In the lab, scientists have found that nano-particles spontaneously self-assemble into crystalline structures with desirable properties. This makes it easier to create useful materials and accelerates the pace of discovery.
This is how data alchemy works. It enables faster, more granular, and more accurate decisions even as the rate of change continues to accelerate. Visit for Data Science Course in Pune.
In the past, companies gathered data only when they knew where to look for needles in a haystack. That works fine when the company knows exactly what it’s looking for, but it’s not very effective when haystacks are constantly changing.
Consider the case of China’s Ant Financial, whose approach to lending embodies data alchemy. The company developed an algorithm that determines the interest rate for each routine loan application from a small- and medium-sized enterprise. The algorithm continually refines itself by ingesting new data from billions of sources every day, including e-commerce platforms operated by the Alibaba Group, such as Taobao. The default rate is less than 1%—far lower than the rate a financial-services firm would obtain with traditional gold-mining techniques—and it will likely drop even further as the system becomes more accurate.
Embracing data alchemy will require more than just hiring software engineers. It will require rethinking decision-making processes and deploying skilled human experts to work with machine specialists. This will require a combination of technical and creative skills that are in short supply today.
Embedding A.I. in Decision Making
Embedding data alchemy in decision-making allows companies to become more agile and more competitive. Whether your business is a traditional player or a digital native, it’s time to make the switch to this powerful technology.
For example, Chinese lender Ant Financial employs data alchemy when granting loans to its customers. This technology replaces the old method of analyzing potential risk by examining past credentials and forecasting financial performance. It also streamlines the internal process by turning multiple decision layers into one seamless procedure.
Other industries are catching up as well. British Airways, for instance, used data alchemy to optimize ticket prices when the COVID-19 pandemic swept across the globe last year. Likewise, a growing number of insurance companies use this solution when underwriting policies. It allows them to see how the unforeseen effects of new variables like quarantine restrictions and social distancing rules would affect their business models and customer behavior. Moreover, this approach prevents them from making costly mistakes.
Algorithms and Exception Managers
During the COVID-19 pandemic, many companies struggled to cope with a myriad of factors: changes in workplace dynamics (including the prevalence of remote working), customer engagement, political circumstances, public and private investment, travel and tourism, international supply chains, and health care.
The old data-collection methods work well when firms know in which haystack to search, but the haystacks are constantly shifting. Using an algorithmic approach, companies can sift through massive amounts of new data quickly and find the needle in a sea of variables.
A company like British Airways uses this technique to constantly reroute flights and adjust ticket prices according to things like weather forecasts, news reports in 67 languages, reports on animal and plant diseases, blog entries, and airline-ticketing statistics. While the algorithms make all routine choices, human managers take over for decisions with major strategic implications. They also flag anomalies for a deeper look. These exceptions are handled by a team of experts who can use their expertise to make the right decision.
Throughout the COVID-19 era, the business has been forced to adopt the practice of data alchemy in order to cope with unprecedented volatility across many key areas: customer buying habits, remote working arrangements, employee engagement, new healthcare standards for employees, political circumstances, travel and tourism, supply chains, and more. For these reasons, data alchemy is not a fad or a short-term solution; it’s likely to be an essential part of future decision-making for both incumbent companies and digital natives.
Start your organization’s journey to data alchemy by identifying key processes and decisions where the level of accuracy has dropped significantly due to the unprecedented rate of change your company has experienced. This is a strong indicator that shifting to a data-alchemy model will bring value quickly.