Data for analytics comes in many forms, from many sources. This data holds invaluable insights for business, but currently business intelligence teams are spending as much as 80 percent of their time preparing and cleansing this data, rather than analyzing it. The challenge for today's BI and data science teams is to make this data preparation phase more efficient, so they can combine data from multiple sources - on premise and in the cloud - and shape it to be fully optimized for analytics. This webinar will demonstrate how new cloud applications and services can enable an ecosystem where data preparation, movement and analytics are seamless, for both the technical and non technical user within the enterprise.

1. Data prep: A Key Ingredient for
Cloud-based Analytics

2. ©2015 IBM Corporation
Hernando Borda, Offering Manager, IBM Cloud Data Services
@hernandoborda
hernando@us.ibm.com
https://developer.ibm.com/bluemix/author/HernandoBorda/
Welcome!

3. The Problem
80% of the time doing analytics isn’t spent
doing analytics, but data preparation
Shortage of skilled data scientists makes
leveraging citizen data scientists imperative
Data diversity makes it hard to find, mix &
match, & analyze data
Traditional ETL requires skilled that are in
limited supply
Business users spend between 40% and 80% of
their time finding, cleaning, aggregating and
formatting before using it to produce any business
insights or analytics.
Cloud and Big Data trends introduces multiple new
sources of un-structured or semi-structured data as
well as large volumes of information that creates new
challenges to analyze it and create business insights.
The ratio of skilled Data Scientists to Business
Analyst is 1 to 100 or lower
Using traditional ETL introduces weeks to months
or delays in the analytical processes and is pushing
businesses to any data instead of the right data

4. ©2015 IBM Corporation
Use Cases
Blending data from multiple sources
Access data from any of the supported sources
and combine the data to create
a file/table relevant to the target analytics task.
Access to data in a hybrid cloud environment
Access data wherever it resides by relying on connectivity
to the most common industry data sources, and easily
and securely reach into data behind a firewall.
Shape raw data for analytics
Filter values and columns from the source data,
sort, remove duplicates, and understand the quality
of the data through standardized scores.

5. ©2015 IBM Corporation
Use Cases
Load data for analytics
Access prepared data, wherever it is, and load
it to a data service on the cloud.
Control data workflows from a web app
Use DataWorks APIs to create and control workflow
activities from an application.
Map relational/structured data to semi-structured data
Load normalized tabular data into Cloudant’s NoSQL
store.

6. ©2015 IBM Corporation
A fully managed data preparation and movement service that enables analysts,
developers, data scientists and data engineers to put data to work. DataWorks
empowers technical & non-technical users to discover, cleanse, standardize,
transform, and move data in support of application development & analytic use cases.
§ Access & Publish data to & from on-Premise and cloud sources
§ Fully managed by IBM with PAYG & Subscription options
§ Powered by Spark for a speedy and responsive experience
§ Available in Bluemix and Watson Analytics
DataWorks
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7. ©2015 IBM Corporation
Dynamic Shaping and Productive Use

8. Productive Use With Secure Gateway

9. Q & A

10. Thank You!!