1.
MODERN BI
Jyoti Jain : S-31
DEMOCRATIZATION
Puneet Bhalla : S-49
SAND BOX ANALYTICS
Karthik : S-43
DATA BECOMES EQUAL
Suvin: S-49
SELF-SERVICE
DATA ANALYTICS
Raj Kumar Misra: S-53
NATURAL LANGUAGE
GENERATION
Prayas S:48
Embedded BI
Rajendra : S-54
Move to Cloud
RS Rawat: S-53
DATA LITERACY
Dinesh Yadav : S-25
Group 1
TOP BI TRENDS

2.
MODERN BI

3.
What makes us alive ?
MODERN BI
Centrally Provisioned,
Highly Governed &
Scalable System-of-
record Reporting
Analytical Agility &
Business User
Autonomy
Data is Life Blood of the organization

4.
MODERN BI
Shift of BI & Analytics Platforms
Business User-Centric Platforms
IT Led
Enterprise
Reporting
Business Led
Self Service
Analytics

5.
Strategic Assumptions - 2018
Self Service tools to prepare data for analysis
Integration of these self-service platforms
Convergence of data discovery platforms

6.
Shifting Categories
Infrastructure
Data
Management
Analysis &
Content
Creation
Sharing

7.
Infrastructure
Platform
Admin
Cloud BI
Security Connectivity

8.
Data Management
Governance &
Metadata
Self Contained
ETL
Self-Service
Data
Preparation

9.
Analysis and Content Creation
Embedded
Advanced
Analytics
Analytic
Dashboards
Interactive
Visual
Exploration
Mobile
Exploration
and Authoring

10.
Sharing
Embedding
Analytic
Content
Publishing
Analytic
Content
Collaboration
& Social BI

11.
Collaborative Analytics
Democratization of DATA- About 56,60,000 results (0.57 seconds)

12.
What is Democratization
Contribution
Exploitation

13.
BI Growth
Gartner 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

14.
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

15.
Deadly Combo
IT Enabled development of Analytical Content by Business Users
BI
Analytics
Platforms
Democratization
of Analytics

16.
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

17.
Challenges
Collaborative Analytics
Integration
Trust
Licensing
FLEXIBILITY, RESPONSIVENESS AND AUTONOMY

18.
Ref

19.
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

20.
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”.

21.
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”

22.
Data Becomes Equal

23.
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

24.
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.

25.
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.

26.
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.

27.
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.

28.
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.

29.
Self-Service Analytics

30.
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.

31.
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.

32.
PLATFORM FOR SELF-SERVICE DATA ANALYTICS

33.
TYPES OF SELF-SERVICE DATA ANALYTICS
Gartner, Inc. is the world's leading information technology research and advisory company

34.
BENEFITS OF SELF-SERVICE DATA ANALYTICS
Faster time to insight
Analysts can extract insights in
minutes rather than hours.
No up front data modeling
Data sources are prepared for analysis
on the fly, eliminating the need for
complex ETL processes.
UI for Non-technical users
Data sources can be easily blended via
drag and drop
Expected range of data sources
Greater ease of use makes it possible
for analytics to connect to more data
sources.

35.
Embedded BI

36.
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.

37.
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.

38.
Embedded BI

39.
Embedded BI

40.
Natural language Generation

41.
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

42.
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

43.
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.

44.
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

45.
Different Variations of NLG

46.
Business impact
• Brokerage Firms
• Travel Distribution Systems
• Accounting
• FMCG
• Weather Service
• Oil and Gas
• Financial Services

47.
Transition to Cloud

48.
 Organizations moving their data to the cloud
 Analytics also to move to cloud
 “Data Gravity”
MOVE TO CLOUD

49.
Big data
Cloud computing
On-premise
Analytics
DATA GRAVITY

50.
Security and Compliance
 Clouds have similar security as on premise
 Compliance is an issue- related to geography
MOVING TO CLOUD: ISSUES

51.
Cost benefit
 Cloud cost effective
 Cost of migration
 Availability of cheap resources on cloud
 Elasticity
MOVING TO CLOUD: ISSUES

52.
NATURE OF BIG DATA
 How big is big?
 How to scale on premise storages and architecture

53.
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

54.
 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

55.
 Medium Term- hybrid cloud-on premise
 Long term- Cloud based BA
 Hybrid- maintain on-premise infrastructure
 Possible for processes which are fragment-able
across network
 Choice of infra- software-app align with cloud
native features
 Ready to move to cloud
BUSINESS ANALYTICS: STRATEGY

56.
Advanced Analytics

57.
Data Literacy – Fundamental Skill

58.
 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

59.
 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

60.
 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

61.
 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

62.
 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

63.
Thank You