Communication and data science
CONTEXTUAL ANALYSIS
Kaplan Jaroslav
Kaplan Research Company
Founder of the Business IQ project
Kaplan Jaroslav
Kaplan Research Company
Founder of the Business IQ project
PDF scan of the article: https://drive.google.com/file/d/1uV4y2wANaDQFhNydy2eqV8w88pqMP33Q/view?usp=drive_link
Abstract
Data is the new gold: more than 80% of executives believe data is valuable to the overall success of their business. And IDC's 2019 survey of Russian companies found that more than 90% of companies "face the challenge of analyzing Big Data to some degree "1. However, the steadily increasing volume of data is creating more and more barriers to transforming this flood of information into actionable insights. An appropriate data strategy can help companies capitalize on the value of their data and use it to make informed decisions.
Keywords:
Data Science, strategic management, context-aware approach, data analytics.
Data is the new gold: more than 80% of executives believe data is valuable to the overall success of their business. And IDC's 2019 survey of Russian companies found that more than 90% of companies "face the challenge of analyzing Big Data to some degree "1. However, the steadily increasing volume of data is creating more and more barriers to transforming this flood of information into actionable insights. An appropriate data strategy can help companies capitalize on the value of their data and use it to make informed decisions.
Keywords:
Data Science, strategic management, context-aware approach, data analytics.
How can we turn data into useful knowledge? How can we use data to make strategic decisions? With the right strategy for handling this data, you can easily utilize this treasure trove for your company.
Companies have a lot of data, but often lack the time, expertise or methodology to turn it into actionable insights and tangible results.
What do data scientists do? A data scientist's job is to analyze data for actionable insights. In the book Doing Data Science, the authors describe the duties of a data scientist as follows: "More generally, a data scientist is someone who knows how to extract meaning from data and interpret it, which requires the tools and techniques of statistics and machine learning, as well as being human..." [O'Neil, R. Schutt 2013].
Here it would be interesting to reflect on what "being human" means... Without going into details, we can say that the task of a data scientist is to identify the most important, relevant factors for a given field of activity, which stand out relative to all others and require special attention from management.
This way, the attention of entrepreneurs and managers is freed from secondary matters and allows them to focus on the main things.
Separately, it should be said about the role of data scientist specialists in increasing the effectiveness of external communications of the company.
In the area of company communications, the key problem is the perception of the company's interaction with its target audience. What will "go in" and what will go unheeded? There are many well-known practical solutions here. However, I would like to focus on one "subtle" point related to the principle of least effort.
It is a broad theory that covers various fields, from evolutionary biology to web page design. According to this principle, animals, humans, and even well-designed systems naturally choose the path of least effort.
This phenomenon is best known, or at least well-documented, in the fields of librarianship and information science.
The principle states that an information-seeking customer will tend to use the most convenient rather than the most efficient search method. Once minimally acceptable results are obtained, the information search will cease.
The key question that arises in this regard is: what exactly (and for whom) is this minimally acceptable result? So here we are faced with another challenge for data scientists: finding a way of obtaining data that is convenient for the target audience. This step, if omitted, will create a high difficulty for users to find information and leave the necessary messaging unattended by the public.
Activity Analysis
No activity happens on its own, rather within a specific space in which such activity "unfolds". If within this space there are conditions in which such an activity can potentially be performed, then there is a probability that this or that activity will be performed.
If, on the other hand, the conditions existing within that space prevent such an action from occurring, then the null result of such an action is obvious.
For example, you will not be able to sled on water or float on one in the air. The laws of physics will not allow you to do it. Such limiting conditions exist in every area of the entrepreneur's activity, whether he or she knows about them or not.
To make an activity in any field possible, some degree of alignment of that activity with the environment is required. For example, you cannot build the foundation for a house in water or air - such an activity is incompatible with those types of environments. Similarly, GR-activities are built in accordance with the adopted regulatory and legal framework and should not go beyond the rules defined by the legislation [Gurov 2011].
Thus, the most important task of data analysis is to understand the boundaries within which certain events or phenomena can exist. And then, when these boundaries are known, it becomes possible to structure the data within these boundaries, establish their relationships, and build a knowledge system.
The problem with analyzing data is context. Here is a very illustrative example from a study of employee compensation programs for training [Johns 2006].
At first glance, compensation should create a desire for employees to learn, grow in competence, and ultimately lead to lower turnover. This sounds logical. But in reality everything is not so unambiguous. The main influence on the promotion prospects of employees in the company was the existence of another program - a career management program. If such a program existed, it helped to reduce staff turnover. If not, it at least compensated employees for training. Ultimately, contrary to the expected outcome, it increased staff turnover. It turns out that the same factor (tuition reimbursement program) leads to completely opposite outcomes depending on the presence or absence of another component (career management program).
This is a clear example that for some organizations, the assumption that turnover reduction programs are effective will be true for some organizations and false for others.
The answer to the question, "Do tuition reimbursement programs help employees manage turnover?" should be, "It depends...". Here we have a context-dependent approach.
In the case of the example described, it is the career management program that will be the primary context. Without identifying this context, much of the data will be impossible to put into practice. On the other hand, identifying this context makes communication about the effectiveness of employee tuition reimbursement programs simple and clear.
As you can see, at the heart of data scientists' communication is their knowledge and understanding of the contextual factors in their research.
To summarize this thought, I want to make two more statements that I think more fully reveal this idea: Entrepreneurial problems have a solution (and often meaning) only in a given context. The choice of solution depends largely not on the problem itself, but on the environment in which it exists.
It is the context that makes the roles of the participants in the interaction relevant. For example, the context of family interaction gives meaning to the roles of "husband"/"wife", "son"/"daughter". Out of context, these roles are inappropriate.
Understanding the context of consumer interactions is a preliminary condition for analyzing data. (If knowledge is built on data gained through experience or education, then understanding can be defined as the ability to interpret that data or events within some known framework, context).
To summarize the article, let's emphasize that the impact of context on an area of activity is amazing. You will not be able to miss a punch from a boxing master if you are "lucky" enough to meet him - he will surely knock you down. The impact of context on an entrepreneur's field of endeavor is nothing less.
The problem with analyzing data is context. A major barrier to data analysis is the overconfidence of professionals who suffer from being cut off from observations.
Companies have a lot of data, but often lack the time, expertise or methodology to turn it into actionable insights and tangible results.
What do data scientists do? A data scientist's job is to analyze data for actionable insights. In the book Doing Data Science, the authors describe the duties of a data scientist as follows: "More generally, a data scientist is someone who knows how to extract meaning from data and interpret it, which requires the tools and techniques of statistics and machine learning, as well as being human..." [O'Neil, R. Schutt 2013].
Here it would be interesting to reflect on what "being human" means... Without going into details, we can say that the task of a data scientist is to identify the most important, relevant factors for a given field of activity, which stand out relative to all others and require special attention from management.
This way, the attention of entrepreneurs and managers is freed from secondary matters and allows them to focus on the main things.
Separately, it should be said about the role of data scientist specialists in increasing the effectiveness of external communications of the company.
In the area of company communications, the key problem is the perception of the company's interaction with its target audience. What will "go in" and what will go unheeded? There are many well-known practical solutions here. However, I would like to focus on one "subtle" point related to the principle of least effort.
It is a broad theory that covers various fields, from evolutionary biology to web page design. According to this principle, animals, humans, and even well-designed systems naturally choose the path of least effort.
This phenomenon is best known, or at least well-documented, in the fields of librarianship and information science.
The principle states that an information-seeking customer will tend to use the most convenient rather than the most efficient search method. Once minimally acceptable results are obtained, the information search will cease.
The key question that arises in this regard is: what exactly (and for whom) is this minimally acceptable result? So here we are faced with another challenge for data scientists: finding a way of obtaining data that is convenient for the target audience. This step, if omitted, will create a high difficulty for users to find information and leave the necessary messaging unattended by the public.
Activity Analysis
No activity happens on its own, rather within a specific space in which such activity "unfolds". If within this space there are conditions in which such an activity can potentially be performed, then there is a probability that this or that activity will be performed.
If, on the other hand, the conditions existing within that space prevent such an action from occurring, then the null result of such an action is obvious.
For example, you will not be able to sled on water or float on one in the air. The laws of physics will not allow you to do it. Such limiting conditions exist in every area of the entrepreneur's activity, whether he or she knows about them or not.
To make an activity in any field possible, some degree of alignment of that activity with the environment is required. For example, you cannot build the foundation for a house in water or air - such an activity is incompatible with those types of environments. Similarly, GR-activities are built in accordance with the adopted regulatory and legal framework and should not go beyond the rules defined by the legislation [Gurov 2011].
Thus, the most important task of data analysis is to understand the boundaries within which certain events or phenomena can exist. And then, when these boundaries are known, it becomes possible to structure the data within these boundaries, establish their relationships, and build a knowledge system.
The problem with analyzing data is context. Here is a very illustrative example from a study of employee compensation programs for training [Johns 2006].
At first glance, compensation should create a desire for employees to learn, grow in competence, and ultimately lead to lower turnover. This sounds logical. But in reality everything is not so unambiguous. The main influence on the promotion prospects of employees in the company was the existence of another program - a career management program. If such a program existed, it helped to reduce staff turnover. If not, it at least compensated employees for training. Ultimately, contrary to the expected outcome, it increased staff turnover. It turns out that the same factor (tuition reimbursement program) leads to completely opposite outcomes depending on the presence or absence of another component (career management program).
This is a clear example that for some organizations, the assumption that turnover reduction programs are effective will be true for some organizations and false for others.
The answer to the question, "Do tuition reimbursement programs help employees manage turnover?" should be, "It depends...". Here we have a context-dependent approach.
In the case of the example described, it is the career management program that will be the primary context. Without identifying this context, much of the data will be impossible to put into practice. On the other hand, identifying this context makes communication about the effectiveness of employee tuition reimbursement programs simple and clear.
As you can see, at the heart of data scientists' communication is their knowledge and understanding of the contextual factors in their research.
To summarize this thought, I want to make two more statements that I think more fully reveal this idea: Entrepreneurial problems have a solution (and often meaning) only in a given context. The choice of solution depends largely not on the problem itself, but on the environment in which it exists.
It is the context that makes the roles of the participants in the interaction relevant. For example, the context of family interaction gives meaning to the roles of "husband"/"wife", "son"/"daughter". Out of context, these roles are inappropriate.
Understanding the context of consumer interactions is a preliminary condition for analyzing data. (If knowledge is built on data gained through experience or education, then understanding can be defined as the ability to interpret that data or events within some known framework, context).
To summarize the article, let's emphasize that the impact of context on an area of activity is amazing. You will not be able to miss a punch from a boxing master if you are "lucky" enough to meet him - he will surely knock you down. The impact of context on an entrepreneur's field of endeavor is nothing less.
The problem with analyzing data is context. A major barrier to data analysis is the overconfidence of professionals who suffer from being cut off from observations.
Jaroslav Kaplan
Author of the book "Business Incognita. How to push the boundaries of entrepreneurial thinking". Expert in the field of sustainable development of organizations and discovering new sources of growth. Developer of the methodology of contextual market research. Member of the International Association of Strategic and Competitive Intellect Professionals SCIP (USA).
Blog: https://www.kaplanresearch.pro/eng
In this light (yet profound) business fable a very magical and sincerely nice goldfish, Goshio, navigates her aquarium and the seas of the Paraquarian world beyond. The heroine's journey is an allegory of the entrepreneurial world (and of life) – based on the author's own research journey to circumnavigate the fascinating World of Entrepreneurship. www.goshio.com
Contact:
E-mail: work@kaplan4research.com
Linkedin: www.linkedin.com/in/jaroslavs-kaplans-11255b
Author of the book "Business Incognita. How to push the boundaries of entrepreneurial thinking". Expert in the field of sustainable development of organizations and discovering new sources of growth. Developer of the methodology of contextual market research. Member of the International Association of Strategic and Competitive Intellect Professionals SCIP (USA).
Blog: https://www.kaplanresearch.pro/eng
In this light (yet profound) business fable a very magical and sincerely nice goldfish, Goshio, navigates her aquarium and the seas of the Paraquarian world beyond. The heroine's journey is an allegory of the entrepreneurial world (and of life) – based on the author's own research journey to circumnavigate the fascinating World of Entrepreneurship. www.goshio.com
Contact:
E-mail: work@kaplan4research.com
Linkedin: www.linkedin.com/in/jaroslavs-kaplans-11255b
Big Data analytics as a tool for business innovation
https://filearchive.cnews.ru/img/files/2019/05/27/20190424idchitachiwpbdafin.pdf
(date of reference: 12.12.2022).
https://filearchive.cnews.ru/img/files/2019/05/27/20190424idchitachiwpbdafin.pdf
(date of reference: 12.12.2022).
References
1. Gurov, F. N. GR-technologies at the service of IT-company / F. N. Gurov // Voprosy novaya ekonomika. 2011. № 2(18). P. 20-24.
2. Johns G. The essential impact of context on organizational behavior // Academy of Management Review. 2006. No. 31(2). Р. 386-408.
3. O'Neil C., Schutt R. Doing Data Science. USA: O'Reilly Media, Inc, 2013. 406 p.
1. Gurov, F. N. GR-technologies at the service of IT-company / F. N. Gurov // Voprosy novaya ekonomika. 2011. № 2(18). P. 20-24.
2. Johns G. The essential impact of context on organizational behavior // Academy of Management Review. 2006. No. 31(2). Р. 386-408.
3. O'Neil C., Schutt R. Doing Data Science. USA: O'Reilly Media, Inc, 2013. 406 p.
