We have too much data piled up ready to be processed using data science. We have too many ready to use tools and technologies, easy-to-use programming languages that do not need a considerable software engineering background to use, and "Data Scientist" a sexy title for, sometimes, an unknown position that many companies are hiring to fill.
The philosophy and concept of Data Science is changing from the area researchers and scientists knew to a new area of applying ready-to-use tools and technologies to create decision support insights, sometimes a quick jump into inaccurate conclusions due to spurious correlations, the lack of domain knowledge, bad automation, etc.
While taking a one day to one week course might be enough to apply for a data science job these days, the question is how real data science can be understood, rescued, and correctly applied, particularly in industry, considering today's inconsistent mentality about Data Science?
Pooia Lalbakhsh
I think that your post "The side effect of Data intoxication" and the question "how real data science can be understood, rescued, and correctly applied, particularly in industry ..." is a rare sober voice in a hyped world of buzzwords. The same question can be asked on artificial intelligence (AI), Big Data, machine learning, etc.
As for Data science (DS), I believe that its purpose is inferring some insights (quantitative and qualitative) from an empirical collection of various data obtained usually without preliminary planning (just because the data are available). However, data science does not offer a causal explanation of any relationships between the variables (factors, features) but it explores merely some empirical patterns, associations / correlation of the past data and attempts making a formal projection of these data to forecast some future events. (By the way, is the future forecast always possible?-See the attached presentation).
Now, there are two distinct areas concerned with using the data potential. One is
focused on Technology for storing, processing and managing large amounts of data of various nature- this is the current trend. This trend leads to fitting a company’s arsenal with data-savvy tools. Value is too often framed as something that increases solely by the collection of more data. This means investments in data-focused activities center around the tools. This leaves an organization with a big set of tools, and a small amount of knowledge on how to convert data into something useful.
Another area is Methodology for making business decisions using modeling and simulation based on data specifically collected to address some business problems. This is called Business Analytics (and more closely related to genuine Data Science). It is getting momentum but still beyond the radar for too many companies.
Business Analytics focus steps:
(i) Defining a business problem, (ii) identifying an analytic method (algorithm) or simulation approach, (iii) collecting data required to feed the algorithm, (iv) turning solution into the actionable managerial decisions.
In summary:
•Data should NOT be a starting point in business analytics • More data do not always result in more accurate predictions • Analytics is NOT a side effect of collecting, keeping and presenting / querying data in general. • Data have business value only in a specific business context. •Currently there is a trend to focusing on data itself instead of focusing on business context and methodology.
You can take a look for more details at the (attached) presentation ' DATA ANALYTICS FOR SOLVING BUSINESS PROBLEMS: SHIFTING FOCUS FROM THE TECHNOLOGY DEPLOYMENT TO THE ANALYTICS METHODOLOGY "
Pooia Lalbakhsh
I think that your post "The side effect of Data intoxication" and the question "how real data science can be understood, rescued, and correctly applied, particularly in industry ..." is a rare sober voice in a hyped world of buzzwords. The same question can be asked on artificial intelligence (AI), Big Data, machine learning, etc.
As for Data science (DS), I believe that its purpose is inferring some insights (quantitative and qualitative) from an empirical collection of various data obtained usually without preliminary planning (just because the data are available). However, data science does not offer a causal explanation of any relationships between the variables (factors, features) but it explores merely some empirical patterns, associations / correlation of the past data and attempts making a formal projection of these data to forecast some future events. (By the way, is the future forecast always possible?-See the attached presentation).
Now, there are two distinct areas concerned with using the data potential. One is
focused on Technology for storing, processing and managing large amounts of data of various nature- this is the current trend. This trend leads to fitting a company’s arsenal with data-savvy tools. Value is too often framed as something that increases solely by the collection of more data. This means investments in data-focused activities center around the tools. This leaves an organization with a big set of tools, and a small amount of knowledge on how to convert data into something useful.
Another area is Methodology for making business decisions using modeling and simulation based on data specifically collected to address some business problems. This is called Business Analytics (and more closely related to genuine Data Science). It is getting momentum but still beyond the radar for too many companies.
Business Analytics focus steps:
(i) Defining a business problem, (ii) identifying an analytic method (algorithm) or simulation approach, (iii) collecting data required to feed the algorithm, (iv) turning solution into the actionable managerial decisions.
In summary:
•Data should NOT be a starting point in business analytics • More data do not always result in more accurate predictions • Analytics is NOT a side effect of collecting, keeping and presenting / querying data in general. • Data have business value only in a specific business context. •Currently there is a trend to focusing on data itself instead of focusing on business context and methodology.
You can take a look for more details at the (attached) presentation ' DATA ANALYTICS FOR SOLVING BUSINESS PROBLEMS: SHIFTING FOCUS FROM THE TECHNOLOGY DEPLOYMENT TO THE ANALYTICS METHODOLOGY "
Dennis Mazur
Indeed, data science seeks a lot of empirical statistical associations, data correlations and patterns in hope that they can be helpful for discovering some hidden insights. But it is the reason why data science cannot be called real science at all.
In general, science can be broadly defined as a logical system of getting and organizing insights in some area of knowledge as a set of some principles, explanations that can be used for making some previously unknown but testable predictions. In order to qualify as a principle, an insight must be both highly general (applicable to many settings) and stable (relevant now and in the future developments).
What are specific data science principles that make it a real science? Data science is just a loose collection of various computational methods applied to empirical data sets. An empirical association can, of course, be a starting point but it is never becoming real science until it includes a causal relationship between the variables (factors or features), i.e. understanding.
New methods of analysis are being developed for new problems, and some methods are becoming obsolete, or inefficient....This is a normal scientific process methodology, i.e. a continual development to address and solve new problems and issues created in the course of technical and societal evolution, that demand explanations and understanding based on causal relationships, not just empirical statistical data associations.
Very good point Alexander Kolker , the issue is the "Science" part. In ordinary data science, generalization happens inside the provided data set, there is no out-of-the box thinking; mathematically speaking, the data set rapidly restricts the domain of the problem without considering the universal set. As an example, I receive many emails from the store I buy groceries from, all trying to recommend specific products that I might be interested in which is populated based on other customers like me or the products I normally buy. So the constructed solution is completely based on product and customers datasets of this company. The out-of-the box thing here is that the algorithm cannot have any idea on other products that I buy from other stores!!! because the solution is completely biased to the current store's datasets as well as my purchase history.
Pooia Lalbakhsh
I agree with your point that in current data 'science' state ".. generalization happens inside the provided data set, there is no out-of-the box thinking; the data set rapidly restricts the domain of the problem without considering the universal set". Your example is very relevant. I have the same experience with laughable so-called machine (algorithmic) recommendations to buy something based on my prior purchase history (be it data science or AI or machine learning, you name it..).
I am glad that we are on the same page.
Just found a nice article on "Data Science: Nature and Pitfalls ";
Highly recommended. see the link:
https://ieeexplore.ieee.org/abstract/document/7579413
- Badges
- Science topic