Schumpeter's Creative Destruction
🔗The new Artificial ways of Natural Selection
“Curly: Uh-oh, he's snuffocatin'! Larry: Don't worry, I know the Heineken maneuver!” -The Three Stooges.
Today’s public/private goods and services providers face a novel and difficult task, keeping up with data processing, its accumulation, protection, and its controlling regulation, together with the need of achieving what many have called the “new industrial revolution” standards.
Schumpeter's creative destruction is not just a fancy name, it is the process by which long standing practices are dismantled, giving way to new ways of innovation.
Many executives and public organization managers still incur in notorious mistakes in regards to what Data Science can do for their organizations, considering the most advanced technologies in the market to be an expenditure, instead of the most reasonable investment to be financing.
The above mentioned quote has a clear intention (besides the giggles it may provide), to show that many organizations today are being dragged into obsoletion, victims of their lack of data processing capabilities, without even noticing this elemental deficiency. As Curly, the unsurmountable amount of data is not aiding this organizations, as it is just contributing to their definite suffocation.
For those who do notice this severe problem, their are not always able to identify the methods which are appropriate for overcoming the new requirements imposed by our age of digital selection.
🔗- Data Science. Not an option anymore
For almost every action, transaction, sale, complaint, interaction between subjects, or individual behaviours, data is generated.
Data is to be regarded nowadays as what it really is, the enriching substance that provides the information needed for process and product optimization, development, marketing, and every other aspect of value generation in the modern economy.
Available Data is in no way diminishing, as everyday insurmountable amounts of it are created. However, the availability of professionals with the required abilities for its processing and utilization is not proportionate to its demand.
Data grows in an unstoppable way, forcing not only for more and better Data Science professionals, but also forcing businesses and organizations to look for methods that allow them to process such rich material, hitherto unavailable for many of them.
The problem is clear, not every organization has access to the benefits provided by data science, which in turn leaves them significantly behind from those competitors that actually have the tools and individuals to reap its benefits. Organizations that fail to adopt data driven, and data informed strategies, suffer the advance of competitors capable of gaining the market share made available only to them.
The disparity between organizations that have a clear and proficient data science program, and those that don’t, is manifest, as the latter will not have access to any of the tools provided by this technology (data visualization, data analysis, data processing, data mining, etc.). Kodak, Nokia, Blockbuster, Blackberry, are just some of the myriad of organizations that failed to adopt data driven and innovative market solutions.
🔗- A. I. Terminator likes to help.
“Algorithms capable of making predictions do not eliminate the need for care when drawing connections between cause and effect; they are not a replacement for controlled experiments. But what they can do is extremely powerful: identifying patterns to subtle to be detected by human observation, and using those patterns to generate accurate insights and inform better decision making The challenge for us is to understand their risks and limitations and, through effective management, unlock their remarkable potential.” - Harvard Business Review.
Mere data collections would be utterly useless if there was nobody to analyze them, similar to libraries filled with texts written in an incomprehensible language (programmers are familiar with such ironies).
However, technology nowadays has surpassed our own expectations, as systems are being developed which allow to process and analyze data automatically, contributing to supply organizations with skilled digital brains that will help them access certain data sets.
Most of the time, Data Scientists, in combination with Business Experts, are the ones administering data analysis and utilization, being their skills highly valued throughout the digital market as a whole. Despite this, new technologies objectives are not to substitute, but to aid this scientists, contributing and providing tools for the Data professionals to utilize and, simultaneously, improve and optimize their own work.
It is at this point that A. I. systems make their appearance, as they are a vital tool for analysing data sets, aiding the job of analysts while also providing accurate information on determined tasks (v. gr. fraud detection, text processing via NLP, etc.).
We should note that Machine Learning is a fundamental part of A. I. implementation, as a set of data-driven algorithms enable the creation of high accuracy prediction models, as well as many other functions required by the use of data.
Machine learning is essentially the process by which a given program learns how to make intelligent and highly accurate decisions, using large amounts of data. Traditional software uses human coded logic to make decisions.
The novel approach that Machine Learning allows for is that it does not require human experts, as it does not require the coding of the strict logical rules that are needed to conduct data driven decisions.
Artificial Intelligence encompassing a wide range of methodologies. Nowadays, Machine Learning is a driving aspect of AI development and modern business growth.
ML is not necessarily new, as many of the implemented ideas were originally developed developed in the early 1900’s.
What changes today, besides technological advancements, is that, because we record and store so much information, the ML algorithms that have been developed are becoming ever more useful.
During the 1950’s, there was no conceivable way of googling pictures of cute cats to generate a cat picture data set (poor souls).
Machine Learning helps Businesses accelerate the time that it takes to extract valuable information from vasts amounts of unstructured, inconsistent, and high volumes of data. Classical statistics are also adding more value every day as the amount of data available to analysts grows exponentially.
🔗- Data Processing & Storing. GDPR, no tricks allowed
For anyone who has not been living under a rock (provided no wifi is available), it is clear that Data Regulations today are starting to catch up to the technological benefits reaped until this day. Abusive and non-consensual data mining, processing, storing and distribution have generated increased outrage among certain countries and political classes, resulting this in more strict data controlling and storing methods.
The most clear example of new regulations is the General Data Protection Regulation (EU) 2016/679, or GDPR.
Compliance with the requirements and restrictions established in the referred instrument are to be achieved, in order to avoid the sanctions and restrictions also contemplated by this document
In other words, Data Storing, Processing, Mining, Transfering & Analyzing must comply with the standards set by the GDPR, or else, penalties will be imposed on organizations, according to the State in which the infringement is carried out (Art. 84. Penalties).
Any solution that allows businesses to mitigate the risks associated with data compliance contributes to their transparency, while also diminishing the operational costs derived from fines and penalties.
🔗- RPA. Robotic Process Automation.
"Domo Arigato Mr. Roboto." -Styx.
ROBOTIC PROCESS AUTOMATION (intended bold capital letters) is not just a tool, it is a paradigm which has the potential to revolutionize the current labor market.
Many confuse the terms AI and RPA when it comes to automated processes within an organization, which is not only a common, but a pretty significant mistake. RPA is the use of virtual robots to conduct activities, mimicking human work with additional machine computation, in order to automate business processes.
Due to the wide amount of human activities that are conducted within an organization, RPA systems allow for a wide range of use cases and implementation practices. RPA’s can be simple rule based robots that perform simple tasks (like data entry), or they can be a fully orchestrated variety of sub-robots and AI models, automating more complex and sophisticated business practices.
RPA is a self sufficient technology, as the market for this particular solution has grown in such manner that it actually leads the tip of digital transformation, having impacted every organizational aspect, from customer management, to infrastructure and business models as a whole.
“The top three RPA software companies are approaching values in excess of 10 billion.” Craig Le Clair, Principal Analyst. Forrester.
🔗- True Value.
One can not deny the value that RPA is creating for industries and companies as a whole, serving states and enterprises in optimizing and reducing operational costs.
In the case of government institutions, digitizing and automating processes allows for the most potential productivity gains. Governments should aim to digitize high volume services, labor intensive and costly processes, as their are to obtain the highest gains by focusing on this much needed categories.
The companies that develop RPA technologies are currently fast-paced, highly-valued organizations, as they are the ones that drive general process optimization around the world, having the potential to accelerate the implementation of all the other technologies we have talked about.
“The most successful companies in the world today run on software and employ fewer employees than their predecessors. Facebook employs only 25, 000 people. It generates a staggering $1.6 million in revenue for each one. Apple generates $1.85 million per employee, and Google parent Alphabet generates $1.2 million.” - Craig Le Clair, Principal Analyst. Forrester.
“At first I was afraid, I was petrified.” -Gloria Gaynor. “I will survive”.
As Charles Darwin exposed in his work “On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life”, adaptation is key for evolutionary survivability. Those beings which are not capable of facing a changing environment will eventually disappear.
Taking into account the fast pace of our current technological developments, everyone should be informed of the benefits that the most advanced technologies provide for them. Those who do not adopt newer methods and processes will be obsolete, victims of the newer waves of innovation.
As Schumpeter described, obsolete practices will be dismantled by the Creative Destruction process, as the capitalist system exhibits this relentless drive towards progress and innovation.
https://www.mckinsey.com/industries/public-sector/our-insights/when-governments-turn-to-ai-algorithms-trade-offs-and-trust “When governments turn to AI: Algorithms, trade-offs, and trust”, Anusha Dhasarathy, Sahil Jain, & Naufal Khan, McKinsey & Company, McKinsey Insights, February, 2019.
https://itnext.io/cloud-native-rpas-with-python-a22cdb6690d0 “Building Cloud Native RPA’s in Python using Destructible Infrastructure”, David Emmanuel Katz, IT Next, Linkit, Medium, 2019, May 29.
https://www.forbes.com/sites/danielnewman/2019/08/14/rpa-in-the-real-world-driving-marketing-analytics-productivity-and-security/#3841b9232c4e “RPA In The Real World: Driving Marketing, Analytics, Productivity And Security”, Daniel Newman, Forbes, 2019, Aug 14.
https://go.forrester.com/blogs/predictions-2019-automation-technology "How Automation Is Impacting Enterprises In 2019”, Craig Le Clair, Blogs, Forrester, Apr 23 2019
https://www.mckinsey.com/industries/public-sector/our-insights/transforming-government-through-digitization “Transforming government through digitization”, Bjarne Corydon, Vidhya Ganesan, and Martin Lundqvist, McKinsey & Company, McKinsey Insights, November 2016
“Algorithms Need Managers, Too.”, Michael Luca, Jon Kleinberg and Sendhil Mullainathan, Harvard Business Review, Reprint R1601H, originally January-February 2016.
Schumpeter, Joseph A., "Creative Destruction: From Capitalism, Socialism and Democracy". Harper, New York, 1975, orig. pub. 1942.