Se ha denunciado esta presentación.
Se está descargando tu SlideShare. ×

Big Data, Big Disappointment

Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Anuncio
Próximo SlideShare
Mexican Landscape of DS & AI
Mexican Landscape of DS & AI
Cargando en…3
×

Eche un vistazo a continuación

1 de 26 Anuncio

Más Contenido Relacionado

Similares a Big Data, Big Disappointment (20)

Anuncio

Más reciente (20)

Anuncio

Big Data, Big Disappointment

  1. 1. Big Data, Big Disappointment A diagnosis and prescription (sort of) for (somewhat) successful analytics efforts in medium to large firms in Mexico (c) 2015 Jesus Ramos 1
  2. 2. “Big Data has arrived, but big insights have not” - “Big data: are we making a big mistake? Tim Harford. Financial Times. (c) 2015 Jesus Ramos 2 And with all the money Gartner says we’re to fork over, the question is…
  3. 3. Why? (c) 2015 Jesus Ramos 3
  4. 4. In mature businesses, mostly because… •  False positives are ignored •  Correlation implies causation •  We don’t care about sampling •  Machine Learning for all (c) 2015 Jesus Ramos 4 From “8 Reasons why Big Data projects fail”. Matt Asay. InformationWeek. 8/714
  5. 5. And in the rest of us, because… We don’t understand what Big Data is! So…we need definitions: (c) 2015 Jesus Ramos 5
  6. 6. BD is a 2-part deal (c) 2015 Jesus Ramos 6 Big Data Technology for storing and processing large amounts of data Analytics The insights gained from such large data “Without ‘analytics’, Big Data is a sleeping giant!” - me
  7. 7. Don’t talk about ‘BD’ w/o the ‘A’ From this slide on, and for the rest of your professional lives, I urge you to please add the ‘Analytics’ suffix to the buzzword ‘Big Data’. (c) 2015 Jesus Ramos 7
  8. 8. Why this distinction matters? (c) 2015 Jesus Ramos 8 Big Data Quality Attributes to watch out for: Analytics Quality attributes to watch out for: -  Performance -  Fault-tolerance -  Replication -  High Availability -  Integration with current ecosystem -  Read Performance -  Insert Performance -  Integration with Analytical Tools -  In-DB Analytics
  9. 9. Why this distinction matters? (c) 2015 Jesus Ramos 9 We might end up buying/building the wrong technology.
  10. 10. The purpose of BDA 1. Development of new products 2. Gain operational efficiencies 3. Support decision-making (c) 2015 Jesus Ramos 10 If our BDA initiative doesn’t touch these goals, we’re doing it wrong!
  11. 11. CEO/COO CFO CTO CDO The right place for BDA within the firm… (c) 2015 Jesus Ramos 11 In a startup: BDA BDA BDA BDA Analytics is part of the org’s DNA
  12. 12. The right place for BDA within the firm… (c) 2015 Jesus Ramos 12 In an mature org: CEO CTO CFO COO CDO BDA CEO sponsorhip needed to break cultural resistance! BD
  13. 13. The WORST place for BDA within the firm… (c) 2015 Jesus Ramos 13 CEO CTO/CIO COO CFO BDA Why?
  14. 14. Reasons why BDA should not be born in IT (unless core biz is tech) 1.  Asking the wrong questions 2.  Lacking the right skills 3.  Culture change happens elsewhere (c) 2015 Jesus Ramos 14
  15. 15. Asking the right questions Even though IT enables the value chain through technology, burning operational questions may be out of our reach, grasp, or jurisdiction. (c) 2015 Jesus Ramos 15
  16. 16. Lack of the right skills Forget Drew Conway’s Venn Diagram. The problem is deeper: 1.  IT is a labor of engineering. 2.  The fundamental question of engineering is ‘How’. 3.  To answer questions we need statistics. 4.  The fundamental question of Stats is ‘Why’. 5.  When we answer ‘Why’ we gain insight. (c) 2015 Jesus Ramos 16
  17. 17. Lack of the right skills (2) •  Of course, our engineers could go through training to become statisticians, and when they do, they are sometimes better at it than classically-trained statisticians. •  Only this training is long, and often requires a change of mindset to become true Data Scientists. (c) 2015 Jesus Ramos 17
  18. 18. Culture change happens elsewhere If tech is not the core business nor is central to strategy, IT will not have enough ‘gravitas’ to pull the entire org from a hunch-based decision management, to a data-driven one. (c) 2015 Jesus Ramos 18
  19. 19. A case for for giving birth to Analytics in IT (c) 2015 Jesus Ramos 19 Survey of +200 data professionals. Those closer to SW dev had a negative correlation to those closer to the business. When the pale red dot turns into a tight, upward-facing, dark blue oval, not only will be have software built with a purpose, but also SW devs turned excellent data analysts. Source: Entry survey for @TheDataPub meetup
  20. 20. If you have no choice but give birth to BDA in IT… 1.  Set up a DWH (if not present). 2.  Federate data. 3.  Establish data ingestion frequency (must match my decision-making frequency) & pipeline. 4.  Hire the right people with the right skill (and keep the BI people at bay lest they spread an illness called Reportitis Operativitis). 5.  Seize IT’s presence all across the value chain and acquire political capital. 6.  Address the low-hanging fruit of analytics. (c) 2015 Jesus Ramos 20
  21. 21. 1.  Set up a DWH (if not present). 2.  Federate data. 3.  Establish data ingestion frequency (must match my decision-making frequency) & pipeline. 4.  Hire the right people with the right skill (and keep the BI people at bay lest they spread an illness called Reportitis Operativitis). 5.  Seize IT’s presence all across the value chain and acquire political capital. 6.  Address the low-hanging fruit of analytics. If you have no choice but give birth to BDA in IT… (c) 2015 Jesus Ramos 21 Big Data Analytics
  22. 22. Where do I get the right people (in Mexico) ? 1.  MSc Data Science – ITAM. 2.  MSc Analytic Intelligence – U. Anahuac. 3.  BS Applied Maths + MSc Economics/ Econometrics. 4.  BS Industrial Engineering + MSc Computer Science. 5.  BS Actuarial Sciences + MSc Computer Science (c) 2015 Jesus Ramos 22
  23. 23. Where do I get the right people (in Mexico) ? •  Note that they’re all master degrees, so don’t expect to pay average developer salaries. •  Industrial Engineering and Economics appear a lot because those guys know how to measure processes. •  Note that when we mention Computing, it’s Computer Science, not Engineering. (c) 2015 Jesus Ramos 23
  24. 24. Take aways: •  BigData does nothing without Analytics. •  BDA must deliver 1) new products, 2) operational efficiency, 3) decision support. •  The right place for BDA is a position of influence. •  BDA living in IT has many drawbacks related to skill + political capital. •  But IT is in a priviledged position to deliver value through BDA if it blends with the business. (c) 2015 Jesus Ramos 24
  25. 25. Pending discussions: •  Big Data Ethics •  Beware Data Charlatanry! •  Analytics team-building •  Data Science + Software Engineering •  What mexican education system needs to produce data professionals. (c) 2015 Jesus Ramos 25
  26. 26. (c) 2015 Jesus Ramos 26 Thanks! tw: @xuxoramos linkedin: xuxoramos ramos.cardona@gmail.com

Notas del editor

  • Titulo fancy para decir ‘esto es lo que me funciona, y espero que a uds tambien’.
  • Del 64% de empresas que según Gartner invertiria en BigData en 2013, solo el 30% lo hizo, y de este 30%, solo 120 organizaciones han extraido los beneficios.
  • Si estan pensando que el problema es la estadística (o la falta de), van por buen camino.


    Algo que hay que resaltar: cuando hablamos de negocios analiticamente maduros estamos hablando de todo el espectro de capital: desde las startups, hasta telcel, walmart, target.
  • Necesitamos tomar 2 pasitos hacia atrás y ver the whole picture.
  • Dicen que 2as partes no sonbuenas, pero ahí esta Batman El Caballero de la noche para demostrarnos que si.
    Big Data es la basesota de datos
    Tenemos que vigilar atributos de calidad como performance, HA, automantenible, etc.

    Analytics es lo que hacemos con los datos de la basesota
    Los invito a que de ahora en delante hablemos de analytics cuando platiquemos de bigdata analytics.
    Aquí vigilamos atributos de calidad que perdemos de vista si solo hablamos de big data: que corra in-database analytics, que se integre con nuestra plataforma analitica y que sea buenisima para contestar preguntas

    Si no hablamos de Analytics, BigData no sera mas que un gigante dormido.
  • Todo lo que hagamos en BDA, debe caer en una o mas de estas 3 cubetas.
  • En una startup la analitica emana desde el CDO, y es practicada por TODAS las areas! Google, Facebook, aunque no son startups, asi estan estructuradas. LinkedIn esta de otra manera, pero eso no lo platicaremos.
  • En una firma mandura con gran capital y N niveles jerarquicos, es mejor que el CDO sea staff del CEO, para que los insights generados tengan impacto en toda la org,vencer resistencias al cambio y tener acceso a los datos de toda la org. Ojo que el CTO forma parte crucial de esta colaboracion, porque la basesota vive con el.
  • El peor lugar para el nacimiendo de esta iniciativa es a veces IT. Por que?
  • Asking the wrong questions
    IT may not be close enough to value chain to know its problems.
    Lacking the right skills
    Analytics=Stats&Math + Domain Knowledge + Programming. IT only has, at best, the latter two. En IT somos ingenieros, y la pregunta fundamental de los ingenieros es COMO. Aquí se requiere otro perfil donde la pregunta fundamental es POR QUE.
    Culture change happens elsewhere
    Going from hunch-driven decisions to data driven decisions requires culture change. IT can’t pull entire org.
  • Setup DWH: implica tambien vigilar que el WH resuelva los atributos de calidad necesarios para la analitica.
    Federate data: Si tenemos silos de datos, volaremos a traves de la iniciativa tuertos y mancos. Es crucial federar la info de finanzas, RH, planta, operation, etc.
    Establish data ingestion frequency: BMV toma decisiones de milisegundos, mientras que Walmart puede tomarlas diario.
    Hire the right people: la gente de BI va a querer participar en esto, lo malo es que ellos padecen de reportitis operativitis, y dificilmente formularan preguntas de valor para el negocio.
    Seize IT’s position: IT esta en una posicion ventajosa porque toca a todas las areas de negocio, asi que podemos establecer sensores y comenzar a tomar metricas de TODO, y poder entregar valor con vista a toda la org.
    Address the low hanging fruit: proyectos de analitica chicos, baratos y que tengan gran impacto para ganar confiabilidad.
  • Setup DWH: implica tambien vigilar que el WH resuelva los atributos de calidad necesarios para la analitica.
    Federate data: Si tenemos silos de datos, volaremos a traves de la iniciativa tuertos y mancos. Es crucial federar la info de finanzas, RH, planta, operation, etc.
    Establish data ingestion frequency: BMV toma decisiones de milisegundos, mientras que Walmart puede tomarlas diario.
    Hire the right people: la gente de BI va a querer participar en esto, lo malo es que ellos padecen de reportitis operativitis, y dificilmente formularan preguntas de valor para el negocio.
    Seize IT’s position: IT esta en una posicion ventajosa porque toca a todas las areas de negocio, asi que podemos establecer sensores y comenzar a tomar metricas de TODO, y poder entregar valor con vista a toda la org.
    Address the low hanging fruit: proyectos de analitica chicos, baratos y que tengan gran impacto para ganar confiabilidad.

×