MODERN BIJyoti Jain : S-31

DEMOCRATIZATIONPuneet Bhalla : S-49

SAND BOX ANALYTICSKarthik : S-43

DATA BECOMES EQUALSuvin: S-49

SELF-SERVICE DATA ANALYTICS

Raj Kumar Misra: S-53

NATURAL LANGUAGEGENERATIONPrayas S:48

Embedded BIRajendra : S-54

Move to CloudRS Rawat: S-53

DATA LITERACYDinesh Yadav : S-25

Group 1 TOP BI TRENDS

MODERN BI

What makes us alive ?MODERN BI

Centrally Provisioned,

Highly Governed &

Scalable System-of-

record Reporting

Analytical Agility & Business

User Autonom

y

Data is Life Blood of the organization

MODERN BI

Shift of BI & Analytics PlatformsBusiness User-Centric Platforms

IT Led Enterprise Reporting

Business Led Self Service Analytics

Strategic Assumptions - 2018

Self Service tools to prepare data for analysis

Integration of these self-service platforms

Convergence of data discovery platforms

Shifting Categories

Infrastructure Data Management

Analysis & Content Creation

Sharing

Infrastructure

Platform Admin Cloud BI

Security Connectivity

Data Management

Governance & Metadata

Self Contained ETL

Self-Service Data

Preparation

Analysis and Content Creation

Embedded Advanced Analytics

Analytic Dashboards

Interactive Visual

Exploration

Mobile Exploration and

Authoring

Sharing

Embedding Analytic Content

Publishing Analytic Content

Collaboration & Social BI

Collaborative AnalyticsDemocratization of DATA- About 56,60,000 results (0.57 seconds)

What is Democratization

Contribution

Exploitation

BI GrowthGartner Inc. (NYSE: IT)

Worldwide BI and analytics market would reach $16.9 billion this year, up 5.2 percent Advanced analytics market would grow at a 14-percent clip this year to $1.5 billion

The new Grocery Store

I want to buy Data for consumers who are women

living in Delhi who have purchased a Jimmy Choo in the

past one year

Deadly Combo

IT Enabled development of Analytical Content by Business Users

BI

Analytics Platforms

Democratization

of Analytics

How BI Generation is Changing

•IT Produced------IT Enabled

•No Upfront Modeling

•Content Authoring – By BUSINESS USERS

•Freedom from Predefined Models

•Free from Exploration

•Distribution through reports – delivery via sharing

Challenges

Collaborative Analytics

Integration

Trust

Licensing

FLEXIBILITY, RESPONSIVENESS AND AUTONOMY

Ref

SAND-BOX ANALYTICS• DATA EXPERIMENTATION ISN’T RIGHT FOR

EVERYONE. • SHOULDN’T BE SHARED COMPANY-WIDE OR

EVEN DEPT.-WIDE.• POTENTIALLY USEFUL DATA-

– RIGHT EXPERIMENTATION– FINESSING– CLEANSING

WHAT IS SANDBOX ANALYTICS

• CREATING SMALL ISOLATED GROUPS OF BI USERS TO PRODUCE, EXPERIMENT WITH AND SHARE DATA BEFORE SHARING COMPANY-WIDE.

• REDUCE TIME TAKEN FOR A BUSINESS TO “CONVERT DATA INTO KNOWLEDGE”.

DOES YOUR ORGANIZATION NEED AN ANALYTIC SANDBOX.

CORE OBJECTIVE

DISCOVERY OF NEW PRODUCTS, MARKETS / CUSTOMER SEGMENTS / SITUATIONAL ANALYTICS.TEST VARIETY OF HYPOTHESIS.

END USERS DATA SCIENTISTS / DATA ANALYSTS

BUSINESS SCOPE

MIXING POT OF DATA SOURCED FROM MULTIPLE SYSTEMS

DATA VOLUME AS PER PROJECT REQUIREMENT

TECHNOLOGY HADOOP CLUSTER + QUERY ENGINE

OUTPUT DATA MINING MODELS (FORECASTING, PREDICTIONS, SCORING)

LIMITED LIFE EXPECTANCY & NOT MISSION CRITICAL “FAIL-FAST”

Data Becomes Equal

All data becomes equal !!!! Value of data will no longer tied to rank or size Quickly and easily access the data and explore it alongside other data to answer questions and improve outcomes Environmental shift toward - people can explore data of all types, shapes, and sizes, and share insights to impact decision-making

All data becomes equal !!!!Data growing at a faster rate

Live in the moment -- the benefits of big data will be lost if the information isn’t processed quickly enough. Hence the concept of “fast data”

Processing speeds requires two technologies: handle developments as quickly as they appear data warehouse capable of working through as arrives

These velocity-oriented databases - support real time analytics & complex decision making in real time, while processing a relentless incoming data feed.

As complicated – it seems, it’s absolute must to compete, particularly in the enterprise space.

All data becomes equal !!!!So much data, so little time Google alone, users perform more than 40,000 search every

second. But when every second -- or millisecond -- can lead to lost data

Each business needs a dedicated platform to capture and analyze data at these increasingly rapid speeds.

How companies use big data to solve problems, test hypotheses and improve product offerings will vary by industry

Being on the very precipice of fast data, startups in the enterprise space must consider the following to get real value from their data.

All data becomes equal !!!!1. Empower all employees through data.

Central business teams will no longer “own” software Responsible for disseminating insights to the other departments Time lag can hurt business

Everyone within the organization needs access to that platform Not only to analyze data But to also gain insights specific to their individual roles.

Enterprise companies need to take data analysis one step further Requires a contextual understanding of each person’s role at the company Offering tangible insights to improve job performance and efficiency through

speedy updates and the streaming of initial analytics.

All data becomes equal !!!!2. Leverage multiple data sources

90% of all existing data developed within a period of just two years

Whether it’s transactional data from POS terminals or sensor data from home appliances, the sources of data are predicted to keep increasing

Difficult for companies to build these “integration pipes” on their own

Important that they ally with partners or utilize public APIs.

All data becomes equal!!!!

3. Use data proactively

Big data isn’t just a guide for the inexperienced

It’s a tool for solving problems and testing hypotheses. Understanding the underlying data sets behind big data is the key to utilizing the technology properly

Big data is only as useful as its rate of analysis. Otherwise, businesses won’t gain access to the real-time suggestions and statistics necessary to make informed decisions with better outcomes

With fast data, information becomes more plentiful, more actionable and more beneficial to an organization.

Self-Service Analytics

SELF-SERVICE DATA ANALYTICS

Self-service Data Analytics is an approach that enables business users to access and work with Corporate Data even though they do not have a background in Statistical Analysis, Business Intelligence or Data Mining.

PLATFORM FOR SELF-SERVICE DATA ANALYTICS

Self-Service Data Analytics provides the ability to easily prep, blend, and analyze all data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours, not weeks.

It allows end users to make decisions based on their own queries and frees up the organization's business intelligence and information technology (IT) teams from creating the majority of reports and allows those teams to focus on other tasks that will help the organization reach its goals.

PLATFORM FOR SELF-SERVICE DATA ANALYTICS

TYPES OF SELF-SERVICE DATA ANALYTICS

Gartner, Inc. is the world's leading information technology research and advisory company

BENEFITS OF SELF-SERVICE DATA ANALYTICS

Faster time to insightAnalysts can extract insights in minutes rather than hours.

No up front data modelingData sources are prepared for analysis on the fly, eliminating the need for complex ETL processes.

UI for Non-technical usersData sources can be easily blended via drag and drop

Expected range of data sourcesGreater ease of use makes it possible for analytics to connect to more data sources.

Embedded BI

Embedded BI

Business intelligence, or BI, is an umbrella term that refers to a variety of software applications used to analyze an organization's raw data. BI as a discipline is made up of several related activities, including data mining, online analytical processing, querying and reporting.Important quotes “ Turn data into opportunity for everyone -Guided decisions, Confident action, Opportunity realized”Embedded BI (business intelligence) is the integration of self-service BI tools into commonly used business applications. BI tools support an enhanced user experience with visualization, real-time analytics and interactive reporting. A dashboard may be provided within the application to display relevant data, or various charts, graphs and reports may be generated for immediate review. Some forms of embedded BI extend functionality to mobile devices to ensure a distributed workforce can have access to identical business intelligence for collaborative efforts in real time.

Embedded BI

Unlike traditional reporting software that works with a narrowly defined set of variables from a single data source, embedded BI is expected to allow significant customization that lets end users author reports that combine data from multiple data streams to fit their precise needs. Ideally, business users can make business intelligence a part of their decision-making process as they carry out assigned work activities. At a more advanced level, embedded BI can become part of workflow automation, so that certain actions are triggered automatically based on parameters set by the end user or other decision makers. Despite the name, embedded BI typically is deployed alongside the enterprise application rather than being hosted within it. Both Web-based and cloud-based BI are available for use with a wide variety of business applications.

Embedded BI

Embedded BI

Natural language Generation

What is NLG?

• Definition (McDonald 1992): the process of deliberately constructing a NL text in order to meet specified communicative goals.

• Input: non-linguistic representation of info

• Output: text, hypertext, speech

NLG system #1: FoG

• FoG: Forecast Generator• Input: weather map• Output: textual weather report in English and

French• Developer: CoGen Tex• Status: in operational use since 1992

NLG system #2: SumTime-Mousam

• FoG: Forecast Generator• Input: weather data• Output: textual weather report in English• Developer: University of Aberdeen• Status: Used by one company to generate

weather forecasts for offshore oil rigs.

NLG System #3: STOP

• Input: Questionnaire about smoking attitudes, history, beliefs

• Output: a personalized smoking-cessation leaflet

• Developer: University of Aberdeen• Status: undergoing clinical evaluation

Different Variations of NLG

Business impact

• Brokerage Firms• Travel Distribution Systems• Accounting • FMCG• Weather Service• Oil and Gas• Financial Services

Transition to Cloud

Organizations moving their data to the cloud

Analytics also to move to cloud

“Data Gravity”

MOVE TO CLOUD

Big dataCloud computing

On-premise Analytics

DATA GRAVITY

Security and Compliance

Clouds have similar security as on premise

Compliance is an issue- related to geography

MOVING TO CLOUD: ISSUES

Cost benefit

Cloud cost effective

Cost of migration

Availability of cheap resources on cloud

Elasticity

MOVING TO CLOUD: ISSUES

NATURE OF BIG DATA

How big is big?

How to scale on premise storages and architecture

Agility and Self service

On-premise- create infra first- software-applications

All resources at one place- cloud

Allow infrastructure to change on the fly

Elasticity-cloud allows to scale up

MOVING TO CLOUD: ADVANTAGES

Lift and Shift approach

Replicate on cloud

Cheaper and faster

Does to fully utilise cloud-native features

Use big data infrastructure made for cloud

MIGRATION PROCESS

Medium Term- hybrid cloud-on premise

Long term- Cloud based BA

Hybrid- maintain on-premise infrastructure

Possible for processes which are fragment-ableacross network

Choice of infra- software-app align with cloud native features

Ready to move to cloud

BUSINESS ANALYTICS: STRATEGY

Advanced Analytics

Data Literacy – Fundamental Skill

2016 - LinkedIn listed BI as one of the hottest skills to get one hired

2017 - Data Analytics will become a mandatory core competency for professionals of all types

Competency in analytics, a staple in the workplace

Expectation - Intuitive BI platforms to drive decision-making at every level

Analytics and data programs permeate higher education and K-12 programs

Data Literacy – A Fundamental skill for Future

Critical data skills shortage that’s gripping the business community

Importance of data in running an effective business – and in gaining faster, deeper market insight and competitive advantage – unequivocally recognized

Data scientists in more demand than ever before

Data Literacy – A Fundamental skill for Future

Maintenance/ broad management of data - a job for the technical experts alone? Is it possible to leave data analysis to the few

specialists? Organisations obsessed with hiring people with very

specific digital skills It’s common approach and thought processes which

are the most important Rely on methodical, analytical way of thinking and

that’s what companies should look for in new hires and existing employees

Analytical Thinking - Across all departments and every line of business needs

Coding vs Thinking Analytically

Self-service Analytics Tools - Coding no longer a must-have skill

Latest generation of data solutions delivers a user-friendly interface

Shift away from reliance on specific people with specific technical capabilities – accords agility

Business changes in the next 12 months, and a skill you’ve hired in is no longer relevant? Far better to hire recruits with an overarching

methodical mentality than a group who can navigate a specific coding language

People with analytical mind set bring richer, more diverse mix into the company, united by a systematic approach to business

Boosting Business with Self - Service

Modules/ courses in business analytics and related fields in Management and Business Schools

Data-driven culture - no longer means that everyone should know SQL Server, Python or R

Every member of the business should understand that each of the firm’s decisions are made based on data, and that frequently interrogating data and making business decisions accordingly is how a company succeeds

Looking to the Future

Thank You

Jyoti, Karthik,

Suvin, Misra

, Prayas, R

ajendra, Rawat, Dino & Bhalla