Some emerging trends in analytics

04/10/2023 Some Emerging Trends in Analytics 1

Some Emerging Trends in Analytics

Prescriptive AnalyticsIn-memory Computing


In God we trust, others must bring Data

-W Edwards Deming


Prescriptive Analytics

Introduction, Origin & Process, Applications


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.


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


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


If you don’t know how to ask questions, you know nothing.

-W Edwards Deming


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.


Identify objectives

(values)

Identify Alternatives

Decompose & Model Problem

Choose best alternative

Perform Sensitivity

analysis

More analysis needed?

If NO, Make

recommendation

Prescriptive Analytics Process


What gets measured, get managed.

-Peter Drucker


Potential decisions

The interactions between decisions

The influences that bear upon these decisions

The Prescriptive Process Analyses


•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


•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


Those who do not remember the past, are condemned to repeat it.

-George Santayana


In-memory Computing

Introduction, Benefits, SAP HANA, Applications


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


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


The price of light is greater than the cost of darkness.

-Arthur C Neilsen


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.


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.


Not everything that can be counted counts, and not everything that counts can be counted.

-Albert Einstein


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


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


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


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


SAP ERP 1

SAP ERP 2

NEW APP 1

NEW APP 2

HANA

NON SAP

Source: SAP AG


It is a capital mistake to theorize before one has data.

-Arthur Conan Doyle


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.


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


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



Thank You!

Srushti, Rahul, Saurabh, Achintya, Divya, Prashant, Krithika