La evolución de la analítica descriptiva - Diego Aguirre
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La evolución de la analítica descriptiva - Diego Aguirre
- 1. IBM Business Analytics :: IBM Confidential :: © 2016 IBM Corporation Transformando reportes en historias Diego Aguirre Data Science & Business Analytics Client Leader, Spanish South America BIG DATA Summit, Lima. 25 de Agosto de 2018 La evolución de la analítica descriptiva
- 2. 2IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation Agenda Analítica Descriptiva Un poco de historia Situación actual Desafíos Qué es lo que viene?
- 3. IBM Business Analytics :: IBM Confidential :: © 2017 IBM CorporationIBM Confidential :: © 2017 IBM Corporation Ciclo de vida de Analítico: Que es la analítica descriptiva?
- 4. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 4 Un poco de historia – La Evolución
- 5. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 5 Un poco de historia – La Evolución 80s. - Principios 90s Reportes a medida Código estático Lenguaje programación + Lenguaje de consulta Experiencia limitada Alto costo
- 6. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 6 Un poco de historia – La Evolución BI tools Usuarios de negocio construyendo reportes Capacidades limitadas Herramientas complejas y poco flexibles 1995 - 2000
- 7. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 7 Un poco de historia – La Evolución Dashboards Mejora la experiencia Se mantiene el alto costo BICC Dashboard / Scorecard: Son diferentes 2000-2010
- 8. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 8 Un poco de historia – La Evolución 2010- Hoy Self Service Indepencia de IT Power users generando su propio contenido Democratización Fácil de Mantener Experiencia superior
- 9. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 9 Self Service - Definición “Self-Service Analytics is a form of business intelligence (BI) in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support. Self-service analytics is often characterized by simple-to-use BI tools with basic analytic capabilities and an underlying data model that has been simplified or scaled down for ease of understanding and straightforward data access.”
- 10. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 10
- 11. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 11 #1 - No todo es self service “The irony of self-service analytics is that it requires standardization.” Business Users “Many companies that have deployed self-service analytics have become inundated by a tsunami of conflicting reports, spreadmarts, renegade reporting systems, and other data silos.” Data Scientists 2% Usuarios Casuales (Executives, managers, front-line workers)90% Power users Su función es analizar10% “Many companies that have deployed self-service analytics have become inundated by a tsunami of conflicting reports, spreadmarts, renegade reporting systems, and other data silos.” 11 Source: Chart data and quotes from “Eckerson Group: A Reference Architecture for Self-Service Analytics”, Wayne Eckerson, Barry Devlin, September 2016 Exploradores de Datos 30% Consumidores de datos 60% Data Analysts8%
- 12. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 12 Enterprise Reporting + Self Service Managed Reporting • Data presentation • Value add by managing the: • Security and Governance • The integrity of the output • Efficient distribution of the output Self-service (Dashboards, Stories) • Data exploration (ad-hoc) • Primary use cases • Answering questions • Brainstorming /socialization of ideas and concepts • Prototyping Platform • Enterprise architecture - security, scalability, integrity • Managed reporting and self service LOB user Skilled user (Power/IT)
- 13. 13IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation • Los análisis “self service se reducen” a un subconjunto del universo de datos bien conocido por el analista. • La construcción del análisis está influenciada por los conocimientos del analista (acerca del negocio) y el conocimiento de la herramienta. #2 Sesgo Data consumers
- 14. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 141 4 Smarter Self Service • Análisis guiado • Sugerencia de visualizaciones (a demanda y proactivas) • Preguntas y respuestas • Identificación de correlaciones • Análsis diagnóstico basado en capacidades cognitivas Smarter Self Services IBM Business Analytics
- 15. 15IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation #3 Usar el lado derecho del cerebro
- 16. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 16 La evolución del Self-Service S m a r t e r D a t a D i s c o v e r y G o b i e r n o y E n t e r p r i s e R e p o r t i n g C r e a t i v i d a d
- 17. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 17 La evolución del Self-Service: Cognos Analytics
- 18. IBM Business Analytics :: IBM Confidential :: © 2017 IBM Corporation 18 Diego Aguirre daguirre@uy.ibm.com
Notas del editor
- This deck is intended to be used with new and existing clients. It is recommended that you select only the slides that are relevant for your prospect. Additional client examples are at the end of the deck - 2 are included with videos from the customer.
- Our demonstration is modeled after the way companies work -- we start with their revenue plan and mid year, they realized they are not tracking to plan. They want to understand why some stations are underperforming and leverage “Smarts” to fully understand all of the business drivers impacting performance. Then, you will see advanced analytics that recommends how to optimize performance in plain English and show the expected impact on the P&L. All of what you will see is controlled by Analytics Governance to ensure completeness, correctness and eliminate bias. The solution itself looks almost simple and straightforward but bear in mind that underneath the elegant front end are the most modern and powerful tools – Cognitive Analytics, Predictive modeling that includes weather data – both historical but also real time forecasts. You will see examples of machine learning, pattern based planning and optimization all working to aid our business user work smarter.
- The graphic at the top shows how the market has shifted from the line of business driving the demand to IT and back again. We think that we are now in a point in time where the balance between agility for the business and control for IT are hitting equilibrium. Both parties have different priorities and industry experts such as Gartner Research are advocating a BI-modal strategy - for BI that means managed reporting and self service. One way to look at bi-modal is mission critical reporting to RUN the business and ad hoc analysis to hunt for opportunity to GROW the business. Formal Gartner definition: Bimodal is the practice of managing two separate but coherent styles of work: one focused on predictability; the other on exploration. Mode 1 is optimized for areas that are more predictable and well-understood. It focuses on exploiting what is known, while renovating the legacy environment into a state that is fit for a digital world. Mode 2 is exploratory, experimenting to solve new problems and optimized for areas of uncertainty. These initiatives often begin with a hypothesis that is tested and adapted during a process involving short iterations, potentially adopting a minimum viable product (MVP) approach. Both modes are essential to create substantial value and drive significant organizational change, and neither is static. Marrying a more predictable evolution of products and technologies (Mode 1) with the new and innovative (Mode 2) is the essence of an enterprise bimodal capability. Both play an essential role in the digital transformation. Source: http://www.gartner.com/it-glossary/bimodal/
- The graphic at the top shows how the market has shifted from the line of business driving the demand to IT and back again. We think that we are now in a point in time where the balance between agility for the business and control for IT are hitting equilibrium. Both parties have different priorities and industry experts such as Gartner Research are advocating a BI-modal strategy - for BI that means managed reporting and self service. One way to look at bi-modal is mission critical reporting to RUN the business and ad hoc analysis to hunt for opportunity to GROW the business. Formal Gartner definition: Bimodal is the practice of managing two separate but coherent styles of work: one focused on predictability; the other on exploration. Mode 1 is optimized for areas that are more predictable and well-understood. It focuses on exploiting what is known, while renovating the legacy environment into a state that is fit for a digital world. Mode 2 is exploratory, experimenting to solve new problems and optimized for areas of uncertainty. These initiatives often begin with a hypothesis that is tested and adapted during a process involving short iterations, potentially adopting a minimum viable product (MVP) approach. Both modes are essential to create substantial value and drive significant organizational change, and neither is static. Marrying a more predictable evolution of products and technologies (Mode 1) with the new and innovative (Mode 2) is the essence of an enterprise bimodal capability. Both play an essential role in the digital transformation. Source: http://www.gartner.com/it-glossary/bimodal/
- The graphic at the top shows how the market has shifted from the line of business driving the demand to IT and back again. We think that we are now in a point in time where the balance between agility for the business and control for IT are hitting equilibrium. Both parties have different priorities and industry experts such as Gartner Research are advocating a BI-modal strategy - for BI that means managed reporting and self service. One way to look at bi-modal is mission critical reporting to RUN the business and ad hoc analysis to hunt for opportunity to GROW the business. Formal Gartner definition: Bimodal is the practice of managing two separate but coherent styles of work: one focused on predictability; the other on exploration. Mode 1 is optimized for areas that are more predictable and well-understood. It focuses on exploiting what is known, while renovating the legacy environment into a state that is fit for a digital world. Mode 2 is exploratory, experimenting to solve new problems and optimized for areas of uncertainty. These initiatives often begin with a hypothesis that is tested and adapted during a process involving short iterations, potentially adopting a minimum viable product (MVP) approach. Both modes are essential to create substantial value and drive significant organizational change, and neither is static. Marrying a more predictable evolution of products and technologies (Mode 1) with the new and innovative (Mode 2) is the essence of an enterprise bimodal capability. Both play an essential role in the digital transformation. Source: http://www.gartner.com/it-glossary/bimodal/
- The graphic at the top shows how the market has shifted from the line of business driving the demand to IT and back again. We think that we are now in a point in time where the balance between agility for the business and control for IT are hitting equilibrium. Both parties have different priorities and industry experts such as Gartner Research are advocating a BI-modal strategy - for BI that means managed reporting and self service. One way to look at bi-modal is mission critical reporting to RUN the business and ad hoc analysis to hunt for opportunity to GROW the business. Formal Gartner definition: Bimodal is the practice of managing two separate but coherent styles of work: one focused on predictability; the other on exploration. Mode 1 is optimized for areas that are more predictable and well-understood. It focuses on exploiting what is known, while renovating the legacy environment into a state that is fit for a digital world. Mode 2 is exploratory, experimenting to solve new problems and optimized for areas of uncertainty. These initiatives often begin with a hypothesis that is tested and adapted during a process involving short iterations, potentially adopting a minimum viable product (MVP) approach. Both modes are essential to create substantial value and drive significant organizational change, and neither is static. Marrying a more predictable evolution of products and technologies (Mode 1) with the new and innovative (Mode 2) is the essence of an enterprise bimodal capability. Both play an essential role in the digital transformation. Source: http://www.gartner.com/it-glossary/bimodal/
- The graphic at the top shows how the market has shifted from the line of business driving the demand to IT and back again. We think that we are now in a point in time where the balance between agility for the business and control for IT are hitting equilibrium. Both parties have different priorities and industry experts such as Gartner Research are advocating a BI-modal strategy - for BI that means managed reporting and self service. One way to look at bi-modal is mission critical reporting to RUN the business and ad hoc analysis to hunt for opportunity to GROW the business. Formal Gartner definition: Bimodal is the practice of managing two separate but coherent styles of work: one focused on predictability; the other on exploration. Mode 1 is optimized for areas that are more predictable and well-understood. It focuses on exploiting what is known, while renovating the legacy environment into a state that is fit for a digital world. Mode 2 is exploratory, experimenting to solve new problems and optimized for areas of uncertainty. These initiatives often begin with a hypothesis that is tested and adapted during a process involving short iterations, potentially adopting a minimum viable product (MVP) approach. Both modes are essential to create substantial value and drive significant organizational change, and neither is static. Marrying a more predictable evolution of products and technologies (Mode 1) with the new and innovative (Mode 2) is the essence of an enterprise bimodal capability. Both play an essential role in the digital transformation. Source: http://www.gartner.com/it-glossary/bimodal/
- The graphic at the top shows how the market has shifted from the line of business driving the demand to IT and back again. We think that we are now in a point in time where the balance between agility for the business and control for IT are hitting equilibrium. Both parties have different priorities and industry experts such as Gartner Research are advocating a BI-modal strategy - for BI that means managed reporting and self service. One way to look at bi-modal is mission critical reporting to RUN the business and ad hoc analysis to hunt for opportunity to GROW the business. Formal Gartner definition: Bimodal is the practice of managing two separate but coherent styles of work: one focused on predictability; the other on exploration. Mode 1 is optimized for areas that are more predictable and well-understood. It focuses on exploiting what is known, while renovating the legacy environment into a state that is fit for a digital world. Mode 2 is exploratory, experimenting to solve new problems and optimized for areas of uncertainty. These initiatives often begin with a hypothesis that is tested and adapted during a process involving short iterations, potentially adopting a minimum viable product (MVP) approach. Both modes are essential to create substantial value and drive significant organizational change, and neither is static. Marrying a more predictable evolution of products and technologies (Mode 1) with the new and innovative (Mode 2) is the essence of an enterprise bimodal capability. Both play an essential role in the digital transformation. Source: http://www.gartner.com/it-glossary/bimodal/
- The graphic at the top shows how the market has shifted from the line of business driving the demand to IT and back again. We think that we are now in a point in time where the balance between agility for the business and control for IT are hitting equilibrium. Both parties have different priorities and industry experts such as Gartner Research are advocating a BI-modal strategy - for BI that means managed reporting and self service. One way to look at bi-modal is mission critical reporting to RUN the business and ad hoc analysis to hunt for opportunity to GROW the business. Formal Gartner definition: Bimodal is the practice of managing two separate but coherent styles of work: one focused on predictability; the other on exploration. Mode 1 is optimized for areas that are more predictable and well-understood. It focuses on exploiting what is known, while renovating the legacy environment into a state that is fit for a digital world. Mode 2 is exploratory, experimenting to solve new problems and optimized for areas of uncertainty. These initiatives often begin with a hypothesis that is tested and adapted during a process involving short iterations, potentially adopting a minimum viable product (MVP) approach. Both modes are essential to create substantial value and drive significant organizational change, and neither is static. Marrying a more predictable evolution of products and technologies (Mode 1) with the new and innovative (Mode 2) is the essence of an enterprise bimodal capability. Both play an essential role in the digital transformation. Source: http://www.gartner.com/it-glossary/bimodal/
- Self service is a key element in BI as it is how you are able to engage the masses - however self service is a spectrum not one size fits all - different users have different behavior and different roles. In fact, research shows the 90% of BI users are casual users. Within that group, there are sub-categories of users. This view is based on Eckerson Group research of analytic user behaviors Data Consumers typically view reports and dashboards created for them, they may interact, search, drill and create snapshots, they represent the majority at 60% Data Explorers edit and author ad hoc reports and dashboards without coding, this assists them in their job but is not the primary focus – represent 30% Power users make up the remaining 10% - these folks are hired by the business to work with and analyze data •Data Analysts are business savvy data experts, they author and share content and do root cause analysis •Data Scientists are more specialized in skills (usually computer science background) who know how to code with SQL, Java, Python, Hive, and Pig. They understand statistics and data mining tools and can create predictive and machine learning models. They want access to granular data and care about the algorithms. According to Eckerson Group in their paper Reference Architecture for Self-Service Analytics not every organization has done self-service well: “Many companies that have deployed self-service analytics have become inundated by a tsunami of conflicting reports, spreadmarts, renegade reporting systems, and other data silos.” “The promise of self-service analytics is not to eliminate IT from the equation, but instead to foster greater collaboration between business and IT” In fact they note the irony that self-service to be done well requires standardization.
- Looking a level deeper, there are conceptually three pillars to Cognos Analytics. Self-service/Dashboards are almost synonymous in the market today, and the “thing” that many organizations and the LOB will put on the top of their priority list. But forget about the name for a second, and think about the purpose of the capability. It’s about data exploration, or exploring and analyzing data in an ad-hoc way. So if dashboards are for data exploration, managed reporting and its skilled authors provide data presentation, and add significant value by ensuring security, trust and integrity, and efficient distribution. Both self service and managed reporting sit on top of the platform. And while the specifics of the platform and the underlying architecture are very much an IT discussion, the essence of the platform is that it provides management of the entire environment, and predictability.
- This is an example of a centrally authored, yet interactive, report that show sales growth. There is the desire to filter the report, do groupings etc. to gain better insight into performance of different products, maybe in different regions. The interactivity level is set by the author.
- This slide is your single slide definition of Cognos Analytics. For existing customers of Cognos BI with support contracts it is the next release and they are entitled to upgrade to it at no additional costs. For new and existing customers this is our go forward platform, so be sure to cover these bullet points when talking with your customers or prospects.
- Storytelling Analytics y storytelling se complementan, requiere que usemos nuestra capacidad analitica combinada con la creativa. La historia agrega el lado emocional que los datos por si solos no pueden transmitir 22x más efectivas que hechos asilados. Los datos y las visualizaciones dan credibilidad a la historia. La historia puede brindar, un punto de vista, una conclusión y próximos pasos Las historias tienen un héroe
- We have seen this market go through several generations. IBM's point of view is that we are now entering the next phase that goes beyond descriptive and diagnostic analytics to deliver SYSTEMS OF INSIGHT. These Systems of Insight are a product of the key trends of self-service analytics, governance and smarter, more embeddable analytics. IBM brings together Mode 1 (centralized and managed) and Mode 2 (distributed / agile) analytics. An analytics solution with built-in smarts can look at data and surface insights automatically minimizing user bias on the analytics process. Built in smarts can also enhance the user experience by automating tasks and guiding the user in accomplishing their intended goals. Finally the ability to take an insight and share it with others and operationalize it within a business process is how insight is brought to life.
- We have seen this market go through several generations. IBM's point of view is that we are now entering the next phase that goes beyond descriptive and diagnostic analytics to deliver SYSTEMS OF INSIGHT. These Systems of Insight are a product of the key trends of self-service analytics, governance and smarter, more embeddable analytics. IBM brings together Mode 1 (centralized and managed) and Mode 2 (distributed / agile) analytics. An analytics solution with built-in smarts can look at data and surface insights automatically minimizing user bias on the analytics process. Built in smarts can also enhance the user experience by automating tasks and guiding the user in accomplishing their intended goals. Finally the ability to take an insight and share it with others and operationalize it within a business process is how insight is brought to life.
- The graphic at the top shows how the market has shifted from the line of business driving the demand to IT and back again. We think that we are now in a point in time where the balance between agility for the business and control for IT are hitting equilibrium. Both parties have different priorities and industry experts such as Gartner Research are advocating a BI-modal strategy - for BI that means managed reporting and self service. One way to look at bi-modal is mission critical reporting to RUN the business and ad hoc analysis to hunt for opportunity to GROW the business. Formal Gartner definition: Bimodal is the practice of managing two separate but coherent styles of work: one focused on predictability; the other on exploration. Mode 1 is optimized for areas that are more predictable and well-understood. It focuses on exploiting what is known, while renovating the legacy environment into a state that is fit for a digital world. Mode 2 is exploratory, experimenting to solve new problems and optimized for areas of uncertainty. These initiatives often begin with a hypothesis that is tested and adapted during a process involving short iterations, potentially adopting a minimum viable product (MVP) approach. Both modes are essential to create substantial value and drive significant organizational change, and neither is static. Marrying a more predictable evolution of products and technologies (Mode 1) with the new and innovative (Mode 2) is the essence of an enterprise bimodal capability. Both play an essential role in the digital transformation. Source: http://www.gartner.com/it-glossary/bimodal/