Skip to main content

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.

 

Comments

Post a Comment

Popular posts from this blog

Certified Enterprise Architect Professional (CEAP) - Module 5 - Architecture Frameworks

Architecture Frameworks: An Architecture Framework is a theoretical structure that has the purpose of developing, executing, and maintaining an Enterprise Architecture. Advantages of EA framework: Simplify Breaks down areas of the business process Organise business components and create and identify relationships between business Determine the scope Customization in the existing framework Disadvantages of EA framework: Need to follow process Provides only direction and not information It's based on goal and objective Need creativity and proactive thinking Zachman Framework: The Zachman Framework is a widely used model in Enterprise Architecture (EA) that provides a structured way to classify and organize an organization's information infrastructure by defining different perspectives from various stakeholders, allowing for a holistic view of the enterprise and facilitating alignment between business needs and technology solutions; essentially acting as a template to organize arc...
Post a Comment
Read more

Daily Agile Scrum stand-up meeting guidelines

Followers of the Scrum method of project management will typically start their day with a " stand-up meeting ". In short, this is a quick daily meeting (30 minutes or less) where the participants share the answers to the three questions with each other: • What did I accomplish yesterday?  • What will I do today?  • What obstacles are impeding my progress?  Some people are talkative and tend to wander off into Story Telling .  Some people want to engage in Problem Solving immediately after hearing a problem. Meetings that take too long tend to have low energy and participants not directly related to a long discussion will tend to be distracted. These are the minimum number of questions that satisfy the goals of daily stand-ups. Other topics of discussion (e.g., design discussions, gossip, etc.) should be deferred until after the meeting.  Here are few tips for running a smooth daily meeting:  • Everyone should literally stand-up and no one should sit down ...
Post a Comment
Read more

Empiricism (Scrum)

Empiricism asserts that knowledge comes from experience and making decisions based on what is observed. Pillars of  Empiricism . Various practices exist to forecast progress, like burn-downs, burn-ups, or cumulative flows. While proven useful, these do not replace the importance of empiricism . In complex environments, what will happen is unknown. Only what has already happened may be used for forward-looking decision making. Each artifact contains a commitment to ensure it provides information that enhances transparency and focus against which progress can be measured: ● For the Product Backlog it is the Product Goal. ● For the Sprint Backlog it is the Sprint Goal. ● For the Increment it is the Definition of Done. These commitments exist to reinforce empiricism . The sum of the Increments is presented at the Sprint Review thus supporting empiricism .
Post a Comment
Read more