In this presentation, Divyatmika introduces SMAC and associated trends. Divyatmika's interest area is Big Data analytics in cloud where she intends to not only do due diligence on the size and processing power of the cloud platform but also develop solutions to give people access to critical analysis.

1. Social
,Mobility,Analytics
and Cloud
Name : Divyatmiika
E-mail:divyatmika2010@gmail.com
Twitter ID : @Divyatmika
University: NITK,Surathkal
Year/Semester: Third yr,Vith sem.
Branch:Computer and Science
Engineering

2. Introduction :
 SMAC is the new leading disruptor in the business-technology
ecosystem. Businesses are becoming more and more agile, and
technologies such as social media, mobility, analytics and cloud
computing are coming together to unleash unlimited opportunities
for everyone involved.
 Social Media :A social media strategy has become a must for all
enterprises. While most enterprises use social media for their
customer service function only, many firms have now started using
social media in tandem with their sales and marketing functions.
This in turn enables firms to use data generated by the customers
effectively to service their larger pools of customers. Example :
Boost post marketing using Facebook APIs ,Twitter APIs , Linkedin
etc.

3. Mobility : Mobile devices have changed the way people access digital content.
Smart phones and tablets have brought rich, digital content to the fingertips of
consumers. Mobile banking has emerged as one of the most innovative products
in the financial bandwagon, and ensure that their applications are mobile ready.
Analytics Every year, companies and individuals generate billions of gigabytes of
data. Data, which properly analyzed and used in time, can emerge as an
unbeatable competitive advantage. Enterprises need to recognize the prospect
analytics represents and should adapt their IT strategy to capture such
opportunities. Example: They can perform Sentimental analysis to increase their
respective sales by knowing the response of the customers.
Cloud computing :The undeniable power of cloud computing to foster innovations
and improve productivity is now accepted by both IT vendors and their customers.
While the financial services and government sectors are mostly moving to a
private cloud model due to information security concerns, other industries like
healthcare and retail have adopted public cloud. Moreover, their existing
infrastructure has helped telecom players to emerge as providers of cloud
computing, leading to erosion in boundaries between IT and telecom vendors.

4. Trends:
 Mobility: More and more enterprises are adopting a formal ‘Bring Your Own
Device’(BYOD) policy. M2M as ubiquitious: From smart homes to connected
cars to intelligent medical devices, a wide universe of products will embrace
mobility as a 'must-have’. Other key trends in mobility are productivity
collaboration and line of business, productivity, expense reporting, business
intelligence etc.
 Analytics : Technologies like Hadoop, for example, that make it functionally
practical to access a tremendous amount of data, and then extract value from
it. Uses of analytics are customer retention, cross selling products ,fraud
detection etc. We can also use Machine learning and predictive analytics
methods to learn and process the data at a lower cost.
 Cloud : This technology is known for its benefits – cost effectiveness, agility,
and less capital intensive. The growth is driven by the emerging segment of
Infrastructure-as-a-Service (IaaS), which includes cloud compute, storage and
print services.

5. Data analytics in Cloud Computing :
 In coming few years, data will grow tremendously, so the main focus relies on
the usage of data reducing the costs.
 With all the benefits data analysis (predictions done using Machine learning or
data mining)and big data offer, much of their potential is missed because
employees lack quick, reliable access to said information.
 As analysis moves towards cloud drives, data analysis gains accessibility as
company employees can access company information remotely from any
location, freeing them from being chained to local networks and thus making
data more accessible.
 Aside from its increased accessibility and utility, big data analysis on cloud
drives also exports many IT demands, such as hosting and maintaining servers,
to cloud service providers. Companies can spend less money on servers and
instead focus on bolstering their staff and product. Software-as-a-Service, or
SaaS, is another popular function for cloud data analysis. Salesforce CRM,
Google Apps, and DeskAway are all examples of SaaS.

6. Interest area: Data analytics in
Cloud Computing.
• I would like to work on data analytics in cloud computing. As clouds become
more secure, reliable, and affordable, the use of data analytics in cloud
computing will also continue to grow.
• When choosing which cloud storage device could best fit a business, the
question becomes how much data storage is needed and what performance
demands will be placed on the cloud. As the name implies, big data is a large
collection of data often varying in scope that grows as additional data is
recorded and processed. Given the inherent size of big data, companies must
determine exactly how big is there big data, as not renting enough space from
a cloud service provider could end up giving a company significant
infrastructure issues and perhaps not allow them to use their big data analysis
as intended or to its full capabilities.
• Likewise, determining the computer power of a cloud drive is of
importance, as underestimating the demands placed on it could slow service
and make the cloud less effective.

7.  Given the scope of big data, some clouds still cannot host or
analyze certain sets of data regardless of their size or capability
given the scope of some data sets. Thus, understanding the needs
and size of big data and how it will be processed is essential in
reaping the benefits of data analytics on cloud drive.
 Hence, I would intend to work upon data analytics in cloud
computing . We can plan to use cloud-based machine learning and
predictive analytics to analyze or extract patterns of the data and
later host the same on cloud to make accessibility easier and avoid
budgetary overheads.

8. Thank you