Increasing Analytic Value by Bringing Cognos Analytics and Watson Analytics Together
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The document discusses the integration of Cognos Analytics and Watson Analytics to enhance business decision-making and data utilization. It emphasizes the need for businesses to adopt self-service analytics, increase data accessibility for employees, and foster a culture of curiosity. The focus is on providing analytic tools that empower users while managing data governance and encouraging predictive and prescriptive analytics.
Increasing Analytic Value by Bringing Cognos Analytics and Watson Analytics Together
- 1. INCREASING ANALYTIC VALUE BY BRINGING COGNOS ANALYTICS AND WATSON ANALYTICS TOGETHER Hyoun Park Founder and Principal Investigator, Amalgam Insights
- 2. HYOUN PARK Founder and Principal Investigator, Amalgam Insights, an advisory firm focused on Technology Consumption Management Previously: Chief Research Officer at Blue Hill Research IT Manager at Bose, Teradyne Campaign Treasurer, Boston City Councilor Fantasy Baseball Website Editor
- 3. "A LEADER’S PRIMARY OCCUPATION MUST BE TO DISCOVER THE FUTURE” RON SHAICH, CEO PANERA BREAD
- 4. SETTING THE STAGE • Fundamental change in analytic demand from a management and leadership perspective • Extracting value from Cognos Analytic and Watson Analytics • Key value propositions and examples
- 5. COGNOS AND WATSON ANALYTICS? • How do Cognos Analytics and Watson Analytics fit into strategic business needs and value propositions? • Let's set the stage, then explore CA and WA through the lens of business value and our ability to drive leadership as analytic providers.
- 6. What How Why
- 7. What – Cognos Analytics How – Planning Analytics Why – Watson Analytics
- 8. Business Need Quote to Remember Solution Understanding Why "I have no special talents. I am only passionately curious." - Albert Einstein Data Discovery What is the answer? "Simplicity is the ultimate sophistication" - Leonardo da Vinci Contextualized Analytics How did we get here? "Keep your fears to yourself, but share your courage with others." Robert Louis Stevenson Storytelling, Analytic Sharing What should we do next? Knowing that things could be worse should not stop us from trying to make them better. - Sheryl Sandberg Predictive Analytics, Root cause analysis What is our focus? How should we fix it? Why is this a problem? "All of the great leaders have had one characteristic in common: it was the willingness to confront unequivocally the major anxiety of their people in their time. This, and not much else, is the essence of leadership." --John Kenneth Galbraith Anomaly detection, Prioritized and prescriptive analytics
- 9. "YOU ARE RESPONSIBLE FOR YOUR LIFE. IF YOU'RE SITTING AROUND WAITING ON SOMEBODY TO SAVE YOU, TO FIX YOU, TO EVEN HELP YOU, YOU ARE WASTING YOUR TIME." - OPRAH WINFREY
- 10. OPRAH’S ANALYTIC COROLLARIES • Employees expect immediate access to tools that fit their needs, skills, and challenges • Businesses are asked to be increasingly driven by analytics and data science
- 11. TO AVOID NEW SILOS OF SHADOW IT • Every company needs a roadmap to get all governed/corporate data into end user hands and end user tools. • Legacy and foundational data must be integrated into today's analytics world, but rip-and-replace is a stupid idea. • IT is changing into a world of rapid, best-in-breed augmentation, not of core systems replacements.
- 12. DATA IS A PRECIOUS THING AND WILL LAST LONGER THAN THE SYSTEMS THEMSELVES.” SIR TIM BERNERS- LEE, INVENTOR OF THE WEB.
- 13. THE PAST DIDN’T GO ANYWHERE… 65% of enterprise workloads are still on- prem 13% in the cloud (5:1 ratio) - Uptime Institute The concept of BI is in its late 20s (Thanks, Howard Dresner!)
- 14. EXTRACTING MEANING REQUIRES A COMBINATION OF CAPABILITIES • Self-service discovery • Direct recommendations • Translating data into stories • Trusted data - peer and company vetted • Identifying the real problems • A culture that asks questions
- 15. AND A COMBINATION OF ANALYTIC CAPABILITIES Historical Reports Descriptive Reports Predictive Forecasting Root Cause Analysis Consultative Analytics
- 16. AS WE FOCUS ON ANALYTIC PROGRESSION IT- Handholding •Tools require IT support to conduct basic tasks Self-Service •Tools providing individuals with analytic access Guided Service •Trusted recommendations and context to users Human-guided Improvement •Humans teaching tools how to work better Automated Machine Learning •Autonomous and self-improving tools
- 17. THE ABILITY TO ESTABLISH, GROW, EXTEND, AND RESTORE TRUST IS THE KEY PROFESSIONAL AND PERSONAL COMPETENCY OF OUR TIME.” - STEPHEN COVEY
- 18. “The Right Thing” depends on the maturity of your users: match your delivery to your users’ needs.
- 19. DON’T BE THE ADMIN: BE THE GUIDE For analytics to provide value, you are tasked to lead end users through multiple stages of maturity and provide an analytic environment that grows with the user Basic Questions and Queries Dashboards and Reports Presentation and storytelling Data and analytic models Data and metadata networks, ontologies, and definitions
- 20. "THE UPSIDE OF PAINFUL KNOWLEDGE IS SO MUCH GREATER THAN THE DOWNSIDE OF BLISSFUL IGNORANCE." - SHERYL SANDBERG
- 21. FINALLY, WHAT YOU CAME FOR… Cognos Analytics • Answers the question of What Happened? • Combines reporting, self-service analytics, and data modeling • Visualization, mapping, storytelling, OLAP and Relational packages Watson Analytics • The Big Why? • Removes need for knowing statistics to get statistical guidance • Provides initial guidance for knowing where to look for relationships and root-cause
- 22. "THOSE WHO CANNOT REMEMBER THE PAST ARE CONDEMNED TO REPEAT IT." - GEORGE SANTAYANA
- 23. • On-Prem or on cloud • Cognos Framework Manager connection for Cognos Analytics • OLAP Dashboards • TM1/Planning interoperability • Cognos Analytics serves as a self-service bridge to foundational data
- 24. “EVERY SECOND OF EVERY DAY, OUR SENSES BRING IN WAY TOO MUCH DATA THAN WE CAN POSSIBLY PROCESS IN OUR BRAINS.” – PETER DIAMANDIS, CHAIRMAN/CEO, X-PRIZE FOUNDATION.
- 26. USING STORYBOOKS • Using Expert Storybooks: guided templates for translating analytics for human consumption • Good for maintaining analytics over time and providing both scheduled and ad-hoc updates • Example - monthly reports and presentations to CIO, Controller
- 27. "A MAN ALWAYS HAS TWO REASONS FOR DOING ANYTHING: A GOOD REASON AND THE REAL REASON." J.P. MORGAN • WA also provides "real" reason capabilities in a variety of ways that traditional BI has never considered • Sentiment analysis for Alchemy API - Native data connectors for Watson Analytics • Data-specific Social Media and weather analysis
- 28. LOOKING FOR THE QUICK WIN The cost and productivity benefits associated with an initial analytics project focused on end user or departmental change typically results in: a payback period of less than one year, and an initial net annual benefit in the $50,000 to $500,000 range. Don’t start with an enterprise-wide expectation of value: focus the value mapping on the early adopters. For successful analytics, this initial ROI ends up being a proof of concept for other departments and not an end-all and be-all of project value.
- 29. COGNOS ANALYTICS Construction customer: IBM Cognos Analytics reduced ongoing BI costs by 2/3rds for an environment that supported roughly 100 users. Keys to value: Moving to the cloud Eliminating on-site infrastructure, Reducing consultant and report creation headcount,
- 30. WATSON ANALYTICS Retail Manager: A veteran branch manager used Watson Analytics to identify key profitability indicators and found a new factor that was not being tracked. This increased visibility provides 3 - 5 percent additional visibility into revenue and profit recognition.
- 31. EVEN IF THEY HATE A PROCESS, HATE A SYSTEM, WHEN YOU TRY TO CHANGE SOMETHING, YOU HAVE TO PEEL THEIR HANDS AWAY FROM IT." SAFRA CATZ, CO-CEO, ORACLE
- 32. "IF YOU WANT TO BUILD A SHIP, DON'T DRUM UP THE MEN TO GATHER WOOD, DIVIDE THE WORK, AND GIVE ORDERS. INSTEAD, TEACH THEM TO YEARN FOR THE VAST AND ENDLESS SEA." ANTOINE DE SAINT-EXUPERY
- 33. COGNOS ANALYTICS AND WATSON ANALYTICS • CA and WA are new approaches: built to bridge your foundation and practical end user trends in the contradictory world where both Trust and Agility matter • Governed data, self-service, predictive analysis, and graduated user experiences are now all part of the same corporate BI environment
- 34. “IT IS BETTER TO BEG FORGIVENESS, THAN ASK PERMISSION.” - GRACE MURRAY HOPPER • Don't just support the leader, be the leader for curiosity, • Bring trusted data to your managers and key stakeholders or they will analyze their own data. • Identify an analytic executive who you can work with to develop analytics focused on improving management, leadership, and business change • Push self-service and focus on service access, not core technology management and report building. Disrupt yourself or have it done for you.
- 35. "GROWTH AND COMFORT DO NOT COEXIST." GINNY ROMETTY, CEO, IBM
- 36. THANK YOU! For more information or to work with Amalgam Insights on data, IT, or revenue recognition management, please contact Amalgam Insights at: Hyoun@amalgaminsights.com Phone: +1 (415) 754 9686 @AmalgamInsights
Editor's Notes
- #3: Amalgam Insights: Strategic Analyst firm focused on emerging consumption strategies for analytics, IT, and finance management to support new business models. Technology Consumption Management focuses on the purchase and broad-based use of emerging enterprise technologies. Hyoun Park: 20 years IT experience, 9 years as analyst/consultant Core Challenge: allow employees to innovate and disrupt Behind the buzzwords of self-service, data insights, Moneyball, and Industry 4.0 are a set of management technologies that need to be maintained at an enterprise level: self-service analytics, data management, cloud resources, and revenue
- #4: Is this true? Do you currently support this?
- #5: Key Changes in understanding Why, Self-Service, Data, and Trust Changing your management approach - Courage Analytics is now a foundation for corporate management and operational benefit. The transformation of analytics and BI can change our businesses and our management efforts The value of analytics must be seen not just in basic process management, but in impacting strategy, action, and leadership
- #6: 1: Set the stage for why these trends are meaningful Goal is to establish common ground 2: Look at CA and WA as solutions to expand analytic usage and adoption 3: Provide recommendations for optimizing the value of CA and WA
- #7: From Simon Sinek’s Start With Why BI has traditionally started and ended with “What” – Cognos TM1 is great at this But instead, BI should start with “Why” Matches up with IBM’s new mode of Why – Watson Analytics How – IBM Planning What – Cognos Analytics
- #8: From Simon Sinek’s Start With Why BI has traditionally started and ended with “What”
- #9: What are the challenges that we face in analytics? What do you have in your organization to solve these needs? Does your analytic approach match any of these great thinkers? Doe your organization have a Sandberg approach? A Stevenson approach? A Da Vinci approach?
- #10: Tough words from Oprah. But this is a mantra that employees have actually embraced. Employees are self-reliant and pride themselves on being able to get work done by themselves. Don’t underestimate the Oprah factor in your organization’s expectations of data and analytics. But they lack the technical skills to truly set up a fully self-service, governed, and complete view of their data. This conflict between individual preferences for independence and the realism that enterprises require team efforts that are often massively distributed leads to a fundamental conflict in data and analytics. Pictures From Flickr:https://www.flickr.com/photos/59632563@N04/5617518534
- #11: Another false dichotomy: Legacy vs. New/ Reporting vs. Analytics Reality is that companies need both and that line-of-business managers and executives are increasingly being asked to do both. Companies must keep the past and build for the future This approach requires a combination of descriptive and predictive analytics In trying to do both, line-of-business departments are currently using the analytic tools that are custom-built to their own use case or department while losing track of the enterprise’s long history of data collection: Sales analytics, marketing analytics, service analytics, and ERP analytics are becoming increasingly nuanced and define analytic functions in Line-of-Business terms. – But this approach lacks enterprise governance and reuse of existing data assets.
- #12: Rapid Best-in-Breed is now the norm – even IBM consulting, such as GBS, looks for quick 2 week – 3 month wins in their DataFirst approach.
- #13: From https://en.wikipedia.org/wiki/Tim_Berners-Lee#/media/File:Tim_Berners-Lee-Knight-crop.jpg Second Key: Legacy data is a foundational capability. A core challenge is connecting Legacy Data is a foundational asset How do we connect Oprah and Tim?
- #14: To keep it real: Foundational data still reigns in the typical enterprise The concept BI is in its late 20s (Thanks, Howard Dresner!) So, the real challenge is to increase value: augment, contextualize, and enhance our foundational data, reports, and existing analytical queries.
- #17: BI needs to get to a “Guided Service” state where IT provides governance and analytic templates while giving end users the ability to explore relevant data and create basic reports and findings. The last 2 steps are more Watson-based. They will be important, but Artificial Intelligence and Machine Learning have not been sufficiently embedded into enterprise analytics to make this viable for end user consumption yet.
- #18: Step 3 - Trust Trust in our data is a core component of accurate and valuable analytics. The value chain of trust is quickly broken by poor business data governance, including both technical governance and end user governance Picture: https://en.wikipedia.org/wiki/Stephen_Covey#/media/File:Stephen_Covey_2010.jpg
- #19: Based on the Hierarchy of Needs – Demonstrates how analytic functionality needs to be prioritized. End user functionality always comes first, which is why Excel rules as an entry-level BI tool. Don’t fight it: accept it.
- #20: Over time, users will evolve if they trust the data
- #21: Starting with the basics is still important – Pushing beyond end user capabilities is difficult, but employees need to know how to provide painful knowledge Reporting is still the top use case for BI, even in a future-facing analytic world. Finding knowledge is still a core challenge Picture: https://en.wikipedia.org/wiki/Sheryl_Sandberg#/media/File:Sheryl_Sandberg_2013.jpg
- #22: WA is useful for analysis across: sentiment, trends retention, churn productivity, effectiveness Governed data is important from a Trust perspective – Support for Cognos Framework Management is a key change for WA in working in conjunction with CA
- #23: Business analytics must start with foundational data. New analytics + New data ignores the multi-million dollar investments in both technology and labor that have created enterprise data over the past 20-30 years.
- #24: Cognos Analytics On-Prem or Cloud Cognos Framework Manager for drilldown and data definitions TMI/Planning interoperability – CA is a part of Planning Analytics
- #25: Analytic and Data Overload means that we often cannot hunt down key drivers on our own.
- #26: From IBM – November 2016 Watson Analytics is fundamentally different from BI because it provides questions and results in both charts and relationships.
- #27: Example: monthly finance reports that controllers and finance managers often provide. Why recreate these charts and reports each month? Or recreate the formatting? Easier to maintain the analysis in a presentation-ready format.
- #28: Sentiment for normal documents and Social media are especially useful for any text-based analysis
- #29: Thinking about financial value – You don’t have to eat the entire ox to get the taste of beef. Create the proof of concept: more important to figure out what is needed in your company to create ROI rather than rely solely on a template. Every company has its own portfolio of tech, resources, and governance. That said, here’s are some basic benchmarks from documented successes.
- #32: Change isn't just about rational understanding, but about relationships, trust, emotion, tradition Picture: https://www.flickr.com/photos/oracle_images/5007038338
- #33: The real challenge – Cultural Change – Teaching and advocating for discovery Who here would be deploying Watson Analytics? Who here would teach users how to ask questions in Watson Analytics? Expect shared functional map based on RAVE visualization Engine Mapping/Geo analytics improvements Data Connectors
- #34: CA and WA combine for Governed + Self-Service + Analytics (both historical and predictive) – sounds simple, but still relatively rare.
- #35: Recommendations for practical implementation and expanding value in the business include: analytic and data staging, onboarding, or ongoing business alignment Work is increasingly focused on specialization and education, not on mid-level skills
- #36: Instead of ending on a high note, ending on a realistic note CA and WA can spread, but require the willingness to change.