Big Data: Using Microsoft Enterprise Information Solutions to make Smarter Business Decisions

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Technology

Published on March 14, 2014

Author: perficientinc

Source: slideshare.net

Description

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.

Big Data and the Intelligent Enterprise facebook.com/perficient twitter.com/Perficientlinkedin.com/company/perficient Presented by the Microsoft BI Practice

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

• 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

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

Our Microsoft Practice

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

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

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?

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.

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.

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

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/

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:

Analyzing Big Data What is a DW appliance?

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)

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

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

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

Visualizing Big Data How can we visualize Big Data?

Visualizing Big Data

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

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

Visualizing Big Data Placeholder for Power Map creation video

Visualizing Big Data Placeholder for Power Map HC video

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

The Future of Big Data Monitoring patients posture

The Future of Big Data ..or joint rotation

The Future of Big Data ..or strength

The Future of Big Data ..or heart rate

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

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