DATA & AI

5

DataScience

ArtificialIntelligence

MachineLearning

Artificial Intelligence (AI)

Mimics human behavior. Any technique

that enables machines to solve a task

in a way like humans do.

Data Science

A multi-disciplinary field that

uses scientific methods,

processes, algorithms and

systems to extract knowledge

and insights from data.

Machine Learning (ML)

Algorithms that allow

computers to learn from

examples without being

explicitly programmed.

Example:

Siri

Example:

Netflix

Example:

Self-driving car

Cloud Data & Apps team as the part of theHPE UK&I Pointnext Services Hybrid Cloud

Foundational Platform

Identity – Security

Backup – DR - Availability

Systems Management

Governance

Managed Service

On PremiseMulti-Cloud

Networking

DevOps

Cloud Environment

Data Platform

App(Data

Analytics)

App(Data

Science)App

Cloud Data & Appsteam

Cloud Platformteam

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Dis

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Stra

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Man

aged Service

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Data platform & apps in the hybrid cloud:main challenges for our customers

Foundational Platform

Identity – Security

Backup – DR - Availability

Systems Management

Governance

Managed Service

On PremiseMulti-Cloud

Networking

DevOps

Cloud Environment

Data Platform

App(Data

Analytics)

App(Data

Science)App

Data integration & consolidation Data lifecycle management Data transformations/migrations

Smart automation: ML/AI capabilities Data-driven decision making: (big) data

analytics and visualisation (BI) App transformations/migrations

We are ready to address customer challenges with the four key market offerings that drive customer efficiency

Digital transformation is the integration of digital technology into

all areas of a business, fundamentally changing how

companies operate and deliver value to customers.

Enterprise agility is a company’s ability to outperform the

competition and drive growth in new, ambiguous situations by learning and adapting when

confronted with foreseen and unforeseen circumstances,

dilemmas, crises, and complex problems.

Companies with large application portfolios continue to ask the question, “Where are the best-fit destinations for my application workloads to deliver against my business objectives?”

With the maturity of public cloud services and private or co-located clouds based on hyperconverged and composable infrastructure, the answer is hybrid cloud.

Market offering #1(Re-)build data platform

HadoopBlueData

Cloud – AWS, Azure, GCP

Market offering #2Transform / Integrate / Consolidate

apps & data

6Rs – Rehost / Replatform / Repurchase / Refactor / Retire / Retain

DatabasesAPIs

Market offering #3Data-driven decision

making

(Big) data analytics(Big) data visualisation

Market offering #4

Smart automation

Data scienceMLAI

journey to hybrid cloud

(app & data migration)

Explore Experiment Evolve

Produce descriptive

statistics & findings

report

Integrate AI to

existing

applications

Clarify & outline problem & objectives Create and model a

proof of concept

Explore the best use cases and technologies

Determine data availability

Gather, explore and validate your data

Our approach in 3 simple steps

Validate & evaluate

e.g. on live data,

A/B testing

Discovery & Analysis Phase

Visualisations and

dashboards

Build & DeployDesign & PoC

HPE Data Sciences Skills Summary

Data Analysis &

Exploration

Development Languages &

PlatformsLibraries Visualisation &

Dashboards

Processing Models

DistributedSERIAL

Data Sciences Team & Skills Matrix

Data Sources

StructuredData

UnstructuredData

DatabasesFiles

Cloud – Azure data platformOur reference architecture

Event Hub

Analysis Services

Data Catalog

IoT Hub

Machine

Learning

Data Lake Cosmos DB

SQL

Database

Logic Apps

SQL Data

Warehouse

Databricks

Data Factory

Stream Analytics Power BI

Service

Key Vault

Cold Path (Augmented data)

Cold Path (Transformation)

Function Apps

Hot Path (Live analytics)

Batch

Power BI

Cold Path (Augmentation)

Excel /

Power Query

JSON Files CSV Files

Databases

API Endpoints

JSON Files

CSV FilesAPI Endpoints

Hot Path (Real-time data)

Notebooks

Bot Service

DevOps PipelinesBoards Repos Test PlansKubernetes

Complex Data

Complex Data

Salesforce

ServiceNow

Artifacts

Active Directory

Warm Path (Persistence)

Warm Path (Hybrid data)

Hot Data

Cold Data

Warm Data

Historical / Current:

DescriptiveWhat happened?

DiagnosticWhy did it happen?

Forward Looking:

PredictiveWhat will happen?

PrescriptiveWhat should I do about it?

Web AppsAPI Management

Applications

APIs

IoT Devices

Conclusions

Models

IoT Edge

Advanced AI tool suite

Cognitive Services

Credential repository

Data lineage and metadata

User authentication

Simple computations

Pipelines & data flows

Unstructured data

Relational data

Key/value,document & graph data

Long term,high volume,

augmented data

Report & dashboardrepository

Triggers toautomate actions

Highly scalablebig data analytics

Aggregation & semantic layer

Predictive analytics

Real-time analytics

Live device data

Real-time data ingestion

Data science& exploration

Complex computations

Human / AI interface

Containerised solution deployment

CI/CDmanagement

Test planning

Source control

Work planning

DevOps tool suite

Private work packages

Key/value

Columnar/ family

Relational

Graph

Tabular/ cube

Document

Consolidated API governance

CustomAPIs

Care UK

The Challenges

- Care UK want to enable the elderly to stay in

their homes as long as possible, whilst giving

their relations piece of mind that they are safe

- How to collect the information to ensure safety?

- False positive alerts take up valuable

emergency resource and leads to unrequired

stress to relatives

- Speed of incident response is key to saving

lives

Enabling the elderly to

stay in their homes as

long as they can; Care

UK uses data to alert

and action incidents in

real-time.

Solution

WellWatch is a wearable device monitoring

the wearers heart rate, activity, and telemetry

Data streamed to Azure IoT hub

Data is analysed in real-time and triaged into

different categories of alerts

Messages can be sent to wearer asking for

further information or to friends and relatives

5million rows of data per user per week are

collected

If an algorithm detects a fall an alert is

triggered which may result in a call to the

emergency services

Fleet Management Example

The Challenges

- Vehicle fleet management company needs to

identify bad driving

- Existing in-car telematics can not differentiate

normal driving incidents from risky driving

- False positive information makes data

irrelevant. Speed bumps and pot holes showing

as bad driving

- Build a data model to accurately identify risky

and bad driving incidents

How does a fleet

management company

use data to identify

incidents of bad driving

accurately?

Solution

Collect data from in-car telematics

Collect video data and correlate against

telematics data set

Machine learning models predict events

based on in car telemetry

Used Azure based architecture for the

solution

Solution able to differentiate risky driving

incidents from general driving issues with

90% accuracy

Tesco Global Hackathon 2019

The Challenges

- 24 hours to produce a working prototype for an

idea that tackles one of the proposed

challenges

- Bringing together Tesco and HPE employees to

form a team

- Limited knowledge and access to Tesco data

- Required quick and creative approach to

develop a working prototype

How can Tesco use

their customers’ data to

serve them better?

Solution

Business value for Tesco: Enabling access

all areas for disabled customers

Before a disabled customer reaches a store,

it is important that their needs can be met,

therefore removing any barriers and

improving their experience with Tesco

Using Tesco’s Store Location API

Speech recognition prototype developed for

the Tesco app

Speech recognition gives full accessibility to

the app. Customer can find a store with

desired facilities to meet their needs e.g.

“Find me a Tesco in London with Wheelchair

Access”

Customer Engagement ScenariosWhat we can build?

Real-Time AnalysisStreaming analysis of real time data e.g. IoT

Predict Future OutcomesForecasting, demand and cost prediction, customer churn

Anomaly DetectionFraud & risk detection, pattern detection, alert & monitoring

Cluster AnalysisCustomer segmentation, behavioural analytics

Text Analysis

Sentiment analysis, recommendation engines

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured;

a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyse actual phenomena" with data.

Data Science Use Cases in Retail

Optimization

Recommendation

engine

Customer lifetime value

Dynamic pricing /

personalised promotions

Behaviour

prediction

CRM /

Customer

Insights

Market

research

Personalized

marketing &

products

Inventory management

Store location

Route planning

Customer segmentation

Trend detection and

prediction

Buying behaviour

Manage customer churn

Customer satisfaction

Sentiment analysis

A/B testing

Customised campaigns

Market basket analysis

Data Science Use Cases in Finance, Insurance & Capital Market Industries

Process

Automation

Customer Segmentation

Customer Lifetime Value

Identify customer

propensity to buy an

insurance on a given

quote

Security Underwriting

& Credit

Scoring

Algorithmic

Trading

Personalized

Marketing &

Products

Categorizing

Documents

Loan Eligibility

Determinator

Recommendation

systems for the staff

about how to help

customers

Fraud Detection

Classifications of

transactions in financial

networks

Identify whether or not a

bank note is authentic

Predict the likelihood

that a customer would

default on a potential

loan

Predict credit card score

Predict the cost of

claims

Identify risk measures and

factors for investments

Categorize investments

based on earnings or risk

Identify accounting

anomalies

mailto:vedran.podobnik@hpe.com