EXAMPLES OF BIG DATA ANALYTICS:

We have been hearing about “Big Data” analytics everywhere. Over the last couple of years, there has been an extensive collection of articles on this topic. Let us start with couple of examples showcasing the power of big data analytics in three distinctly different areas. As presented in this Harvard Magazine article,[1]big data analytics was able to connect the extent of decrease in “radius of generation”, which refers to people’s daily and weekly commuting patterns, with the magnitude of cholera outbreaks in Rwanda. Another example presented in the same article shows the ability of data analytics to provide better conclusions on the outcome of Supreme Court cases compared to the qualitative judgments of 87 law professors. Similarly, in another article presented on HR data analytics, we can find that big data analytics can provide an opportunity for organizations to optimize salary increase based on employee’s affinity for the organization.[2]

“It turns out, as much of our other research shows, that this “normal distribution” curve of pay is a big mistake. What the research found was that employees in the second and third quintile of performance (good solid performers) would stay with the company even if their raise was as low as 91% of average increases in their job class. So these folks were being overpaid. On the other hand, people at the top of the performance curve would leave the company unless they received 115-120% of the average pay increase for their job class, indicating that the payroll money should go here.”

Based on these examples, we can see not only the power of data analytics, but also its ability to transcend across various areas disrupting and creating a new marketplace. If this were to be the power of data analytics, then why should I caution on big data, and call it a “Red Ocean” or “Desert with one tall building” strategy.

This title came about after an interesting article on the risks of big data to society due to big data analytics proliferation.[3] The objective of this article is to present a holistic analysis on advantages, risks, and the potential impact of rapid big data analytics in the long run. In this way, all the stakeholders involved, namely everyone, can act proactively to this next paradigm in technology.

BLUE OCEAN STRATEGY:

Prior to putting it all together, let us first start the discussion with “Blue Ocean Strategy”. This concept was captured in great detail in Chan Kim & Renee Mauborgne’s book.[4] To capture a quick snapshot of this strategy, it involves value innovation, which give organizations the ability to combine differentiation and low cost at the same time. This strategy also touches upon the need to strategically sequence in the right direction to align it towards the “big picture” vision, thus creating a new marketplace reaching beyond the current demand.

Certainly, Big Data appears to be a great example for Blue Ocean Strategy. Based on a limited set of examples presented before, it is obvious that big data promotes innovation, connects various areas at a rate never seen before, thus creating a new marketplace. Further, it also helps on the cost front at the same time.

DOUBLE-EDGED SWORD & DISTORTED PARETO RULE:

Now moving onto the next aspect of big data analytics, and how it is connected to a “Red Ocean” or “Desert” strategy, we need to look at a recent article that presents the risks associated with the rise of big data.3 In this article, along the lines of any other disruptive innovation, the key findings present the double-edged sword nature of big data proliferation. The five key concerns as outlined in this article being,

1. Data localization in the hands of public and private sectors

2. Outdated privacy laws not aligned with the rise of big data

3. Risk for discrimination against minority groups without the intent of one; potentially promoting unconscious biases

4. Online privacy and transparency issues

5. Need to enact new legislation on response procedures for data breaches

To elaborate on some of these concerns, let us go back to the HR example. Though big data analytics helped in cutting down the compensation for 2nd tier employees, it has the potential to negatively impact employees with limited scope for movement outside the organization due to financial situation, personal commitments, loyalty towards organization, ethical reasons or lack of “right” connections. Moreover, the HR performance metrics is not completely objective, and can be impacted at least by unconscious biases as repeatedly presented in the literature, thus taking away significant cumulative compensation over a 30-year period. Moreover, such practices can further impact the motivation and morale of employees, which is already at a record low level.[5] This reduced level of motivation would digress employees’ focus away from their jobs, and instead focus solely on building connections to identify their new opportunity. Thus, in the long run, organizations can lose talented and loyal employees for short-term gains.

The next question that we need to ask pertains to larger ramifications of such actions. Such rapid disruptive growth has the potential to result in further shortsighted compensation structure, and potentially reduce the average life span of leading US companies from 67 years in the 1920s to approximately 15 years observed now to even a lesser time frame in the future.[6] This reduction in life span would further increase job uncertainty, which along with low morale and steeper student debt, certainly can widen the gap between the bottom 80% and the top 20% with larger share of such operational enhancement going to the top 1%.

Another example to point out being categorizing customers based on social media data. For example, “Ethnic second-city struggler” category[7] namely, an individual who lost his/her job recently can be targeted for high interest loans. This targeted product offerings exploiting an individual’s situation, which along with direct or indirect collusions between various involved larger institutions can increase unhealthy consumer practices, and reduce financial stability amongst financially “less-savvy” sections of the population.

Further, big data analytics, unlike in the past, provides an opportunity for first degree price discrimination where each person can get a different price not limiting to his/her ability to pay, and demand for the product, but also based on other personal information collected by the company.[8] For example, an ISP can find out a lot more about an individual customer based on Internet usage, nature of usage, and financial status of that family to name a few variables, and can use it to their advantage. This first-degree price discrimination has the potential to completely compress supply/demand dynamics, and eliminate any price reduction from cost compression expected due to technology maturation. Further, with such finer details available to organizations, an already fragmented customer base with asymmetric information would always be at the receiving end of any negotiation. Such actions can also transform an oligopolistic market segment to a monopolistic one.

ECONOMIC RESEARCH REPORT ON TECHNOLOGY & INEQUALITY:

As an extension to this discussion, it is important to discuss the work of Prof. Acemoglu on technology & inequality. There was an extensive article[9] on the impact of technology on inequality, the key take-home message of this work being that the disparity in wealth can increase with technological advancements.