelings is to trigger emotional engagement. As the students ponder these questions they will probably feel negative emotions, such as anger or guilt, as they relive in their minds these experiences. The anger and guilt they feel are then used by the teacher to show them how they need to fight oppression. The oppressed student will use their anger for change while the students who are the oppressors will use their guilt to push for change.
It is important to note that nothing the students experienced was real in terms that it happened to them or did they do it to someone else. However, the emotions make the injustice real for the students even though it may not have taken place in their own lives. It is similar to going to a movie on racism and leaving the theater angry or guilty because of what you saw in the movie. However, if the injustice has been experienced in the student’s life it makes it all the more powerful when blame is placed on a structure of power.
To be fair, Friere used generative themes to teach reading. For example, if through dialog the generative theme of poverty was discovered. The teacher is supposed to teach the students how to spell and read the word poverty. However, students are often so distracted by the emotional strange of discovery injustice that academic skill development is secondary to awakening a need for action.
Conclusion
A major problem with generative themes is their emotional focus. Students do not necessarily develop thinking skills in this context. Instead, they learn to express strong emotions, which generally should be controlled. In addition, fighting for justice is so attractive for teachers that it is pushing out the development of actual skills in an academic setting. If students feel the system is unjust and they do not have any competency in basic literacy. The only kind of employment they could do would be community activism and protesting, which when overdone is not necessarily beneficial to society.
Authentic tasks are meant to be as realistic as possible. The video below provide several examples of listening tasks that are authentic for ESL students.
Removing identifying information in data is a self-explanatory term. The purpose of removing identification from data is to protect the people who the data came from. These people can be customers, employees, or other groups for which data has been collected. De-identification can also be performed for compliance reasons and or as a security measure.
People who are responsible for privacy and or data governance in their organization need to be familiar with ways to de-identify data. Therefore, in this post, we will look at two commonly used techniques for removing identification from data. These two methods are.
Pseudonymization
Anonymization
Pseudonymization
A pseudonym is a false name. Therefore, in the context of data, pseudonymization is the process of giving false names to data that can help identify somebody. It is similar to having a secret identity in the superhero world. For example, Peter Parker and Spider-Man are the same person but most people do not know this because of the use of a false name.
ad
Practical ways to achieve pseudonymization with data can include changing text to numbers such as names. Removing information such as date of birth and or removing parts of data in a column such as keeping only the last four digits of a person’s social security number.
One advantage, or perhaps disadvantage, of pseudonymization is that the data can be returned to its original state. This is because whoever altered the data used the same rules for every change they made. The downside to this is if someone else can determine how the data was altered it would allow them to see the original data which could be used to identify someone.
Anonymization
Anonymous means no name. Therefore, anonymization is the process of removing all personal identifying information in a dataset. When this is done the process is not reversible and thus there is no way to determine the identity of the people in the dataset.
An example of anonymization would be to completely remove the names of people in a dataset along with other information such as date of birth and the total removal of phone numbers. Anonymization provides heightened protection but at the loss that even the people who anonymized the data have no idea who the original people are. Whether this is good or bad depends on the context in which the data will be used.
There are industry-specific ways of achieving either pseudonymization or anonymization. Examples include the fields of health care and education. However, at the macro level, all industries are using some combination of pseudonymization and anonymization.
Conclusion
Data privacy is a major concern in the world today. The concern with privacy needs to also be balanced with the need to analyze data for insights. For this reason, many have turned to various ways to de-identify data to support the conflicting concerns of privacy with analysis.
Intensive listening is one of many ways to develop listening skills in ESL students. The video below provides several different activities for listening in the language learning classroom.
Protecting data is a major concern of organizations today. With so many people sharing so much about themselves online organizations must be careful and aware of ways to secure the data that they have. In this post, we will look at two different security models that are commonly deployed today. These two models are the CIA Triad and the DIE model. Either of these models is commonly used when developing a data governance plan for an organization.
CIA Triad
There are several different models used by organizations to examine data privacy. One example is the CIA triad. The CIA triad provides 3 concepts that must be kept in mind when attempting to protect the privacy of users.
“C” stands for confidentiality, in other words, organizations must be sure that the data they have cannot be accessed by others. The “I” stands for integrity. Integrity involves ensuring that data is not altered or changed without authorization. If the data is manipulated without user knowledge any insights derived from the data would be considered questionable.
ad
The last letter in the CIA triad is “A.” The letter “A” stands for availability. Availability means that the data system is operational and can access the data. In other words, the security system cannot be so complex that nobody can get the data that is being protected.
DIE Model
Another security model that is commonly is the DIE model. DIE stands for distributed, immutable, and ephemeral. Distributed means that data should not be limited to one source in case of failures. For example, having multiple copies of data in multiple sources.
The “I” in DIE stands for immutable. Immutable in this context means that the infrastructure being used is replaceable without data loss whenever there is a problem. Again this relates to the idea of having multiple sources of the same data. Lastly, the “E” in DIE stands for ephemeral. Ephemeral means that if there is a data problem that it does not take a long time to get back up and running in the event of a data failure or breach.
Compare and Contrast
There are some similarities and differences between the CIA triad and DIE. Both are focused on data being available. For the CIA this is the “A” and for DIE this is the “I.” In addition, both models are focused on protecting data in terms of preventing changes and this is covered in the letter “I” in both models.
However, there are also some differences. The DIE model is considered much more scalable than the CIA triad. As such, smaller organizations may lean towards the CIA triad while larger organizations may lean towards the DIE model. Furthermore, DIE is focused on hardware and infrastructure while CIA is more data-focused.
Conclusion
Every model has its strengths and weaknesses. The best model depends on the needs of the organization. In either case, the CIA Triad or the DIE model can guide an organization that is looking for a roadmap for securing its data.
Reading comprehension is one of the main purposes of education. In the video below, the three main levels of reading comprehension are shared and discussed.
Data classification is a critical part of many company’s strategy for protecting data. In this post, we will look at data classification in terms of its purpose, types, and steps for the implementation of this process.
Common Purposes
The main reason for data classification is to ensure confidentiality. Many data systems have personally identifiable information such as credit cards, social security numbers, and more. Such information needs to be protected and the only way to know it needs to be protected is through classifying it as something that must be shielded.
Availability is another reason for data classification. Through classifying data, it helps a data governance team to know who should have access to what kinds of data. For example, the manager may have full access to all data while the assistant may only have access to data that is not considered confidential. Classification helps in determining access to data.
ad
Data integrity is yet another reason. By ensuring that the data represent what it claims to be assessing what the data stands for. If data is classified as sensitive but does not contain any sensitive information it indicates a problem.
Data Types
There are also several different ways data can be classified. Data can be public which is generally not protected as it is accessible to all for the most part. Data can also be personal which is data that can be used to identify individuals and is usually strictly protected. Data can also be classified as sensitive which means data that requires access authorization.
Lastly, there is confidential information which is data that may have legal restrictions associated with it. The examples above are common forms of data classification. Individual organizations may use all or some of these classifications. In addition, there is nothing to stop an organization from creating its own distinct categories.
Steps
The process of classifying data is rather simple. First, you need to gather all the information that is needed to classify data. Part of this process is supported by having a data catalog that provides information on the location, owners, and content of the data asset.
Once it is clear what data is going to be classified, step two involves the development of a framework. This framework provides the structure for determining how to classify the data. The team involved in this process must develop the criteria for determining which category to place data in. When the categories are developed the data will be tagged. Once this is down the process can be automated using software.
Step three involves making sure the rules developed in step 2 are consistent with the standards that have been developed in the data governance policy. In other words, the classification must not violate the data governance policy because of compliance issues. There must be administrative consistency between data classification rules and the data governance policy
Step 4 involves the application of the rules developed in Step 2. Once this is completed the data classification is over at that moment.
Conclusion
Data classification is another tool that can be used to support an organization. This tool in particular is useful in protecting data based on its characteristics. Therefore, when it’s time to protect data a data classification can help you to determine what data to protect.
Writing techniques for ESL are activities that can be used to help students develop their skills primarily in writing. The video below provides different levels of complexities along with appropriate assignments for each level.
A data governance policy is a set of guidelines that allows an organization to manage its data consistently and properly. What is contained within this policy will vary from one organization to another but some of the topics addressed include data quality, access, usage, integration, and security.
ad
The topics listed above are included in a data governance policy because they relate to the topic of managing data. If a data governance team ignores data quality, access, security, etc. It could have negative ramifications for the organization.
Components
The topics of a data governance policy are described above. However, we will not look at the structure of a data governance policy. Generally, the following components are used in a data governance policy.
Statement of purpose-The goal of the document
Scope and goals-What are and what are not covered in the document along with a breakdown of core beliefs about what data governance should do.
Roles & responsibilities-Who is in charge of what
Principles and rules-THese are a further breakdown of goals into observable behaviors that are called rules. Goals and principles are highly similar and it might be too confusing to have both. Therefore, consider choosing one or the other.
Definitions of terms-It is important to define keywords that will be frequently used on the document. The level of detail depends on the audience.
These are some of the main components of a data governance policy. However, what to include in such a document depends on the local context and challenges of an organization. Some or all of these pieces may be needed or other components not mentioned may be appropriate.
Process
The steps for making a data governance policy are as follows
Make an inventory of the data that will be covered under the data governance policy. Most of the time, not all data in an organization is under the policy.
Build a team that includes a leader along with other stakeholders of the data.
Define scope and goals
Assign roles and responsibilities
Develop standards
Define metrics. Metrics help you to determine if you are achieving your goals.
Make a draft of your document and revised it as needed.
As you can see, the process is similar to the components. The order of developing the components matters as it is better to build broader policies before focusing on behavioral objectives.
Conclusion
A major step in the development of a data governance program is the development of a data governance policy. The policy allows a data governance team lay out what they are trying to do and how they will do it. Such a document is critical to helping the team to stay on the same page and to consistently seek the same. objectives.
Data continues to become more and more important. With this growth, there has been a corresponding need for standardizing and managing. In this post, we will look at master data and how one can go about managing it.
Definition
Master data is a uniform set of data that is used throughout an organization. By uniform, it means that this data is exactly the same wherever it appears in any data set. This is highly important because it is natural for data to change a little as people use it or if it is merged and edited in various stages of the workflow. Master data is so important and fundamental that it must remain unchanged for the sake of consistency when different departments within an organization need to integrate data.
ad
Therefore, master data management is the process of protecting master data from the changes that can happen from people and systems interacting with data. Unfortunately, preserving data is not an easy task and at times this can be complex and difficult.
Master Data Management Forms
There are different forms or ways to develop master data. The analytical approach feeds whatever master data an organization has into a data warehouse where it can be referred to as needed. The operational approach involves master data in the core business or organizational systems. Essentially, the difference between these two approaches is at what level of granularity they are implemented. Analytical is across an organization while operational is within a sub-unit of the organization.
Whichever method is used there are several ways that the approach is implemented. A registry process involves creating a unified master data source with making any changes to local systems. This means there are two different systems which mean that people need to be aware of when to refer to the registry.
Consolidation is another way and involves updating the registry master data whenever the local system is updated. Lastly, the transaction method is the opposite and involves the local system being updated whenever the registry is.
Steps
The steps to selecting and standardizing master data are explained below Step one involves selecting what is considered master data. This will vary from organization to organization and will involve some disagreeing and negotiation. The same applies to step two which is agreed on data standards and the master data approach. Examples of things that involve data standards can include capitalization of text, number of decimals, number of digits, maximum text length, abbreviations, etc. All these must be worked out together. Should states be abbreviated or spelled out fully? Should phone numbers have dashes in them? These are just some of the challenges to address.
Step three involves deploying the software to find and standardize the master data. This can be done manually and this happens in smaller organizations but for larger organizations, this is the only practical way to do this. Step 5 is the cleansing of the data which can include dealing with duplicates. Once all of this is completed it is now appropriate to use the master data.
The Team
Most projects require a team effort and master data management is no exception. Often you will want a manager who oversees the project. Another person who may be involved is a master data specialist who maintains the system. Data stewards are generally involved as they are the ones most familiar with whatever data they are responsible for. In addition, you may need leadership sponsors and stakeholders involved as well particularly when picking master data and assigning data standards.
Conclusion
Master data is a critical component of many organizations which means it must be managed and controlled as well. Some practical ways to address this have been shared here. However, the best way to approach this will vary from one organization to another.
The data protection impact assessment (DPIA) is a tool associated with GPDR that is used to determine the level of protection a data needs within an organization. Protection is determined by finding potential risks that might negatively affect data within the organization. In this post we will look at the benefits of conducting a DPIA, assessing when to conduct an assessment, and a brief look at the process for completing a DPIA.
Benefits
As mentioned earlier, conducting a DPIA allows an organization to document risk. Documenting risk allows for strategies to be developed to reduce the said risk. Other benefits include allowing an organization to assess the cost or level of a particular risk. Lastly, a DPIA can provide unique insights into specific data protection needs and risks.
ad
In general, the DPIA provides the initial data needed to develop a roadmap for supporting data protection within an institution. As such, this is a critical first step in a complex process.
When to do DPIA
Considering the importance of conducting a DPIA a natural question to consider is when should such an assessment be performed. There are several situations that warrant a DPIA. One example is whenever an organization is moving to some form of auto processing such as a program that identifies at-risk students. Since this system is automated it is important to make sure the data is protected.
Another situation that may warrant a DPIA is a situation in which individuals are judged and or evaluated. For example, collecting what users watch on Youtube to make recommendations. Lastly, instances of data integration may require a DPIA to make sure there is no loss of protection from combining data.
Process
There are several steps to actually completing a DPIA. Step one often involves describing the data flow. By data flow, it is meant how data movies throughout the organization in terms of its collection, storage, as well as sources. Step two involves determining the scope of the data. Scope is referring to what types of data will be assessed, the amount of data to be assessed, and or how long will the data be stored.
Step three involves defining the benefits of data processing. Data processing is the cleansing of data so that it can be used for analysis. How this is done varies wildly and depends on the situation. Step four looks at how processing affects the consumer. Explaining this is difficult but for example, complex data processing could slow down the user experience.
Steps 5 and 6 involve talking to stakeholders about this new project and checking for compliance. Stakeholders will explain any concerns that they may have while compliance involves legal matters such as regulations and laws.
Steps 7 and 8 are where various risks are identified and solutions are proposed. For example, if it is discovered that some of the data is revealing people’s identities it might be appropriate to make the data anonymous. Once all of the problems and solutions are developed, step 9 is the official approval of the DPIA.
Conclusion
Completing a data protection impact assessment is a practical way to take the first steps in data privacy in an organization. With the insights developed an organization can inspire confidence in their stakeholders that the data within the organization is not only accurate but safe as well.
In this post, we will look at some ideas and tools to keep in mind when addressing data privacy issues.
Data Concerns
If an organization needs to gather and collect data from customers and or stakeholders several concerns need to be addressed. For example, the organization must develop a privacy policy that explains how data is collected, and its legal ramifications, identifies who the data is shared with and how, and explains how an individual can opt out of this process. Some experts also recommend a cookie policy but that relates primarily to organizations that solicit data from individuals who visit the organization’s website.
ad
Once the privacy and or cookie policies are developed they need to be published on the website. Publishing the policies helps with informing consent and allows individuals to decline participation in sharing their data with the organization. The policy also needs to include a contingency plan for data breaches.
Obligation Management& Data Collection
When dealing with data, an organization must also know what data is being collected, how this data is collected, and as was already mentioned how consent can be given or revoked. A privacy team must know how and where data came from to set in place proper procedures for governing this data.
There are two main ways that data is collected and that is directly and indirectly. Direct data collection is a request that the organization makes that a person complies with. For example, when entering a website to purchase something it is common to have to supply an address and credit card information.
Indirect data collection is data that is collected with a direct request to the individual. For example, many websites have cookies and track IP addresses to determine the person’s location. Many people provide this information without being aware of it.
Data Movement
Data movement addresses many of the same ideas already discussed. In general, several key questions must be answered to determine how data moves within and out of an organization. For example, it is important to know how data was collected, what data was collected, why it was collected, how it will be stored, how it will be shared, and if necessary, how it will be destroyed.
Again most of these questions have been addressed but the main difference is for what purpose. Data movement can be used to track the journey of data through an organization in a way that is beneficial for data lineage.
Acronym
Many of the ideas expressed in this post can be captured in the acronym PREACH, which is listed below
P (purpose)-What is the reason for asking for data
R (Right to change)-Can changes be made to the data on request
E (Easy to understand)-Are the policies for data comprehensible
A (Alerting)-Will a person be alerted if there is a problem with their data
C (Consent)-Do people give permission for their data to be used
H (How)-How will the data be used
Conclusion
There will always be challenges with managing the privacy of data. Despite this, there are several ideas to keep in mind when trying to protect user data. The ideas presented here provide a baseline for privacy leaders.
Two ideas that are discussed widely today are online learning and justice. When looking at these two ideas together it leads to the question of what rights do students have online?
Fortunately, one group has attempted to address this question. ProctorU has proposed a seven-point student bill of rights for online students. This bill of rights provides a framework for discussing this topic. It is not necessary to agree with every point that ProctorU makes. Instead, it is better to consider this as laying the groundwork for a deeper discussion. ProctorU’s framework covers such topics as teaching, academic dishonesty, privacy, and data collection. Below are the seven points.
Have your questions answered
Have your work presumed to be honest and accurate
Expect compliance with all privacy laws and policies
Review and understand policies protecting you and your work
Review and understand policies keeping others from disadvantaging you
Understand data collection, retention, and dissemination
Expect that data collection to be specific and limited
We will look at the points below. Rather than examine each one individually we will group them on the following topics: teaching, academic dishonesty, and privacy.
Teaching
The first point of the Student Bill of Rights deals with asking questions. Answering questions is related to communication between students and teachers. In online learning, teachers must communicate quickly as this establishes a presence in the online context.
ad
Communication is a trait of excellent teaching both in the online context and in the traditional teaching format. Other examples of excellent teaching include providing clear, well-planned lessons, and reflection. In general, students have a right to teaching that does not cause confusion.
Students expect their teachers to provide excellence even if the students do not always show the same commitment. ProctorU has made the claim that answering questions is a right for online students which means that educators need to take notice.
Academic Dishonesty
Points 2, 4, and 5 of the Student Bill of Rights deal with ideas related to academic dishonesty. Academic dishonesty is a major headache in online teaching. Plagiarism and copy/paste, sharing work, etc. are just some of the problems found.
Point 2 of the student bill of rights implies that the work students do is done ethically. However, academic dishonesty is so common that this assumption seems misplaced. Despite this, the teacher is the one who needs to prove that academic dishonesty has taken place
Points 4 and 5 are related to procedural justice. Students have a right to review their school’s policies and know that they are not at a disadvantage compared to their peers. If students think a course is unfair they may justify academic dishonesty to make the course “fair.” To provide an extreme example, if a teacher demands that students memorize 500 words many students would consider that unreasonable and resort to cheating to pass the class.
Lastly, point 5 explains that students who cheat have an advantage over those who do not. Therefore, the teacher has a responsibility to maintain the fairness of the learning environment.
There are ways to reduce the temptation for students to cheat or commit other forms of academic dishonesty. For example, explaining the expectations of assessments is critical to helping students. When developing an online assessment a teacher should be aware of the course objectives, the type of assessment (formative or summative), the process or product focus of the assessment, and whether students will work alone or with others. It is also important to consider the technology needed for the assignment as students may not have the needed technology or lack the skill to use it
It is also critical that all directions are written down and available for the students to look at when needed. There are several things to consider after students complete an assessment. If the students do poorly, it is necessary to reteach the content. However, anyone who has taught online knows how difficult it is to change the content found in the learning management system. One way to address the need for reteaching online is to leave the original activities the same but add additional material for reviewing while also allowing students to retake a quiz or other minor assessment until mastery is achieved. Adding material is easier than rearranging in many circumstances. Providing these opportunities to fix past mistakes is one example of fairness and could discourage academic dishonesty
Privacy
Points 3, 6, and 7 all deal with privacy and data collection. Within the United States, several laws explain the school’s responsibility for data protection. For example, the Family Educational Rights and Privacy Act (FERPA) provides protection for students’ privacy concerning academic data, immunizations, and special needs, also known as personally identifiable information (PII). Upon turning 18, even parents must receive consent from their own child to access the child’s educational records. During the pandemic and the explosion in online learning, the Department of Education provides clarifications about FERPA.
Another law related to privacy is the Children’s Online Privacy Protection Act (COPPA). COPPA is a law in the United States that controls how websites gather data on minors under the age of 13 (Federal Trade Commission, 2013). For educators who may work at the K12 level and or get involved in private industry or non-profit work, it is important to be aware of this as online classes often point students to various websites, and an out-of-compliance website could cause problems.
The Teach Act defends the right of students to learn from copyrighted materials. The Teach Act explains that it is okay for schools to use copyrighted material in traditional or online teaching without prior permission. What this means, as an example, is that a teacher could upload a video and allow students to watch it. A law like this protects an online student’s right to learn.
There are also laws internationally outside the United States that protect a student’s privacy. For example, the EU General Data Protection Regulation (GDPR) provides tough restrictions on the use and sharing of data. What makes this law unique is that it applies not only in Europe but to any institution that has business or students in Europe.
Conclusion
Students have rights and teachers and institutions need to be aware of them. It is more than likely that laws on privacy will become more stringent as people look to protect themselves online.
Data privacy is a topic that many organizations are addressing. In this post, we will go through several steps that must be taken to implement a data privacy program.
Leadership Sponsor
As with any major initiative, data privacy is going to need the support of leadership. In particular, there will be a need for an advocate on the leadership team who will support the vision of improving data privacy. Who this person is will naturally vary from organization to organization.
ad
The sponsor is not only an advocate but also serves as a medium of communication between the data privacy team and leadership. The sponsor serves as the eyes and ears for the privacy team to help them to avoid pitfalls is deal with concerns that are not shared directly from the leadership team to the privacy team.
Put Someone in Charge
Implementing any program or strategy requires that someone take the lead. Therefore, when it is time to develop a privacy approach someone needs to be in charge. The selection of the leader will naturally vary from one place to the other. The point is that the leadership sponsor needs someone they can talk to directly about the challenges and concerns that may be made at the leadership level.
Depending on the size of the project there might be more than one person identified as a leader. However, it is generally wiser to start small and scale as appropriate.
Examine the Data
Before any action can take place it is important to take an inventory of available data. Another name for this is the compiling of a data catalog. A privacy leader must know what data needs to be held private. Without this information, it is hard to ensure the quality.
Knowing the data works in combination with the policies and procedures that need to be made. For example, if the data includes personal information this will influence how privacy is maintained versus data that does not contain such information.
Compliance Expectations
Knowledge of the data is used concerning compliance expectations. For a corporation, the compliance standard might be GPDR. For other organizations, compliance might be determined by local laws or organizational standards.
Generally, a privacy team must provide evidence that they are implementing and or obeying compliance standards. Therefore, a team might have to document and archive how they comply with regulations in the event of a data breach and or audit.
Assess Risk
Assessing risk helps to inform the privacy team in terms of what sort of policy and or procedures to implement. Fortunately, it is not necessary to develop this risk assessment in a vacuum. There are risk assessment frameworks such as ISO 31000 or ISO 27005. Either of these frameworks or others can help you to determine the level of danger your data is potentially facing.
Create Policies and Procedures
Policies are broad guidelines based on the context in which it is being developed for. Most websites have some sort of privacy policy that explains how and what data is collected along with its purpose. Privacy policies can include an idea of the roles and responsibilities of the data privacy team as well.
Procedures are the steps that need to be taken to fulfill the policies that were created. In other words, data procedures provide step-by-step guidance of policies. For example, if the policy speaks about the importance of only certain people having access to data a procedure for this might be how to set up a password or to seek permission to access a particular database. Essentially, policies inspire procedures.
Controls
Controls are inspired by risk assessment. In this step, you are implementing ways to mitigate risk to data. For example, it might have been uncovered that sensitive data is too easy to access. The control for this example may be to move the data to more secure data or to ensure that the data is password protected.
The main point here is that all of these measures must be integrated and working together. The data catalog and knowledge of compliance inspire the policies and procedures which in turn helps with the development of controls
Training& Monitoring
Now that almost everything is in place it is time to train people on the new privacy rules. The training will be context specific but is critical for getting buy-in to the new system. Without the cooperation of the masses, there is no hope for the success of the program.
After training, the training is assessed through monitoring. Monitoring assesses how well the program is running. It deals with such challenges as whether people are obeying the new procedures that have been implemented. Monitoring also helps in providing feedback in terms of where there might be growth opportunities. No system is perfect and monitoring provides critical information to strengthen the program.
Conclusion
Data privacy can be improved in any organization. The ideas presented here provide information on how to start a data privacy program. Naturally, all of these steps may not work for each organization but many valuable ideas have been shared to support the protection of privacy.
A table of contents can be useful when having to navigate a large document. In the video below, you will learn how to create a table of contents when using R Markdown
Privacy by Design is an idea found within the General Data Protection Regulation, which affects the data privacy practices of organizations. In this post, we will define this term and explain several principles of privacy by design.
Definition
Privacy by design is a concept in which data protection happens through the appropriate development of technology. Essentially, data protection should not be limited to one place or one feature instead data protection should be layered throughout the system of an organization.
ad
There are several ways to begin this initiative. A common method is to have a privacy policy that is up-to-date and readable. Another way to begin this process is to establish someone as the data protection officer. Lastly, it is also common to conduct some sort of assessment of data protection to determine areas of improvement before using an individual’s personal data.
Principles
There are seven principles of privacy by design. Below is a list with explanations.
Proactive rather than reactive-There should be an effort to prevent privacy loss rather than trying to fix a situation in which people’s personal information is inappropriately accessed.
Privacy by default-Maintaining the privacy of data should be the first thing an organization thinks about and can include restricting use/access, and or deleting data that is no longer needed.
Embedding of privacy-EMbedding involves such tools as encryption, authentication, and the testing of vulnerabilities. In other words, privacy is used as a foundational aspect of developing a website or application.
Full functionality-This idea is a reminder that data privacy should not make it difficult to use a website or application. Protect data but avoid sacrificing the user experience.
End-to-end security-This is similar to principle number two and is essentially a reminder that privacy protection must be comprehensive from the time the data is received until the data is destroyed.
Visibility and transparency-People should know what is being done with the data an organization has of them.
Respect for user privacy-People should still have authority over their data after it is collected. What this means is that they can grant or rescind consent to their data at any time.
ImplementationPerspective
There are several perspectives from which the implementation of privacy by design that must be considered and these are systems, processes, and risk management perspectives.
The system perspective involves documenting the organization’s commitment to data protection, appointing a data protection officer or leader, providing training for employees, checking security measures, developing a record-keeping system, and conducting a self-assessment. All of these steps are used to develop an initial system for data privacy.
For processes, it is necessary to determine roles within privacy such as people in IT, legal, etc. who support privacy with their technical expertise. It is also important to document the data processing process and privacy risks. Privacy controls for users and the implementation of security measures from the systems perspective are critical as well.
Risk management is another key perspective that needs to be addressed for data privacy. Risk management involves the legal purpose of processing data. It also includes tracking who has access to data, controls for accessing data, what to do in the event of a breach, and minimization, anonymization, and pseudonymization of data. Lastly, measures for data accuracy are developed here.
Data catalogs and data silos are two ideas that are commonly associated with data governance. In this post, we will look at these two terms by defining them and share either how to implement them or prevent them.
Data Catalogs
Data catalogs are a rather recent phenomenon. They were first developed in the 2010’s with the exact origins not defined. A data catalog is a reference application that contains metadata on the various datasets within an organization. Usually, this document is in a searchable format so that people can find datasets they may need within an organization.
ad
The data catalog essentially tracks available data within an organization. The main reason for tracking data is to prevent loss and or secret data. Within a data governance framework, data is considered an asset. Therefore, just as an organization prevents the loss of inventory because of its monetary potential the data catalog prevents the monetary decision-making loss of data within an organization.
Tips
There are also several tips for developing and using data catalogs. For example, a data catalog should track the roles of various people concerning individual datasets. Roles can include who is the owner of the data, the steward, the custodian, etc. Tracking roles helps in assigning responsibility for data.
Another tip is to develop data dictionaries concerning the data catalog. Data dictionaries contain metadata not from all data but just from one dataset. An analogy would be maps. Some maps cover the whole world like a data catalog while other maps only cover a city or county like a data dictionary. The data dictionary is useful one an analyst needs more information when preparing to use data.
It is also important to make the data catalog user-friendly. Making a data catalog user-friendly for stakeholders involves the support of IT with a strong concern for the user experience. Nobody will use a data catalog if its user interface is useless. However, the solution to this would be lots and lots of training
Data Silos
Data catalogs help to prevent what are called data silos. Data silos are sources of data that are controlled in an isolated place within an organization. When silos are developed it can lead to analyses that are incomplete because of incomplete data. In multiplication, silos can lead to a breakdown in collaboration which can cause duplication of efforts and reduced productivity. Lastly, people may also struggle within an organization to find data that is needed for analysis.
Data silos are often developed in organizations that have a decentralized IT strategy. A decentralized approach frequently leads to every department doing what they want in terms of data storage and technology utilization which is chaotic. Other motivations for data silos can include a lack of common goals when it comes to data management. No goals means everyone does what they want.
Breaking Silos
Two main ways of breaking data silos are the development of data governance and data integration. One step in data governance is developing a data catalog as mentioned early. Once a data catalog is developed the team can start to create policies and standards in data governance to establish expectations regarding data use and storage.
A second strategy that is related to the first is data integration. Data integration is the processing of combining data from different tables into one. Upon completing this more analysis can take place. Combining data makes it hard to isolate because data must be available for use.
Conclusion
Data catalogs and silos are a part of the daily life of the information professional. Therefore, in the context of data governance, it is important to be familiar with these two terms so that support can be provided.
A field closely related to data governance is data privacy. In this post, we will look at what data privacy is as well as principles that need to be kept in mind when trying to keep people’s data private.
Data Privacy
Privacy is a term that is difficult to define. For our purposes, data privacy is the amount of control a person has over personal information in terms of how this information is collected, managed, and stored. This definition gives the impression that people have little data privacy because we are so often compelled to share our information online.
ad
Websites often require some surrendering of personally identifiable information (PII) such as name, address, phone number, etc while in the medical field, there is demand for personal health information (PHI). Sharing information about yourself can be frustrating for many but is the cost of doing business online. Naturally, once these various online companies have your data they must be sure to protect it.
Data security is not about collecting or managing data. Rather, data security is focused on the protection of data from unauthorized access. Securing data is critical to protect individuals and organizations from harm because of security breaches. For example, there can be serious financial repercussions if someone’s credit card number is stolen online.
Fair Information Practice Principles
With all the concerns regarding data privacy, it was natural that frameworks would be developed to help organizations with data privacy. One such framework is the Fair Information Practice Principles (FIPPs) developed by the Organization of Economic Development back in the early 1980s. Below are the eight principles in this framework.
Limits on data collections-Every organization need to determine the smallest amount of data they can connect while still maintaining success
Data quality-Data that is collected needs to be accurate and pertinent to the purposes of the organization.
Purpose determination-There must be a clear compelling reason to collect data.
Limits of use-Personal data must only be used for its intended purpose.
Security-Data must be protected
Transparency-People should know that their data is being collected
Individual participation-People whose data has been collected have the right to access their data, have it corrected, and or erased
Accountability-Whoever collects this data is responsible for adhering to the principles listed above
The principles shared above have been adopted by many organizations to provide a foundation on which they can develop their own data privacy policies and philosophy.
Conclusion
Data privacy is a major concern in the world today. Organizations whether online or offline continue to demand more information about their customers. As such, this implies that there must be safeguards in place to ensure the protection of this information.
R Markdown is a tool within RStudio that is beneficial for reporting results from an analysis that was done in RStudio. The video below provides several basics ways that R Markdown can be used in a document.
Within the field of data governance, there are different ways of approaching data and the definition of truth. In this post, we will look at different approaches to data and also how truth can be defined with a data governance framework.
Defense
A defense approach to data is focused on controlling data. This can involve security and stringent governance of data through a highly centralized setting. In addition, the defensive data approach is concerned with minimizing risk and ensuring compliance with standards and expectations. Preventing theft and tracking the flow of data through an organization is also important.
ad
When analytics are used they are used to detect fraud and unusual activity. How defensive an organization is depends on the field or industry. For example, banking and health care are highly defensive due to the type of data they gather.
Offense
An offensive approach to data is focused on developing insights with data. The goal is not to protect but to develop insights for decision-making. An offensive approach to data is characterized by flexibility and being focused on the customer. This style of approaching data is generally emphasizing a decentralized style of data governance.
Organizations that find themselves in highly competitive environments often are forced to become more offensive as they search for insights to maximize profits. How much offensive and defensive an organization needs does vary. However, in general, most if not all organizations start defensive and slowly become more offensive in nature.
Truth
Whether the approach to data is offensive or defensive it is important to determine what is the truth when it comes to data in an organization. Every organization needs a single source of truth (SSOT) for critical data. The SSOT is language used within data that is the same across an organization. For example, sometimes the same name can be entered in multiple different ways in an organization’s data. Take the company AT&T as an example it could be entered in some of the following ways
ATT
att
Att
AT and T
AT&T
Each of the examples above can be considered different and can lead to chaos when it is time to analyze data for insights. This is because redundant names can lead to redundant costs. For example, if AT&T was a vendor for our fictitious company there might be several different contracts with AT&T with several different divisions who all spell AT&T differently. To prevent this the SSOT will define the one way to code AT&T into the system and determine what it represents.
However, keeping the offensive approach to data in mind. There are times for the purpose of analysis that the SSOT can be modified. Doing this leads to what is called multiple versions of truth (MVOT). An example of MVOT is a department that classifies our example of AT&T different way from the SSOT. Accounting might see AT&T as a vendor while marketing might see AT&T as their internet provider, etc. Since everyone knows what the SSOT is they are aware when they make a MVOT for their distinct purpose.
Conclusion
Each organization needs to decide for themselves what approach to data they want to take. There is no right or wrong way to approach data it really depends on the situation. In addition, every organization needs to determine for itself how they will define truth and there is no single way to do this either. What organizations need to do is address these two topics in a way that is satisfying for them.
In this post, we will look at juvenile justice programs, practices, and policies. Each of these terms plays a different role within the juvenile justice system.
Program
A program in juvenile justice is a designed package that has clear procedures for delivery, has manuals, and provides technical assistance. In addition, the outcome is commonly related to some sort of change such as recidivism. Two commonly implemented programs are multisystemic therapy and functional family therapy.
ad
Another key aspect of a program within juvenile justice is recidivism. Programs are generally designed for specific use through the use of a logic model. A logic model is a visual depiction that shows the relationship among the resources, activities, outcomes, and outputs of a program. In other forms of social science research, the logic model is called a conceptual framework however these two concepts are not exactly the same. Logic models depict relationships while conceptual frameworks are a proposed theory that is attempting explain why certain outcomes take place.
Practice
PRactices in juvenile justice are not as clearly defined as programs are. In general, practices in juvenile justice are essentially programs that are more flexible in their application and use. For example, the design may not be as rigorous and the instructions made not be as detailed.
Due to their more flexible nature practices are often more general in nature and can thus be applied in different situations. To make things more confusing some programs are considered practices if they are more flexible than highly controlled programs. One common practice is the Treatment in Secure Corrections for Serious Juvenile Offenders.
Policies
Policies are regulations that apply to the general population. Policies generally lack empirical evidence for their usefulness in supporting youth. However, policies do provide guidance and structure which shows that they serve a different role than what is found with programs and practices.
Evaluating Programs and Practices
There are times when programs and practices are evaluated for their usefulness. Below are some commonly used ways to evaluate programs and practices.
One way to evaluate a program is the quality of the evidence or data. For example, randomized controlled experiments are considered the gold standard. Therefore, other methods of collecting data such as quasi-experiments and surveys will affect the perceived quality of a program.
A second criterion is looking at the quality and extensiveness of the research of the program. What this means is the quality and quantity of research that has assessed the value of a program. If a program has multiple studies that are a witness to its worthiness and these studies are of high quality it raises the value of this program.
A third criterion is the expected impact of a program. By expected impact, it is meant the effect size. The effect size is something that is extracted in the data analysis aspect of a study and helps to provide a number of the impact of a study. Programs with stronger effect sizes are seen as better.
Finally, another criterion for program/practice quality is the adoption rate. In other words, how many other people are using the program/practice? Tracking adoption is higher but there are program registries that have vetted programs and recommend them. Examples of program registries include crimesolutions and blueprints. Both of these registries have graded programs and provide links to studies about the programs.
Conclusion
Programs, practices, and policies all play a critical role in helping youth in the juvenile justice system. People who work within this system need to be aware of the meaning of these terms as well as how to judge good from bad programs.
Autonomy is an important skill for a student to develop in order to take control of their learning. In the video below we will look at strategies for developing student autonomy.
Data governance is becoming more and more common in today’s world. In this post, we will look at one commonly used process of implementing data governance. The steps are explained below.
Scope & Initiation
The first step in setting up a data governance system is to determine the scope of data governance. By scope, it means how deep and wide the program will be. In other words, you have to determine what will be governed and how thoroughly it will be governed.
It may surprise some that not all data is governed by data governance. For each organization, it will be different but generally, all organizations have data that is excluded from data governance. For example, some organizations will include emails under data governance while others will not. It depends on the situation and there is no single rule.
ad
In addition, it is important to determine how thorough the governance will be. An example of this would be the tolerance for data quality issues. There are times were some data errors are permissible as long as they do not exceed a certain threshold but this also depends on the context
Assess
At the assessment stage, the purpose is to determine an organization’s ability to govern data and be governed by policies. Generally, there are three ways of assessing this and they are measuring the capacity to change, the culture of data use, and the ability to collaborate.
The capacity to change is self-explanatory and is a measure of an organization’s ability to accept new policies such as data governance policies. The data use culture is looking at how an organization uses data at that moment. Lastly, collaboration looks at how well people within the organization can work together. Collaboration is critical because data governance generally affects the entire organization and people from multiple departments must work together.
Vision
The vision is where terms are defined and steps going forward are set. For example, the organization needs to define what data governance is for them. In addition, requirements for doing data governance are also developed.
Vision setting is a theoretical experience and this is often boring for the more practical action-oriented individuals. However, setting the vision sets the tone for the rest of the project. Therefore, this must be planned and developed.
Align & Business Value
Aligning and business value is for determining the financial value of incorporating data governance into an organization and also refining how things will be measured. For profit-seeking organizations business value is critical. Most projects need to make or at least save money in this setting. For non-profit organizations, the motivation might be to increase efficiency or the ability to better serve stakeholders.
It’s not enough to talk about savings. Evidence must be provided for determining actual savings. This is where metrics come into play. There must be ways to measure the value of a data governance project. Again, how to do this will vary from place to place but it needs to be addressed.
Functional Design
Functional design is focused on the actual process of doing data governance. What will be done must be determined as well as established roles that support this process as well. Principles are often developed at this step and principles are similar to goals in terms of what is expected from implementing data governance. Following principles, the next thing that is developed are standards which are similar objectives in education in which you have some sort of measurable action.
Best practices often encourage data governance to be embedded within existing roles and responsibilities. In other words, setting up another department within an organization and calling it data governance is generally not considered the best way to make this happen.
Governing Framework Design
Once the plan has been developed it is time to find the people who will implement it. governing framework involves assigning processes to people and setting up the various roles associated with data governance. Generally. a lot of the aspects of data governance are being done at an organization but in a disjointed unaware way. Therefore, the main benefit here is not so much to give out more work but rather to make it clear who is already doing what and make sure they are aware of it.
Road Map
The road map step involves data governance going live. This is the point where data governance is integrated into the existing organization. Other things that are done at this step are designing metrics and reporting requirements. In other words, how good or bad does performance have to be on a standard and how will this be reported?
Change management is also addressed here and involves dealing with resistance and making sure that the scope and or goals of the project do not change. There are times when a project will wander from its original purpose which can be frustrating for people.
Rollout and Sustain
Roll out and sustain involves executing the plan and checking its effectiveness. Essentially, this step involves monitoring the data governance implementation and making corrections as necessary.
Conclusion
Data governance is a critical part of most organizations today. However, it can be tricky to figure out how to make this a part of an organization. The information above provides an example of how this could be done.
