Data is the New Oil: Presented By Naveen Narayanan, Global Client Partner of KPISOFT
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Data is the New Oil: Presented By Naveen Narayanan, Global Client Partner of KPISOFT
- 4. Birth of Big Data • 1994, • What scares me about this is that you know more about my customers after three months than I know after 30 years • 2x market
- 5. Why most Orgs get data journeys wrong? Humans = emotional & < rational Emotion is a motivating force Daniel Kahneman 2 system theory Humans hate being contradicted Data & WIIFM Humans are lazy in ”right” choices
- 6. So just having data stare at you wont make you change • By 2020 – 80% of orgs will initiate mass data literacy programs • 50% of Orgs will lack talent • 20% more profitable • By 2022, false and real information lines will blur
- 7. 4 Steps to Data driven organizations • Insight Factory Vs Problems to solve • Small Vs Perfect Data • Data Warehouse / Data Lake • “Speed” of insights. • Perfect vs good. • involve user groups. • bite(byte) size & Pull
- 10. Big Data – 4 V - volume, variety, velocity, veracity
- 11. The market
- 14. 14 • Like the movies, days of silent dashboards will be a distant past
- 15. 15Augmented Analytics as The Next Wave WHAT ARE WE CHANGING IN ENTERPRISES ?
- 16. 16 WHAT ARE WE CHANGING IN ENTERPRISES ? Augmented Analytics is the Future
- 20. Then, what is deep learning? • ML on steroids • Technique to amplify even the smallest patterns • Deep Neural Network – It has many, many layers of simple computational nodes 20
- 21. 3 Questions Your Peers Are Asking • What platform do I buy for today and tomorrow? • How do I link decision intelligence, data discovery, semantic modeling, APIs and governance • How do I deliver scale, transformation and value?
- 22. Future of how we will consume Data Open Query via voice Auto Cure, Auto ML – yet to come Interfaced with Behavioural Economics
- 23. Orange Box is AA
- 26. Data Spillage - Let us consider Google as a case study. Between July and December 2016, Google received over 45,500 requests from governments all over the world to reveal user information. Out of all these requests, Google complied with 60%, which means that more often than not, Google will provide private user information to governments when requested.
- 27. AI is like some vice in college (think any) • Everyone talks about it • No one knows how to do it • Everyone thinks everyone is doing it • So everyone claims they are doing it
- 29. Benefits of Analytics · IMPROVED DECISION MAKING · REDUCE RISK AND IMPROVE GOVERNANCE · ACHIEVE GREATER OPERATIONAL EFFICIENCY · GAIN MARKET PRESENCE AND INCREASED REVENUE · ACCELERATE INNOVATION · GAIN BETTER INSIGHTS ON CUSTOMER BEHAVIOURS · INCREASE EMPLOYEE PERFORMANCE · CONTROL ONLINE REPUTATION
- 31. For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income. Retailers who leverage the full power of big data could increase their operating margins by as much as 60%. 73% of organizations have already invested or plan to invest in big data by 2016 Poor data can cost businesses 20%–35% of their operating revenue. Bad data or poor data quality costs US businesses $600 billion annually.
Editor's Notes
- Humans are wired to be emotional and less rational – subconscious pathways and neural stimuli are wired emotionally. Emotion is a motivating force Daniel Kahneman System 1 – Amygdala – Fast, Stereotype, emotional and subconscious System 2 – Slow, effortful, logical, infrequent use, conscious, calculating Humans hate being contradicted, especially deeply entrenched beliefs Every conversation is “data and business benefits” and rarely on Data and its effects on beliefs and motivations By 2020 – 80% of orgs will initiate mass data literacy programs 50% of Orgs will lack talent in AI and Data Companies that have deep data/digital skills will be 20% more profitable By 2022, most people in mature economies will not know between false and real information
- Before beginning your insight factory, clear understanding on what you want to solve. Customer Churn? Source the raw materials – start with “small data” and not perfect data. Data Warehouse is perfect over long term. Start with say POS data and move it with competitor data/traffic, share of wallet etc Produce insights with speed – finite time limits to insights factory . Perfect vs good enough. Deliver goods and Act – involve the user groups in insight. They need to be bite(byte size). Create pull
- Average Repair Time is 30% below Target. The Decline is generated from the Elevator Repairs carried out by Contractor XYZ. If Not, Corrected, FY 19 Revenue Performance will be down by 2%. Ask your contractor to initiate Coach-ins with their installation team. YoY gross margins are up 19 % over same time last year. Elevator installation services gross margins are up 23 % due to our new New Projects starting in Q2. Let's remind our contractors about margins from new agreements.
- WhatcharacteristicsandcapabilitiesshouldIexpectfrommydataandanalyticsplatform, which addresses both my current and future business scenarios? Howarecomponents,suchasdecisionintelligence,datadiscovery,semanticmodeling,APIs and exchange standards and governance services, related in my data and analytics platform? HowcanIcreatedynamicdataandanalyticsorganizationmodelsfordigitalbusiness transformation that deliver scale and business value?
- For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income. Retailers who leverage the full power of big data could increase their operating margins by as much as 60%. 73% of organizations have already invested or plan to invest in big data by 2016 Poor data can cost businesses 20%–35% of their operating revenue. Bad data or poor data quality costs US businesses $600 billion annually.