Loras College 2016 Business Analytics Symposium Keynote
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Leaders who embrace data have a profound impact on their organizations yet too few seize the opportunity. Biases in decision making, technology myths, data quality and analytical skills and are the most frequently cited obstacles by organizations of all sizes. Technology advances have neutralized the scale advantage and have democratized analytics for every organization – so now what? Are you to engage more data in your management decisions? Do you have an analytic strategy that has two speeds – one for innovation and one for scale? Are you investing in your top talent so they can ask new questions? We’ll explore these topics and how to create an analytic culture in your organization. We’ll share how leaders have transformed their organizations by innovating their analytic processes, re-designing the way they work and embracing new technology innovation. We’ll dispel myths about technology and provide you a foundation for building your journey to analytic excellence.
!["By 2018, one third of the top 20
market share leaders will be
significantly disrupted by new
competitors that use the 3rd
platform to create new services and
business models.”
- IDC Predictions 2014
[people driving analytic transformation is key]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Loras College 2016 Business Analytics Symposium Keynote
- 1. Creating a Transformational Impact Rich Clayton ‘87 Loras Regent Vice President, Oracle Corporation @richardmclayton
- 2. "By 2018, one third of the top 20 market share leaders will be significantly disrupted by new competitors that use the 3rd platform to create new services and business models.” - IDC Predictions 2014 [people driving analytic transformation is key]
- 3. “To be ‘Ubered’ has become a catchphrase that goes beyond transportation.” Financial Times
- 4. Don’t be Ubered Today you must run twice as fast as your competition • The threats are greater • Irrespective of size / industry • Innovation is a must
- 5. Digital Disruption is Everywhere [using data to accelerate innovation isn’t new] 20002000 2005 20102005 2010 20152015 Amazon (Books) Apple (Music) Lending Club (P2P Finance) Airbnb (Lodging) Michelin (Fleet) Tesla (Automotive) Stratasys (3-D Print) Uber (Transport)
- 6. Analytics is Well Worn Trail [but still a ton of opportunity] 6% more profitable 5% more productive
- 7. Transformation Requires a Strategy ASSESS STARTING POINT DEVELOP ANALYTIC MISSION PLAN YOUR JOURNEY
- 8. “The object of your mission is to explore the Missouri river, & such principal stream of it, as, by its course & communication with the waters of the Pacific Ocean, whether the Columbia, Oregon, Colorado and/or other river may offer the most direct & practicable water communication across this continent, for the purposes of commerce.” Thomas Jefferson [broad and ambitious] Develop Your Mission
- 9. Around-the-clock Availability Consumer Expectations Real-time Fulfillment Flawless Engagement Personalized Service Information any time, Anywhere Business Model Insight Speed to Market Employee Expectations New Markets Analytic Opportunities Span Every Function
- 10. Pendleton & Sons Storefront Sensors Driving Growth Dynamic merchandising New revenue streams Personalized offers
- 11. Developing Your Analytic Mission [start with the why] SIMPLIFY OPTIMIZE INNOVATE
- 12. Starting with Simplifying Information Access • Self-service Reporting • Centralize data assets • Eliminate spreadsheets • Centralize assumptions • Access to Customer Data • Lack of performance visibility • Inefficient planning process • Reporting is time consuming • Insufficient IT resources
- 13. Optimizing Existing Processes • Order to Cash • Hire to Retire • Procure to Pay • Record to Report • Embed insights into process • Establish process benchmarks • Find new process signals
- 14. Innovating Your Business Model • Explore New Data • Automate Next Best Offer • Create Data Asset • Customer Experience • Crowd-sourced Design • Pricing Strategy • Recruiting
- 15. Land O’ Lakes Precision Agriculture Higher Margins; Greater Yield Analytics @ The Edge
- 16. Assess Starting Point [start with your people] People Data Decision Processes Technology
- 17. [and there is no shortage of data]
- 18. Data Literacy Key to Advancement “Data modeling, simulation, and other digital tools are reshaping how we innovate.” And that has changed the skills needed by our employees. To meet this challenge, P&G created a baseline digital-skills inventory that’s tailored to every level of advancement in the organization.” Bob McDonald CEO
- 19. One Analysts Point of View [time to develop your own]
- 20. Several Evaluation Frameworks [just pick one]
- 21. Take a Multi-dimensional Approach to Assessment [example framework]
- 22. Recognize Data Quality is Never Perfect 23 28 29 43 51 Quality, reliability or comprehensiveness of data Lack of effective systems to gather and analyze data Lack of skills required to interpret data Lack of widespread understanding of what data is used for Concern over confidential corporate information [but not a reason to delay]
- 23. Data is Among Top Intangible Assets [just ask Facebook]
- 24. Six Ways to Value Your Data
- 25. Executives on Decision Processes Good decisions were too infrequent 28%Quality of strategic decisions was good 60%Bad decisions just as frequent as good ones 12% [good analysis + good judgement ≠ good decision]
- 26. Top Five Most Common Biases in Management Decision Processes [what biases do you bring to decisions?]
- 27. Process 6x More Important Than Analytical Detail in Measuring Decision Effectiveness
- 28. Technology Considerations • Consistent view of operating performance • Independence from IT analyst • Ease of generating new scenarios • Visually see and share the signals • Mobile access to key performance indicators • Quickly mashup new sources with existing • Predictive analytic capabilities [these are just the basics]
- 29. Plan Your Journey Core Competencies Organizational Design Social Responsibility Explore All Data Invest in Platform
- 31. Build Diversity in Analytic Skills Storytelling Scenario Analysis Analytic Process Design Data Modeling Predictive Analytics Data Visualization Data Wrangling Text Analysis
- 32. Become a Better Storyteller [picture is worth a 1000 words] turn it upside down learn to distill think infographic
- 33. Diversity in Discovery Dr. Steven Shapiro University of Pittsburgh Medical Center Integrating genomic and clinical information across 140 breast cancer patients Two modes of research working in tandem Challenging pattern recognition biases
- 34. And Yet We Do This….
- 35. When This is Possible…
- 36. Optimizing Supply Chain and Labor in Retail Beverage Temperatures By Store Over Time
- 37. Form a Data Club Rules The first rule of data club: You don’t admit to being in data club The second rule of data club: You don’t admit to being in data club The third rule of data club: no spreadsheets
- 38. Digital Ethics on Data Governance “By 2018, 50 percent of business ethics violations will occur through improper use of big data analytics.” [so what’s your plan]
- 39. General Mills Data 4 Good Data Science Competition Partnered with Generation Next Identify opportunity gaps for students of color in MSP Contribute to Social Causes
- 40. Explore All Data Possibilities External Data Your Data ERP HCM CRM Social Media Machine data Web logs • New relationships • Behavior patterns • Predictive outcomes Government Sources Sensor data Mobile Geo-Location
- 41. Replay Edison’s Invention Factory
- 42. Form a Data Lab #1 Encourage analytic creativity #2 Scale to support a large portfolio of data projects #3 Connect to the Data Lab to commercialize Empower your innovators Formalize your invention process Start with the data Connect the people Connect the data Connect the infrastructure Create a culture of well- managed data projects Welcome a broadened team Support any type of analysis on any data
- 43. Get a Good Wagon [this is the technology bit]
- 44. Two Speeds for Analytic Excellence Fast for InnovationSlow for Scale Cloud Personal Data Mashups Enriched Data Model Visual Analytics Mobile and Wearables On-Premises Enterprise Data Controlled Data Model Robust Reporting Desktop Dashboards Consumer ExperienceAdmin &Upgrade
- 45. Inspire Others to Follow in your Tracks
- 46. Q & A
Editor's Notes
- A study in 2012 showed that companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors. But that’s no longer enough, now that many organizations are using analytics for better decisions. Today, companies are realizing that data should be treated as a new form of capital, to be invested throughout their enterprise for competitive advantage. [CLICK] There is tremendous inherent value in that data itself. In fact, the McKinsey Global Institute says that data capital, basically “what’s your data worth to the bottom line”, explains most of the valuation premium enjoyed by what they call digitized companies. That’s companies that actively collect and use data. [CLICK] It goes on to say that the accumulating global value of digital-capital investments has reached more than $6 trillion. Analytics is the tool to extract that value from the data. Sources HBR Big Data: The Management Revolution, Andrew McAfee and Erik Brynjolfsson, October 2012 McKinsey: Measuring the full impact of digital capital http://www.mckinsey.com/insights/high_tech_telecoms_internet/measuring_the_full_impact_of_digital_capital Source Forbes, The Rise of Data Capital, Paul Sonderegger, February 2015, http://www.forbes.com/sites/oracle/2015/02/24/the-rise-of-data-capital/
- Local butcher in London. Loyal customers, good reputation. Library closed and supermarket moved into space on their small space impacting foot traffic and revenues. Simple sensors to monitor foot traffic and impact of window displays and promotions. Look at conversion. Sandwich board sign. Integrated weather data to plan meal suggestions and recipes for week ahead. Cell phone detection sensors using cloud-based BI platform from sensor vendor. Refine their displays and messaging based on what worked, not just intuition. Also pointed out new revenue opportunity. Two pubs located down the street. More passerbys @ 9pm til midnight. Looked at Google trends on what foods are popular. They pulled their pork burger with chorizo. Expand use of data even further to targeted offers in email and seasonal promotions.
- Rolls Royce Michelin
- Beyond butter, Land O’Lakes is a diversified company and owns WinField Solutions, which is one of the largest wholesale distributors of agricultural seed and crop protection products in the US. It tests numerous seed hybrids in 200 different farm test plots located around the country, to understand how each variety works in different soil types and different weather. Those tests GENERATE a lot of data. WinField is also able to COLLECT DATA FROM FARMERS in the U.S. about what they’ve planted and their crop yields. When combining publicly available data with WinField’s own sales data and its other data sources, Land O’Lakes saw an opportunity to create a software solution that will help growers buy the best seeds, and help WinField and their farm cooperative partners sell them. Land O’Lakes built its analytics solution using Oracle’s Endeca Information Discovery. Land O’Lakes said implementing its system using Endeca cut two years and $3 million off the software project. In addition it helped OPTIMIZE SALES CYCLES to help increase sales performance. Volumes and profits are both up. Turns out data on soil type combined with a lot of other data can indeed help Winfield and its partners sell more seeds.
- What doesn’t get used, doesn’t get fixed.
- Action – excessive optimism, competitor neglect Interest – individual incentives, emotional attachment, misaligned corporate goals Pattern – storytelling, false analogies, confirmation, power of the champion, Social – align with the leader, group think Stability – loss aversion, sunk cost fallacy, status quo
- Think about the conclusion and actions you want the data to affect. Test thesis and iterate Learn to distill data rapidly. Avoid infoxication – too much data has the same affect as too much alcohol Let the data speak for itself. Clean and simple.
- Cancer is a complex disease involving many genes and we’ll never understand it if we get bogged down in the time consuming process of testing cause-and-effect relationships one at a time. Need to measure as many variables as possible in as many cases as we can collect without bias from preconceived ideas. Two modes of research working in tandem each enhancing the other. Greatest impediment to cancer research has been the sheer volume of data: billions of measurements and combinations of measurements on a disease-by-disease basis. Greatest impediment to cancer research is sheer volume of data. Billions of measurements and combinations – each tied to one or many diseases Human genome has more than 3 billion base pairs requiring about 1TB / person Cancer is a complex disease involving many genes and we’ll never understand it if we try to analyze cause-and-effect – one relationship at a time. Need to measure as many variables as possible in as many cases as we can collect without bias from preconceived ideas. Using Oracle Big Data Solution – UPMC combined over 200 data sources and use both modes of research – each in tandem and enhancing the other.
- Competency centres, policies, procedures are pretty dry topics and our presentations in the early days were pretty dry. However over time we found that as the team were able to roll out good solutions to the business that required less rework and had less defects, the group of people started to evolve into a real team. Instead of using the ‘EICC’ moniker bestowed upon them, they started to call themselves the ‘data club’
- SUMMARY: More than just invention, Edison’s invention factory encompassed all stages of innovation through commercialization. We can still learn from him ------------------------------------- In 1876, Edison created an industrial research facility in Menlo Park, New Jersey. That’s where he developed the lightbulb, among other great inventions. But that wasn’t his true genius. Edison was the first to see invention as what we now call innovation—invention, research, development, and commercialization . And he did not work alone, gathering a diverse team of workers like a glassblower, a clockmaker and a mathematician, to help him. He created a new institution: the industrial research laboratory. Edison called it the Invention Factory. [CLICK] He vowed to turn out a minor invention every six weeks and a major invention every six months. And he did, putting a process around innovation to great commercial success. Sources: The Thomas Edison Papers, Rutgers, http://edison.rutgers.edu/ Thomas A. Edison and the Menlo Park Laboratory, Henry Ford Museum, https://www.thehenryford.org/exhibits/edison/ The Thomas Edison Center at Menlo Park http://www.menloparkmuseum.org/history/thomas-edison-and-menlo-park/