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What are The Top 10 Areas for Data Analytics Application


Following is the list of top 10 areas where Data Analytics has been applied extensively and still hold a lot of potential for continued growth and improvement.

1.    Policing/Security

Apprehending criminals and anticipating criminal activities has become easier with advanced data & text mining tools. Data Analytics tools have been used extensively to investigate crimes and monitor communication of suspected terrorists. By applying predictive analysis to geographical and historical data, most accurate predictions can be rendered regarding criminal activities and the areas likely to be targeted.

2.    Banking and Finance

Advanced Data Analytics can be very benefiting to banks in solving multiple business problems by quickly detecting correlations in business information and market prices. Applying Data Analytics techniques to customer data collected during the loan application and the data related to their recent expenditure helps in inferring possible defaulting customers.

3.    Delivery Logistics

Analytics have been used to parse real-time GPS data and local weather data to improve operational efficiency of delivery logistics companies. As it helps in figuring out suitable routes for delivery, the best delivery time and means of transport while avoiding loss of packages and unanticipated accidents costing life and money. Companies like UPS, DHL, FedEx, and etc. have successfully applied Data Analytics to achieve cost efficiency, customer satisfaction, and reliability.

4.    Smart Cities Planning and development

There are many ways in which Data Analytics can be applied to smart city planning, spending, energy management and web provisioning. Data analytics modeling would help in deciding upon the best location of erecting structures while avoiding future problems like the accessibility of specific areas or services and overcrowding. Data Analytics can be applied to energy management to implement energy optimization, smart-grid management, distribution of energy and etc.

5.    Customer Relationship Management

Direct and indirect customer data can be analysed to reveal customer behavior and preferences. When applied to e-commerce and retail, the Data Analytics can reveal trends, detect potential customers, optimize pricing models, and generate personalized and real-time offers etc.

6.    Healthcare

Healthcare Analytics industry is growing fast and has been predicted to reach more than $18.7 billion by 2020 in the US only. The key focus areas being clinical analysis, supply chain analysis, financial analysis, frauds and HR analysis. Data analytics can be used in predicting the possible outbreak of diseases, pinpointing their true source and possible areas they could spread to, saving thousands of lives at once.

7.    Internet/Web Search

Companies like Google, Bing, Yahoo, Duckduckgo, and AOL etc. servicing web search, utilize heavy data analytics to crawl and index the internet pages that are reliable and credible. Given the size of the internet and the rate at which it still grows these companies have realized the need for faster scanning and indexing and have resolved to spearhead the research and development in Big Data Analytics.

8.    Education

Educational Data Mining along with Analytics aims at predicting learning behavior, analyzing educational support effects, and advancing scientific knowledge of learning. An intelligent curriculum that can adjust and adapt to the needs of students, their life experiences, learning pace and previous knowledge is the next step in the education sector. Data analytics can help institutions in crafting learning experience in accordance with ability, learning style and preference of the students and the teachers.

9.    Research and Development

Researchers spend a lot of time in testing, iterating, tweaking, and repeating while they develop a solution to a particular business problem. With data analytics applications, researchers can accelerate their research by compensating for varying data with automatic model generation and training.

10.    Bioinformatics

Mining and analyzing biological data can reveal useful knowledge related to protein function inference, gene finding, protein and gene interaction network reconstruction, and etc. Predictive analysis can be particularly useful in disease diagnosis, prognosis, treatment optimization, and protein sub-cellular location prediction and etc.


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