Add Sophisticated Analytics to Your Repertoire with Data Mining, Advanced Analytics and R

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Augusta Skinner
6 years ago
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1 Add Sophisticated Analytics to Your Repertoire with Data Mining, Advanced Analytics and R

Why Advanced Analytics Companies that inject big data and analytics into their operations show productivity rates and profitability that are 5% to 6% higher than

2 Why Advanced Analytics Companies that inject big data and analytics into their operations show productivity rates and profitability that are 5% to 6% higher than those of their peers.

3 Stuck in Aspiration? While many companies escalate to production with Advanced Analytics, many more are stuck in an aspirational state.

4 MicroStrategy Bridges the Gap

5 Advanced Analytics Spectrum Descriptive Diagnostic Predictive Prescriptive What Happened? Why did it happen? What will happen? What should happen? Analytical functions Descriptive models Predictive models Optimization Math, OLAP & Financial, Operators & Statistics Cluster & Association, Link & Factor Analysis Regression & Time Series, Forecasting & Classification Linear Programming Simulation

6 MicroStrategy Analytic Map MicroStrategy Functions Library MicroStrategy Native Data Mining Features R Analytics

7 MicroStrategy Functions Library 300+ analytical functions Statistical Reporting Date and Time Math Functions Data Mining Permut FTest HeteroscedasticTTest HomoscedasticTTest MeanTTest PairedTTest VarTest Forecast ForecastV Growth GrowthV Intercept Pearson RSquare Slope SteYX Trend TrendV Beta CDF Binomial PDF/CDF ChiSquare CDF/Inv ChiSquareTest CritBinomial Exponential PDF/CDF F CDF/Inv Fisher PDF/Inv Gamma PDF/CDF/Inv Hypergeometric PDF Lognormal CDF/Inv NegativeBinomial PDF Normal PDF/CDF/Inv Poisson PDF StandardNormal CDF/Inv T CDF/Inv Weibull PDF/CDF AvgDev Confidence Correlation Covariance Kurtosis Skew Standardize Average Mean Count Sum Maximum Minimum Median Mode Product Rank Percentile N -Tile N-tile by Step N-tile by Value N-tile by Step and Value Add Days Add Months Current Date Current Date & Time Current Time Day of Month Day of Week Day of Year Days Between Month Start Date Month End Date Months Between Year Start Date Year End Date Absolute A-cosine Hyp A-cos A-sine Hyp A-sine A-tan A-tan2 Hyp A-tan Ceiling Combine Cosine Hyp Cosine Degrees Exponent Factorial Floor Integer Ln Log Log10 Mod Power Quotient Radians Randbetween Round Sine Hyp Sine Square Root Tan Hyp Tan Truncate Association Rules Clustering General Regression Mining Neural Network Regression Rule Set Support Vector Machine Time Series Train Association Train Clustering Train Decision Tree Train Regression Train Time Series Tree Model Statistical Aggregate Variance of a Standard Deviation Population Standard Deviation Geometric Mean Pop Average Deviation Variance Kurtosis Skew Financial Accrued Interest Accrued Interest Maturity Amount Received at Maturity Bond-equivalent Yield for T-BILL Convert Dollar Price from Fraction to Decimal Convert Dollar Price from Decimal to Fraction Cumulative Interest Paid on Loan Cumulative Principal Paid on Loan Depreciation for each Accounting Period Days In Coupon Period to Settlement Date Days In Coupon Period with Settlement Date Days from Settlement Date to Next Coupon Double-Declining Balance Method Discount Rate For a Security Effective Annual Interest Rate Fixed-Declining Balance Method Future Value Future Value of Initial Principal with Compound Interest Rates Interest Rate Interest Payment Internal Rate of Return Interest Rate per Annuity Macauley Duration Modified Duration Modified Internal Rate of Return Next Coupon Date After Settlement Date No of Coupons Settlement and Maturity Date Nominal Annual Interest Rate No of Investment Periods Net Present Value Odd Last Period / Yield Prev Coupon Date Before Settlement Date Price Per $100 Face Value w Odd First Period Payment Payment on Principal Price Price Discount Price at Maturity Present Value Prorated Depreciation for each Period Straight Line Depreciation Sum-Of-Years' Digits Depreciation T-BILL Price T-BILL Yield Variable Declining Balance Yield Yield for Discounted Security Yield at Maturity OLAP Functions Running Total Running Std Deviation Running Std Deviation of Population Running Minimum Running Maximum Running Count Moving Difference Moving Maximum Moving Minimum Moving Average Moving Sum Moving Count Moving Std Deviation Moving Std DeviationP First /Last in Range Exponential Weight Moving Avg Exponential Weight Running Avg

8 MicroStrategy Native Data Mining Capabilities Linear regressions Training Scoring Ensembles Create Dataset Select Variables Develop Model Deploy Model Logistic regression Detailed/Summary Clean/ Sample Explore/Transform Discover Patterns Train Model Validate Model Score Records Present Results Decision tree clustering Time series Score, neural networks, rule set, SVMs Predictive metrics are deployed like any other metric to anyone, anywhere, anytime

9 Interface Driven Predictive Model Model Visualizations Quality Info and Simulator Descriptive Statistics

10 What is R R is a language and environment for statistical computing and graphics Over 5,000 packages: Calculations and graphics Millions of practitioners Vibrant user communities # 1 choice of data scientists worldwide

11 R Integration Pack R is a language and environment for statistical computing and graphics MicroStrategy Desktop MicroStrategy Web MicroStrategy Mobile MicroStrategy Office Integrate R in 3 Steps Write R script Capture its Signature Deploy as a metric

12 Competitive Advanages Usable in any MicroStrategy Environment One Analytic: Multiple Outputs, Multiple Applications, Standalone Scripts Flexible applications with parameters/prompts/transaction services Transparent R environment Error Logging

13 Usable in any MicroStrategy Environment Analytics Desktop Web/Visual Insight Developer Mobile Available on-premises or Cloud

14 One Analytic: Multiple Applications via Standalone Scripts Don t re-invent the wheel Eg. Use the same clustering analytic to cluster stores, employee, customers and products Maintain and update multiple applications through a single script Inline scripts Maintenance nightmare Rife with errors Complex for end-users Edits must be made within each calculation Standalone scripts Easy to Maintain Error Proof Abstracts complexity Edits made in a single location

(Choose a metric to forecast, choose a level at which to aggregate) Transaction Services Score on-the-fly by entering in variable

15 Flexible applications with Parameters/Prompts/Transaction services Parameter Submit scalar values at metric execution time, e.g. (Number of Clusters, % Confidence Interval, Train vs Score) Prompts Utilize all types of prompts to maximize flexibility, e.g. (Choose a metric to forecast, choose a level at which to aggregate) Transaction Services Score on-the-fly by entering in variable values on your mobile device, e.g. (Customer comes into car dealership, enter attribute values, and predict what car will be bought.)

Transparent R environment Access to File System Read/Write data files and images Models don t disappear into the stratosphere Persist R environment for

16 Transparent R environment Access to File System Read/Write data files and images Models don t disappear into the stratosphere Persist R environment for further investigation Send insights to data scientists for validation Silent Installations of R Package Don t burden administrators with package configuration

17 Error Logging No Error Message Reported: Errors Logged and Reported:

18 Demo

Additional resources Try us now today https://rintegrationpack.

19 Additional resources Try us now today Join our Webcasts

20 Thank you.

Lancet Data Sciences and Bluestem Brands

Lancet Data Sciences and Bluestem Brands Man vs. Analytics Neil Gunn Bluestem Brands Inc. BI Manager Neil.Gunn@bluestembrands.com Jason Todd Lancet Data Sciences Practice Leader & AE jtodd@lancetdatasciences.com