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Data & Analytics

Data and analytics is the management of data for all uses and the analysis of data to drive business processes and improve business outcomes through more effective decision making and enhanced customer experiences.

Four Types of data analytics:

1.       Predictive data analysis

Predictive analytics may be the most commonly used category of data analytics. Businesses use predictive analytics to identify trends, connections between data, and relationship between data.

2.       Prescriptive data analytics

Prescriptive analytics is where AI and big data combine to help predict outcomes and identify what actions to take. Prescriptive analytics can help answer questions such as “What if we try this?” and “What is the best action?” You can test the correct variables and even suggest new variables that offer a higher chance of generating a positive outcome.

3.       Diagnostic data analytics

While not as exciting as predicting the future, analyzing data from the past can serve an important purpose in guiding your business. Diagnostic data analytics is the process of examining data to understand cause and event or why something happened. Techniques such as drill down, data discovery, data mining, and correlations are often employed.

Diagnostic data analytics help answer why something occurred.

Discover and alerts notify of a potential issue before it occurs.

4.       Descriptive data analytics

Descriptive analytics are the backbone of reporting—it’s impossible to have business intelligence (BI) tools and dashboards without it. It addresses basic questions of “how many, when, where, and what.”

This report sent monthly or generated and sent based on the business need.

Data analytics used in several industries like medical care, climate monitoring, research, cyber security, customer care, market campaigns, market promotions, insurance, and manufacturer warranty.

Data analysis process:


 Data analytics is performed to convert monolithic application to microservices application.

Data consistency and data integrity are critical challenges for managing data in the microservices architecture, as microservices manages its own data.

 Benefit of Data & Analytics:

·         Analyzing big data helped a large printer manufacturer to cut the attrition rate at their call centers by over 20% – a significant and tangible financial saving.

·         A large local bank by market capitalization in Asia that operates in 15 countries world wide was able to achieve higher customer engagement and increase customer satisfaction by 20% compared to a control group. The bank was able to benefit by responding to the customer actions, personal lifetime events and demographic profiles.

·         Businesses collect customer data from many different channels, including physical retail, e-commerce, and social media. By using data analytics to create comprehensive customer profiles from this data, businesses can gain insights into customer behavior to provide a more personalized experience.

 

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