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Building for the future of AI and Machine Learning at scale
- 1. BUILDING FOR THE FUTURE OF AI AND MACHINE LEARNING AT SCALE Data and Analytics Executive Summit - NYC #AlteryxLive
- 2. GUESTS Randy Schafer Technology Innovation Principal Director Heather Harris Solutions Architect & Data Scientist Tom Davenport Best Selling Author, Key HBR author, and Co-Founder & Advisor of International Institute of Analytics Olivia Duane Adams Chief Customer Officer Ashley Kramer Vice President, Product Management #AlteryxLive
- 3. OLIVIA DUANE ADAMS Founder and Chief Customer Officer Alteryx #AlteryxLive
- 4. Source: Innosight, “Corporate Longevity: Turbulence Ahead for Large Organizations “, 2016 #AlteryxLive Evolution or Extinction
- 6. THOMAS DAVENPORT Babson College, MIT, Deloitte, IIA #AlteryxLive
- 7. Artificial Intelligence for the Real World Thomas H. Davenport Babson College/MIT/Deloitte/IIA Alteryx Data and Analytics Executive Summit April 19, 2018 7 | 2018 © Thomas H. Davenport All Rights Reserved
- 8. Data on Enterprise Use of AI/Cognitive Technologies • The 2017 Deloitte State of Cognitive Survey: Bullish on the business value of cognitive • Data source: 250 “cognitive aware” senior managers in large U.S. companies • Other surveys from McKinsey, Gartner, Teradata, etc. • Harvard Business Review: “Artificial Intelligence for the real world” • Data source: 152 consulting projects • 30 company interviews Copyright © 2017 Deloitte Development LLC. All rights reserved. Deloitte Analytics: Smarter Insights. Stronger Outcomes.
- 9. • Technologies used • Benefits achieved and expected • How ambitious are companies? • What does it mean for the workforce? • What capabilities do companies need to build? What’s Happening with AI in the Enterprise?
- 10. Source: Deloitte State of Cognitive Survey, August 2017, n = 250 A Diverse Set of Technologies 59% 58% 53% 49% 34% 32% 2% Robotic process automation Statistical machine learning Natural language processing or generation Expert or rule-based systems Deep learning neural networks Physical robots Other (Please describe) None Total (n=250) Robotic process automation and statistical machine learning are the top two widely used AI/cognitive technologies.
- 11. The 152 cognitive technology projects studied fell into three categories: 11 Routine and data-intensive administrative tasks Granular statistical insights from structured data Language or image- based interaction with customers/employees Three Types of Cognitive Projects 71 57 24 Robotics and Cognitive Automation Cognitive Insights Cognitive Engagement
- 12. Examples of Each Project Type 12 ► Robotics and Cognitive Automation—perform digital tasks ► Transferring data from email and call center speech to other systems ► Replacing lost credit or ATM cards without human intervention ► Producing automated investment reports for wealth management ► Cognitive Insights—deliver granular insights, detect patterns ► Employing thousands of customer propensity models ► Identifying credit/claims fraud in real time at banks/insurers ► Identifying probabilistic matches of similar data across databases ► Cognitive Engagement—interact with customers, employees ► Virtual digital assistants for customer service ► Internal HR or IT service sites for employee questions
- 13. From the edge to the center The majority of Fast Laners (71%) say AI will become “much more” important to their company’s strategy in next three years—but 31% for Slow Laners and 24% for Waders. “How will the importance of AI/cognitive technologies to your company’s strategy change in the next three years? ”% “much more”: • 92% believe that cognitive technology is an important aspect of their internal business processes. • 87% reported it will play a significant role in improving their products and services. • Looking ahead, even more (89%) see it playing a larger role in shaping company strategy. 24% 31% 71% Waders Slow Lane Fast Lane Benefits: AI is a Business Imperative
- 14. 83% of respondents said their companies have already achieved either moderate (53%) or substantial (30%) benefits from their work with these technologies; more experience yields more benefit. Source: Deloitte State of Cognitive Survey, August 2017, n = 250 Happy (Early) Returns
- 15. Beyond cost savings: New products, new markets, and more For example, 26% of Fast Laners report using AI to help them create new products or create new markets. Only 12% of Waders share this goal. When asked about the primary benefit of AI/cognitive technology, here’s how many respondents said “create new products”: 38% AI: Primary benefits to companies 51% Fast Lane Benefits in Products, Decisions, Operations……Not Automation 19% 14% 12% 12% 10% 8% 8% 7% 7% 2% 15% 10% 12% 14% 10% 9% 9% 11% 8% 17% 11% 8% 10% 16% 8% 8% 12% 8% 51% 35% 32% 36% 36% 25% 25% 30% 22% 0% Enhance the features, functions, and/or performance of our products and services Make better decisions Create new products Optimize internal business operations Free up workers to be more creative by automating tasks Pursue new markets Capture and apply scarce knowledge where needed Optimize external processes like marketing and sales Reduce headcount through automation Other (Please specify.) Don’t know Total n=250 Rank 1st Rank 2nd Rank 3rd ∑ 1-3
- 16. The “Moon Shot” Approach A highly ambitious project to treat certain forms of cancer • CEO-driven • $62M spent • Very high media visibility … • No patients treated • No EHR integration • Project put on indefinite hold The “Low Hanging Fruit” Approach A series of projects to improve patient satisfaction, operational efficiency, and financial returns • CIO-driven • “Care concierge” for patients’ families • Identified patients needing help w/ bills • Helping staff with IT problems … • Patient satisfaction up • Financial condition improved • Cognitive CoE established • Many similar projects underway A Tale of Two Ambitions: M.D. Anderson Cancer Center
- 17. The “Moon Shot” Approach An ambitious project to create a “robo-advisor” • One of earliest Watson projects • Goal to capture DBS knowledge and market developments to recommend investments (through relationship managers) … • Watson couldn’t handle graphic info • Variation in research formats • Pilot not as good as human advice The “Low Hanging Fruit” Approach A series of projects using multiple technologies to improve operations and customer relationships • Chatbot for digital bank in India • Machine learning models replenish cash • Predicting churn of salespeople • Identifying and reducing trading fraud … • 55X reduction in cash outages • 85% accuracy in predicting attrition • Calls to human call centers down by 15% • Many similar projects underway A Tale of Two Ambitions: DBS Bank in Singapore
- 18. The “Moon Shot” Approach At least two ambitious projects to create automated checkout at Amazon Go stores and manage drone data • One pilot convenience store in Seattle • Some teething problems with the automated checkout technology • Initially for Amazon employees— now open to the public • Drone information isn’t public The “Low Hanging Fruit” Approach A series of “invisible” projects using machine learning to improve operations and customer relationships • Demand forecasting • Product search ranking • Product and deals recommendations • Merchandising decisions • Fraud detection • Machine translation Bezos: “Though less visible, much of the impact of machine learning will be of this type – quietly but meaningfully improving core operations.” A Tale of Two Ambitions: Amazon.com
- 19. 47% 40% 12% 1% 0% It’s important to strive for large-scale, transformational change with cognitive technologies. Cognitive technologies have a lot of value, but right now it is better to “pick the low hanging fruit,” such as starting with robotic process automation or… Cognitive technology is important, but we can wait a few years until the technology matures before we start using it. Cognitive technologies don’t provide any current or future value to our company. None of these Feeling About AI Today Transformational or incremental? The Fast Lane votes for transformation. % of respondents who chose “It’s important to strive for large-scale, transformational change with cognitive technologies”: 27% 41% 62% Waders Slow Lane Fast Lane Moon Shots, or Low-Hanging Fruit? Source: Deloitte State of Cognitive Survey, August 2017, n=250
- 20. AI predicted to cause both gains and losses Source: Deloitte State of Cognitive Survey, August 2017, n=250 5 years from now 10 years from now 3% 4% 4% Job Loss or Job Shift? 11% 14% 22% 51% 36% 28% 17% 23% 15% 18% 23% 28% 3% 4% 7% 3 yrs from now 5 yrs from now 10 yrs from now Relationship Between AI and Workforce in Future Don’t know at this point We are likely to see many new jobs from AI/cognitive technology AI/cognitive technologies are not likely to have much impact on the workforce over this timeframe Workers and AI/cognitive technologies are likely to augment each other to produce new ways of working Workers are likely to be displaced in substantial numbers by AI/cognitive technology-driven automation
- 21. Primary Challenges with Enterprise AI 21
- 22. 1. Understand the technologies • Executives need to know what AI does what, benefits, risks • May need a formal educational offering 2. Identify a portfolio of projects • Knowledge bottlenecks, scaling challenges, too much data 3. Launch some pilots • Try out multiple technologies • Small projects adding up to a big impact • Probably need a Cognitive Center of Excellence 4. Scale up • Lots of change management • Hard because of integration issues • Need some productivity, competitive advantage Becoming a Cognitive Company: A Four-Step Framework
- 23. ASHLEY KRAMER Vice President of Product Management Alteryx #AlteryxLive
- 24. “THE REASON WHY IT IS SO DIFFICULT FOR EXISTING FIRMS TO CAPITALIZE ON DISRUPTIVE INNOVATIONS IS THAT THEIR PROCESSES AND THEIR BUSINESS MODEL THAT MAKE THEM GOOD AT THE EXISTING BUSINESS ACTUALLY MAKE THEM BAD AT COMPETING FOR THE DISRUPTION.” Clayton Christensen , Author of The Innovator’s Solution and The Innovator’s Dilemma #AlteryxLive
- 25. DATA CHAOS + ANALYTIC OBSCURITY HBR, Data Blending: A Powerful Method for Faster, Easier Decisions * IDC, The State of Data Discovery and Cataloging ** *2017 The State of Data Science & Machine Learning O F O RG A N I Z AT I O N S U S E M U LT I P L E S O U RC E S F O R T H E I R DATA * A N A LYS T S N E E D F I V E O R M O R E D I F F E R E N T DATA S O U RC E S * A N A LYS T S T I M E W A S T E D S E A RC H I N G F O R DATA * * T I M E W A S T E D G OV E R N I N G DATA * * 94% 64% 37% 23% A N A LY T I C S S H A R E D V I A E M A I L * * *28%#AlteryxLive
- 27. THE CULTURE FACTOR of hi ghl y di gi tal l y advanced companies use cr oss - functional teams to or gani ze wor k and char ge them w i th i mpl ementi ng di gi tal pri ori ti es. 70% MIT Sloan Review Survey, 2017#AlteryxLive
- 28. ©2018 Alteryx, Inc. 28 Business User 300M Advanced Spreadsheet + SQL Users 20-30M DS ~1M Business Users >350m Citizen Data Scientists 50M+ Data Scientists 1.5-2M 2015 2025 IMPORTANCE OF BUILDING PLAYER COACH RELATIONSHIPS #AlteryxLive
- 29. 26 HOURS 8 HOURS6 BILLION $60 BILLION DOLLARS Hours per year spent working In spreadsheets Per week wasted working In spreadsheets Per week wasted repeating the same data tasks Per year wasted on analysts doing repetitive manual work in spreadsheets Source: IDC: The State of Self- Service Data Preparation and Analysis Using Spreadsheets ANALYTIC WASTE #AlteryxLive
- 30. GAR- BAGE IN…. 97.3% Share of departments whose data records were “Not Acceptable” 2.7% Share of departments whose data records were “Acceptable” *Harvard Business Review, January 2018 #AlteryxLive
- 31. CROSSING THE DEPLOYMENT CHASM OF ALL DATA SCIENTIST M ODE LS GE T DE P LOYE D 13% *Rexer Analytics Data Science Survey, 2017 #AlteryxLive
- 32. D I S C O V E R + C O L L A B O R A T E P R E P + B L E N D A N A L Y Z E + M O D E L D E P L O Y + M A N A G E THE MODERN ANALYTICS PLATFORMALTERYX D A T A S C I E N C E & A N A L Y T I C S C U L T U R E C O M M U N I T Y #AlteryxLive
- 33. PANEL DISCUSSION Tom Davenport, Best Selling Author, & Co-Founder & Advisor of International Institute of Analytics Heather Harris, Solutions Architect & Data Scientist, Alaska Airlines Randy Schafer, Technology Innovation Principal Director, Accenture Ashley Kramer, Vice President, Product Management, Alteryx Olivia Duane Adams, Chief Customer Officer, Alteryx #AlteryxLive
- 34. Demographic Engine Introduced Spatial Engines (drive time, geocoder) Alteryx Designer (45 tools) Advanced Analytics Predictive Functions Alteryx Server Scaling and Sharing Alteryx Connect Data Discovery and Collaboration Alteryx Promote Operationalizing Models The Alteryx Platform HISTORY OF TRANSFORMING ANALYTICS
- 35. OUR PRODUCT MISSION • Expand beyond traditional analytic platform capabilities • Create more citizen data scientists • Bridge the gap between data science work and business value • Break the barriers between data scientists, IT, and business analysts • Unify the data and analytics processes within enterprises
- 36. RETAIL RESTAURANTS CONSUMER PRODUCTS TELECOM & CABLE MEDIA PROFESSIONAL SERVICES ENERGY & UTILITIES HEALTHCARE MANUFACTURING FINANCIAL SERVICES TRAVEL & HOSPITALITY TECHNOLOGY DIVERSE CUSTOMER BASE 36Note: Customers as of December 31, 2016
- 37. 1,200 HOURS SAVED 36 DATABASE INPUTS 31 TRANSACTIONS P/S Analytic Automation • 63 Gbs of data each month analyzed in minutes • Cut production from 100 hours to 4 min • Expanded fraud analysis due to time savings
- 38. Demand Forecasting and Optimization • Automated merchandise planning • Improved store performance across all locations • 2 to 4% lift to top line sales within 6 months $76B REVENUE GAIN 2,000 STORES 160K SKUS
- 39. Player Coach Relationships • Codify manual processes for use by analysts • Data scientists taking on more complex tasks • Unifying the analytics language spoken between teams $1M SAVINGS 24HR AFTER INSIGHT CREATED 4,600 DATA SOURCES 1000S USE CASES ACROSS THE ORG
- 40. Advanced Analytics and Model Deployment • Near real-time data about claims and statuses • Open net new revenue streams • Deployed model in first day of Alteryx trial 8-12 PRODUCTION MONTHS SAVED 15+ USE CASES 700 HRS SAVED A MONTH
- 41. 41 D I S PA R AT E D ATA C U L T U R E C R I T I C A L C O M P L E X M O D E L S D E P L OY A N Y W H E R E Answers to business questions often lie in many disparate data sources Enterprises are moving to the cloud at different paces Sharing insights and collaborating is critical to building a culture of analytics Business needs are driving more complicated workflows CUSTOMER TRENDS
- 42. MODERN END-TO-END ANALYTICS PLATFORM 42 C O N N E C T D E S I G N E R S E R V E R P R O M O T E D A T A S C I E N C E & A N A L Y T I C S C U L T U R E C O M M U N I T Y
- 43. MODERN END-TO-END ANALYTICS PLATFORM 43 D I S C O V E R + C O L L A B O R A T E P R E P + A N A L Y Z E S H A R E + S C A L E D E P L O Y + M A N A G E D A T A S C I E N C E & A N A L Y T I C S C U L T U R E C O M M U N I T Y
- 44. 44 D I S C O V E R + C O L L A B O R A T E S E LF - S E RVICE DATA DIS COVE RY, INS IGHT S , AND COLLABORAT ING WHE RE VE R YOU WANT, HOWE VE R YOU NE E D
- 45. 45 DISCOVER, COLLABORATE AND CATALOG YOUR ANALYTIC ASSETS DISCOVER Disparate Data / Apps CATALOG Lineage / Governance COLLABORATE Visibility / Impact Analysis I N S I G H T S
- 46. ©2018 Alteryx, Inc. ALTERYX CONNECT SEARCH ANY ASSET IN YOUR ORGANIZATION
- 47. 47 P R E P + A N A L Y Z E T RANS FORMING HOW YOU INT E RACT WIT H DATA, CONS UM E IT AND ACT ON INS IGHT S IN A CODE - FREE AND CODE - FRIE NDLY WAY
- 48. ALTERING YOUR ANALYTICS PIPELINE 48 D E S C R I P T I V E V I S U A L I Z A T I O N S & R E P O R T S Spatial Production and Output Data Cleansing BE FORE Data Prep and Blending Predictive and Prescriptive Production and Output Data Cleansing Spatial Data Predictive, Prescriptive Modeling V A L U E F I N D I N G , V A L U E S A V I N G I N S I G H T S ALTERYX Data Prep and Blending
- 49. 49 • Powerful data & analytic workbench with 250+ tools • Enrich with Spatial, Demographic and Census data – including TomTom • Wide range of preconfigured statistical and predictive models CODE- FREE
- 50. CODE- FRIENDLY 50 • Developed to bridge the gap between the analyst and data scientist • R, Python code support • Extensible via SDKs • In-Database Analytics for unstructured data
- 51. 51 S H A R E + S C A L E S HARING DE E P INS IGHT S ACROS S YOUR ORGANIZAT ION AT E NT E RP RIS E S CALE
- 52. CONTROL MEETS FREEDOM ANALYTIC GOVERNANCE 52 Share workflows, models and insights across teams Automate and schedule workflows & reports Flexible deployment options – AWS, Azure and Google Role-based access controls
- 53. 53 S E CURE LY AND CONFIDE NT LY DE P LOYING ALT E RYX ACROS S YOUR ORGANIZAT ION D E P L O Y + M A N A G E
- 54. MANAGE AND DEPLOY ANALYTIC MODELS 54 DYNAMIC PRICING Suggest prices through a website or app REAL-TIME CREDIT SCORING Determine the credit approval via embedded app FRAUD DETECTION Automatically determine if transactions are fraudulent RECOMMENDER SYSTEMS Use predictive models to forecast consumer behavior • Deploy R & Python into production without recoding • Embed machine learning models into business processes • Manage and monitor models, version control and iterate
- 55. DEPLOY DATA SCIENCE MODELS IN MINUTES A LT E RY X P RO M OT E
- 56. MODERN END-TO-END ANALYTICS PLATFORM 56 D I S C O V E R + C O L L A B O R A T E P R E P + A N A L Y Z E S H A R E + S C A L E D E P L O Y + M A N A G E D A T A S C I E N C E & A N A L Y T I C S C U L T U R E C O M M U N I T Y
- 57. See what Alteryx can do for you! Download a free trial of Alteryx alteryx.com/trial Alteryx,com THANK YOU
Editor's Notes
- Good afternoon to all of you in the room. And good, afternoon, good morning or good evening to all of you in our virtual audience who have tuned in today. Thank you for joining and participating with us for out Data and Analytics Executive Summit today. We are thrilled you could make it. We want to get the most value of out of today, and encourage you in the room to participate in the panel discussion. As well as encourage all of you in the virtual audience to participate in the virtual conversation, with each other, as well as with us. We have someone in the room here who will be letting me know of your questions and we will be asking your questions to our guest panelists as well. Please ensure to use the #AlteryxLive for your questions. We have the hashtag listed on every slide to make it easy for you.
- Today I am happy to welcome our guest speakers: Tom Davenport, Author of best-seller, Competing on Analytics, Tom Davenport is an innovator in the analytics literature space and is a frequent contributor to the Wall Street Journal, Fortune, and Harvard Business Review. Tom has been named one of the top three business/technology analysts in the world, one of the 100 most influential people in the IT industry, and one of the world’s top fifty business school professors by Fortune magazine Ashley Kramer Ashley is the Vice President of Product Management at Alteryx and is responsible for driving the direction of the Alteryx platform. Ashley brings tremendous knowledge and experience to Alteryx to help scale its product organization, facilitate the development of cloud-based offerings and strengthen strategic technology partnerships. Prior to Alteryx, Ashley was a Software Engineer for NASA and Oracle before transitioning into product roles at Amazon and recently at Tableau where she was the Head of cloud Strategy at Tableau. Randy Schafer Randy has worked more than 35 years in premier global financial institutions as both employee and management consultant, leading a range of innovation, renovation and direction-setting initiatives, several with enterprise-wide impact. Most recently, at Goldman Sachs where he led global payment- and liquidity-related initiatives dealing with policy and program management. Before that, DTCC, SWIFT, MasterCard, Chase Manhattan Bank, Oliver, Wyman and Merrill Lynch doing a variety of strategy and innovation programs. Heather Harris Heather Harris is a Solutions Architect and Data Scientist for Alaska Airlines. She leads the deployment of emerging data science and analytics technologies across the airline to elevate the use of data as a key corporate asset. By applying a three-dimensional approach to software deployments that include people, processes and technology, Heather achieves successful business outcomes and sustainable solutions.
- We are in this incredible moment today, and it’s why all of you are in this room and all of you in our audience at home are here, it’s because the world has woken up and realized that Analytics is a major disruptive force in our global economy. This demand for insights is almost insatiable, and it’s as if you can’t work long enough, hard, enough of fast enough to keep up with it. The quest to have analytics powering every department and every single business decision has only increased with time. And now, there is a tremendous amount of focus on AI and Machine learning in analytics today, that organization are looking to deliver on the promise of even more innovation and transformative analytics. Yet there is a lot of hype and hyperbole surrounding the concepts of AI and Machine learning. There seems to be a pervasive concept that achieving AI and Machine Learning will catapult you into the upper echelons of analytic nirvana. Today, we’re going to be dispelling with some of the smoke and mirrors of the AI and Machine Learning analytics promised land to cut to the heart of what these technology really mean in the real-world contexts. We’ll also walk you through the real issues are that are preventing organizations from achieving higher-order analytics, after which we will turn to our panel discussion to give you pragmatic steps and advice you can take back with you.
- Before we begin I want to set the stage today. Why the pressure is on for analytic teams and leaders across all industry sectors, and why have organizations begun to turn to such a heavy focus on AI and ML. A lot of it has to do with the fact that there is a creative destruction that is starting to whip through corporation's faster then ever before. In a mere 54 years, the companies that every one of you in this room, and everyone one of you tuned in on the virtual stream, know today may not exist. If we take a look at the lifespan of an organization, which is what you’re looking at in this graph, you’ll see that the half-life of a company is shrinking incredibly fast. If we go back further then what this graph shows, and look at a company that started in the 1920s, that company had nearly 70 years before another company disrupted its hold on a market ―no real innovation required. Now, If we bring this timeline in a little bit more and look at companies started in the mid 60’s companies still had a good run rate, with a half life of about 33 years before market disruption. Today a company has about 15 years before competitive entry or market disruption. The creative destruction whipping through organizations today is burning so fast that it’s predicted that 40-percent of today's Fortune 500 companies on the S&P 500 will no longer exist in 10 years, and by 2027 75% , 75 percent, of the S&P 500 will be replaced. The pressures of staying ahead of the digital innovation race is one of the biggest reasons you and analytic teams like yours are facing such a tremendous amount of pressure, because analytics is the centerpiece of digital business transformations. In fact, organizations that are putting digital innovation at the epicenter of there strategy are the top 5 most valuable companies in the world, vs. those 10 years ago, representing over $2 Trillion in market value. The key for everyone in this room and in watching live, is that the pace, and the rate of digital turmoil, is the ability to support and deliver insights for decision-making at velocity will me more important then ever. Creating an analytics competency that fuels digital innovation is no small task, and is either exciting or intimidating depending on what side of the analytic effectiveness spectrum you’re sitting on when it comes to digital transformation.
- When it comes to transformation there are three areas in which we see analytic teams whom have embraced the velocity of change required to thrive in the changing world. More often than not it’s not a lack of vision, but a lack of resolve to fully commit to new methods and technology that hold analytic teams back from achieving more. Conviction in the analytics destination of your choice is going to be key, analytic leaders must be bold catalysts of change, you have to envision a future that is far different from the status quo. We often see this translating into a hybrid approach to aligning technology, people and process, resulting in old and new methods being stitched together, trying to bridge the old with new demands. And though it’s often a place of comfort, where the lull and comfort of learned patterns slow the rate of analytic innovation and evolution. What we also often see is an over focus on strategy and planning, but the simple reality is it is people, the analytic talent, within your organizations that will bring this vision to fruition. Analytic teams that will thrive will posse flexibility, diverse talent, and bring new levels of speed and efficiency, it’s your responsibility as analytic leaders to ensure that the processes and technologies you’re brining into your organization support your all of the analytic talent within your organization needed to execute on your analytic vision, and ensure that you create systems and structure that support encourage ongoing analytic innovation. With the hyper focus on digital transformation, it’s important to keep in perspective what analytic innovation means in that context……..is that is isn’t always about new things, it’s about new value. And with that, I’s like to introduce you to Tom Davenport who will walk you through the balancing act of the world of AI and ML in the context of new value vs new analytic innovation.
- Tom spoke about rethinking the AI and ML projects you’re going after to help cut through some of the hype around AI and ML. I’m going to walk you through some of the operational and culture barriers we see organizations struggling with as they work toward taking on more and more of the analytic projects Tom just mentioned.
- The transformative analytic benefits of AI and ML will be difficult for many organizations to achieve. And that’s because for most of you in this room, or those of you watching in the virtual stream, everything that you do today that makes you great at your existing business actually makes you bad at competing for the disruption. More often then not, we find that analytic teams are starting from an insufficiently strong position, attempting to innovate with legacy holdovers of analytics processes, technology and team alignments. Holding on to these relics are the biggest barriers to analytic alignment and innovation. Yet, many organizations that we talk to, are still struggling with this, and it can be clearly seen by the simple fact that there are fundamental operational issues that still plague analytic teams today, and until you solve for the core issues that will hold you and your analytic teams back.
- This isn’t the high-profile analytic strategy problems that the news, or business execs want to hear about, but not addressing this basic issue will prevent you from achieving value-driving insights, but this is a big problem that we see time and time again – the problem of data chaos and analytic obscurity. Today, only 6% have all their data in one place, and the majority of analysts have to pull data from 5 or more sources – spreadsheets, cloud applications, social media, and that data warehouse. And that’s once they find it! Right now at this very minute while we’re sitting in this room with you, your analytic team is spending close to 40% of their time trying to even find all the data they need. They haven’t event begun the work of modelling and this just doing basic analytic tasks, the world of AI and ML analytics is only going to exacerbate these issues. Then there is the fact that your data talent is spending more of their time governing and searching for data than they are on extracting value, and that’s the just data. And what about the state of your other analytic assets? The models that each of your individual data workers has built that is sitting on their personal system which are scattered all across your organization. The most common means of collaboration and information sharing regarding analytics is EMAIL and every idea and insight shared via email are effectively lost to you organization's knowledge base, and more so it translates into lost productivity, too: brainstorming, experimentation, research, even coding must be recreated from scratch -- each and every time it's needed.
- You need to have a solution in place that will bring decentralized data, analytic assets and users together - matching needs and haves, in ways that bypass traditional, legacy methods in order to unlock true value [CLICK] By focusing on extending tribal knowledge across your analytic teams not only will you ensure that your capturing the analytic IP being created day in and day out within your analytic teams today, but will accelerate the evolution and expansion of your analytic IP across your organization. These are just the core operational elements that need to be aligned if you’re looking to gain the transformative benefits of higher-value analytics at scale. There is another component that is key………….
- In order to achieve next-levels of analytic maturity that drive digital transformation there needs to be a focus on breaking the barriers between analytics talent and teams. Digital transformations are about blurring or even obliterating traditional boundaries. It requires more cross function collaboration and cooperation, and that includes analytics teams and groups. MIT Sloan researched what set digitally advanced companies apart from their peers, and a key underpinning to creating true business value and competitive advantage was in their ability to collaboratively work together within among teams as well as with key stake holders and partners both internally and externally. The same principle holds true for analytics teams. Moving up to the next level of analytic maturity requires a new way of looking at your analytic process, how you empower your analytic talent, and how you help them to work together. This is critical if you're trying to build for AI or ML are scale because everyone in this room is in a talent drought.
- Today, there are around 1 Million true data scientist globally by 2025 this number will at most double. Basically there 8x more people in NYC today then there will be Data Scientists globally in 2025! Conversely, there is this population of spreadsheet and SQL data workers in your organizations who have the data literacy to actually take on more sophisticated analytic tasks is growing and evolving, yet they’re not being empowered to take on more tasks. In order to get to the world of AI and ML at scale you have to rethink your analytic talent, and rethink what they’re doing how in three ways: You have to free up your specialized analytic talent so they focus their full efforts on the higher value, and more complex analytic work that needs to be done, and reskill them to think about creating reusable templates that the general analytic population can use so they can become hyper productive At the same time you have to upskill your existing data talent to take on more sophisticated analytic tasks. And most importantly you have to build player coach relationships between these two teams and help them to share and collaborate on analytic tasks, The challenge is these two analytic talent types are today don’t speak the same analytic language and what we see often is that they only common tool they have to try and bridge that analytic language is spreadsheets.
- Spreadsheets are great for some things, but it is not an analytics tool, nor is it a collaborative analytics tool. In fact it’s is extremely costly tool if being used for analytic work. On average data workers spend 26 hrs per week working in spreadsheets, what’s worse if 8 hrs of that is wasted doing the same data task over and over again. When we calculate just the time lost on repetitive data tasks by data analysts it’s equivalent to flushing $60 billion dollars. You simply can’t afford these levels of inefficacy and cost waste in your analytic teams today regardless of where you are on the analytics maturity continuum. A solid analytic foundation can’t be built with antiquated tools that weren’t purpose built for the analytic demands and needs of today.
- When it comes to higher-value analytics and the trust factor and strategic alignment between the business and analytics team must be extremely high due to the high cost of poor decision made on inaccurate information. Unfortunately, what we’re seeing is that traditional analytic process and methods are lacking in providing that. The Harvard Business Review recently had researchers in executives programs analyze their most recent 100 records processed by their departments. On average 47% contained a critical error, and less then 3% was deemed acceptable. The process of moving your data and models back and forth between analytic tasks and tools opens the flood gates of introducing errors. Particularly with AI and ML models, which will require you to move back and forth more between data and modeling in order to test, tune and train them. You need to build an agile analytic pipeline on that freely allows your teams to move back and forth between tasks, iterate quickly and can see what is happening to the data at every stage of the analytic process in order to build sound insights that business can trust and depend on.
- The last mile of analytics is in making it actionable, and this often the hardest barriers for analytic teams to cross. - only 13% of data scientists surveyed by Rexer analytics saying their models always get deployed. What we see is month long projects requiring special development teams and IT in order to deploy analytic models into business systems. This should be the hardest part of the analytic process and yet for many it is. And, once they finally get deployed it’s just as challenging to monitor and manage these models. Simply hiring more developers to help deploy your analytic models isn’t going address the real problem. You have to make it actually easier to deploy models without requiring developers.
- Many organizations are trying to innovate and accelerate their analytic capabilities leveraging patchwork of yesteryears technology and overlaying new point tools that were really built for the 1% of your analytic population. The reality is legacy platform and point solutions fundamentally lack these properties essential to modern analytic needs. Collaboration, agility and an elegant analytic pipeline that is flexible are critical in achieving advanced analytics at scale across your organization Reproducible, versionable, scalable and open are operational necessities to achieving the efficiency needed to take on even more sophisticated analytic tasks. Having a single solution that is purpose built for today’s analytic talent, needs, and demands and moving out of custom disconnected products and into PLATFORMS that are open and flexible to change are what is will set the analytic innovators apart from the analytic laggers.
- Langley already hit worth saying again…huge market opp Expand beyond prep and blend –powerful core need to know how to sell it but also expand beyond it Huge opportunity to help create more CDS But we can’t ignore the DS or they will dismiss us –bridge that gap CDOs are looking for platforms that offer end to end analytics They no longer want their orgs using 15 data and analytics tools They’re moving from best of breed point solutions to full-service platforms
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- Background: Adhering to financial service regulatory requirements, needed to Problem find fraud (emulating a terminal) and social engineering (calling victim and pretending someone they know is at a terminal and in need of money) within the complex framework of this global payments provider. In total there are 36 different database inputs, 63 GBs of data each month that then have to be analyzed to detect that fraud. Before Alteryx: Using Excel, Access and MYSQL, just the data preparation was taking 100 hours per month. Excel crashing, Access freezing, and MYSQL just wasn’t powerful enough. After Alteryx: Analysts learned the product basics in 30 minutes, built a repeatable workflow in 2 hours and the exact same process was reduced from 100 hours to just 4 minutes, saving saved 1,200 hours from one single workflow. Used Alteryx to develop more sophisticated analysis and detection of malicious insiders and hackers and expand the fraud analysis and detection at Western Union
- Background: Home Depot, a relatively new customer of just a few months, building out assortment planning workflows in Alteryx and optimizing the merchandize mix in each store. Wanted to improve store performance by eliminating markdowns, eliminating stock outs, reducing returns to manufacturers Needed to ingesting POS data covering 160,000 SKU’s along with several other data sets for all 2,000 stores building store clusters, then comparing the actual performance to anticipated performance Monumental task knowing that there are roughly 16 billion combinations of SKU’s and stores Before Alteryx 2 weeks covering just 5% of merchandize After Alteryx now runs 10 times a day, covering 100% of merchandise allowing them to double the margins of each store analyzed. Results In the first 6 months of deploying the macro for merchandise optimization, they point to huge performance improvements including a 2 to 4% lift to top line sales, which currently is $76 Billion a year.
- Used AYX to drive their digital transformation Ford elevated data-driven decision-making to C-Level status, a crucial step in the journey to digital transformation. Player coach relationships story – bridge the gap between teams Goals Establishing consistent data management, data quality and data governance practices. • Gaining comprehensive knowledge of available data and acquiring experience with how that data is best used. • Avoiding redundant and inconsistent analyses and, therefore, the dreaded problem of multiple versions of the truth. • Sharing scarce data science talent by not confining experts to serve in a single area of the business. • Developing broadly capable and experienced analytics experts who have been exposed to many aspects of the business Problems Mix of experts including veterans formerly embedded within departments as well as new hires, promotes the development of talent, sets and coordinates analytic priorities and champions infrastructure and data investments to the benefit of all business units. Not enough data science talent. Many processes very manual, tedious process. And it was prone to problems, because as skill sets changed, experience changed moved, it wasn't very repeatable and stable. Results: Moved over key processes into Alteryx, essentially codify manual processes so it could be automated and repeated. Place these type of models in the hands of the analysts, allowing Data science team to, incorporate new insights such as clustering algorithms to augment insights. bring all these different pieces and business partners together into one room. So now we have the operations team, the purchasing team, the IT support team-- everybody sitting in one room, speaking the same language, solving the same problem altogether. Using Alteryx as the underlying technology to upskill data talent to take on more analytic activities using data scientists to take on more activities.
- Looking to increase the efficiency of Model Management and Deployment Specifically looking to embed predictive models into their web apps for online insurance applicants. and drive deeper analytic insight creation across all business functions. With Alteryx: Deployed model within the first days of their trial, immediate return in real-time. R models. retraining model Cut production time of building and embedding predictive models by 8-12 months Generated a net new revenue stream, and selling applicants who didn’t meet their threshold to referral partners in real-time. Net new referral model, predicated to provide begin driving 1 million -8 million in additional revenue. This is just one model, expecting to roll out to several others. This is just one use case - Rapid Expansion - In under 8 months, Alteryx has over 15 documented use case across all major functional areas of the business Claims, Compliance, Data Science (Yhat use case), Corporate Marketing & Distribution, Digital Marketing, Support Desk, Underwriting, Customer Service, “Agency” – Sales for Agencies, Finance & Audit, Cash Management, HR (future use case), Special Investigations Unit, Product, IT The General is collectively saving over 700 hours a month across all functional areas
- Notes from Seth: “according to us, as we see if from our Customers”
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- WHAT IT IS Create powerful code-free workflows for statistical, predictive, and spatial modeling (title) Leverage dozens of powerful built-in analytic tools to create and modify models – for example, decision trees, forest models, regressions Incorporate other advanced analytic models written in R & Python using our code-friendly tools In-line Visualytics to see your results along the way – check your math along the way WHAT IT MEANS Flexibility – delivers support for everything from basic transformations to advanced analytic models. Visibility - using embedded visualytics within your analytic workflows to check your math along the way. Less Time/Speed – decrease the time to insights and the ultimately, the time to decision.
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- WHAT IT IS Deploy analytic models whenever & wherever you choose - real-time or in batch; on-premises or cloud Embed R and Python advanced analytic models into production applications for real-time insights Manage models including permissions & versioning Rank models based on performance WHAT IT MEANS Speed – decrease the time it takes to get analytic models from development into production. Compatibility / Openness - Models can be consumed by a variety of existing users, workflows, and applications. Confidence – Model scoring increases confidence for users & lines-of-business. Security – Ensure model versions are managed appropriately and by the right people.
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- Thank you all for joining us from our virtual audience, That concludes our live streaming portion. For our in-person audience we’ll be walking you through our vision of how we are helping leaders get to the next-level of analytics