DATA SCIENCE – An opportunity or a Challenge ?

As we are getting into more and more of digital era, it generates a gargantuan amount of data every second or we can say while we are entering an epoch of ‘Big Data’, the need for data science keeps on increasing. If we have to explain what data science is in simple words, I would say, Data Science is nothing but managing, working and analyzing the enormous amount of data using scientific methods and specific tools such as Hadoop.  

 Walmart handles more than 1 million customer transaction every hour. Facebook effectuate approximately 40 billion photos from its user base & 500 terabytes of new data every day while Boeing 737 will generate 240 terabytes of flight data during a single flight across the US. This much amount of data being spawn almost every day and every minute require people to manage and analyze the data inducing the need for the role of data science and data scientists. Data Science helps in analyzing various root level problems as well such as which product will be a success, when should a company sell its particular product and how, who are their target customers or audience, so on.

 There’s a famous saying “Nothing comes for free”, this also means a real beautiful subject or domain such as data science also have some disadvantages such as data of a particular company or organization can be mishandled, tools required for analyzing the data are expensive. Also, these tools are complex in nature and necessitate people with a specific skill set.

Unlock the Opportunities of Data Science post COVID era

 

 To combat COVID-19 crisis, more than 400 clinical trials are conducted worldwide. The trials employs a living database that garners and curates data from various trial registries. Through this, public health professionals and experts finds out the spread of the disease, bringing new treatments and chalk out a contingency plan to deal with the clinical management of the pandemic. Here the concoction of Data Science and Analytical techniques will help to forecast, prepare, and respond in a proper fashion to battle out this pandemic and its after effects. As per the exeprts opinion, owing to the widening market shift, data science professionals require an increasing analytical  pace to bring innovations in the post COVID era. Here are some of the latest technologies that data and analytics leader should focus on since they will be the pathfinder for the new normal.

Smarter and Responsible AI

Towards the fag end of the 2024, almost 75% of the enterprises will make a certain shift from piloting to operational AI, augmenting a five times increase in streaming data and analytics. Under the current pandemic situation, AI techniques such as machine learning (ML) and natural language processing (NLP) gives you a detailed information about the spread of the virus and efficacy of the countermeasures.

 The use of AI techniques like reinforcement learning and distributed learning can create adaptable flexible systems in order to tackle complex business situations. Decisions taken on the historical data will be replaced by agent based systems. Implementation of new chip architectures like neuromorphic hardware are deployed to fast track AI and ML computations. Progressively,  this will help increasing AI solutions leading to greater impact on business.

 

Agile Data Science

 Machine learning models adds natural value, but the development time is generally four to eight weeks and after a thorough analysis there is a clear idea scope of the use case and required data to validate and test the models.

 Companies like PWC used agile methodologies to develop EIRD (Susceptible-Exposed-Infected-Recovered-Death) model of COVID-19 progression for almost 50 US states within a week's duration.

 

Decision Intelligence

 As per the recent estimates, almost 33% of the large corporations will have data analysts or scientists working on decision modelling. It covers both decision management as well as decision support. It provides a framework in the hand of data analysts who design, build, monitor, as well as tune decision models in regards to the various business processes and behavior.

 

Augmented Data Management

 This branch mainly uses AI and ML techniques to bolster operations. It is used in transmitting meta data from being used in auditing, lineage, and adding it to the power dynamic systems. Augmented data management products will deal with sets of operational data covering performance data, actual queries, as well as schemas. Data Scientists in nera future will use augmented data management to streamline their architecture. This increases the scope of automation to revamp their redundant data management systems.

 

Vast Array of Scopes for Data Scientists

 Worldwide panic of COVID 19 brings majority of the corporates under a one roof and function from their home remotely. Thanks to the growing demand of  technology and the people across the globe slowly trying to cope up with this new normal. This created a surge of hiring as far as Data Science domain is concerned. Recruiters are looking on hiring Data Scientists and Machine Learning engineers. The mean reason is that big data and analytics can bring some mind-boggling change like contact less driving and flying using AI, drones for surveillance, increased home delivery orders for merchandise, and many more.