Data Mining & Advanced Analytics

Transcription

1 Data Mining & Advanced Analytics Expandiend el alcance de sus mdels predictivs Marian Urman Sales Engineering Manager 1

2 Current Situatin 2

3 Users f Advanced Analytics Data Mining Users BI Users Tw types f users: Builders f analytical mdels Users f analytical mdels In an enterprise, there tends t be just a handful f builders In thery, there shuld be many, many mre users 3

4 Mdel Deplyment Challenges Hw d yu get a mdel int the hands f the persn wh will be using it? Hw d yu ensure the data used t scre the mdel is cnsistent with the data used t build that mdel? Is the data the same? Is cnsistency autmatically enfrced? Mdels evlve ver time hw can the mdel be updated withut causing a lt f re-wrk? Answer: Cmbine Analytics and Business Intelligence 4

5 MicrStrategy Data Mining Visin 5

6 Data Mining Services Visin Leverage and Extend the BI Infrastructure Deply Advanced Analytics t Business Users Relevant, Practive & Prvide a Cmpetitive Edge Leverage and Extend the BI Infrastructure 50-75% f data mining is preparing a gd dataset Enterprises using MicrStrategy have business rules & definitins that can be easily leveraged fr Advanced Analysis. Users can fcus n analysis, rather than cllecting, integrating and mdeling data frm disparate systems. Deply Advanced Analytics t Business Users Prvide deep, rich analytics that hide the cmplexity f the underlying statistics. Users dn t have adapt t new applicatins r interfaces, the applicatin delivers sphisticated results t the tuch pints already used by MicrStrategy users: Web, Dashbards, Excel, PwerPint, Alerts, Mbile, etc. Relevant, Practive & Prvide a Cmpetitive Edge Organizatins gain a cmpetitive advantage when they can increase the sphisticatin f their applicatins t g beynd basic histrical reprting t deliver strategic practive insight. Specifically including predictive and mdel driven metrics, as well as predictive analytic alerts. 6

7 Analytical Cmpetitrs Sprts: Plitics: The A s & Mneyball MLB Sccer, NFL, Everywhere Vter Vault & 2004 UK Parliament, Dems, Everyne Financial Services: Fraud Detectin Credit Targeting, Churn, Must Have Pharmaceuticals: Prescriptins Wh Wh Why, Requirement Entertainment: Mtin Pictures DVDs Netflix, Gaming, Prerequisite In Every Industry: Healthcare, Pharma, Retailers, Shipping, Telc, Travel, Key Challenge: Mst cmpanies have the data; It s what t d with it! 7

8 Advanced Analytics Frecasting: Sales, Csts, Prfits, Inventry, Classificatin: Churn, Campaign management, Risk management Assciatin: Market Basket Analysis, Clustering: Custmer Segmentatin, Prduct Segmentatin, 8

9 Cmbining Analytics & Business Intelligence 9

10 The Full Spectrum f Business Analytics in One Seamlessly Integrated Platfrm Predictive Analytics ANALYZE OLAP Analysis Data Discvery MONITOR Enterprise Reprts Dashbards ACT Alerts Transactins 10

11 Building and Scring : Hw Data Mining Can Be Integrated Int BI Applicatins Create Dataset Detailed/Summary Clean/Sample Select Variables Explre/Transfrm Discver Patterns Develp Mdel Train Mdel Validate Mdel Deply Mdel Scre Recrds Present Results Scring Building a predictive mdel is ften an iterative prcess that requires knwledge f mining algrithms Scring is the prcess f applying the mdel t new data All business users can take advantage f these scres Predictive analytics can be presented just like descriptive analytics Screcards, Dashbards, Persnalized, Slice-and-Dice BI PhD is nt required! 11

12 The Different Ways t Perfrm Scring Three Appraches: 1. Data Mining Tl des the scring 2. Database des the scring 3. BI des the scring MicrStrategy is the nly BI Vendr t supprt them all! Business Intelligence Database Technlgy Data Mining Tl f(x)= BiXi 12

13 Example: Screcard cmbining descriptive & predictive 13

14 Example: Screcard cmbining descriptive & predictive 14

15 Example: Screcard cmbining descriptive & predictive 15

16 Advanced + Agile Analytics Experiment with insightful visualizatins t find patterns, trends, and relatinships 16

17 Dem 17

18 Summary 18

19 Summary Turn yur enterprise data int a cmpetitive advantage End users can simultaneusly use descriptive and predictive analysis alng side traditinal BI capabilities Prmpting Slice and Dice Threshlds and Alerts New Metrics based n predictive metrics Deliver cntent with predictive metrics via , mbile, etc.. Screcards and Dashbards with predictive metrics Be an Analytical Cmpetitr by using BI t Out-Think & Out-Execute the rest f yur industry 19

20 MicrStrategy prduct lgs can be fund Gracias!