analytics, business intelligence, and data science - what's the progression?

The First Step in Information Management

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Sept. 7, 2017

Analytics, Business Intelligence and Data Science:What's the Progression?

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Topics for Today’s Analytics Webinar

Defining Business Intelligence (BI), Analytics and Data Science

Differences in Architectures

When to Use Analytics, BI and Data Science

Evolution Between Analytics, BI and Data Science

Key Take-Aways

Q&A

pg 2© 2017 First San Francisco Partners www.firstsanfranciscopartners.com

Why is Today’s Topic Important?

Organizations struggle with where to place and how tomanage the use of data. The addition of powerful analytics just adds another item to the stack of data

usage that needs to be managed. Organizations need to be clear about where the capabilities lie – and who is

responsible for successful application of all the varieties of using data. There are numerous alternatives, and there is no one reference model. Too many organizations are going the self-service route and are failing at

meeting their data needs. Without a good understanding of what will work in your organization, you

are at risk.


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Defining Business Intelligence (BI), Analyticsand Data Science

Definitions

No solid demarcation between these “styles” of using data


Definitions


Business intelligence ‒ *an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.

Then does it include analytics?


*Source: Gartner IT Glossary

Definitions


Business Intelligence - *an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.

Analytics ‒ *Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain and so on).



Definitions


Business intelligence - *an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.

Analytics – *Analytics has emerged as a catch-all term for a variety of different business intelligence (BI)-and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain and so on).

Data science – (grouped in with Advanced Analytics definition) *the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. Advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks.



Definitions

Business Intelligence

Analytics

Data Science

Which one(s) do I use?



Differences in Architectures

Architecture Drivers


Backward-looking Forward-looking

Operations and “better decisions” Data science, new insights, strategy

Steady state of sources Dynamic Sources

TRADITIONAL CONTEMPORARYBU

SIN

ESS

GO

ALS

AND

INFO

RMAT

ION

REQ

UIR

EMEN

TS

TRADITIONAL DIFFERENCES

AUDIENCE

DATA SOURCE DIFFERENCES

BUSI

NES

S IN

TELL

IGEN

CEAN

ALYTICS & DATA SCIEN

CE

Quality, reliability and precision Enablement not control

MANAGEMENT & GOVERNANCE

Architecture Differences


ETL, EAI, data quality Anything that works

Suitable for batch or near-time Streaming and high volume

Queries have limitations Tuned for huge volumes

TRADITIONAL CONTEMPORARY

INFRASTRUCTURE

DATA VOLUMES

BUSI

NES

S G

OAL

S AN

DIN

FORM

ATIO

N R

EQU

IREM

ENTS

BUSI

NES

S IN

TELL

IGEN

CEAN

ALYTICS & DATA SCIEN

CEDATA USAGE

It’s a Continuum

Effective use means exploiting data assets

Various standard architectures are presented to allow for understanding;the reality is no singlestyle of architecture can address all situations

Note that algorithms and query complexity are not called out, because you can run complex algorithms against anything


ReportsDimensional

QueryPredictive Modeling

Scenario-based

Forecasting

Goal Seeking Models

Normalized Data

Structures

EDW and Marts Load

HadoopSchema on Read

Hadoop

Structure and flexibility

Sourcing and data types

FSFP Reference Architecture – Abstract


Data Insight Architecture

1

DataMovement/

Logistics

ContextMonitoring

Controls

Management Layer Metadata, Lineage, Work Flow, Models, Reference Data, Rules, Canonical Data

Data Access LayerReports, Visualization Visualization, Prediction, “Closed Loop,” Edge Analytics

Traditional Area

ERP

CRM

Finance

Traditional Data Collection

EDW

Data Marts

Contemporary Area

Edge Processing

Ingestion

Smart Machines

Social

Bots

Business Strategy

Data Scientists

Traditional Stakeholders


Principles

Determining which part of the data use spectrum to use isa function of principles

Most organizations just declare an architecture; e.g., “We need a lot of data so it has to be a data lake.”

Principles to apply: − Architectures to deliver BI and analytics need to reflect business needs − Supporting organizations around BI and analytics need to reflect true self service − Final architecture solution must be based on support of both modes or vintage

and contemporary environments


Other Decision Factors

For BI, answer these questions as YES− Are the results intended to be repeatable? − Will the result be made operational?− Are you using the result to make decisions or monitor progress?

Analytics and Data Science is more variable− What is the level of experimentation?− Is AI or machine learning involved? − Are there algorithmic models involved?

Other questions to consider− Does any of the data leave the organization? − What are the regulatory constraints?


An Engineering Process to Define the Correct Architecture

Break all the previous notions. Remember thereis no mandate for any particular architecture, like Data Warehouse, Data Mart, Operational Data Store and a Data Lake.

Any combination is possible, as long as it meets business needs.

17

Understand Business Strategy

and Goals

Determine Needs for

Operations and Mgmt.

Determine Data Needs for Planning and Analysis

Determine Org.

Support

Develop Best-Fit

Architecture

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Organizational Considerations

What’s the Progression?


Data analytic tools and approaches go in and out of favor.

CENTRALIZED

DE-CENTRALIZED

Data Science

• Focused group of advanced data processing

Business Intelligence Competency Center

• Centralized capability to enable efficiencies

Analytics Workbench

• Facilitation of self-service analytics via centralized toolset

Self-Service Business Intelligence

• Shifting greater flexibility to the user

Business-driven Analytics

• Purchase and implementation via cloud, independent of IT

Citizen Data Scientist

• More automated, visual data processing enabling broader adoption

TOOLS APPROACHES

Organizational Drivers


IT DrivenBusiness Used

Business DrivenIT Supported

STRATEGY

RegulatoryExternal / Reputation

Internal ExperimentationChaos Drives Innovation

RISK TOLERANCE

SpecializedHard to find

More GeneralCross-functional

SKILLS

Centralized Decentralized

Organizational Principles/Decision Factors

Business volatility/variability− How frequently does your business change?

Skills− How adaptable are your people?

Alignment− How well do you collaborate across functions?

Regulatory requirements− How tightly does your data need to be controlled?



Evolution Between BI, Analytics and Data Science

Hierarchy of Data Use Solutions


AI

Operational Analytics/CEP

Analytic Experimentation

What If

Investigate

Operate

Monitor

DESCRIPTIVE

DIAGNOSTIC

PREDICTIVE

PRESCRIPTIVE

HIN

DSIG

HTIN

SIGH

TFO

RESI

GHT

Summary: When to Use One or the Other


USAGE BASIS

WHAT HAPPENED?

WHY DID IT HAPPEN?

WHAT WILL HAPPEN?

MAKE IT HAPPEN BY

ITSELF

WHAT DO I WANT TO HAPPEN?

WHAT SHOULD WE

DO NEXT?

PERCEIVED MATURITY

REPORTING ANALYZING PREDICTIVE OPERATION-ALIZE

ADAPTIVE FORESIGHT

SOLUTION CATEGORY

CAPABILITY

REPORTING BUSINESS INTELLIGENCE INITIAL ANALYTICS

ADVANCED ANALYTICS / DATA SCIENCE

SURVIVAL/ OPERATE

OPERATE/MANAGE MANAGE/ PLAN

ANTICIPATE/AUTOMATE


What Comes Next

Data Science Enables the Future of Analytics


User 1.0

To each their own..•Persona•Tools/Language•Containers

Data 1.0

Fragmented Datasets•Isolated controls•Orphaned Models•Access patterns

Technology 1.0

To each their own…•Analytical

Tools/Algorithms•Visualization Models•Platforms –

exploration to deployment

User 2.0 Self-service power persona

Data 2.0Integrated, secure, logical data warehouse

Technology 2.0

In-placeAnalytically completePlatform virtualization

Analytics 1.0Aggregate

Dashboards/BI

Analytics 2.0Connected/Mashed

Datasets

Analytics 3.0Analytics-in-place at

Scale

Analytics 4.0Cognitive/Multimodal

Insights; Deep Learning

Hypothesis testing

Rapid Experimentation

In-situ/CEP Insights

Artificial Intelligence

The rise of Deep Learning

Source: May 2017 DIA webinar (Data Scientist interview)

Key Take-Aways

There are many definitions for BI and Analytics. ‒ Your environment to deliver data will never fall into one single

definition.

The architectures for delivery will vary widely over time within a single organization.

– Focus on fit for purpose.

Use a formal process to determine where and how the data supply chain is sourced, executed, managed and supported.

‒ Do not adopt external reference architecture without an alignment exercise.

BI, Analytics and Data Science will continue to evolve.– Don’t be afraid to “fail fast” within a comfortable cost structure.


Questions?


MONTHLY SERIES

Thank you for joining – thanks, also, toLooker.com for sponsoring the webinar.

Please join us Thursday, Oct. 5 for the“Data Lake Architecture” webinar.

Kelle O’Neal @[email protected]