The Analytics Revolution and What You Need to Know
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Analytics has been maturing for more than half a century, but in the last decade there's been a real revolution in how businesses are using data to drive business strategy. What is the history? Why is the current age of analytics different? What role is technology playing in the revolution? How can you engage in the revolution?! This is a talk I presented to the business analytics program at the University of Central Missouri in Warrensburg, MO on October 18, 2018. https://www.ucmo.edu/academics/programs/majors/big-data-and-business-analytics-bsba-46-640/index.php
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The Analytics Revolution and What You Need to Know
- 1. The Analytics Revolution andWhatYou Need to Know October 19, 2018 – Paul Boal
- 2. Innovation andValue 1. The purpose of analytics is to enable agility in a way that creates value for the business. 10/19/2018 2
- 3. Analytics as a Partnership 1. The purpose of analytics is to enable agility in a way that creates value for the business. 2. Analytics can only create value when it is done in a cooperative partnership that is intermittently lead by business leaders and then technology leaders. 10/19/2018 3
- 4. Constancy of Change 1. The purpose of analytics is to enable agility in a way that creates value for the business. 2. Analytics can only create value when it is done in a cooperative partnership that is intermittently lead by business leaders and then technology leaders. 3. The rate of change in business is accelerating, accelerates the demand for analytics, and is accelerated by analytics. 10/19/2018 4
- 6. Early Analytics Concepts 10/19/2018 6 “Consider a future device … in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory.” Vannevar Bush 1945 1960’s Researchers at MIT, Carnegie Mellon, Harvard, Dartmouth, and others begin work researching the idea of computerized models to assist in decision making. 1970’s 1980’s 1990’s Business journals first begin reporting on the idea of computerized decision support systems. ACNeilson and IRI develop fact / dimension model for retail sales. Berry Devlin and Paul Murphy coin the term “data warehouse.” Teradata releases first database designed specifically for decision support. Ralph Kimball and Bill Inmon publish seminal books and articles on data warehousing and business intelligence systems.
- 8. Business-Driven • Business users identified the opportunity and need • Business users defined the requirements • Business users set the priority and the timeline • Technologists established standards and repeatable patterns 10/19/2018 8 ETLDW Rpt XLS P
- 9. • Sophisticated business users spend more time making decisions and less time crunching numbers. • Technologists develop business patterns for building systems faster and more reliably. 10/19/2018 9 • Business users set the direction, specify the requirements, and verify the work is done correctly. • Technologists build systems. • As soon as a developer is done delivering one product, the requirements are sure to change. • Users always have new questions. Innovation and Value Analytics as Partnership Constancy of Change $
- 10. Pushback During the TeenageYears 10/19/2018 10 • Self-Service Reporting • Master Data Management • Data Stewardship • Data Quality Management • Data Governance • Self-Service Data Prep
- 12. Blame the Business 10/19/2018 12 http://dilbert.com/strip/2008-05-07
- 13. Making Life Easier forTechnologists 10/19/2018 13 1990 Dimensional modeling Low-code ETL tools Self-service reporting tools Data warehouse appliances On-line analytical processing Wal-Mart andTeradata create the world’s largest data warehouse at 24TB 1999
- 14. 10/19/2018 14
- 15. • Self-Service Reporting enables easier drag-and- drop reporting. • Master Data Management allows users to edit business configuration data. • Data Governance becomes a business function. 10/19/2018 15 • Technology deploys more sophisticated and user- friendly tools. • Business users begin focusing more energy on looking at information rather than waiting on developers. • Vendors continuously leap frog each other, leading users to want to continuously switch. • Everyone builds their own version of each report. Innovation and Value Analytics as Partnership Constancy of Change $
- 16. Data and Analytics Maturity Model 10/19/2018 16 PRENATAL INFANT CHILD TEENAGE ADULT SAGE Financial Systems Executive Systems Analytical Systems Monitoring System Strategic System Business Service Static Reports Spreadsheets OLAP/Ad Hoc Reports Dashboards Scorecards / Analytics Customer / Embedded “Cost Center” “Inform Executives” “EmpowerWorkers” “Monitor Performance” “Drive the Business” “Drive the Market”
- 18. Data and Analytics Methodology 10/19/2018 18 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Requirements Analysis Data Warehouse Data Profiling Data Modeling Data Mapping ETL Development ETL Testing Workflow Scheduling System Testing Information Delivery Report Design Report Development Report Validation User Acceptance Testing Report Deployment
- 19. • Emergence of the Chief Data Officer and Chief Analytics Officer who drive best practices and efficiencies across an organization. • Ongoing refinement and automation of data warehousing and business intelligence design patterns. 10/19/2018 19 • Organizations create an Analytics Center of Excellence and shared services models to provide optimized delivery of analytics. • Relationships between requestors and providers of analytics become formalized with service level agreements. • Incremental improvement and planned change are the standard practice. • Analytics roadmaps have three to five year time horizons. Innovation and Value Analytics as Partnership Constancy of Change $
- 20. 10/19/2018 20
- 21. Who’s On First? Business requests Technology to do what Technology is enabling Business to do so that Technology doesn’t have to do what Business wants Technology to do at the request of Business. 10/19/2018 21
- 22. 10/19/2018 22
- 23. Time for a Revolution! 10/19/2018 23 2000’s Big Data DataVisualization Machine Learning & Artificial Intelligence 2010’s Agile & DevOps “The Cloud”
- 24. What is Big Data? 10/19/2018 24 New patient data in the US would create a stack of paper 1,000 miles high every year. 1,000 miles 250 miles International Space Station 340 miles HubbleTelescope 35,000 ft (6.6 miles) CommercialAirplane
- 25. 10/19/2018 25
- 26. Largest DataWarehouse - 1999 10/19/2018 26 24,000,000,000,000 Bytes
- 27. Largest DataWarehouse - 2014 12,100,000,000,000,000 Bytes 10/19/2018 27
- 28. What is big data? Any data set that is too complex for traditional data-processing application software to adequately deal with. 10/19/2018 28
- 32. 10/19/2018 32
- 33. Traditional Programming vs Machine Learning • In traditional programming, we write a program that takes input data, applies a set of instructions, and produces output data. 10/19/2018 33 COMP 01001110 01101101
- 34. Traditional Programming vs Machine Learning • In machine learning, we provide a computer with sample inputs and outputs, and the computer produces a reusable program. 10/19/2018 34 ML 01001110 01101101 COMP
- 35. 10/19/2018 35
- 37. What is Agile? • Agile • DevOps • DataOps 10/19/2018 37
- 38. What does Agile look like? 10/19/2018 38 • Deployment plan • Database server provisioning • Database software installation • Database configuration • Database object creation • BI server provisioning • BI server software installation • BI desktop software installation • Setup BI database connections • Integration server provisioning • Integration software installation • Integration software configuration • Setup integration database connection • Schedule data integration jobs • Schedule BI reports • Setup ad hoc reporting > git clone data-analytics > kubectl run data-analytics Traditional Setup Steps Using Agile / DevOps Tools
- 39. You Know the Cloud 10/19/2018 39 You OnlyThink
- 40. …As A Service • Infrastructure as a Service • Database as a Service • Platform as a Service • Identity as a Service • Enterprise Integration as a Service • Machine Learning as a Service • Internet ofThings as a Service • Artificial Intelligence as a Service • Labor as a Service 10/19/2018 40
- 41. What Revolution Looks Like 10/19/2018 41
- 42. Innovation andValue 1. The purpose of analytics is to enable agility in a way that creates value for the business. In order to create sustainable business agility, the the processes and technology behind delivering analytics delivering analytics must be even more agile. Technology is cool and should be used to deliver innovative solutions to business objectives. • Machine learning and artificial intelligence • Business-centric analytics • Decomposition techniques 10/19/2018 42 Understand
- 43. Analytics as a Partnership 2. Analytics can only create value when it is done in a cooperative partnership that is intermittently lead by business leaders and then technology leaders. Sometimes business needs will drive technical choices choices and at other times, the technology will open the open the business to new insights, business models, and models, and create entirely new markets. Imagine what you can accomplish working together and be open to collaboration! • The Uber model • Precision Medicine • Smart Everything 10/19/2018 43 Embrace Understand
- 44. Constancy of Change 3. The rate of change in business is accelerating, accelerates the demand for analytics, and is accelerated by analytics. Consumers have come to expect change. Smarter Smarter businesses adapt and create new competitive competitive forces faster. Markets are being disrupted disrupted regularly. You need to embrace and be able to drive change! • Agile software development • DevOps and DataOps • Cloud deployment and containers • Flexible data and solutions architectures 10/19/2018 44 Embrace Accept Understand
- 47. Q & A 10/19/2018 47
- 49. Bibliography • https://hbr.org/2006/01/a-brief-history-of-decision-making • http://dssresources.com/history/dsshistory.html • https://en.wikipedia.org/wiki/Decision_support_system • https://www.theatlantic.com/magazine/archive/1945/07/as-we-may-think/303881/ • http://www.dataversity.net/brief-history-data-warehouse/ • https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone- should-read/#560e1e3a60ba • https://blogs.saphana.com/2014/03/05/guinness-world-record-largest-data-warehouse/ • https://siliconangle.com/2014/05/29/virtual-reality-brings-big-data-visualization-to-life/ • https://www.inc.com/guadalupe-gonzalez/amazon-artificial-intelligence-ai-hiring-tool-hr.html • https://www.bloomberg.com/view/articles/2018-10-16/amazon-s-gender-biased-algorithm-is-not-alone 10/19/2018 49
Notes de l'éditeur
- For a very long time, even before human “computers” and tabulating machines we had the notion that the work of business decision-making could be supported by offloading time intensive work of collating data and tabulating numbers to non-decision-making individuals. The origin of the analytics movement was the idea that businesses could fundamentally improve and accelerate the rate of improvement by learning from its past decisions and past performance. Business leaders had always been able to do this, and they were able to pass that down to subsequent generations through mentorship of family members and apprenticeships. What was new was the idea that a business could institutionalize and systematize this process of improvement. From this basic notion, we’ve had all kinds of great business theories emerge.
- For many of these early years, the use of technology was mostly about the automation of business computations that were needed to compile data and calculate business metrics. This allowed decision makers to react to new information more quickly that competitors, to see trends more precisely, to understand complexity more easily. We weren’t making a significant transformational change in the kinds of analysis being done, though – just the difficulty and speed with which the analysis could be done.
- The need, the insights, and the direction were primarily business driven.
- As teenagers tend to do, the adolescent phase in the history of analytics was one of push back to business users. Data warehouse and BI developers got tired of doing the same old work over and over again and decided that they should build tools to let more businessy people write their own data integration jobs and reports. In came the era of self-service.
- Data Governance Master Data Management Data Quality Management Reference Data Management
- By the way – this is what 24 TB can look like today.
- As data analytics started to mature, the industry developed standard processes and maturity models that helped everyone feel good about what they were doing, even if they didn’t measure and see the business impact.
- TODO: Update
- With all of the talk about “maturity” you’d think we hit some kind of Zen.
- We weren’t really achieving some kind of Zen, though. The pile of business challenges was increasing and we were building ever larger and more complex technologies and processes to try to deal with those big challenges.
- Creve Maples, a pioneer in advanced data visualization, talked in 2011 about work that he’d done with Goodyear Tire Company. The engineers at Goodyear had been working for two years to figure out how to make their tires go faster without failing under the stress. Dr. Maples provided a sophisticated and immersive environment to explore the data and within five minutes, the engineers literally “saw” the answer.
- Every NBA game has six high definition cameras recording the game at 25 frames per second. Each game produces about 54 GB per game. Over a season, that’s more than 66 TB of data for the games… not to mention the tapes of practices. NFL has chips inside shoulder pads that track their position, speed, and direction continuously during games. They use that for tracking players but they’re also using that to train robotic tackle dummies that can be used to help reduce damage from repetitive brain injuries. Now players are starting spend several hours training while sitting at a desk with VR goggles on. Running plays over and over to train their brain to see what the play should look like, which enhances their performance in learning the play physically and then executing it in games.
- Amazon’s hiring algorithm has gender biases just like we do.
- When I started, we were all reading “The Mythical Man Month” as a lesson about project management, estimation, and how long things really take to get done. Now, the personal marketplace, Etsy, deploys new updates to it’s website thousands of times per day.
- In my personal experience, the process of setting up a full BI stack of technology could take multiple days - for a test environment let alone a complex production environment. Database – we had to install the database and set all of the company standard settings; then we had to change all of those settings to fit the BI vendor recommendations. Then we’d have to tweak them for performance as we learned more. ETL Tools – usually at least two layers of servers plus the desktop development and administration tools plus the database configuration on all the developer desktops as well as on the servers. And make sure you have right version of the database drivers – sometimes both 32-bit and 64-bit. BI Tools – also usually multiple servers. In fact, this was somewhere that we almost always have a cluster of servers. So, they have to be able to talk to each other as well to the databases and to the user web browser and all the desktop development tools.
- Infrastructure as a service and virtualization… but the cloud is way more than that!
- Understand: Modern analytics is about innovation and business value Embrace: Business / Technology partnerships Accept: Change used to happen in big steps, occasionally. Not it happens continuously and incrementally.
- Agile Dev Ops Dimensional Modeling Data Visualization Statistics Machine Learning Business Artificial Intelligence