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  • 1 1

    The Analysts Perspective: Ad-hoc Analysis with Microsoft PowerPivot and Office 2010 Excel

    Rafal Lukawiecki Strategic Consultant, Project Botticelli Ltd rafal@projectbotticelli.com

  • 2 2

    Objectives

    Introduce powerful self-service analysis with PowerPivot

    Show use of Microsoft SQL Server 2008 Analysis Services Data Mining

    The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Microsoft makes no warranties, express, implied or statutory, as to the information in this presentation. Portions 2010 Project Botticelli Ltd & entire material 2010 Microsoft Corp. Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Microsoft Copyright ownerships. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE.

    This seminar is based on a number of sources including a few dozen of Microsoft-owned presentations, used with permission. Thank you to Chris Dial, Tara Seppa, Aydin Gencler, Ivan Kosyakov, Bryan Bredehoeft, Marin Bezic, and Donald Farmer with his entire team for all the support.

  • 3

    PowerPivot

  • 4 4

    Massive Data Volumes With a few mouse clicks, a user can create and publish intuitive and interactive self-service analysis solutions

  • 6

    1. Analysing Massive Data Volumes Using PowerPivot

    2. Slicer as a Better Filter

  • 7 7

    Published

    Reports

    SharePoint

    Farm

    Report-Based

    Data Feeds

    OLTP and OLAP Data Sources

    Reporting Services as a Data Source

  • 8

    1. Report as a Data Source for Analysis

  • 9 9

    Share and Collaborate

    With SharePoint:

    Publish your PowerPivots as Web applications for your team

    Schedule data refreshes to keep your analysis up-to-date

    Manage security just like a document

  • 10 10

    PowerPivot Infrastructure Overview

    SharePoint Farm

    WFE

    App Servers

    Content dBs

    NLB

    Excel, RB, PerfPoint

    Power User

    Data Sources

    Excel Services

    PowerPivot Mid-Tier

    AS Engine

    Browser

    Standard User

    PowerPivot Add-In

  • 11 11

    PowerPivot Infrastructure: Excel

    SharePoint Farm

    WFE

    App Servers

    Content dBs

    NLB

    Excel Services

    Gemini Mid-Tier

    Gemini Engine

    Browser

    Standard User

    Excel, RB, PerfPoint

    Power User

    Data Sources

    Use of IMBI Engine: In-Memory Column-

    Based store

    Once data is imported, all calculations

    are performed on client

    Excel now has its own local SSAS

    engine

    Added Excel power functions for Gemini

    called DAX (Data Analysis eXpressions)

    Use of new compression algorithm to

    significantly compress the data ~ 10:1

    Added slicer functionality: not just for UI

    but for smoother SharePoint integration

    PowerPivot Add-In

  • 12 12

    Excel, RB, PerfPoint

    Power User

    Data Sources

    Browser

    Standard User

    SharePoint Farm

    WFE

    App Servers

    Content dBs

    NLB

    Excel Services

    PowerPivot Mid-Tier

    AS Engine

    PowerPivot SharePoint Integration: ECS Viewing

    Excel Web Access

  • 13 13

    Excel, RB, PerfPoint

    Power User

    Data Sources

    Browser

    Standard User

    SharePoint Farm

    WFE

    App Servers

    Content dBs

    NLB

    Excel Services

    PowerPivot Mid-Tier

    AS Engine

    PowerPivot SharePoint Integration: Server Action

    Excel Web Access

  • 14 14

    Data Analysis Expressions (DAX)

    Simple Excel-style formulas

    Define new fields in the PivotTable field list

    Enable Excel users to perform powerful data analysis using the skills they already have

    Has elements of MDX but does not replace MDX

  • 15 15

    Data Analysis Expressions (DAX)

    No notion of addressing individual cells or ranges

    DAX functions refer to columns in the data

    Sample DAX expression Means: = [First Name] & & [Last Name] String concatenation just like Excel

    =SUM(Sales[Amount]) SUM function takes a column name

    instead of a range of cells

    =RELATED (Product[Cost]) new RELATED function follows

    relationship between tables

  • 16 16

    DAX Aggregation Functions

    DAX implements aggregation functions from Excel including SUM, AVERAGE, MIN, MAX, COUNT, but instead of taking multiple arguments (a list of ranges,) they take a reference to a column

    DAX also adds some new aggregation functions which aggregate any expression over the rows of a table

    SUMX (Table, Expression)

    AVERAGEX (Table, Expression)

    COUNTAX (Table, Expression)

    MINX (Table, Expression)

    MAXX (Table, Expression)

    16

  • 17 17

    More than 80 Excel Functions in DAX Date and Time Information Math and Trig Statistical Text DATE ISBLANK ABS AVERAGE CONCATENATE DATEVALUE ISERROR CEILING, ISO.CEILING AVERAGEA EXACT DAY ISLOGICAL EXP COUNT FIND EDATE ISNONTEXT FACT COUNTA FIXED EOMONTH ISNUMBER FLOOR COUNTBLANK LEFT HOUR ISTEXT INT MAX LEN MINUTE LN MAXA LOWER

    MONTH Logical LOG MIN MID NOW AND LOG10 MINA REPLACE SECOND IF MOD REPT TIME IFERROR MROUND RIGHT TIMEVALUE NOT PI SEARCH TODAY OR POWER SUBSTITUTE WEEKDAY FALSE QUOTIENT TRIM WEEKNUM TRUE RAND UPPER YEAR RANDBETWEEN VALUE

    YEARFRAC ROUND

    ROUNDDOWN ROUNDUP SIGN SQRT SUM SUMSQ TRUNC

  • 18 18

    Example: Functions over a Time Period TotalMTD (Expression, Date_Column [, SetFilter])

    TotalQTD (Expression, Date_Column [, SetFilter])

    TotalYTD (Expression, Date_Column [, SetFilter] [,YE_Date])

    OpeningBalanceMonth (Expression, Date_Column [,SetFilter])

    OpeningBalanceQuarter (Expression, Date_Column [,SetFilter])

    OpeningBalanceYear (Expression, Date_Column [,SetFilter] [,YE_Date])

    ClosingBalanceMonth (Expression, Date_Column [,SetFilter])

    ClosingBalanceQuarter (Expression, Date_Column [,SetFilter])

    ClosingBalanceYear (Expression, Date_Column [,SetFilter] [,YE_Date])

  • 19

    1. Simplicity of DAX to Relate and Analyse Data

  • 20

    Data Mining

  • 21 21

    What does Data Mining Do?

    Explores Your Data

    Finds Patterns

    Performs Predictions

  • 22 22

    Typical Uses

    Data Mining

    Seek Profitable Customers

    Understand Customer

    Needs

    Anticipate Customer

    Churn

    Predict Sales &

    Inventory

    Build Effective

    Marketing Campaigns

    Detect and Prevent Fraud

    Correct Data During

    ETL

  • 23 23

    Analysis Services Server

    Mining Model

    Data Mining Algorithm Data Source

    Server Mining Architecture

    Excel/Visio/SSRS/Your App

    OLE DB/ADOMD/XMLA

    Deploy

    BIDS Excel Visio SSMS

    App Data

  • 24 24

    Mining Model Mining Model Mining Model

    Mining Process

    DM Engine DM Engine

    Training data

    Data to be

    predicted Mining Model

    With

    predictions

  • 25

    Microsoft Decision Trees

    Use for: Classification: churn and risk analysis

    Regression: predict profit or income

    Association analysis based on multiple predictable variable

    Builds one tree for each predictable attribute

    Fast

  • 26

    1. Decision Trees for Classification of Customers Buying Potential

  • 27 27

    Profitability and Risk

    Finding what makes a customer profitable is also classification or regression

    Typically solved with: Decision Trees (Regression), Linear Regression,

    and Neural Networks or Logistic Regression

    Often used for prediction Important to predict probability of the predicted, or expected profit

    Risk scoring Logistic Regression and Neural Networks

  • 28 28

    Neural Network & Logistic Regression

    Applied to Classification

    Regression

    Great for finding complicated relationship among attributes

    Difficult to interpret results

    Gradient Descent method

    LR is NNet with no hidden layers

    Age Education Sex Income

    Input

    Layer

    Hidden

    Layers

    Output

    Layer Loyalty

  • 29

    1. Neural Networks for Predicting Lending Risk

  • 30

    Time Series

    Uses: Forecast sales

    Inventory prediction

    Web hits prediction

    Stock value estimation

    Regression trees with extras

  • 31

    1. Foerecasting Sales with Time Series

  • 32 32

    Data Mining Techniques Alg

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