Download - Latest trends in Business Analytics
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