Share:
Imagine, you have a problem. You also have found a solution. The solution initially works very well. However, after some time, the solution – instead of solving the problem – makes the problem even worse. This is the ‘Cobra effect’ – an interesting concept from political economics.
The term came from the story of a policy issued by the British government in India. Once, there was a huge population of venomous cobras in a part of the country. The government declared a silver coin as a reward for each dead cobra. This motivated people to proactively haunt cobras and eventually get rewarded. Everything was going fine until some smart people found it more profitable to breed cobras in-house and later convert them into silver coins. When this news came out – the government scrapped the policy. The cobra breeders had to set the cobras free as it was no longer a lucrative trade option. Eventually, with the released cobras, the cobra problem became even worse!
Just to interpret the story, sometimes a good intention to curb a problem – does not necessarily end with a desirable result.
In today’s world, we have a lot of digital data, and organizations are using it to generate insights and solve business problems. This created a supersonic era of Data Science. Data Science is a buzzword. However, sometimes the results are not what we intend.
Recently, I have talked to several industry practitioners, thought leaders, and researchers about this issue, and they’ve shared some examples of the Cobra effect from data science. Let’s take a look at a few of them.
Consider a large organization. It has several product divisions and business functions. Often, they work independently and in silos. Data models of a division often do not talk to the data models of others. As a result, the outcomes are often sub-optimal and counter-intuitive.
For example, the analytics manager of procurement advises buying in bulk to leverage the economies and higher discounts from the suppliers. However, the benefits are often negated by inventory holding costs, opportunity costs, and the risk of the products being obsolete shortly. Similarly, an inventory manager asks her data science team to come up with a great inventory optimization model that thrives to save on inventory holding costs. Though, the model can save some dollars on the inventory – it may fail to fulfil a million-dollar order due to material shortages – if the model does not take inputs from the sales and marketing team!
Such examples show how the original intention of a business division can be great, but without a holistic view of the overall business landscape, it may fail to bring in the desired benefits. It’s important for organizations to break down data silos and ensure that data models talk to each other, to achieve the best outcomes for the entire business.
In an organization, there are usually several data science or analytics teams. Now each analytics team builds sophisticated visualization and business reports. These are discussed in the board room. Often, the data source and model assumptions are different, and hence the reports capture only partial, incomplete, and conflicting views of the business. Hence the executives are left dark and clueless.
To please the executives, more reports are produced again. And the process keeps on going across teams. Over some time, there is a flood of reports without a consensus. It takes a significant amount of time and effort to build those reports – which ultimately gets wasted.
It’s important for organizations to ensure that all teams are working with the same data sources and assumptions so that the reports generated give a complete and accurate view of the business. This way, executives can make informed decisions based on the data, without getting lost in a sea of conflicting reports.
The data science team builds sophisticated predictive models to solve a business problem. The analysts spend long hours collecting, cleaning, processing, and modelling the data. Sometimes, the models are too complex and turn out to be an unexplainable black box.
On top of that, during the development, often there is not much involvement of the domain experts. As a result, the models are off the ground – without proper business alignment or foundation. Such models look okay basis the given training data. However, when deployed in production, the models fail badly and the result is disastrous.
The other day I was talking to John, who’s the boss of a marketing company, and he was explaining to me about a machine learning model they tried to use to find out which customers might stop using their service. The idea was to offer those customers special deals and things to make them stay. But the model didn’t work as well as planned. It mistakenly labelled some customers as likely to leave when they weren’t really planning to. So, those customers got special treatment for a while until the model recalibrated them as not being risky! The company stopped offering the free stuff now. A major portion of such customers left as they felt ignored.
The same thing happened with the police department in the city. They started using a machine learning model to figure out where to put more police officers and support staff in areas where crimes were happening. But the model wasn’t perfect either. It identified some areas as being high risk when they really weren’t and sent too many patrols there. Meanwhile, other areas that were actually risky didn’t get enough attention, and the crime rate increased unexpectedly!
Organizations aim to deploy an IT-enabled intelligent end-to-end business process – giving the best customer experience. However, the most ignored part is the current business process. Is it optimal, customer-centric, and stable under various uncertain settings?
Typically, IT automation increases the efficiency of an efficient business process and the inefficiency of an inefficient business process.
My friend Patrik leads an advertising company. The company recently started using web analytics and a high-end optimization engine to show the right product(s) to the right customer(s). However, the problem was that the way they were collecting data didn’t match up with how customers actually behaved in the portal. Moreover, they didn’t have an A/B testing process.
With automation, the ML models built on misaligned data started damaging customer experience everywhere and ended up with a massive failure!
Thus, a business with sub-optimal or poor business processes, when automated with IT and sophisticated data technologies, can only expect serious bottlenecks during IT-enabled execution. Hence, the original idea to deploy cutting-edge technology and IT automation to scale up the business would fall flat on the ground. The cobra-effect!
Many organizations invest heavily in enterprise technologies, sophisticated hardware, and software. For example, a large retail company might invest in a customer data platform, a machine learning tool for demand forecasting, and an inventory management system. However, in a large enterprise, there are often different data science teams working on similar areas in silos. In the absence of proper coordination, the teams can end up duplicating technology or tool investments.
To make it worse, in many cases, the technologies are not even used properly due to a lack of awareness and willingness to change. From an overall organizational point of view, this significantly reduces the ROI. And this creates a barrier to the adoption of future technologies, continuing the cycle of ineffective investments.
New technologies like AI/ML, IoT, Blockchain, and Cloud are the buzzwords in the business world. Many companies are investing big bucks into them, even acquiring startups that specialize in these areas. But, unfortunately, the return on investment (ROI) can be quite low or even negative in some cases due to conflicts with existing legacy systems.
For example, let’s say a company invested in IoT systems to gain an edge in data-driven insights. However, their 25-year-old systems can’t handle the massive amount of streaming data from IoT devices. This conflict creates problems with the traditional skill set of the company and a lack of proper governance. If the conflict is not resolved effectively, the investment could end up causing more harm than good.
It’s just like the cobra effect in action once again!
Now, let’s talk about ethics, morality, societal impact, and sustainability.
Data Science and the latest AI/ML systems are programmed to make decisions based solely on data and algorithms. They don’t have a sense of right and wrong, and can only make decisions based on what they are programmed to do.
Hence, it is easy to create biased, incorrect, and myopic viewpoints. For example, it might prioritize profits over human lives, the environment, and other moral considerations. Ethics and morality can be compromised big time with fake and unsolicited information. This is a slippery path that could lead to a world where moral values are no longer prioritized or respected.
Next, social systems can also be negatively affected by AI/ML. As automated AI/ML continues to increase, it’s likely that many jobs may become obsolete, leading to unemployment and potentially widening the gap between the rich and poor. Additionally, social media algorithms can lead to polarization and division among different groups of people.
Finally, the environment is another area where AI/ML has severe negative impacts. The energy required to train and operate AI/ML models can significantly increase carbon footprints and greenhouse gas emissions and other environmental problems. For example, the latest buzzword ChatGPT is supposed to be one of the largest Neural Network models with 175 billion parameters. The carbon footprint for training GPT-3 is estimated to be the same as driving to the moon and coming back! In short, our short-term gains are compromised big time with overall sustainability, leading to serious damage to our planet.
To summarize, data-driven emerging technologies are there to help enterprises to fly. However, a good intention to adopt such technologies is just not enough. Organizations need to do a few checks internally before they sign up with such ideas:
The above steps when done correctly must negate the cobra effects. It cannot happen in a single day. Only a good intention with the right focus, a well-thought strategy, and collaboration across all levels will win in the end.
Have you seen any other cobra effects? Please, let me know.
Keywords: Data Science, Artificial Intelligence, Machine Learning, Data Science in Industry, Data Science Case Studies
© 2023 DataScienzz. Powered by DataScienzz
