some emerging trends in analytics
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04/10/2023 Some Emerging Trends in Analytics 1
Some Emerging Trends in Analytics
Prescriptive AnalyticsIn-memory Computing
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In God we trust, others must bring Data
-W Edwards Deming
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Prescriptive Analytics
Introduction, Origin & Process, Applications
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Introduction
(not just what and when, but also why…)
It automatically synthesizes big data, multiple disciplines of mathematical sciences and computational sciences, and business rules, to make predictions and then suggests decision options to take advantage of the predictions.
It can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options.
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The Final Step in Analytics…
Descriptive
• The use of data to find out what happened in the past
• Data modeling, trend reporting, regression analysis
Diagnostic
• The use of data to find out why it happened
• Postmortem analysis, trend analysis
Predictive
• The use of date to find out what could happen in the future
• Data mining, predictive modeling
Prescriptive
• The use of data to prescribe the best course of action for the future
• Optimization, simulation
The Analytics Sequence
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Why is It Needed?
Prescriptive model emerged to check the shortcomings of the predictive models.
Optimal solution is not realistic, so prescriptive models predict case-based suggestions
Integration of the descriptive and predictive components is critical
Availability of huge data and advanced tools has made application of this model more feasible
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If you don’t know how to ask questions, you know nothing.
-W Edwards Deming
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Origin of Prescriptive Analysis
Got its early start in 2003 with the R&D focussed organisation DataInfoCom which was later renamed as AYATA.
During 2004 and 2005, AYATA conceptualized the technology footprint for Prescriptive Analytics and went on to release first version of the software in 2007.
In 2011 it was trademarked for two categories: Software and Software as a Service.
AYATA has worked with Apache Corp. to improve production by proactively reducing pump failures in the field.
Later deployed at various Fortune 100 companies including Dell, Microsoft & Cisco.
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Identify objectives
(values)
Identify Alternatives
Decompose & Model Problem
Choose best alternative
Perform Sensitivity
analysis
More analysis needed?
If NO, Make
recommendation
Prescriptive Analytics Process
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What gets measured, get managed.
-Peter Drucker
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Potential decisions
The interactions between decisions
The influences that bear upon these decisions
The Prescriptive Process Analyses
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•To leverage operational and usage data by combining the external factors such as economic data, population demographic trends and population health trends
•This helps us to more accurately plan for future capital investments such as new facilities and equipment utilization, understand the trade-offs between adding additional beds, expanding an existing facility versus building a new one.
Health care strategic planning
•Prices fluctuate dramatically depending upon supply, demand, geo-politics, & weather conditions.
•Gas producers have a keen interest in more accurately predicting gas prices so that they can lock in favourable terms while hedging downside risk.
•Prescriptive analytics can accurately predict prices by modelling internal and external variables simultaneously and also provide decision options and show the impact of each decision option.
Oil and gas industry
Applications
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•Telecom and cable companies have large field service organizations to install, repair and resolve issues at customer sites, both in homes or at businesses.
•Mastering field service operations is critical because field services impact customer satisfaction.
•Prescriptive analytics enables telecom and cable providers to dramatically improve effectiveness and efficiency of field service operations.
Tele-communications
and cable
Applications
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Those who do not remember the past, are condemned to repeat it.
-George Santayana
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In-memory Computing
Introduction, Benefits, SAP HANA, Applications
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In–Memory ComputingIn-memory computing is the storage of information in the main random access memory (RAM) of dedicated servers rather than in complicated and comparatively slow disk drives.
Source: SAP AG
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Why In-Memory Computing Ground breaking Innovation
10,000x improvement in speed of access
Movement to main memory from disk storage: Viable performance with increasing data volumes Affordable servers > 1 TB system memory CPUs – Multi core for rapid parallel processing
Cost feasible technology
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The price of light is greater than the cost of darkness.
-Arthur C Neilsen
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Benefits of In-memory Computing
‘In-memory computing’ is a software that could transform business IT enabling companies to crunch and analyse large volumes of data in near real-time and run sophisticated ‘what if’ simulations.
In-memory computing uses sophisticated data compression techniques to store information in Ram which is 10,000 times faster than standard disks, enabling companies to analyse that data in seconds instead of hours.
Empowerment and increased flexibility is assured because it reduces business users reliance on IT.
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Cost benefits are assured because it reduces hardware and maintenance costs through a flexible, cost-effective, real time approach for managing large data volumes.
Real time visibility is assured as data is pushed from the sources to the warehouses.
Previously the sheer volume of data and computational power allowed only for pre- determined data analysis. With in-memory systems detailed data is loaded into memory where calculations are performed “ on the fly” at query time.
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Not everything that can be counted counts, and not everything that counts can be counted.
-Albert Einstein
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SAP HANA: (High Performance Analytic Appliance)
Engine of Real-time enterprise Provides foundation to build new generation of Analytics applications Enables customers to analyze data from virtually any source, in real time Example showing actual customer performance of a core reporting
process.
77 minutesBefore SAP HANA
13 secondsWith SAP HANA
Runs 350xfaster
Source: SAP AG
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MobilityAccessible Systems
Oracle DB2 SQL Other
Business Applications PerformanceBound by Data
BICS Business Analytics
Systems of Engagement
Info “Out”
Innovated with AnalyticsInnovated with MobilityInnovated with DatabaseHANA Accelerates Data, Applications, AnalyticsHANA is the Database
ERP + LOB
Systems of Record
Data “In”
Innovated with ERP & LOB
Oracle, DB2
HANAIn Memory Database
SQL, Other
ELT or ETL ELT or ETLHANAIn Memory Database
Source: SAP AG
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Local BI
Data Mart
DB
SAP ERP 1
Database
Local BI
Data Mart
DB
SAP ERP 2
Database
Local BI
Data Mart
DB
NON SAP
Database
Enterprise Data Warehouse (BW)
Database
Corporate BI
BWA
Traditional Computing
Enterprise Data Warehouse (BW)
Corporate BI
HANA
Local BI
SAP ERP 1
DatabaseHANA
Local BI
SAP ERP 2
DatabaseHANA
Local BI
NON SAP
DatabaseHANA
In-memory Computing using HANA
Corporate BI
In-memory as Data Layer
Enterprise Data Warehouse (BW)
SAP ERP 1
SAP ERP 2
NEW APP 1
NEW APP 2
HANA
NON SAP
Source: SAP AG
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It is a capital mistake to theorize before one has data.
-Arthur Conan Doyle
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Applications of In-memory Computing
Research shows that organizations that have adopted in-memory computing were not only able to analyze larger amounts of data in less time than their competitors – they were literally orders of magnitude faster.
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Two birds with one stone : Volume and Velocity Relies on latest breed of high powered servers Vast amount of RAM where business information can be
directly stored Zero latency No look up time High speed Multi-core processing
This results in real-time analysis of core business data
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It is most applicable for large enterprises Steep initial investment
43% of companies using in-memory computing have an annual revenue of over $1 Billion
Data should be available in structured format(transaction data, sales figures or product codes)
Should use appropriate analytical applications
When it comes to processing large amounts of structured data very quickly, in-memory computing outperform their peers Almost 3 times the data over a hundred times the speed
Data volumes are growing at the rate of 36% per year
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Thank You!
Srushti, Rahul, Saurabh, Achintya, Divya, Prashant, Krithika