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Latest trends in Business Analytics

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Business Analytics

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Latest trends in Business Analytics

  1. 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. 2. MODERN BI
  3. 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. 4. MODERN BI Shift of BI & Analytics Platforms Business User-Centric Platforms IT Led Enterprise Reporting Business Led Self Service Analytics
  5. 5. Strategic Assumptions - 2018 Self Service tools to prepare data for analysis Integration of these self-service platforms Convergence of data discovery platforms
  6. 6. Shifting Categories Infrastructure Data Management Analysis & Content Creation Sharing
  7. 7. Infrastructure Platform Admin Cloud BI Security Connectivity
  8. 8. Data Management Governance & Metadata Self Contained ETL Self-Service Data Preparation
  9. 9. Analysis and Content Creation Embedded Advanced Analytics Analytic Dashboards Interactive Visual Exploration Mobile Exploration and Authoring
  10. 10. Sharing Embedding Analytic Content Publishing Analytic Content Collaboration & Social BI
  11. 11. Collaborative Analytics Democratization of DATA- About 56,60,000 results (0.57 seconds)
  12. 12. What is Democratization Contribution Exploitation
  13. 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. 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. 15. Deadly Combo IT Enabled development of Analytical Content by Business Users BI Analytics Platforms Democratization of Analytics
  16. 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. 17. Challenges Collaborative Analytics Integration Trust Licensing FLEXIBILITY, RESPONSIVENESS AND AUTONOMY
  18. 18. Ref
  19. 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. 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. 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. 22. Data Becomes Equal
  23. 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. 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. 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. 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. 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. 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. 29. Self-Service Analytics
  30. 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. 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. 32. PLATFORM FOR SELF-SERVICE DATA ANALYTICS
  33. 33. TYPES OF SELF-SERVICE DATA ANALYTICS Gartner, Inc. is the world's leading information technology research and advisory company
  34. 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. 35. Embedded BI
  36. 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. 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. 38. Embedded BI
  39. 39. Embedded BI
  40. 40. Natural language Generation
  41. 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. 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. 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. 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. 45. Different Variations of NLG
  46. 46. Business impact • Brokerage Firms • Travel Distribution Systems • Accounting • FMCG • Weather Service • Oil and Gas • Financial Services
  47. 47. Transition to Cloud
  48. 48.  Organizations moving their data to the cloud  Analytics also to move to cloud  “Data Gravity” MOVE TO CLOUD
  49. 49. Big data Cloud computing On-premise Analytics DATA GRAVITY
  50. 50. Security and Compliance  Clouds have similar security as on premise  Compliance is an issue- related to geography MOVING TO CLOUD: ISSUES
  51. 51. Cost benefit  Cloud cost effective  Cost of migration  Availability of cheap resources on cloud  Elasticity MOVING TO CLOUD: ISSUES
  52. 52. NATURE OF BIG DATA  How big is big?  How to scale on premise storages and architecture
  53. 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. 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. 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. 56. Advanced Analytics
  57. 57. Data Literacy – Fundamental Skill
  58. 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. 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. 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. 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. 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. 63. Thank You

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