Successful companies rely on accurate, timely and integrated information to stay ahead of the competition. By delivering this relevant, focused information, Enterprise Information Solutions (Master Data Management, Data Warehousing, Governance and Quality) help companies make better-informed business decisions, leading to greater performance. Additionally, the EIM industry is sitting at the cusp of a major evolution - Big Data. Companies are assessing how to manage the increasing volume, variety and velocity of their untapped information to create a managed platform that leverages these large volumes of data to derive timely insights, while still preserving their existing investments in information management. This requires companies to think more in terms of creating a complete, collaborative experience, and building and delivering robust data platforms comprised of cutting-edge data exploration and visualization capabilities. In this session, our Microsoft Business Intelligence practice will discuss trends and technologies in the enterprise information management solution space that help organizations take advantage of the latest "MDM - Big Data - Governance - Quality" capabilities, to produce a competitive advantage. This session will also cover relevant cloud solutions to Big Data.

1. Big Data and the Intelligent Enterprise
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Presented by the Microsoft BI Practice

2. Perficient is a leading information technology consulting firm serving clients throughout
North America.
We help clients implement business-driven technology solutions that integrate business
processes, improve worker productivity, increase customer loyalty and create a more agile
enterprise to better respond to new business opportunities.
About Perficient

3. • Founded in 1997
• Public, NASDAQ: PRFT
• 2013 revenue ~$373 million
• Major market locations throughout North America
• Atlanta, Boston, Charlotte, Chicago, Cincinnati, Cleveland,
Columbus, Dallas, Denver, Detroit, Fairfax, Houston,
Indianapolis, Los Angeles, Minneapolis, New Orleans, New
York City, Northern California, Philadelphia, Southern
California, St. Louis, Toronto and Washington, D.C.
• Global delivery centers in China, Europe and India
• >2,100 colleagues
• Dedicated solution practices
• ~90% repeat business rate
• Alliance partnerships with major technology vendors
• Multiple vendor/industry technology and growth awards
Perficient Profile

4. BUSINESS SOLUTIONS
Business Intelligence
Business Process Management
Customer Experience and CRM
Enterprise Performance Management
Enterprise Resource Planning
Experience Design (XD)
Management Consulting
TECHNOLOGY SOLUTIONS
Business Integration/SOA
Cloud Services
Commerce
Content Management
Custom Application Development
Education
Information Management
Mobile Platforms
Platform Integration
Portal & Social
Our Solutions Expertise

5. Our Microsoft Practice

6. Duane Schafer, Business Intelligence Practice Director at
Perficient
• Nearly 20 years in technology consulting, BI architectures and
solution sales including hybrid cloud and DW appliance
architectures
• Responsible for strategy assessments including EIM, BI, MDM
and governance, solutions architecture and management of key
client engagements, as well as BI/DW architecture, analysis and
training within the Microsoft BI stack
Our Speaker

7.  Big Data Defined
 Analyzing Big Data with the Microsoft Platform
 Visualizing Big Data with Excel
 The Future of Big Data
Agenda

8. Original 3 V’s
Volume
Terabytes, Petabytes, Exabytes…
Velocity
How much data is created every minute?
Analyzing streaming data.
Variety
Is your phone watching you?
Different producers/types of data.
The MANY V’s of Big Data
Big Data Defined
…more 3 V’s
Veracity…
Biases and abnormalities in data.
Validity…
Data Quality
Volatility…
How long is it valid and how long should it
be stored?
How many V’s do we need?

9. Voracious
…ate terabytes of other dinosaurs
Velocity
…ate other dinosaurs really fast
Variety
…ate a lot of different dinosaurs
The VELOCIRAPTOR of data!
One V to rule them all
Let’s not get hung up on trying to identify the ‘V needs’
in our organization.

10. Data that could previously not be analyzed.
Big Data Working Definition
 Too much data
 Too expensive to store (relative to its perceived value)
 Appeared to have little/no value (e.g. web logs)
 Technology didn’t exist to capture/store the data
It’s not magic data, it’s just big data.

11. What are some working examples of Big Data?
Big Data Working Example
QA data from plants
+ weather data
= Insight into moisture related
issues in electronics at
plants around the world
Personal Fit data
+ location data
+ weather data
+ medication data
= Insight into patients that
are susceptible to readmitting
with depression symptoms

12. What about audio and video?
Big Data Working Example
Eye level cameras
+ RFID tags in clothing (that know what you have touched)
+ heart rate monitor on clothing racks
+ voice modulation sensors
= Insight into your emotional response
as you look at a piece of clothing, right
before a text based coupon is sent to your phone
http://gigaom.com/2014/01/24/why-video-is-the-next-big-thing-in-big-data/

13. What are some issues with analyzing big data?
Analyzing Big Data
 Managing large amounts of structured, semi-structured and
unstructured data
Structure and store it: Leave it unstructured:

14. Analyzing Big Data
What is a DW appliance?

15. Analyzing Big Data
What is Hadoop?
 Framework for storing and processing large amounts of data
 Uses clusters of commodity hardware
 Underlying technology was created by Google
 Has its own programming model to Map data then Reduce the result
sets down to the final answer. (Map/Reduce)

16. Analyzing Big Data
Why do we need specialized equipment and frameworks?
 Rows Inserted: 142 million (142,204,940)
 Time to insert: 2 minutes

17. Analyzing Big Data
What about retrieving the data?
 Rows Queried : Over half a billion (237,870,702) + (470,654,658)
 Time to query: Less than 1 second

18. What are some other issues with analyzing big data?
Analyzing Big Data
 Querying the structured and unstructured data together

19. Visualizing Big Data
How can we visualize Big Data?

20. Visualizing Big Data

21. Connecting to Big Data
 Native connection in Excel PowerPivot to connect to PDW

22. Connecting to Big Data
 Using Power Query
in Excel to connect
Hadoop, Azure or
Hadoop in Azure via
HDInsight
 Hybrid architectures
(i.e. cloud and on-
premise) are a viable
option

23. Visualizing Big Data
Placeholder for Power Map creation video

24. Visualizing Big Data
Placeholder for Power Map HC video

25. The Future of Big Data
 IoT is reshaping how companies build products
 Smart tags on cartons or pallets (Retail)
 Smart Grids, smart meters (Energy)
 Mobile apps to control your home (Consumer)
 Personal fit devices integrated with your EMR (Healthcare)
 In home health monitors (Consumer healthcare)
 RFID engine bolts (Manufacturing)
http://gizmodo.com/gms-rfid-engine-bolts-prevent-assembly-line-screw-
ups-1493922327
The Internet of Things – “M2M: Everything connected”
…30 billion IP-connected devices and sensors projected to be in operation by
2020, according to ABI Research

26. The Future of Big Data
Monitoring patients posture

27. The Future of Big Data
..or joint rotation

28. The Future of Big Data
..or strength

29. The Future of Big Data
..or heart rate

30. Real-world Big Data
Company Overview: High-end electronics manufacturer.
Company Goal: Build best in class global quality reporting platform.
Solution Proposal:
 QA analytics platform will integrate data from 18 sources
 Manufacturing feed of ~450 million records per month
 Social Sentiment feed from a data aggregator
 Plants, distributors and call centers world wide
 Hybrid platforms including Office 365 and SharePoint Online
 Big Data platform will include MPP architecture (PDW)
Business Value:
• Improved customer satisfaction
• Proactive mining of customer sentiment
• Reduction of capital expenditures due to cloud utilization

31. Connect with Perficient

32. Thank you for your time
and attention today.
Please visit us at Perficient.com