To view recording of this webinar please use below URL: http://wso2.com/library/webinars/2016/11/dealing-with-common-data-requirements-in-your-enterprise/ Today’s enterprises are challenged with fast growing data requirements. Unlike in the past, where organizations relied on a single database or isolated data silos, today’s enterprises need to cope with multiple data sources and complex access control requirements. They also need to analyze large amounts of data in order to gain insights into their business functions. This webinar will discuss how the WSO2 platform can help deal with common enterprise data requirements such as data as service transactions, aggregation of corporate entities and management of fragmented data sources to build an efficient enterprise data management strategy.

1. Dealing with Common Data
Requirements in your Enterprise
Nipun Suwandaratna
Senior Solutions Engineer - WSO2
WSO2 Solution Architecture Best Practices Webinar Series - 2016

2. Agenda
● Organizational Data
● Common Data Challenges of Modern Organizations
● Integrating with Different Messaging Infrastructures
● Data Services
● Data Analytics & Visualization
● High Availability
● Q&A

3. Organizations & Data

4. Old School
DB
Systems
Files
Organization

5. Modern Data Ecosystem
External Systems
External Users

6. Organizational Data
● Master data
Eg: Customer data, employee records, Supplier details, Product related
data etc.
● Transactional data
The data that master data participates in… transactions, discounts on bills
etc. (changes constantly)
● Meta-data
Data about data

7. Common Data Challenges Organizations
Face
● Work with multiple Data Transports and Data Formats
● Data Transformation and Validation
● Exposing data as services
● Secure and managed data access
● Federated data stores
● Data/Entity Aggregation
● Data Analytics
● Visualization of Data

8. Data Transports & Formats
Formats of data, their storage and transport mechanisms vary among
different systems
● Transports: HTTP, HTTPS, FTPS, SFTP, TCP, UDP, WebSocket, POP,
IMAP, SMTP, JMS, AMQP, MQTT
● Formats & protocols: JSON, XML, SOAP, WS-*, HTML, EDI, HL7,Text,
JPEG, MP4, binary formats

9. Integration

10. Integrating with Messaging Infrastructures

11. Message Transformation
● Protocol and Format conversion and Message Translation
○ eg: SOAP to REST and XML to JSON and translate the output from one
system to match the input format required by the other system
● Enrich Content
○ eg: Add or remove data fields; may require accessing a separate data source
● Wrap Content
○ eg: Include additional message header fields or encryption source to query
required data
● Data Validation
○ eg: Validate input data against a schema

12. Enterprise Service Bus

13. Message Transformation Example
Protocol / Content-Type Conversion

14. Data Services

15. Exposing Data-As-Services
Why ?
● Decouple data from the infrastructure and the data sources and expose
them through standard web services interfaces.
● Ability to incorporate multiple data sources/entities into a single data model
(Data Federation)

16. Secure & Managed Data Access
● Transport and Application level security
● Authentication, authorization, confidentiality, integrity and encryption - with HTTP(S)
Basic Auth, WS-Security, WS-Trust, WS-SecureConversation, WS-Policy,
WS-Policy Attachment and WS-SecurityPolicy
● Authorization deals with defining who can access what
● Role based access control
● Fine-grained authorization with XACML
● Throttling access to data

17. Federated Data Stores
● Expose data from multiple data sources through a single service
● Facilitates entity aggregation

18. Data/Entity Aggregation

19. WSO2 Data Services Features
Ref: http://wso2.com/products/data-services-server/

20. Analytics

21. Data Analytics
● Batch Analytics
Analyze a set of data collected over a period of time.
Suitable for high volumes of data.
● Real-Time Analytics
Continuous processing of input data in real time.
Suitable for critical systems where immediate actions is required e.g: Flight radar
systems
● Interactive Analytics
Obtaining fast results on indexed data by executing ad-hoc queries
● Predictive Analytics
Predict future events by analyzing historical and current data

22. Big Data
What is Big data ?
“Big data is a term for data sets that are so large or complex that traditional
data processing applications are inadequate to deal with them” - Ref: Wikipedia

23. Big Data Analysis
Why ?
● Make informed Business decisions - make decisions based on
patterns emerging from analyzing historic data
● Improve customer experience - discover customer preferences,
purchasing patterns and present the most relevant data
● Process Improvements - identify areas of the business process that
needs improvement

24. Big Data Analysis Example
Better customer experience in airline seat reservation/allocation
img ref: http://staticcontent.transat.com/airtransat/infovoyageurs/content/EN/seating-plan-a310-300(1).png

25. Real Time Analytics
● Identify most meaningful events within an event cloud
● Analyze the impact
● Acts on them in real time

26. Real Time Analytics Example
City Transport Control System - Analyzing traffic, monitor movement of busses,
generate alerts based on traffic, speed & route

27. Predictive Analytics & Machine Learning
Approaches:
● Machine Learning
Machine learning is the science of getting computers to act without
being explicitly programmed - http://online.stanford.edu/
● Other approaches such as statistical modeling

28. Predictive Analytics Example
e-Commerce sites use predictive analytics to suggest the most relevant
merchandize, increasing sales opportunity

29. WSO2 Data Analytics
Ref:wso2.com

30. Data Visualization

31. Data Visualization Contd.
What is Data Visualization ?
● View data in a constructive and comprehensible format
● Facilitates interaction with data - drill into the data for visual
analysis
● Detect patterns (e.g: sales patterns) that may go un-noticed unless
data is properly visualized

32. High Availability

33. High Availability of Data

34. CONTACT US !