Data Analytics Lifecycle Bing Data Analytics_.pptx

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1. DATA ANALYTICS LIFECYCLE

2. The Big Data analytics lifecycle can be divided into the following nine stages

3. Business Case Evaluation Each Big Data analytics lifecycle must begin with a well-defined business case that presents a clear understanding of the justification, motivation and goals of carrying out the analysis. An evaluation of a Big Data analytics business case helps decision-makers understand the business resources that will need to be utilized and which b us i n e s s c h a ll e n g e s t h e a n a l y s i s w i l l t a c k l e . Note also that another outcome of this stage is the determination of the underlying budget required to carry out the analysis project.

4. Data Identification I de n t i f y i n g a w i de r v a r i e t y o f d a t a s o u rc e s m a y i n c r e a s e t h e p r o b a b i l i t y o f f i n d i n g h i dde n p a tt e r ns a n d c o rr e l a t i o ns . I n t h e c a s e o f i n t e r n a l datasets I n t h e c a s e o f e x t e r n a l datasets a l i s t o f a v a i l a b l e d a t a s e t s f r o m i n t e r n a l s o u r c e s a l i s t o f p o ss i b l e t h i r d - p a r t y d a t a p r o v i de r s

5. Data Acquisition and Filtering

6. Data Extraction

7. Data Validation & Cleansing

8. Data Aggregation and Representation Data may be spread across multiple data sets, r eq u i r i n g t h a t d a t a s e t s b e j o i n e d t o g e t h e r v i a c o mm o n f i e l d s , f o r e x a m p l e , d a t e o r I D Data aggregation and representation can be complex due to differences in data structure and s e m a n ti c s . Large v o l u m e s c a n m a k e i t time-consuming .

9. an example of data aggregation where two datasets are aggregated datasets A and B can be combined to create a standardized data structure with a Big Data solutions

10. Data Analysis Data analysis can be straightforward or complex, involving data mining and statistical analysis techniques to discover patterns or anomalies. It can b e c l a ss i f i e d a s c o n f i r m a t o r y a n a l y s i s a n d e x p l a n a t o r y a n a l y s i s

11. Data Visualization is dedicated to using data visualization techniques and tools to graphically communicate the analysis results for effective interpretation by business users.

12. Utilization of Analysis Results Common areas that are explored during this stage include the following: Input for Enterprise Systems Business Process Optimization Alerts

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