What is Analytics?
It is the analysis and making sense of data to help us understand, explore, examine and uncover things such as behavior patterns for segmentation (Segment, target, positioning) and business purposes. It is the examination of a large amount of data to uncover hidden patterns and insights.
Data analytics is not a new concept. What is different, though, is the advancement of technology and the availability of more data than ever. Hence, the rise of Big Data and the need for more complex and advanced tools beyond excel spreadsheet to handle the data.
Yet, it is important to note that analytics is essentially a tool and it cannot make decisions for you. Data is put through a software with predefined parameters where it undergoes analysis. The data output allows us to zoom in on certain aspects of the data – patterns, outliers, new trends etc
Use of Data Analytics
There are many reasons why people conduct data analytics. Some examples include cost reduction, faster and better data management and profit increase, new product and services so as to remain competitive.
Data analysis was used in the 2011 movie Moneyball, and here is a classic example of how data is used in baseball to build a competitive baseball team. Based on the book by Michael Lewis in 2003, it demonstrated how a revenue-disadvantaged baseball team - Oakland Athletics won almost two-thirds of its games in 2001 by assembling a team based on analysis, evidence and a sabermetric approach. This reduced analytical errors and biases, and was drastically different from the subjective approach of the past. Instead of using one’s instinct or things like swing style and speed to decide the value of a player before buying the player, in-game activity and rigorous statistical analysis about player performance was measured to observe and determine performance consistency. Besides strategic decisions on player choice, data analysis was also applied in tactical decisions and other small but high impact changes such as habits of walking or getting a run.
There are many other used cases of analytics with commonly known ones being stock trading, finance, customer-service in the airline industry, and even shopping. In the case of shopping, our purchasing data – time date, items purchased are all collected and used to predict our next purchases. One other example worth mentioning is multi health system (MHS). They have assessment tools and methods that can analyse an individual’s behavior, and this is used to aid in a wide range of critical decision making processes. This includes helping parole boards determine who is granted release from incarceration. If a prisoner in the USA wants to be granted parole, they would need to obtain the approval / recommendation of MHS. It seems that MHS is able to analyse the prisoner’s behavior every second, and know what they are doing.
Another example is that of driverless cars. Statistics show that two million people die in the USA as a result of car accidents. With driverless cars, it is hoped that this number will be reduced. Big data will be used to control self-driving vehicles. Traffic, environmental data, and data from car sensors will be used to monitor its position, proximity to pedestrians or other drivers, traffic guides, signals and more. This data driven approach to car transportation is meant to reduce human errors. With this, it is capable of generating even more data for further analysis. However, these are still machines and there are other ethical considerations that we need to concern ourselves with. After all, a self-driving car will not be able to make a decision to speed up if there is an urgent need to rush someone to the hospital. At this point, we then question if machines will overpower our decision-making abilities, our sense of control and us.
We’ve heard examples about how analytics have been used to decide the positioning of items like toothpaste and toothbrushes in the supermarkets to entice us to purchase both. This eventually led to the bundling of both items together. To what extent are we being influenced, persuaded and manipulated by the data output and to what extent are consumers able to be more mindful and not fall into purchasing traps?
What does Analytics mean to us?
For many of us, data is a double-edged sword. It empowers us and yet can be intimidating in its own right. It seems that analytics attempts to find a pattern to things. However, when it comes to predicting human behavior, it is undeniable that human behavior is unpredictable and we don’t always behave according to the statistics. Our behavior may also not be the best representative of the different thoughts and considerations that goes through our minds. To date, we still have very little knowledge of our brain. But we know that our thoughts show itself in the form of behavior. Just like behaviorism in psychology, analytics is the best tool that we have to make sense of human behavior and thoughts. Plenty of work is ongoing in this arena with IBM Watsons analytics.
On the other hand, what causes analytics to fail is the inaccuracy, elements of uncertainty and that it may not have included all factors into the equation. However, do we need 100% accuracy in all instances? Many of us just need some assistance with the data to guide us in decision-making. For example, the identification of an ideal job position to apply for, or the amount that a comedian (in Mumbai) should get paid in terms of the entertainment merit index. Analytics remain a tool for us to use, and we cannot rely merely on it to make decisions. Some say that analytics provide people with a competitive advantage. However if everyone is now on an equal playing field, how do we continue to differentiate ourselves? In the end, it is about the way we make sense of and use the data as in the case of Alpha House’s failure versus House of Card’s success. Both drama series were created using analytics. In Alpha House’s case, they based it merely on the data and patterns of what people like to watch. In the case of House of Cards, it was not just based on that. Human experience was also key.
So, is there a need to fear Analytics? We only fear what we don’t know. Do we know what data is being collected and what companies do with the data?
Recommended books / Reading / Activity from the session:
1. Moneyball: The Art of Winning an Unfair Game by Michael Lewis
Themes explored in the book: Insiders vs. outsiders (established traditionalists vs. upstart proponents of sabermetrics), the democratization of information causing a flattening of hierarchies, and the ruthless drive for efficiency that capitalism demands.
2. Fooled by randomness: The Hidden Role of Chance in the Markets and in Life by Nassim Nicholas Taleb
This is regarding the Black Swan Theory which shows us the possibility to expect the impossible.
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