Posted in Operations & IT Articles, Total Reads: 1237
, Published on 23 November 2014
A documentary by the BBC channel states that data created in the last 5 years is more than the data created in the entire human history. Today, we live in a complex world where every movement (Google Maps and CCTVs) and every mouse click generates a data entry. From the biggest business decisions to the most innocuous ‘likes’ on social media, data today has become an essential part of our digital existence. The emerging field of Data Science as a part of Data Analytics is thus fast becoming a game-changer for several organizations.
Data Science is the art of transforming this data into insights, business decisions and products. Where Data Science exits from the realms of Business Intelligence is in terms of discovering new questions for business organizations as opposed to asking the ones that already exist. It is more future-oriented and inclined towards predictions using real-time data as opposed to analysing past data which today is found in data warehouses of most organizations. So while Business Intelligence would aim to create reports, Data Science is about models and data products.
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Data Science can play a huge role in maximizing revenues for organizations and improving customer service. Take e-commerce for example. Popular e-commerce websites carry out thousands of transactions in a day creating a huge repository of data. Building models around the time slots where maximum hits on the website are received can give the company the knowledge of releasing the right offers at the right time. The distribution of fluctuating prices and the corresponding sales volumes can equip a company with the right price point for a customer where the sales volume and profits earned optimize to a peak point. Intelligent profiling of customers based on their historical transactions can help websites introduce behavioural targeting which increases the effectiveness of the real time recommendations dispensed to customers. Analysing data on the various steps of the supply chain for every order placed till the time it reaches the customer can help websites discover loopholes in their management and can help build models around these problems to come up with the most optimum time that should be spent at each step.
In the B2B markets too, Data Science is gradually finding the right ways to tame the complexities of the business. The sales funnels of most organizations start from the opportunity stage and then narrow down (because of fall-outs) significantly by the time a contract is signed with a client. Data Science finds a massive scope right from increasing the lead-to-opportunity conversions by empowering the salesmen with intelligent data that targets the right customers, to finding the optimum discounts for the salesmen to negotiate with the clients, and improving the efficiencies of the highly complex provisioning systems of organizations in order to decrease fall outs and meet customer due dates. Data Science in the near future could play a huge role is increasing the width of the opportunity funnel and finding new avenues to win customers and increase market shares.
There are challenges that organizations face as far as profitable implementation of Data Science is concerned. As is true for most research activities, finding the best solution requires several failed attempts at a problem. Having a willingness to fail fast, to quickly evaluate one’s mistakes and to explore through complex approaches to arrive at simple solutions is a big challenge faced by organizations around the world. From a different angle, Big Data with its variety and veracity is still in the process of finding efficient tools and technologies. Until that moment, the data warehouses are getting bigger and uncontrollable and are still some miles away before they can become the El Dorados for the business world.
Data Analytics is often used to find patterns on the basis of which several models are subsequently built. One common error that is made in Analytics is the problem of finding patterns when there are actually no patterns. The minimization of this error, often referred to as the Type I error, is another major challenge faced by Data Scientists. Thus, testing of the final models through a pilot study becomes imperative for organizations before they can put the models in production.
As the age of data sets in, Data Science is bound to play a major role in the business edge that most organizations vie for. This presents the greatest obstacle that organizations today are attempting to tackle: the right human resource. It requires an ideal blend of Understanding the Business, knowledge of Computer Science, and an inclination towards Mathematics as a subject. It involves the ability to be both left brained, which lends a person rationality to analytically look at problems, as well as right brained, which aids a person to present creative solutions to a problem. As is the case with Analytics, curiosity and giving attention to intricacies plays a huge role in taking that leap from initiation to a successful data model.
Data Science is here. And it attempts to change the way we look at business. Organizations today look for the slightest of opportunities to gain that edge that would make them powerhouses. Data Science could well be the Oracle that can predict the fortunes of organizations as well as guide them to change their courses towards glory and success.
This article has been authored by Vivek Ohri from FMS-Delhi
• The Field Guide to Data Science – Booz Allen Hamilton, 2013
• The Age of Big Data – BBC Documentary, 2012-13
• Data Science revealed – emc.com
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