In today’s world, we are generating and using data more than ever before. It has been estimated that more than 2.5 quintillion bytes of data are being generated each day. That would take 10 million blue-ray discs to hold the data, the height of which piled one on top of the other, would be as tall as four Eiffel Towers stacked up.
In 2020, for every person on earth, 1.7 MB of data was created every second. These are not mere statistics; they go on to show how data has encompassed our lives. If harnessed appropriately, it can provide us meaningful information. This is where data science comes in – an interdisciplinary field that mainly uses statistics, mathematics, computer science, and scientific computing to build, clean and structure datasets that can be analysed, interpreted and used for making predictions.
In business, decision-making occurs every day at multiple levels. It is of prime importance that these decisions are taken with objectivity and in the stipulated time frame. To make a comprehensive decision, it is wise to use multiple sources of data. Techniques from data science can be used to prepare (collect, clean and organise) the data for analysing it further.
Entities within a business interact among themselves at multiple levels and it is not always humanly possible to track all the factors that influence a particular business decision. Given the abundance of data today, it can be tricky to choose what to pick. We can leverage the high computational power of machines and use data science techniques to process this data in a matter of seconds. Organisations can measure, track, and record performance metrics to facilitate decision-making across the enterprise.
Data science has a huge role to play across industries. To elucidate how data science can be leveraged to make smarter decisions, let us look at a scenario where there are two competing services- one costing substantially less than the other. Data Science can help us analyse both sets of data and help decide whether we can migrate to the cost-effective service without compromising on quality.
When it comes to the fintech industry for instance, several decisions like whether to give a loan to a particular customer, or the optimal time to run a marketing campaign can be automated with the help of data science models. A risk modeling system helps determine if a customer is trustworthy and is eligible for a certain loan or interest rate. Data science models can also help prioritise the collection efforts by predicting how likely a customer is to default. This saves time, cost and resources for the organisation.
Another important aspect of any decision-making process is evaluation of the decision. The effectiveness of the decision can be validated by analysing new data. This allows conveniently updating and modifying strategies if needed.
Predictive analysis is an often-used technique where the chances of an outcome are estimated based on past data. Since data science models are computer simulations, different possible scenarios can be easily tested enabling the stakeholders to take an informed decision.
In 2006, British mathematician and Data Science entrepreneur Clive Humby said that “Data is the new oil”. The demand for meaningful data will only grow in the future. Just like oil, raw unprocessed data needs to be first prepared before it can be used. A very important caveat to keep in mind is that data science is not a one size fits all solution to business problems. Domain knowledge is of utmost importance and data should be processed considering the conditions specific to the domain.
Data science models use historical data and can predict future actions. Data science helps organisations take informed decisions based on quantifiable and data-driven evidence. It rules out factors such as human bias, judgement errors etc.
However, it is important to note that data science cannot be used as an advantage in an organisation unless decision makers understand where the problems lie and give data scientists a clear goal to aim for. Once a goal is established, data scientists can then work their magic and help in making the right business decisions swiftly.
Ria Ghosh is the lead data scientist at MyShubhLife.
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