First Impressions of the New SAP BI Launchpad

Experienced users of Web Intelligence, whom might have become fatigued with the interface of Web Intelligence and other applications using the BI Launchpad, will be excited to see the brand new iteration of the Launchpad in BI 4.2 SP4. This new, yet familiar interface will be sure to please both current and future users alike.

New, Yet Familiar Design

The entire look of the BI Launchpad has been given a major facelift, with the most prominent change being a shift to a tiled icon interface, more consistent with other SAP applications like Fiori 2.0. Upon login, users are directed to the My Home  page, pictured below.

In my opinion, this layout can be a tad bit overwhelming at first glance. Files are listed alphabetically with seemingly no options for grouping by file type, whether the file be a Webi or a Crystal Report. A drop down menu in the top left hand corner does allow you, however, to navigate to your recent and favorite documents for a more consolidated view.

To download full PDF and Continue Reading…

Austin Graham
Sr. Consultant 2
Data & Analytics

Getting Started with BW4HANA

Big Data with SAP HANA & Hadoop

Purpose –

This is part 1 of series of blog to get started with BW4HANA.

In this part we will create a BW4HANA instance from the image deployed by SAP on Cloud Platform like AWS or Azure.

Prerequisites –

  • SAP SID. It usually starts with S and is 11 characters for eg-: S0012345678. You can get SID from SAP.
  • SAP Cloud Appliance Library account (link). Fill in the necessary information using your SID above.
  • Access key and Secret key for your Amazon Web Service (AWS) account if you are using AWS to host your instance.

Let’s Get Started –

  1. Open the SAP Cal (link) in your favorite browser


  1. Login with your SAP ID

1-2.png3. On the top right corner there is a search option, Search for “BW


4. Select the appropriate instance from the below list and click on Create instance.      …

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Categories: HANA

How to secure information views in SAP HANA

As more and more organization implement SAP HANA native or standalone, the need to understand how to provide access and secure information views has emerge.  The intent of this article is to provid…

Source: How to secure information views in SAP HANA

Categories: HANA

Multistore Table Partitions in SAP HANA 2.0

Starting with SAP HANA 2.0 we can now partition a single table between in-memory storage and SAP HANA Extended Storage (AKA.. Dynamic Tiring). This is an excellent feature because it simplifies the…

Source: Multistore Table Partitions in SAP HANA 2.0

Categories: HANA

Building a One-Way ANOVA R Extension in SAP Predictive Analytics

One of the first statistical tools many new analyst use is the Analysis of Variance, a collection of statistical methods used to decompose and understand the causes of variation within a set of data. One-way ANOVA is perhaps the most basic of these methods and a staple of most statistical software. While SAP has included many predictive algorithms in their SAP Predictive Analytics Expert application, it is missing many of the common descriptive algorithms used by data scientists to better understand their data. Luckily, it is relatively easy to build in custom R extensions to accommodate any descriptive statistical needs.

What is One-Way ANOVA?

One-Way Analysis of Variance is used to compare means within 3 or more samples to evaluate whether or not all groups have the same means (in effect, there is no difference in the monitored statistic between the groups). Of course there is natural variation in data, so the actual means of the groups may vary slightly, but the age-old question persists: is the difference statistically significant???

One-Way ANOVA is an omnibus test, which means that if the null hypothesis (all means are the same) is rejected, it offers no additional information on which of the group(s) are different from each other, simply that at least one of them is different enough to reject the hypothesis that they are all the same.

For additional background on One-Way ANOVA, see the relevant Wikipedia article.

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Hilary BlissAbout Hillary Bliss
Hillary is a Senior Manager – Data & Analytics at Protiviti, and specializes in data warehouse design, ETL development, statistical analysis, and predictive modeling. She works with clients and vendors to integrate business analysis and predictive modeling solutions into the organizational data warehouse and business intelligence environments based on their specific operational and strategic business needs. She has a master’s degree in statistics and an MBA from Georgia Tech.

Categories: Predictive Analytics

Reporting & Analytics Interactive 2016








Categories: Events

50 Business Problems I’ve Addressed with Predictive Analytics, Data Science, and Advanced Analytics

I was reading Vincent Granville’s recent blog post and thought I might add a few of the problems I’ve addressed in my career. While these are not all data science they do fall within advanced analytics.

  1. Estimating hybrid yields and crop characteristics across multiple geographies, soil types, climates, and ecosystems based on performance of limited field trials. Estimating the same for heretofore uncrossed inbreds.
  2. Accurately forecasting the demand for promotional items driving the market basket 18 months into the future in order to accommodate an extended supply chain.
  3. Estimating the brand capital associated with consumer brands in the marketplace. E.g. What is the value of a brand in the marketplace in terms of both goodwill on the balance sheet and an organizations ability to leverage the brand capital through marketing to deliver sales.
  4. Network optimization for optimizing supply chains. Optimizing supply chain routing in near real-time.
  5. Optimal scheduling of ship dates for seasonal goods based upon stochastic analysis of probable events along the chain in order to assure supply without clogging the pipe.
  6. Call center optimization.
  7. Forecasting the demand for retail store associates across a large retail chain and optimizing the schedule based upon that forecast.
  8. Predicting what goods are most likely to be purchased during a hurricane warning.
  9. Predicting the annual sales of a prospective retail site based upon demographic, market, competitor and other data.
  10. Optimizing locations for retail site selections using game theory and accounting for cannibalization and impact on key competitors.
  11. Optimal routing for transportation fleets given uncertainty.
  12. Pricing optimization, including setting pricing strategies for every day pricing.
  13. Estimating the impact of pricing changes on demand for high affinity items. Understanding expected impact of promotional pricing on over-all revenues based on affinity items and additional trips.
  14. Estimating the impact of operational activities on competitors and the likelihood of cannibalization.
  15. Estimating sell through dates for seasonal goods and understanding the risk profile for lost sales opportunity and clearance/write-off.
  16. Identifying potentially fraudulent transactions at the register and in the back office regarding cash and check deposits.
  17. Identifying potentially fraudulent workman’s comp claims.
  18. Identifying primary causes associate with employee injury accidents and prioritizing limited resources to prevent and mitigate lost productivity, workman’s comp expenses, and long term liability associated with miss-handled claims.
  19. Identifying potential tax savings due to missed tax benefits across multiple tax jurisdictions.
  20. Analyzing production data to identify root cause of quality issues and productions slow downs in manufacturing environments.
  21. Analyzing clinical trial data for safety and efficacy of treatment protocols.
  22. Analyzing system performance logs to understand bottlenecks in production and analytical lab computing environments.
  23. Analyzing consumer behavior to understand the impact of marketing message theme, channel preference, pricing sensitivity, seasonal good purchase cycle, brand affinities, product affinities, loyalty engagement, net promoter score, customer satisfaction, lifetime value, purchase driver, style preferences, color preferences, size preferences and other brand specific factors.
  24. Integrating consumer behavior data with attitudinal and demographic data to make  cohort level inferences regarding behavior.
  25. Response and uplift modeling to understand the impact of direct marketing efforts in a test and learn environment.
  26. Establishing value of information models to map response/uplift to the financial benefit they bring to the organization.
  27. Establishing a champion/challenger approach to model deployment and consistently measuring the impact the model brings to the business.
  28. Leveraging machine learning to assess when a model needs to be re-scored, refit, remodeled, or replaced.
  29. Integrating insurer, practitioner, and population data, formulary status, and negotiated pricing levels for branded prescription medicines to analyze the impact on long term sales and profits.
  30. Applying artificial intelligence techniques to assist in optimum model selection across predictive analytics solutions.
  31. Forecasting sales and returns in the publishing industry this requires forecasting at the distributor and retailer level and understanding revenue recognition, probability of returns and the publisher’s liability associated with returns.
  32. Integrating omni-channel data in order to model customer response to brand treatments across multiple touch points.
  33. Estimating the most likely customer segment for cash baskets in a retail environment with a high percentage of cash transactions.
  34. Optimizing the retail supply chain for demand driven pull.
  35. Individual store assortment planning for large chain retailers based upon customer behavioral profiles.
  36. Dealer performance estimation and visualization (GIS) in the automotive industry.
  37. OMNI Channel retail performance marketing delivering uplift modeling in a champion/challenger environment and integration into a marketing automation system.
  38. Estimating the customer response to postpaid plan upgrade offers (propensity/uplift) by micro audience and offer theme.
  39. Marketing mix modeling at the individual store level for a larger retailer.
  40. Leveraging early IOT data sources to improve forecasting results.
  41. Analysis of disparate data sources on Hadoop to understand the impact on quality of management decisions.
  42. Automation and application of artificial intelligence techniques in the champion and challenger process for on-going predictive model performance management.
  43. Stochastic optimization of project outcomes (based on data science generated predictors) for the purpose of project portfolio management.
  44. Preparing stochastic based financial projections based upon known cause and effect and predictive relationships (tactics driving KPI’s driving financial performance) for complex businesses.
  45. Estimating future gross margin contribution across a large research and development portfolio for new technology introduction, new product introduction, and continuous product improvements. Application of those estimates to identify key success drivers. Stochastic modeling of the key drivers and estimation models to allocate and optimize annual R&D budgets.
  46. Optimizing balance sheet and off-balance sheet labor costs in a manufacturing environment based on key production predictors including outside economic variables, internal scheduling constraints and forecast demand.
  47. Estimating future resource utilization based on forecasts, historical forecast accuracy, sales pipeline, and existing contract terms.
  48. Predicting customer churn and developing optimal retention policies to prevent it.
  49. Identifying cross sell and up-sell opportunities in a business to business environment.
  50. Identifying root cause of sub optimal brand performance across a portfolio of brands and determining optimal corrective action to improve financial performance.

Patrick McDonald HeadshotAbout Patrick McDonald 

Patrick McDonald is an Associate Director with Protiviti focused on advising clients in the Retail, Manufacturing and Telecommunications industries on analytical solutions. Over a 20 year career in advanced analytics, Patrick completed tours in a big four firm and leading analytics technology and software companies.


Categories: Predictive Analytics