Bp presentation business intelligence and advanced data analytics september 25 2012 icpas, v2, 20130915

1ICPAS Metro Chapter Barrett Peterson September 25, 2013

BUSINESS INTELLIGENCE & ADVANCED ANALYTICS

The Search for Patterns, Waldo, and Black SwansBarrett Peterson, C.P.A.

ICPAS Chicago Metro Chapter, September 25, 2013


WHYBUSINESSINTELLIGENCE?

Information

Good Data

Good Analysis


BIG DATA AND ANALYTICS - WHY

PREDICTION and PATTERN

IDENTIFICATION


• Digitization Datafication • Correlation, more that causality• Reduced emphasis on sampling• “Messy” data usable for many

applications, but not all

BIG DATA AND ANALYTICS – CRITICAL ATTRIBUTES


• Reduced privacy and handling “private” data• Over reliance on, and over confidence in. data

and analysis• Currency – correlations can change over time• Predictions are hard to make, especially about

the future. - Niels Bohr [Not Yogi Berra].

BIG DATA AND ANALYTICS - RISKS


HISTORY AND BACKGROUND


• Computer based business intelligence systems is an idea that is middle aged – about 40 . Previously described as:

– Decision support systems [DSS]– Executive information systems

[EIS]– Management information systems

[MIS]

A LITTLE BACKGROUND

HISTORY

A trip down memory lane


• Internet Development– ARAPNET and others – 1960s– Internet Protocols – 1982, presumably by Al Gore

• IBM researcher Edgar Codd credited with development of relational data base theory in 1970.

• IBM’s Donald Chamberlin and Raymond Boyce develop structured query language [SQL] in the early 1970s to manipulate and retrieve data from IBM’s early relational data base management system

• World Wide Web and 1st web browser invented by Tim Berners-Lee in 1990 by combining the internet, hypertext mark-up language, and Uniform Resource Locator [URL] system. Became Nexus.

• Mosaic, designed by Marc Andressen became the first commercial web browser [Netscape].

• Development of big data enabling database designs and high speed processing during the last 15 years.

A LITTLE BACKGROUND

History

ImportantTechnologyInventions


• Development of the primary infrastructure– Database design– Processing and Storage Hardware– Server Development and Massively Parallel

Processing• Improved telecommunications speed• Hardware miniaturization, capacity, and speed

– Memory [RAM] capacity– Storage capacity and transfer speed– Bus speed– Video processing capacity and speed

• Increased hardware speed and capacity• Digital formats for sensors, cameras, RFID, and

other data collection sources• Mobile computing• “Cloud” capability exploits many of these

developments

A LITTLE BACKGROUND

History

DriversEnablingBI and AdvancedAnalytics


• Analytics• Business Intelligence• Knowledge Management• Content Management• Data Mining• Big Data• Data Integration• Datafication• Gameification• Blob [Binary Large Object]

A LITTLE BACKGROUND

TERMINOLOGY

A consultant’s collection ofconfusing names - a sampler


• CPU speed and power– Moore’s law– Multi-core chips– Solid State Memory

• Storage improvement and cost reduction– Greatly increased capacity – petabytes and

more; IBM’s first hard drive in 1958 was 3.75MB

– Greatly increased access/transfer speed– Greatly reduced cost

• Data collection from a wide range of devices

• Data communications – speed and volume• Database management techniques and

software• Application speed and power

A LITTLE BACKGROUND

DriversAndEnablers ofBigData


BUSINESS INTELLIGENCE AND ADVANCED

ANALYTICSDEFINED


A system comprised of “computer” hardware, storage hardware, operating system, database software, file systems, and application software to:

• Collect, “clean”, filter, “tag”, and integrate data

• Store data [hardware and software]• Provide knowledge management, analytical

, and presentation tools to translate data into decision useful information

TONIGHT’S CRITICAL DEFINITIONS

BusinessIntelligence


• Prehistoric – Mainframe Era– DSS, EIS, MIS– Hierarchical Master Data Files

• The Current Era [Primarily] – Business Intelligence– Primarily “structured” data [data that can

be represented in relational /dimensional tables or flat files], and BLOB [binary large object] formats

– Analysis of “known”, defined ,patterns– Presented in tables, simple charts, and

dashboards

• Emerging – Big Data and Advanced Analytics– to discover new, changing, or variable

patterns– A wide variety of “unstructured” digital

data formats added to “structured” data– Emerging storage structures– “Exploratory” analytics – Zoomable User Interface [ZUIs]– Solid State Memory and Solid-State Drives

TONIGHT’S CRITICAL DEFINITIONS

Business IntelligenceGenerations


THE HARDWARE AND SOFTWARE ELEMENTS OF

BUSINESS INTELLIGENCE


• Computer – CPU, Memory, and Operating System Software• Data Collection

– Master Data Management– Collection Processes and Devices– Data Cleansing Processes and Software

• Data Storage – Petabyte capable– Physical Devices and Storage Management Software– Data Management and Integration– Database Software Storage

• Relational – Traditional ERP/Transaction systems• Dimensional – Traditional Data Warehouse, including

associated BLOB• Distributed , Multiple Server, Storage Systems• NoSQL [Not Only SQL] Distributed Operational Stores• Apache Hadoop for Highly Parallel Processing and

certain Intensive Data Analytics Applications• DBMS System: Apache Cassandra; Amazon Dynamo• Middleware Software• High Speed Data Communications – Petaflop capable• Business Intelligence Application Software

– OLAP, Dashboard, and Chart Reports– Statistical Analysis and Presentation Tools

BUSINESS INTELLIGENCE ELEMENTS

PrincipalComponentsfor MaximumApplication


• Data Governance and Management– Uniform terminology– Uniform meaning– Uniform units of measure– Metadata

• Data Structure and Attributes– Structured - Relational/Dimensional– Unstructured– Rate of change, context, and other

attributes

• Data Collection and Preparation– Filtering, particularly “Big Data”, and

“tagging”– Extract, Transform, Load [ETL] for

“structured data

• Data Base File Systems• Data Storage and Retrieval

– Capacity– Access/Retrieval speed


DATAISSUES:THECORNERESTONE


• Metadata management– Business definitions , rules, sources– Technical attributes, such as type, scale,

transformation methods– Processing requirements – filtering, tagging,

ETL, aggregation, summarization• Data Definitions and data dictionaries

– Name– Unit(s) of measure

• Data collection and filtering or transforming requirements– Sources – internal and external– Context addition/filtering requirements

• Data integration specifications– Multiple platforms and applications– Mapping to intermediate data marts

• Privacy requirements– Personal Identifying data– Laws: HIPPA, Privacy act


MASTERDATAGOVERNANCEANDMANAGEMENT


• Data Structures– “Structured” Data , principally text

and numbers capable of incorporation in relational or dimensional tables

– “Unstructured” Data, not suitable for relational tables, many in newer data formats, including images

• Big Data Attributes– Both “structured” and “unstructured”– The four major “Vs” of big data

• Volume - huge• Velocity – fast changing, unlike

structured• Variety – format and content• Variability – lacks the consistency, and

perhaps precision, of structured data


DataStructuresandAttributesAre CriticalDrivers


• Content Structure – Traditional Financial Data – Numerical– Sign/Debit or Credit– Text Descriptions

• Database Management Structures– Legacy Systems: Hierarchical and Network– Transaction Systems: Relational

• Relations [Tables]. Attribute [columns], Instance [Rows]

• Rules: no duplicate rows; single value for attributes– Warehouse Systems: Dimensional

• Facts [data items, usually a dollar amount or unit count]

• Measures – dollar or count for facts• Dimensions – groups of hierarchies and descriptors of

various aspects or context for the facts/measures

– Big Data Databases Unstructured

• Microsoft Office and Similar File Formats • Photography and Art


Data StructuresITLingo


RELATIONALTABLEILLUSTRATION

“Tuple” is borrowed from mathematics and set theory and is used in database design to refer to the attributes of an “item” or “value” [row], the subject or title of the table. Value examples include customers, vendors, orders, product SKUs

Business Intelligence Elements



MATHCAN BECOMPLICATED


• Numbers and words/letters– Relational/Dimensional– Spreadsheets– Word Processing documents

• Sound and Music• Photo• Video• Video Game• CAD Design• Graphical

– PDF– Raster, Vector Graphics– Statistical Visualization

• Scientific• Signal• XML [Web based mark-up formats]• Geo-Location• Web Logs


DATAFILETYPECATEGORIES,ALMOST ENGLISH


• Collection– Company transaction/ERP systems– Purchased, such as Nielsen, IRI– Vendor supplied, such as bank

transactions– Sensor readings– Cameras– Mobile device traffic – Phones, Tablets

• Filtering– Adding context such as date or location– Eliminating “chatter” from high volume

data– Error correction

• Aggregation & Integration


DATACOLLECTIONAND PREPARATION


DATA COLLECTION - RFID

RFID tag RFID tag reader


DATA COLLECTION

Various sensors Surveillance Camera


DATA FILTERING AND CLEANSING IS IMPORTANT


• Relational – SQL • Dimensional – SQL, OLAP• Binary Large Object [BLOB] – binary data, most

often photos, video, audio, or PDF files• Massively Parallel-Processing [MPP]• Apache Hadoopp Distributed File System [HDFS] –

Java – Google File System [GFS], used solely by Google– Google Map Reduce

• Amazon S3 filesystem [used by Amazon]• NoSQL, MySQL• Storm• Resource Description Framework [RDF] Databases,

like Big Data


DATABASEFILE SYSTEMS



SELECT BIG DATADATABASEMANAGEMENTSYSTEMS

• Significant Originators– Google MapReduce– Google File System [GFS]– Amazon S3 filesystem

• Continuing Developments– Apache Software Foundation

• Apache Cassandra distributed database management system

• Apache Hadoop software framework to support data-intensive distributed applications

• Apache Hive, a data warehouse structure built on Hadoop

• Pig - high level programming language for creating MapReduce programs with Hadoop

– Significant to Technology Development• Facebook [uses MySQL as a DBMS system,

with Memcache]• Yahoo• LinkedIn [Project Voldemort]


• Convergence aspect of mainframes and servers

• Massively parallel , multiple server, distributed processing, in multiple data centers – grid computing

• Multi-core , high capacity, lower power consumption, CPUs

• Memory servers for RAM employing DRAM comprised of Fully Buffered Direct Inline Memory Modules [FBDIMM]

• Solid state flash drive storage• Greatly improved., and less

costly, hard drive storage


COMPUTERHARDWARECONSIDERATIONS


BI CONFIGURATION SIZES

Small – BI, but not Big

Data capable MediumLarge – IBM Sequoia At

Livermore Labs


• Data Storage Terminology– Memory – CPU direct connected, often called

RAM– Storage – not directly connected to the CPU

• Data Storage Device Types– Memory

• DRAM – based• Flash memory – based Solid-State Drives

[SSDs]– Storage

• Hard Disk Drives [HDD]• Optical Drives – CDs, DVDs

• Data Storage Systems– Direct Attached– Network Attached Storage [NAS]– Storage Area Network [SAN]– pNFS – Parallel Network file systems


DATASTORAGEHARDWARE/SOFTWARE


• Traditional Reporting Systems– ERP systems, including extract and

presentation tools– Downloads to Excel and similar programs for

analysis using functions and pivot tables• Presentation Tools• Specialized Analytics

– IBM InfoSphere BigInsights and InfoSphere Streams

– IBM Netezza– ParAccel Analytic Database– EMC Greenplum– SAS High Performance Computing– Information Builders WebFocus

• Exploratory Tools, like IBM SPSS [originally Statistical Package for the Social Sciences]– Data mining with specialized algorithms– Statistical analysis and related charting

software


BIAPPLICATIONSOFTWARE


• BI Reporting• Predictive Analytics• Data Exploration - correlation• Data Visualization - graphical• Instrumentation Analytics• Content Analytics• Web Analytics• Functional Applications• Industry Applications• Location Tracking


ADVANCEDANALYTICSAPPLICATIONTYPES



USESTATISTICALTECHNIQUESAPPROPRIATELY


ALGORITHMS CAN BE TREACHEROUS

DATAMODELSHAVE LIMITS


BI AND ADVANCE ANALYTICS OUTPUT ILLUSTRATIONS


EXAMPLES OF USES


• Sales and Operations Planning• Financial Instruments Modeling• Production Control• Online Retail• Economics and Policy Development• Agriculture/Farming• Weather Analysis/Prediction• Environmental Impact Assessment• Healthcare Diagnosis and Records Management• Genomic Analytics and Pharmaceutical and Medical

Research• Natural Resource Exploration• Research Physics• Road, Rail Traffic Management• Security Surveillance: Business, Government• Astronomy• Logistics Management, Including GPS Tracking• Electrical and Telecommunications Grids Mgmt• Social Media – Facebook, LinkedIn, Google+, Twitter,

YouTube, Pinterest• TV shows – Star Trek, Person of Interest• Cloud Services – computing, Storage• Credit Scoring

SELECTED EXAMPLES OF USES


• Retail– Amazon– Dell– Delta Sonic Car Washes

• Data Services– IBM– Google– Amazon

• Financial Services• Manufacturing

– McCain Foods – Frozen foods– Boeing

• Transportation and Logistics– Logistics – UPS, FedEx– Rail – UP, CSX, TTX– Air – United, AMR, Southwest

• Social Media– LinkedIn– Facebook

• Government– NSA PRISM and Other tools– CIA – Palantir Software

• Medicine and Health– Center for Disease Control (CDC)– J. Craig Venter Institute

• Science– Livermore Labs

SELECTEDUSERS


• Technical Elements– Direct on-line access– Amazon specialized “Big Data”

database – Distributed and extremely large

data centers– Highly automated, high technology

warehouses– High supplier [vendors] integration

• User Benefits– Favorable prices– Suggested associated purchases– Individual interest advertising

SELECTED EXAMPLES OF USE

AMAZON


• Technical Elements– Web driven order entry and

custom purchase configuration– Tracking of sales correspondence

with promotional offers– Supplier re-order integration

• User Benefits– Ability to customize purchase– Reasonable cost– Prompt delivery


DELL


• Technical components– Shared component and assembly

designs– More detailed quality

specifications and product tolerances

– Control of assembly schedule– “Real time” exchange of technical

information– Dissemination of best practices

• Customer benefits– Faster deliveries– Increased product quality– Reduced defects


BOEING


• Techniques employed– Collect cellphone and GPS signals,

traffic cameras, and roadside sensors– Identify accidents, traffic jams, and

road damage– Emergency vehicles can be dispatched– Update traffic websites– Sends messages to drivers’ GPS

devices and cellphones– Uses supercomputers running Intrix

application• Benefits

– Eliminates traffic congestion faster– More timely relief for accident victims– Facilitate road paving scheduling


NEW JERSEYDEPARTMENTOFTRANSPORTATION


• Technical Elements– General LinkedIn Structure

• Personal Profile• Individual Connections• Groups• Company and Other Searches• Endorsements• Attached application partners

– Slideshare, Owned by LinkedIn• User Benefits

– Networking with professional contacts– Personal branding capabilities– Business Development– Job Search enhancement


LINKEDIN

http://www.linkedin.com/


LINKEDIN PROFILE PAGE SAMPLE


Facebook Page Sample


TRENDS• More, bigger, faster – big data gets

bigger• Cloud services continue to expand• Mobile computing expands• Hadoop becomes more common• Interactive data visualization will expand• Social media type platforms will

increase their prominence• Analytics skills demands will increase• Privacy Issues will become prominent


RESOURCES• Books

• Competing on Analytics, Davenport & Harris• Analytics at Work, Davenport, Harris, & Morison• The Data Asset, Fisher• Data Strategy, Adelman, Moss, Abai• Big Data, Cukier, Mayer-Schonberger

• Websites• The Data Warehouse Institute – tdwi.org• IBM data analytics: www.ibm.com, smarter planet

http://www.ibm.com/


SUMMARYWHY USE BI AND ADVANCED ANALYTICS

INSIGHTFROMDATA