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Bardess Moderated - Analytics and Business Intelligence - Society of Information Management (SIM)

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Bardess Moderated - Analytics and Business Intelligence - Society of Information Management (SIM)

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Joe DeSiena, President of Bardess Group Ltd moderated a panel of Information Technology executives titled Analytics and Business Intelligence for the chapter meeting for the New Jersey Society of Information Management.

Joe DeSiena, President of Bardess Group Ltd moderated a panel of Information Technology executives titled Analytics and Business Intelligence for the chapter meeting for the New Jersey Society of Information Management.

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Bardess Moderated - Analytics and Business Intelligence - Society of Information Management (SIM)

  1. 1. Analytics & Business Intelligence: Beyond the Buzzwords 1
  2. 2. Nandan Shah Nandan is Director, CDRA Systems at Regeneron Pharmaceuticals. In this role, Nandan is responsible for providing strategic guidance, oversight and support as the CDRA (Clinical Development and Regulatory Affairs) organization develops and implements information systems. Prior to Regeneron, Nandan was at Sanofi- Aventis where he was IT Head of Solution Centers for Global Regulatory Affairs, Evidence Value Development (EVD), Global Medical Affairs and Global Marketing. Prior to that, Nandan has worked at Cambridge Technology Partners in various roles with increasing levels of responsibility. Mike Prorock Mike is Director of Emerging Technologies for Bardess Group. A career Analytics professional, Mike specializes in enabling enterprises to expand and handle analytics in high data-growth scenarios. He is an Analytics Strategist and Futurist focused on Analytics in an ever-changing Digital world. Kip Olmstead Kip is the Chief Marketing Officer (CMO) of Private Brands at First Quality Enterprises. A career marketing officer, he was EVP, CMO at Crayola and a Marketing Director at Proctor & Gamble for clients such as Duracell and Walmart The Panel 2
  3. 3. Stephen DeAngelis Steve is President and CEO of Enterra Solutions, a firm specializing in Cognitive Computing. Steve is a technology and supply chain entrepreneur and patent holder with more than 25 years of experience in building, financing, and operating technology and manufacturing companies. Named to Esquire magazine’s “Best and Brightest” list in 2006, he was recognized by Forbes as one of the “Top Influencers in Big Data” in 2012. In 2014, he became a contributing member of Wired magazine’s Innovation Insights blog. Joe DeSiena Joe is President of Consulting Services at Bardess Group, Ltd., a Management Consulting firm specializing in data revitalization, business process design, and information technology. He has robust corporate management experience in several industries including data networking, telecommunications, manufacturing, pharmaceuticals, financial services, utilities, travel and entertainment. Joe has spent 20 years in professional services assisting Fortune 500 companies to define and execute their Analytics strategy The Panel 3
  4. 4. • Data • Nexus of Forces • Business Intelligence • Data Discovery • Big Data • Predictive Analytics • Cognitive Computing • Internet of Things The Buzz Words 4
  5. 5. Data is everywhere Data has grown more in the last year than all the other years in history combined Data more than doubles every two years according to IDC By 2020, information managed by Enterprises will grow 14X (to 35 Zettabytes) 5
  6. 6. What is Data? Facts about things, organized for analysis or used to reason or make decisions Raw material from which information is derived and is the basis for intelligent actions and decisions Collections of usable facts or data Processed stored or transmitted data Data in context with precise definition and clear presentation Specific information about something—the sum of what has been discovered or learned Information known and in the proper context Value added to information by people who have experience and acumen to understand its potential The culmination is applying knowledge by utilizing Information for Value which is corporate Wisdom. Corporate Wisdom is therefore a function of a corporation’s capacity to acquire and apply knowledge. This capacity to acquire and apply knowledge, Corporate Intelligence, is predicated upon the initial Quality of Data Assets. Data Information Knowledge Wisdom 6
  7. 7. Data Challenges to Consider Many organizations are concerned that the amount of amassed data is becoming so large that it is difficult to find the most valuable pieces of information. • What if data volumes get so large and varied you don't know how to deal with it? • Do you store? • Do you analyze all your data? • How can you find out which data points are really important? • How can you use it to your best advantage? 7
  8. 8. The Traditional Constraints: Until recently, the sheer volumes of data overwhelmed processing platforms. • Organizations have been limited to using subsets of their data, or • Organizations were constrained to simplistic analyses The Issue: What is the point of collecting and storing terabytes of data if it can't be analyzed in full context, or if it takes hours or days to get results? But, not all business questions are better answered by bigger data. Possible Solutions: 1. Incorporate massive data volumes in analysis. If the answers will be better provided by analyzing all the data: Apply high-performance analytics to analyze the massive amounts of data using high-performance technologies such as grid computing, in-database processing and in-memory analytics. 2. Determine upfront which data is relevant. Traditionally, the trend has been to store everything (data hoarding) and query the data to discover what is relevant. Apply advanced analytics on the front end to determine relevance based on context. Determine which data should be included in analytical processes and what can be placed in low-cost storage for later use if needed. Solutions to the Data Challenge 8
  9. 9. What is The Nexus of Forces ? (Gartner) The Nexus of Forces is the convergence and mutual reinforcement of Social, Mobility, Cloud, and Information Pervasive Access Big Data Global Delivery Circles of Influence 9
  10. 10. Business Intelligence
  11. 11. Set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. • 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. • A data analysis process aimed at boosting business performance by helping corporate executives and other end users make more informed decisions. What is Business Intelligence (BI)? Definitions: 11
  12. 12. • Gartner noted that BI is Top Priority for CIO’s • Gartner noted BI platform revenue reached US$14.1 billion in 2013 • Up over 30% since 2011. BI Software Market Strong and Growing  Growth has been largely through companies investing in IT-led consolidation projects to standardize on IT-centric BI platforms for large- scale systems-of-record reporting  These have tended to be highly governed and centralized, where IT production reports were pushed out to inform a broad array of information consumers and analysts.  As a result the stack vendors have dominated the market 12
  13. 13. Poor customer support Our users need more flexibility and self service Traditional BI We’ve spent millions with little value Too complicated and difficult to manage BI projects take too long to deploy Our BI vendor has increased maintenance fees User adoption is too low Only used for export to .xls 13
  14. 14. A Business intelligence architecture aimed at interactive reports and explorable data from multiple sources. – According to Gartner "Data Discovery has become a mainstream architecture in 2012". • The ability to give business users the means to draw insights from data independently • Knowledge discovery - "the detection of patterns in data. [...] These patterns are too specific and seemingly arbitrary to specify, and the analyst would be playing a perpetual guessing-game trying to figure out all the possible patterns in the database. Instead, special knowledge discovery software tools find the patterns and tell the analyst what--and where--they are.“ • While data discovery is a nebulous term that can be tough to define, it essentially means a far less structured approach to data exploration. Unlike traditional business intelligence, which is geared toward monitoring and reporting, data discovery is more about discovering hidden patterns and trends What is Data Discovery? Definitions: 14
  15. 15. Gartner noted the following in its 2014 BI Magic Quadrant: • There is a five-year trend, where Data Discovery Tools are either complementing or displacing traditional BI tools • New requirements and investments have been more skewed toward business-user-driven data discovery techniques to make analytics beyond traditional reporting more accessible and pervasive to a broader range of users and use cases. • At the end of 2013, Data Discovery tools surpassed $1 billion in annual software sales, Data Discovery is a Fast Growing Segment of the BI Market • From now through 2015, Data Discovery tools will outgrow the overall BI platforms market by a factor of three. Data Discovery Tools are either complementing or displacing traditional BI tools Gartner predicts that most BI vendors will make data discovery rather than static reporting their primary focus by 2015. 15
  16. 16. QlikTech Dominates the Data Discovery Market Latest 2014 Market Share • Qlik down to 42% • Spotfire up to 22% • Tableau up to 18% • Others down to 18%16
  17. 17. Top Down Purchasing Decision Led by IT Bottom up purchasing Led by Business BI Market Dynamics: Purchasing Decision Polarize  Cost Optimization  Integrity  Scalability , Manageability, Reliability  Performance  Competition: ORCL, IBM , SAP, SAS, MSFT  Flexibility  Speed  Visual Exploration  Minimal Modeling  Key Players: QlikView, Tableau, Spotfire Data Discovery Sweet spot 17
  18. 18. BusinessValue Data Discovery: Sustains value Time Traditional BI: Slowest implementation time, lowest analytic value Data Visualization: Biggest ‘Wow factor’ but tends to wear off quickly Data Discovery: Fast time to value, sustained value over time.
  19. 19. BI Market in a period of accelerated transition BI is Moving from a market with systems used for measurement and reporting to those that also support analysis prediction, forecasting, simulation and optimization. What Next? Why did it happen? PredictiveProblemSolving What if? Descriptive Information Delivery What happened, where and when? 19
  20. 20. Big Data
  21. 21. What is Big Data? An evolving term that describes any voluminous amount of structured, semi- structured and unstructured data that has the potential to be mined for information. Although big data doesn't refer to any specific quantity, the term is often used when speaking about petabytes, exabytes or zettabytes of data. >80% of data is unstructured or semi-structured, forcing new approaches to analysis of data A term used to describe the exponential growth and availability of data, both structured and unstructured. 21
  22. 22. • $41.5B by 2018, Total market for Big Data Technology Source: IDC • $23.76 B in 2016, Global Big Data technology and services revenue will grow from $14.26 billion in 2014at an annual growth rate of 18.55% Source: IDC's Worldwide Big Data Technology and Services 2012 - 2016 Forecast. • $9.83 B in 2020-Big Data technology and services will grow from $1.95 billion in 2013 at a CAGR of 26%. Source: Huawei report Big Data & Advanced Analytics in Telecom: A Multi-Billion-Dollar Revenue Opportunity. Big Data Market is prime for explosive growth Analysts Predict Strong Growth • $114B by 2018 Global spending on Big Data hardware, software, and services will grow at a compound annual growth rate (CAGR) of 30 percent through 2018 Source: A.T. Kearney Beyond Big: The Analytically Powered Organization. • $50.1B in 2015, Big Data is projected to be a $28.5 billion market in 2014 growing 76% in 2015, Source: Wikkbon report, Big Data Vendor Revenue and Market Forecast 2013-2017 EMC - $48B by 2017 22
  23. 23. What are the Four V’s of Big Data? 23
  24. 24. Enabling Technologies for Big Data A number of recent technology advancements enable organizations to make the most of big data and big data analytics: • Cheap, abundant storage. • Faster processors. • Affordable open source, distributed big data platforms, such as Hadoop. • Parallel processing, clustering, MPP, virtualization, large grid environments, high connectivity and high throughputs. • Cloud computing and other flexible resource allocation arrangements. 24
  25. 25. Why Big Data Matters? The real issue is not about acquiring large amounts of data, It's about what is done with the data. The Big Data vision is that organizations will be able to acquire data from any source, harness relevant data and analyze it to find answers that enable: 1. Cost reductions 2. Time reductions 3. New product development and optimized offerings 4. Smarter business decision making 25
  26. 26. By combining Big Data and high-powered analytics, it is possible to: • Determine root causes of failures, issues and defects in near-real time, potentially saving billions of dollars annually. • Optimize routes for many thousands of package delivery vehicles while they are on the road. • Analyze millions of SKUs to determine prices that maximize profit and clear inventory. • Generate retail coupons at the point of sale based on the customer's current and past purchases. • Send tailored recommendations to mobile devices while customers are in the right area to take advantage of offers. • Recalculate entire risk portfolios in minutes. • Quickly identify customers who matter the most. • Use clickstream analysis and data mining to detect fraudulent behavior. Big Data Implementation Examples Combining Big Data with Analytics: 26
  27. 27. • What technology advancements enable organizations to make the most out of Big Data analytics? • How do publicly available datasets and web services impact analysis? • What are some key ways of approaching big data problems? 27 Technology for Big Data
  28. 28. Big Data – Data Scientists Dominated by Rapidminer & “R” “R” seeing the fastest growth Significant year over year decline in use of Excel to gain real answers 28
  29. 29. Big Data – Data Volumes used by Data Scientists Median size of the largest analyzed dataset is in the 40-50GB range 29
  30. 30. John Chambers of Cisco in a 2-10-15 WSJ interview believes that: • CIOs need to get the right data at the right time to the right device or right person so they can make the right decision • The role of the network will change dramatically—and that contrary to popular beliefs, the majority of the data will be analyzed and acted upon at the edge of the network— 30 Role of Network in Big Data Analytics
  31. 31. 0 10 20 30 40 50 60 70 Funding for Big Data related Initiatives Defining Our Strategy Integrating Big Data Technology with Existing Infrastructure Integrating Multiple data Sources Obtaining Skills and Capabilities Needed Risk and Governance Issues Determing How to get value from Big Data Percent of Surveyed CIOs Listing these Factors as hurdles or challenges with Big Data Percent The Big Challenges of Big Data – from CIOs Source: Gartner Survey and Wall St Journal 31
  32. 32. What are the Dangers of Big Data? 32
  33. 33. Predictive Analytics
  34. 34. • An area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. – Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future • The branch of data mining concerned with the prediction of future probabilities and trends. – The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior. • The practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. – Predictive analytics does not tell you what will happen in the future. – It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment. • The use of statistics and modeling to determine future performance based on current and historical data. What is Predictive Analytics? Definitions: 34
  35. 35. Predictive analytics describes any approach to data mining with four attributes: 1. An emphasis on prediction (rather than description, classification or clustering) 2. Rapid analysis measured in hours or days (rather than the stereotypical months of traditional data mining) 3. An emphasis on the business relevance of the resulting insights (no ivory tower analyses) 4. (increasingly) An emphasis on ease of use, thus making the tools accessible to business users. What is Predictive Analytics? (Gartner) 35
  36. 36. • $5.2B in 2018--Transparency Research predicts the Predictive Analytics space would more than triple from $2B in 2012 • $14B by 2018 if you consider Machine-to-Machine Analytics to be part of Predictive Analytics domain -- Markets and Markets • $3.4B in 2018 for Advanced and Predictive Analytics (APA) software market is growing 9.9% CAGR from $2.2B in 2013 —Forbes • The global predictive analytics market, valued at USD 2.08 billion in 2012, is expected to see strong growth at 17.8% CAGR during 2013 to 2019.— Dublin Analytics Predictive Analytics Market Size 36
  37. 37. Cognitive Computing
  38. 38. • The development of computer systems modeled after the human brain. – Originally referred to as artificial intelligence, researchers began to use the modern term instead in the 1990s, to indicate that the science was designed to teach computers to think like a human mind, rather than developing an artificial system. • Cognitive computing integrates the idea of a neural network, a series of events and experiences which the computer organizes to make decisions. What is Cognitive Computing? The simulation of human thought processes in a computerized model. – Cognitive computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. – Rather than being programmed to anticipate every possible answer or action needed to perform a function or set of tasks, cognitive computing systems are trained using artificial intelligence (AI) and machine learning algorithms to sense, predict, infer and, in some ways, think. 38
  39. 39. Internet of Things
  40. 40. • A scenario in which objects, animals or people are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. • The interconnection of uniquely identifiable embedded computing devices within the existing Internet infrastructure. • A computing concept that describes a future where everyday physical objects will be connected to the Internet and be able to identify themselves to other devices. – The term is closely identified with RFID as the method of communication, although it also may include other sensor technologies, wireless technologies or QR codes. • Is when the Internet and networks expand to places such as manufacturing floors, energy grids, healthcare facilities, and transportation. • The network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment. What is The Internet of Things (IoT) ? Definitions: 40
  41. 41. What is The Internet of Things? The Internet of Things is a growing network of everyday objects – from industrial machines to consumer goods – that can share information and complete tasks while you are busy with other activities, like work, sleep or exercise. Soon, cars, homes, major appliances and even city streets will be connected to the Internet – creating this network of objects that is called the Internet of Things. Made up of millions of sensors and devices that generate incessant streams of data, the IoT can be used to improve lives and businesses in many ways. The Internet of Things consists of three main components: • The things (or assets) themselves. • The communication networks connecting them. • The computing systems that make use of the data flowing to and from our things. Using this infrastructure, objects or assets can communicate with each other and even optimize activities between them based on the analysis of data streaming through the network. 41
  42. 42. • $19 Trillion dollars opportunity -2014 John Chambers CES Keynote; Cisco IBSG predicts there will be 25 billion devices connected to the Internet by 2015 and 50 billion by 2020. • $300 billion in 2020 --Gartner estimate that IoT product and service suppliers will generate incremental revenue. • $7.1 trillion in 2020 -- IDC forecasts that the worldwide market for IoT solutions will more than triple from $1.9 trillion in 2013. • $10 to $15 trillion for the “Industrial Internet” over the next 20 years -- GE estimate Size of the Internet of Things Market 42
  43. 43. Examples Internet of Things • Sprinkler systems that use forecasts, weather sensors and pay-by-use water rates to optimize the watering of lawns. • Public trash cans that compacts trash as needed and alert city workers when they are full. • Self-parking cars today become fully autonomous cars that taxi people efficiently around a city, stopping to share fares when budget-conscious travelers opt in. • Trucks that haul commerce safely and quickly across the country, avoiding traffic delays and optimizing part replacement needs. • Home security systems that take proactive action - cooling down homes and opening windows, based on user preferences, the existing weather conditions and proximity to homes (more than allowing remote control of door locks and thermostats) Sensors offer unprecedented access to granular data that can be transformed into powerful knowledge. However, without an integrated business analytics platform, sensor data will just add to information overload and escalating noise. 43
  44. 44. Ten Internet of Things facts and predictions 1. The total economic value-add from IoT across industries will reach $1.9 trillion worldwide in 2020, anticipates Gartner. 2. Fifty billion devices will be connected to the Internet by 2020, predicts Cisco. 3. The remote patient monitoring market doubled from 2007 to 2011 and is projected to double again by 2016. 4. The utility smart grid transformation is expected to almost double the customer information system market, from $2.5 billion in 2013 to $5.5 billion in 2020, based on a study from Navigant Research. 5. Wide deployment of IoT technologies in the auto industry could save $100 billion annually in accident reductions, according to McKinsey. 6. The industrial Internet could add $10-15 trillion to global GDP, essentially doubling the US economy, says GE. 7. Seventy-five percent of global business leaders are exploring the economic opportunities of IoT, according to a report from The Economist. 8. Cities will spend $41 trillion in the next 20 years on infrastructure upgrades for IoT, according to Intel. 9. The number of developers involved in IoT activities will reach 1.7 million globally by the end of 2014, according to ABI Research estimates. 10. The UK government recently approved 45 million pounds (US$76.26 million) in research funding for Internet of Things technologies. 44
  45. 45. • Big data" and "evidence-based policy" are the dominant ideas of our moment. A May 2014 White House report put it this way: "Big data will become an historic driver of progress, helping our nation perpetuate the civic and economic dynamism that has long been its hallmark." • The White House report presents big data as an analytically powerful set of techniques. It says the social and economic value created by big data should be balanced against "privacy and other core values of fairness, equity and autonomy." 45
  46. 46. Other Slides
  47. 47. 47
  48. 48. 48
  49. 49. Gartner on Internet of Things – Aug 2014 49
  50. 50. Explosion of Digital Data Limited Access to Powerful Analysis Long Time to get Answers Industry Average Deployment Traditional BI: 18 Months Time to Build One Report traditional BI: 6.3 Weeks 72% 28% Cisco predicts that we will use as many as 50 billion online connected devices by 2020 - and that is leading us to produce as much as 7.9 zetabytes of data globally by 2015.* * Source: http://www.computerweekly.com/blogs/cwdn/2013/04/ca-world-big-data-needs-to-be-productionised.html “ The need for better decision-making is growing, However, most BI is not delivering 22% 78% BI users Non users 50
  51. 51. • Data discovery is often discussed in the same breath as Big Data because it may encompass the three "Vs" typically used to describe Big Data: volume, velocity and variety. That is, folks can work with very large data sets and can get answers quickly. Users can explore data, both structured and unstructured, that comes from a wide variety of disparate sources. Data Discovery and Big Data 51

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