Data science: empowering business decisions

Data science

There are two ways to make profit in a business; by increasing the price of the products or by cutting down the costs while maintaining quality. While the first approach might seem attractive, if employed, it usually results in reduction in sales over a long period. This approach can still be employed if your business is successful in driving positive perception resulting in increased brand value like Apple or Starbucks.

If you business does not command similar brand value it is better to tackle the second approach; that of cutting costs. The trick with this approach is to juggle quality, innovation and consumer demands while optimizing processes to reduce costs. Traditionally, the consumer’s demands were sought out by creating surveys but this method generates restrictive inputs.

With the advent of technology and Social Media, businesses now have access to a multitude f touch points with the consumers. The data gathering is no longer limited to surveys but this has resulted in generation of huge amounts of data. To make any sense out of it, the data needs to be processed and scrubbed. In come the role of Data Science.

Data Scientist is a person has has advanced knowledge of math, statistics and computer science. Data Science deals with taking the data gathered from various sources using statistics to explore, model and draw inferences from this data and creates visual representation of these inferences and how they relate to the business.

Armed with this information and with awareness of the consumer’s sentiment now business can accurately identify the path for innovation. The analysis also gives the business a data-driven look at their process inefficiencies and how to optimize them to reduce costs.

Let’s look at a few example of how some companies have made use of insights gleaned from Data Science practices:

General Electric: Most of the machines today generate copious amounts of data (IoT) including power plants, healthcare equipment,etc. GE’s analytics team analyze the data and re calibrates the machines to improve efficiency.  Even small improvements add up given GE’s scale of operation. By GE’s estimates, data can boost productivity in the U.S. alone by 1.5%, which over a 20-year period could save enough capital to raise average national incomes by as much as 30%.

Uber: Uber analyzed rider and driver data to identify an opportunity in London. Based on the data, they introduced the service called Uber Pool with the ambition of reducing the number of cars on London streets by half. This service was designed to cater to users interested in lowering their carbon footprint and fuel costs. Uber’s business is built on Big Data, with user data on both drivers and passengers fed into algorithms to find suitable and cost-effective matches, and set fare rates.

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