PREDICTIVE ANALYTICS AND BIG DATA WITH HANA TECHNOLOGY

Transcription

1 PREDICTIVE ANALYTICS AND BIG DATA WITH HANA TECHNOLOGY Predictive Analytics enables competitive advantage through the ability to analyze tremendous amounts of data, leverage analytical predictive models, and predict events. Data needs to be treated as a strategic asset and competitive differentiator. Organizations maintain mountains of historical data that continues to expand exponentially every day due to the digitized world. Organizations tend to be Data Rich but Information Poor. Transformation into an analytical organization is the key to competitive advantage. Advanced analytics provides fact-based action-oriented business advice. It falls under the following 3 broad categories: Descriptive Predictive Decisive A price increase of 10% on IPADS will reduce unit sales by 3%. 200,000 IPADS were sold last week. Reporting and Alerts Mining and Statistical Analysis Modeling and Forecasting To maximize profit for next week (holiday period), unit prices for IPADS should be $450. Business Optimization Predicting what if scenarios that are customized and reliable with the added benefit of location intelligence expertise allows clients to make decisions with confidence. It helps answer mission-critical business questions such as: Who are my customers? How far will my customers travel? How does the presence of competition affect my market opportunities? What will my customers buy? How many facilities would be required to support the market? Below are several Public Sector scenarios where predictive analytics could provide significant business value. There are many others. Fraud Detection Large Public Sector programs suffer from monetary leakage due to Fraud, Waste and Abuse. For example, the Revenue department could miss out on Tax and Revenue opportunities by exploitative and non-compliant customers. False Medicare and Medicaid claims are syphon away millions of dollars from State coffers. Predictive algorithms can help dramatically increase revenue with no net increase in headcount.

2 Supply Chain Risk Mitigation SAP HANA transforms businesses by Risk Profiles can be created for parts and subcontractors to identify those most at streamlining transactions, analytics, risk for adversely impacting the production planning, predictive, sentiment data schedule. Models based on Bill of processing on a single in-memory Materials, assembly and manufacturing schedule, natural factors such as weather, database so business can operate in expiring union contracts, etc., can be real-time. Dr. Hasso Plattner, SAP leveraged to create these risk profiles. Understanding the nature and timing and impact of such risks can help organizations develop mitigating strategies such as engaging alternate vendors, stockpiling inventory, and optimizing production and delivery schedules. Sales and Margin Optimization Sales forecasting coupled with product variants (technology, engines, avionics, weapons, etc.) can be utilized to optimize sales and margin. What happens to sales when the price drops on one option? How are the future sales of other options impacted? What is the forecast of overall sales, as well as by product and variant? How can you reduce produce variants while increasing profitability? Maintenance Predictability Analytical models can be developed to predict the probability of a certain part (tank, aircraft etc.) failure based on historical mean time to failure and other variables. Based on these statistical models, a forecasted maintenance schedule and parts replacement schedule can be derived. This could increase uptime, reliability and profitability or, in the case of public sector, enhance national security or effectiveness in the battlefield. Maintenance can be scheduled ahead of time or just in time reducing latency. Predicting Production Learning Curves Predicting production learning curves could be very useful for defense contractors bidding on fixed price aircraft, tank, or missile production. Contractors count on reducing the learning curve and depend on ramping down unit production cycle time to optimize cost and maximize profitability. Inaccurate predictions lead to significantly reduced margins and profitability and even loss. By analyzing the historical data from various programs and variables, a much more accurate costing model can be leverage to improve the accuracy of bids. Impact on Program Cost and Schedule Risks are constant threats to any large program. Program Managers need to assess the impact of risks on program cost and schedule and take necessary pre-emptive action. Predictively doing an advanced impact analysis based on what-if scenarios could highlight the magnitude of the risk and depth of impact. Based on this analysis, Program Managers can develop mitigation plans and take pre-emptive action. 2

3 Development Approach to Implement Predictive Analytics The development approach is anchored in: Business Rules Data Modeling Forecasting Algorithms Visualization SAP HANA Real-time Financial Planning, Intelligent Re-Stock, Warranty Management, Profitability Analysis, Genome Analysis, Demand Signal Management, SOP Planning, Customer Segmentation. The possibilities are endless... Revised Business Rules will be derived from the organization s business strategies and vetted thoroughly with the organization s leadership. New Key Performance Indicator (KPI) libraries will be created or existing libraries will be enhanced based on revised business rules. Before any Data Modeling can be done, data must be identified, validated and cleansed. The organization s transactional, conditional, and master data will be the source. Also, external macro-economic data, industry vertical data, and unstructured data may be considered. Once the relevant data has been identified and data has been cleansed and validated, the Data Model will be built. Statistical and mathematical Forecasting Algorithms will be developed against the Data Model and a series of simulations (what-if scenarios) will be run on known time periods to validate the data model and algorithms. Based on the results, fine tuning and adjustments can be made to the data model, forecasting algorithm, or both. A Proof of Concept (POC) is an ideal approach for Visualization. The Proof of Concept will begin with the Blueprint Phase followed by the Data Model Development and Concept Validation. The following activities will performed within each phase: 3

4 SAP HANA is a hybrid technology, combining both OLTP and OLAP into a single database providing massively lower TCO. Hasso Plattner, SAP Blueprint Phase Project planning Onsite discovery workshop Business and technical requirements planning (data elements and sourcing, data definitions and hierarchies, data cleansing and validation) Business process definition (business KPIs, business rules) Model Development Data Mining and Trend Analysis Feasibility study and data model recommendations Design and implement Sensitivity analysis Configuration and data model monitoring Concept Validation Testing functional, use cases and performance testing and optimization Validating the model against a known time period Dashboards and reporting User validation JTSi s Role in Your Organization s Transformation JTSi has available service offerings for implementing Predictive Analytics: Plan Build Run Plan SAP HANA Assessment including systematic implementation plan and business case Build SAP BW on HANA o Upgrades from SAP BW3.X or 7.X to SAP BW 7.3 o SAP BW 7.3 conversion to SAP HANA 4

5 o Analytics visualization using BOBJ on HANA. Design Custom Data Marts or leverage existing BW. Using HANA studio, develop new data models on HANA etc., o BPC on HANA SAP HANA Install Services o Assistance with HANA landscape design and sizing o Landscape Optimization o Fixed fee installation of HANA o Data replication services from SAP and NON-SAP o Data Quality services using appropriate tool set (Business Object Data Services etc.,) Business Process Re-Engineering and Application Development o Developing next generation applications (business logic, business models, data models, workflow etc.) to run natively on the SAP HANA platform and fully utilize in-memory computing capabilities. Examples include: Predictive MRO using statistical and other mathematical models based on high volume of historical data; Route/Flight optimization; real-time Complex Compliance adherence, etc. o Migrating legacy applications to run faster on HANA technology without costly optimization and application recoding. o Enhancing existing standard applications with missing functionality using HANA technology. o Providing full range of reporting, dashboards and data models that run natively on the SAP HANA platform. o Providing Proof of Concept for predictive analytical capabilities using Big Data and HANA leading to increased revenue, improved profitability, and better decision support. Proof of Concepts include: Blueprinting, Model Development, and Concept Validation. o Improving the performance of existing SAP business suite functionality utilizing the power of SAP HANA. o Standard SAP applications may have mass-data-related performance problems. Using SAP HANA secondary database to read mass data can speed up calculations leading to improved system performance. Examples include: - Faster Operational Reporting: Using the in-memory appliance alongside ERP data replication speeding up long running operational reports: Financial Reporting (DSO reports), Sales Reporting (Sales order analysis), etc. - Accelerate Existing Business Processes: Processing / calculation takes place on HANA with real time data replication from ERP sources. Calculations may include utilizing built-in predictive algorithms or newly written algorithms. Run Application Management Services o End user support 5

6 o Change management o Solution transition to enterprise service oriented architecture o Application management o Business process operations o Custom development management o Technical operations o IT infrastructure management Incremental Renewal Sample List of Predictive Analytical Solutions Commercial o Uncovering correlations to discover further insights such as customer behavior Public Sector o Fraud Detection o Supply Chain Risk Mitigation o Sales and Margin Optimization o Maintenance Predictability o Tax and Revenue solutions to selectively target high yielding targets based on parameters such as ratio of revenues to employees and comparison with industry norms, etc. o Cyber Security Solutions o Crime Fighting Solutions to correlate and integrate diverse data for a cohesive thread such as integrating criminal records, 911 calls, housing records, social services records, vehicle licensing, and other image data bases o Public Health Care Solutions to detect disease patterns o Credit Scoring / Predicting Default o Automating Claims Processing Health Care o Optimize coverage areas for healthcare services or pharmaceutical representatives o Identify epidemiological problem spots Insurance o Visualize parameter drive risk models based on multitude of factors such as smoking habits, obesity, age, geography, etc. o Detection of fraud activities 6

7 Raj Rajendran Director, Strategic Relationship Management Raj joins JTSi with over 30 years of overall experience in Information Technology activities and 15 years of experience selling Professional Services and Solutions into Public Sector, Consumer Products, Manufacturing, Life Sciences, and Banking and Insurance sectors. He is well-attuned to the current IT trends and technologies such as Big Data, Predictive Analytics, In Memory and On- Demand Computing that are major Revenue Enablers and Cost Drivers. Prior to joining JTSi, Mr. Rajendran was a Senior Client Partner at SAP for 15 years selling professional services and solutions. In his latest role at SAP, he sold advanced Business Analytics and HANA into Aerospace and Defense accounts and prior to that into State, Local and Higher Education. Raj was a three-time awardee to the prestigious SAP Winners Circle for achieving sales excellence. Joe Gioffre Director, SAP Consulting Practice, DOD Joe has over 25 successful years of managing large, multi-million dollar projects and programs for both government and industry. His experience includes the full lifecycle planning and implementation of solutions for public and private sector. Prior to joining JTSi, Joe was a Delivery Executive for SAP, responsible for framing new projects, managing major end-to-end ERP implementations, and providing oversight to smaller projects with a focus on rescues from other integrators. We Do What We Say! Locations Corporate Headquarters Commerce Park Drive Suite 410 Reston, VA Programs Office Picatinny Arsenal 3159 Schrader Road Dover, NJ Contact Us Corporate Headquarters Toll Free JTSi (5874) [email protected] 7