BI Basics Finding Return on Your Data

Transcription

1 BI Basics Finding Return on Your Data

2 Allen Smith Consultant Bio Skills Business Intelligence Data Warehousing Architecture SQL Server SharePoint Teaching Industries Healthcare Insurance Energy Recent Projects: Sonic Drive In s GE Oil & Gas Southwestern Energy McJunkin Redman CerKficaKons/Tests: MCTS: SQL Server 2008 Business Intelligence Development and Maintenance

3 Terminology Data as an Asset Value Costs Time Value of Data Role of: Business Intelligence (BI) Data warehousing Big data How To Get There Lifecycle Team Skills MicrosoX BI stack Agenda

4 Image: epicgraphic.com

5 This Session Will: Be Introductory Aimed at decision makers Discuss when to use which technology Will Not: Be Technical Be showing lines of code Require programming

6

7 What is a Data Warehouse? Turning Data into Information Warehoused Central Database A large store of data accumulated from a wide range of sources within a company and used to guide management decisions. - Wikipedia HR Your data Finance Sales Companies have data in: l Different platforms l Different systems l Different Formats l Different Locations l Different Currencies Call (405) G Waterwood Parkway I-1, Edmond, OK 73034

8 What is a Data Mart? Warehoused Central Database Sales Dependent Data Mart Subject Area Data Finance HR Sales Sales Data Marts: - Often hold only one subject area - May be summarized - May be from a given set of source systems - Built with dimensional model Call (405) G Waterwood Parkway I-1, Edmond, OK 73034

9 What is a Data Mart? Sales Independent Data Mart Subject Area Data Finance HR Sales Sales Data Marts: - Often hold only one subject area - May be summarized - May be from a given set of source systems - Built with dimensional model Call (405) G Waterwood Parkway I-1, Edmond, OK 73034

10 What is Big Data? An all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications. Wikipedia Very large, loosely structured data that defies traditional storage Hadoop Illuminated Volume - Velocity - Variety Company data Other data Web logs Weather Stocks Production Sensors Apps Social Wearables Call (405) G Waterwood Parkway I-1, Edmond, OK 73034

11 Maturity of Big Data Technology Source: Gartner s 2014 Hype Cycle for Emerging Technologies hrp://

12 What is Data Science? Image: Creative Commons by Drew Conway

13 What is Business Intelligence? Turning Data into Information Business Intelligence Dashboards A broad category of applications and technologies for: l Gathering l Storing l Analyzing l Sharing l Providing access to data to help enterprise users make better business decisions Scorecards - Gartner Call (405) G Waterwood Parkway I-1, Edmond, OK 73034

14 How does BI work with a Data Warehouse? Business Intelligence Dashboards /Scorecards Turning Data into Information Business Intelligence Services: Microsoft SQL Server Microsoft SharePoint Power BI Warehoused Central database Data Warehouse Services: Extract, Transformationm, Loading Your data HR Finance Sales Source Stems Services: Database Administration Server Tuning Call (405) G Waterwood Parkway I-1, Edmond, OK 73034

15 How does BI work with Data Sources? Business Intelligence Dashboards, Scorecards and Analytic Tools Direct Source Access DW Access Warehoused Central database ETL and Cube Processes SSAS Cubes (OLAP) Cube Access SSAS Tabular Big Data Your data HR Finance Sales Internet of Stuff Call (405) G Waterwood Parkway I-1, Edmond, OK 73034

16 How does Power BI fit in? Business Intelligence Dashboards, Scorecards and Analytic Tools Direct Source Access Warehoused Central database ETL and Cube Processes SSAS Cubes (OLAP) SSAS Tabular PowerPivot Your data HR Finance Sales Internet of Stuff Call (405) G Waterwood Parkway I-1, Edmond, OK 73034

17 Data Has Value

18 Who uses data? Monitor ExecuKves Managers Supervisors Dashboards Summarized InteracKve Analyze Cubes and Reports Needs change oxen Dynamic Analysis Go Summary to details Details Reports Standardized Detailed Can be Scheduled Can be filtered Drill Up Drill Down

19 Types of Data Used? Execs Summary External Data (Markets, Suppliers, Weather, CPI, etc.) Managers Supervisors Details Internal Company Data

20 Data Has Costs

21 Types of Data Costs Storage AcquisiKon ETL Extract Transform & Load Cleansing Data Quality IniKaKves Data Governance Archiving GeneraKng Data

22 Story of a Bank Cost of Acquiring Data Signet Bank Default Rates 2.9% Interest Rate Terms IniKal Default Rates Went to 6% Profitability Credit RaKng Demographics Signet started treakng data as an asset to be invested in by varying rates and terms. Eventually Profits Rose In 2000, the bank was carrying out 45,000 such tests Their consumer credit division spun off as Source: Data Science for Business by Provost & FawceR

23 Signet spun off What s in your data?

24 Time Value vs. Time Costs Typical Business Value/Cost of Data second 1 minute 1 hour 1 shix 1 day 1 week 1 month 1 year n years Data Latency

25 Time Value vs. Time Costs 100 Non- Typical Value/Cost of Data Large Hadron Collider 150 million sensors 600 million par4cle collisions Interested in 100 pieces of data from each collision. Accumulate 30 petabytes annually second 1 minute 1 hour 1 shix 1 day 1 week 1 month 1 year n years Data Latency Value Cost

26 Time Value vs. Time Costs Your Business Value Value/Cost of Data ? second 1 minute 1 hour 1 shix 1 day 1 week 1 month 1 year n years Data Latency

27 Which Path? Data Warehouse An architecture You know the QuesKon. I need Sales, by quarter & territory. Structured Carefully constructed Single version of the truth Compliance and reporkng, such as Sarbanes- Oxley. Existed 25 year Big Data A technology You know the Answer What factors influenced Sales? Unstructured or loosely structured Data quickly loaded Answer depends Good luck with your audit. DefiniKon conknues to change

28 Real Example Data Warehouse + BI Global Oilfield Supplier AccounKng Data & Inventory Data In Global Regions Need Near- Real Time ReporKng & Analysis

29 Example Big Data IBM Study of ackvity on Wikipedia

30 How to Get There

31 DW/BI Lifecycle Planning Project Planning Why do it? Business Value of Data? Cost of Data? Project Charter Analysis & Design Requirements Architecture Infrastructure Development Extract Transform Load (SSIS) MulK- Dimensional Cubes (SSAS) Report Delivery (SSRS) Deployment Training AdopKon Feedback Skills: Project Manager Business Analyst Architect DBA Developers: Database ETL AnalyKcs ReporKng SharePoint QA/TesKng Trainers BI Support

32 Project Planning Phase Who is involved: CIO/IT Manager Business Sponsor Business Analyst Processes: Why do it? Business Value? Cost savings? Added Value? Project Management BudgeKng ROI/Payback Period Time ExpectaKons

33 Analysis & Design Phase Who is involved: Project Manager Business Analyst Subject MaRer Experts Business Users BI/DW Architect Processes: Review Source Systems Evaluate Infrastructure Gather Requirements New Analysis ExisKng ReporKng Evaluate Gaps: ExisKng vs. Desired CommunicaKon/ DocumentaKon

34 Development Phase Who is involved: Project Manager ETL Developer Database Administrator Network Administrator MulK Dimensional Developer Report & Analysis Developer Quality Assurance/TesKng Processes: Infrastructure Changes Stage Data Cleanse & Transform Build Cube Structures Build presentakon layers Unit TesKng & Data ValidaKon CommunicaKons!

35 ImplementaKon Phase Who is involved: Project Manager BI/DW Trainer Business Analyst Business Sponsor Subject MaRer Experts (Users) Rollout Processes CommunicaKons User Data ValidaKon User Training Support Procedures

36 BI Tools

37 2014 Gartner Magic Quadrant hrp:// data/big- data- analykcs/ gartner- bi- magic- quadrant- winners- and- losers/d/d- id/

38 2015 Gartner Magic Quadrant hrp:// data/big- data- analykcs/ gartner- bi- magic- quadrant- winners- and- losers/d/d- id/

39 BI Tool Comparison Build & Develop Turn-Key & Self Service Enterprise BI+ Cross Departmental The BI Market MISO Departmental BI *MISO: Microsoft, IBM, SAP, Oracle Slide Courtesy of Pyramid AnalyKcs (Silver Sponsor today)

40 MicrosoX BI Stack Self- Service BI in Excel 2013 Includes Power Query, Power Pivot, Power View, Power Map Power BI in Office 365 Cloud based self service BI. SharePoint 2013 SQL Server 2014 IntegraKon Services Analysis Services Tabular Analysis Services MulK- Dimension ReporKng Services

41 Self Serve - Power BI Image from Technet Tutorial

42 Machine Learning Free ebook! MicrosoX Azure EssenKals: Azure Machine Learning hrp://bit.ly/mlebook

43 Skills for BI Teams

44 DW/BI Skills Employee This is an example based on a resume cube I built in Cognos on my first project around 2000.

45 DW/BI Team Skills

46 Big Data Team Skills

47 Rate Your Skills and Goals

48 Where does your company want to go? Where are you now? Data goals? Specific Measurable Achievable Relevant Time Bound

49 Terminology Data as an Asset Value Costs Time Value of Data Role of: Business Intelligence (BI) Data warehousing Big data How To Get There Lifecycle Team Skills MicrosoX BI stack Summary

50 Please complete Evaluations Capital One and its logo are trademarks of Capital One.

51 Session Outline This session will you chart the waters through the sea of Business Intelligence (BI) opkons. IT departments need tools to turn data into useful informakon for users. Instead of ROI, this session talks about Return on Data' for IT Managers. In this session we look at the roles of data warehousing, big data and business intelligence systems. The MicrosoX BI stack has evolved from tradikonal OLAP cubes to include tabular models, PowerBI, integrakon with big data, reporkng tools and full integrakon with SharePoint. This session is non- technical.