Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Big Data, Big Disappointment

A diagnosis and prescription (sort of) for (somewhat) successful analytics efforts in medium to large firms in Mexico.

  • Be the first to comment

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

    Be the first to comment

    Login to see the comments

  • JosIvnGarca

    Feb. 12, 2015
  • sumitbajaj

    Feb. 13, 2015
  • kuonen

    Feb. 14, 2015
  • DavidN929

    Feb. 14, 2015
  • apalacheteb

    Feb. 15, 2015
  • IanPetersen1

    Feb. 16, 2015
  • JoeLee30

    Mar. 1, 2015
  • nicolaferraro

    Apr. 5, 2015
  • prakash_paranjape

    Apr. 8, 2015
  • ArivalaganRaju

    Apr. 21, 2015
  • YasinDirlik

    May. 23, 2015
  • RodrigoPalma8

    May. 30, 2015
  • eduardo.vinas.mouta

    Aug. 5, 2015
  • raminorujov

    Aug. 11, 2015
  • OrkhanTanrverdiyev

    Aug. 11, 2015
  • zulfinho

    Aug. 12, 2015
  • WesSauderMBA

    Oct. 7, 2015
  • sciammarella

    Oct. 14, 2015
  • randeroid

    Feb. 8, 2016
  • javier.castanon

    Jul. 4, 2016

A diagnosis and prescription (sort of) for (somewhat) successful analytics efforts in medium to large firms in Mexico.

Views

Total views

4,491

On Slideshare

0

From embeds

0

Number of embeds

159

Actions

Downloads

62

Shares

0

Comments

0

Likes

20

×