1 1 the analyst's perspective: advanced bi with powerpivot dax, sharepoint dashboards, and sql data...

Download 1 1 The Analyst's Perspective: Advanced BI with PowerPivot DAX, SharePoint Dashboards, and SQL Data Mining Rafal Lukawiecki Strategic Consultant, Project

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  • Slide 1
  • 1 1 The Analyst's Perspective: Advanced BI with PowerPivot DAX, SharePoint Dashboards, and SQL Data Mining Rafal Lukawiecki Strategic Consultant, Project Botticelli Ltd rafal@projectbotticelli.com
  • Slide 2
  • 2 2 Objectives Introduce more advanced BI analytics from Microsoft Discuss using SharePoint 2010 as a BI Dashboard environment 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.
  • Slide 3
  • 3 PowerPivot on SharePoint 2010 Manageability
  • Slide 4
  • 4 4 PowerPivot for SharePoint 2010 Managed Self-Service Business Intelligence Collaborative, shared gallery of PowerPivots IT Pro management Lifecycle & Workflow Server Resource Management
  • Slide 5
  • 5 5 Share Insights Common view of organizational performance
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  • 6 1. PowerPivot for SharePoint: Uploading Documents to Server 2. Galleries
  • Slide 7
  • 7 7 Managing the BI Environment User driven application administration and monitoring Manage and facilitate access to secure organizational data
  • Slide 8
  • 8 1. PowerPivot Management Dashboard 2. Anticipating a self-created BI that can become an organisational concern
  • Slide 9
  • 9 9 PowerPivot Client Architecture
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  • 10 PowerPivot Part of SharePoint
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  • 11 PowerPivot Server Architecture
  • Slide 12
  • 12 Excel, RB, PerfPoint Power User Data Sources Browser Standard User SharePoint Farm WFE App Servers Content dBs Excel Services PowerPivot System Service AS Engine PowerPivot SharePoint Integration Excel Web Access
  • Slide 13
  • 13 PowerPivot DAX
  • Slide 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
  • Slide 15
  • 15 Data Analysis Expressions (DAX) No notion of addressing individual cells or ranges DAX functions refer to columns in the data Sample DAX expressionMeans: = [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
  • Slide 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)
  • Slide 17
  • 17 More than 80 Excel Functions in DAX Date and TimeInformationMath and TrigStatisticalText DATEISBLANKABSAVERAGECONCATENATE DATEVALUEISERRORCEILING, ISO.CEILINGAVERAGEAEXACT DAYISLOGICALEXPCOUNTFIND EDATEISNONTEXTFACTCOUNTAFIXED EOMONTHISNUMBERFLOORCOUNTBLANKLEFT HOURISTEXTINTMAXLEN MINUTELNMAXALOWER MONTH Logical LOGMINMID NOWANDLOG10MINAREPLACE SECONDIFMODREPT TIMEIFERRORMROUNDRIGHT TIMEVALUENOTPISEARCH TODAYORPOWERSUBSTITUTE WEEKDAYFALSEQUOTIENTTRIM WEEKNUMTRUERANDUPPER YEARRANDBETWEENVALUE YEARFRACROUND ROUNDDOWN ROUNDUP SIGN SQRT SUM SUMSQ TRUNC
  • Slide 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])
  • Slide 19
  • 19 1. DAX for Creating Calculated Measures 2. DAX for Creating New Columns
  • Slide 20
  • 20 SharePoint 2010 BI Dashboards: PerformancePoint Services
  • Slide 21
  • 21 PPS in SharePoint 2010 PerformancePoint Services in SharePoint 2010 improve over PerformancePoint Server 2007: SharePoint does all security, management, backup, respository of dashboard Decomposition Tree KPI Details Scorecard drilldown, dynamic hierarchies, calculated KPIs Dynamic, up-to-date filters for time intelligence SharePoint Dashboard Designer is smoother Better accessibility Analytic charts with value filtering and server-based conditional formatting
  • Slide 22
  • 22 Monitoring with PPS Business users can build performance dashboards easily
  • Slide 23
  • 23 Analytics with PPS Integration of KPIs and analytics Multidimensional slice and dice, drill-across, drill-to-detail, root-cause analysis, prediction and centralized business logic definitions No coding
  • Slide 24
  • 24 Reporting and Consolidation in PPS Combine operational and financial data into one report No need to reconsolidate manually Dynamic and standard reports Consistent live reports published from Excel to Reporting Services and SharePoint
  • Slide 25
  • 25 Dashboard Designer Details pane Workspace Browser Workspace
  • Slide 26
  • 26 Developing a Dashboard Choose a dashboard layout Assign elements to a dashboard zone Add filters Preview the dashboard Deploy to SharePoint
  • Slide 27
  • 27 1. Building a Dashboard, Scorecard, and a KPI Using SharePoint Server PerformancePoint Services
  • Slide 28
  • 28 Visualising BI with Microsoft Visio and SharePoint 2010
  • Slide 29
  • 29 Two Trends that Lead to The Messy Diagram
  • Slide 30
  • 30 Data Visualization Fault Analysis Tree Status Indicators Color By Value Text Callouts Data Bars
  • Slide 31
  • 31 Data Visualization Manufacturing Specialized Shapes
  • Slide 32
  • 32 Strategy Maps Visualize PPS Scorecard data in context
  • Slide 33
  • 33 Data Mining with SQL Server
  • Slide 34
  • 34 What does Data Mining Do? Explores Your Data Finds Patterns Performs Predictions
  • Slide 35
  • 35 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
  • Slide 36
  • 36 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
  • Slide 37
  • 37 Mining Model Mining Process DM Engine Training data Data to be predicted Mining Model With predictions
  • Slide 38
  • 38 Data Mining Techniques AlgorithmDescription Decision Trees Finds the odds of an outcome based on values in a training set Association Rules Identifies relationships between cases Clustering Classifies cases into distinctive groups based on any attribute sets Nave Bayes Clearly shows the differences in a particular variable for various data elements Sequence Clustering Groups or clusters data based on a sequence of previous events Time Series Analyzes and forecasts time-based data combining the powerof ARTXP (developed by Microsoft Research) for short-term predictionswith ARIMA (in SQL 2008) for long-term accuracy. Neural Nets Seeks to uncover non-intuitive relationships in data Linear Regression Determines the relationship between columns in order to predict an outcome Logistic Regression Determines the relationship between columns in order to evaluate

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