What is Data Science ?

May 9, 2019
What is Data Science

All of us generate data and the volume of data has now become incredibly large. Data has grown from using own data to databases available across industrial verticals and so huge that cloud storage is now the buzz word. Data science and its analytics with the ‘Big’ tag deal with data primarily and the predictions or forecasts from analyzing databases that help with informed decision making in all processes related to business. Any growing business needs big data analytics to have a competitive edge, reduced operational expenses, better productivity, and enhanced customer loyalty and retention. With this, the demand for data scientists and analysts keeps growing and makes an ideal choice of careers that pay well and have plenty of demand.

With traditional tools, one can work with relatively smaller databases that are less than a terabyte size-wise. However, modern data tends to be unstructured and comes in the form of videos, audio clips, blog posts, reviews, and more which are challenging to clean, organize and include huge volumes of data. Data science is thus multidisciplinary and involves gaining meaningful insights for complex resolutions for business and research purposes. The steps are:
• Gaining a deep understanding of data.
• Making inferences from data.
• Developing algorithms for ML.
• Using cutting-edge tools and technology.

Data Science Applications:

Here are some real-life examples of such data analytics applications using very large databases.

• Offering marketing insights:

Data analytics helps change operations and marketing in all businesses. Whether it be effective marketing campaigns, advertisers looking for decision making in purchasing, targeting the right segment, popularizing the best product or just improving efficiency through cost-saving measures the insights and forecasts help make those decisions. Ex: Netflix and its advertising campaigns covering over 100 million customers.

• Boosting retention and Customer-Acquisition:

Companies like Coca Cola are implementers of foresight from data analytics using the business critical customer behaviour data and insights to trigger customer retention and loyalty through better products, services and customer experiences.

• Regulatory compliance and Risk Management insights:

A classic example is Singapore’s UOB which used data analytics for rapid assessment of risks and risk management. Especially in the financial sector, the foresight for regulatory compliance and risk management is a critical investment.

• Product innovations:

A study based on customer acceptance can give gainful insights usable in design, product development and innovations of the product line. Ex: Amazon moving to the food, groceries and fresh-foods segment.

• Management of logistics and supply-chains:

Big data predictions and foresight have helped Pepsico with accurate and clear insights that have led to improved processes, warehouse management, scheduling deliveries, reconciling logistics and shipment needs and more.

Data Science Complexities

Some of the more discernible hurdles in data science are

1. Landscape uncertainties:

The evolving field has new technologies and tools being developed and implemented literally on a daily basis. These in themselves could expose your asset of data to risks, hacking, the problem with storage and so on.

2. The gap in talent:

As in all evolving fields, there is a dearth of personnel to handle such huge volumes of big data and its analytics. Even lesser are the experts available to resolve issues and that could be a major setback.

3. Procurement of clean data on the big data platform:

The infinite increase in data generated, the various tools used by different companies, and the storage of critical databases become a mammoth hurdle for those seeking to use databases.

4. Data source synchronization:

Lack of a single platform or language applicable across databases making the synchronization and cleaning processes time-consuming and lack credibility in providing the right gainful insights.

5. Use of foresight from analytics:

While analytics provides for gainful insights, the information needs to be put into the right hands to be effective. The gap that exists here is real and often those who benefit from the insights never get the right analytics or forecasts and vice versa.

Concluding note:
The tools and techniques involved in the capture, storage, and cleaning of data need necessarily to be updated. One needs faster software that can compare databases across platforms, operating systems, programming languages and such complexities of technology. If you wish to choose Data Science as a career, then do a Data science Course at a reputed institute like Imarticus Learning. It is really outstanding that their course has market acceptability, rounded industry-relevant curriculum and real-time live project work with plenty of hands-on practice. Opt-in today!

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