Understanding What’s Possible: Getting Business Value from Big Data Quickly

Grab some

coffee and

enjoy the

pre-show

banter before

the top of the

hour!

The Briefing Room

Understanding What’s Possible: Getting Business Value from Big Data Quickly

Twitter Tag: #briefr The Briefing Room

Welcome

Host: Eric Kavanagh

[email protected] @eric_kavanagh


  Reveal the essential characteristics of enterprise software, good and bad

  Provide a forum for detailed analysis of today’s innovative technologies

 Give vendors a chance to explain their product to savvy analysts

  Allow audience members to pose serious questions... and get answers!

Mission


Topics

April: BIG DATA

May: CLOUD

June: INNOVATORS


Analyst: David Loshin

David Loshin, president of Knowledge Integrity, Inc, is a thought leader and expert consultant in the areas of data quality, master data management, and business intelligence. David is the author of numerous books and papers on data management, including the “Practitioner’s Guide to Data Quality Improvement.” David is a frequent speaker at conferences and in web seminars. His best-selling book, “Master Data Management,” has been endorsed by data management industry leaders. David can be reached at [email protected], or at (301) 754-6350.


OpenText

OpenText produces enterprise information management software, including solutions for search, content management and customer management

OpenText recently acquired Actuate in order to build out its big data analytics and visualization capabilities

Actuate’s BIRT Analytics Platform includes traditional BI and reporting, as well as advanced and predictive analytics


Guest: Allen Bonde

Allen Bonde (@abonde) is VP of Product Marketing & Innovation at OpenText, where he joined following the acquisition of Actuate. A former McKinsey and Yankee Group analyst, he was previously CMO of Offerpop, and a Partner at Digital Clarity Group covering social business and big data. Mr. Bonde started his career as a data scientist in the telecom sector, is a frequent industry speaker, and has been featured in over 150 publications worldwide.


Perceptions & Questions

Analyst: David Loshin

Brie%ing Room: Considerations for Big Data Analytics

April 14, 2015 David Loshin

Knowledge Integrity, Inc. loshin@knowledge-‐integrity.com

© 2015 Knowledge Integrity, Inc loshin@knowledge-‐integrity.com (301) 754-‐6350 11

“Big Data” Analytics? •  What differenKates “big data”

analyKcs from “regular” analyKcs? –  Scale (lots of data) –  Scope (different sources) –  Speed (rapid Kme-‐to-‐value) –  Sweep (lots of delivery points) –  SoluKons (addressing business

needs)

© 2015 Knowledge Integrity, Inc loshin@knowledge-‐integrity.com (301) 754-‐6350

12

Analytics Users •  Data ScienKst •  Business Analyst

•  IT Developer •  Process Owner


13

Review the past

Explore causality

Consider scenarios

Adjust behavior

OpKmal decisions

What Happened? Why? What If? How? What Next?

Reports Ad Hoc Sta/s/cal Analysis Planning

Forecas/ng Models

Predic/ve Models

Prescrip/ve Analy/cs Search Dashboards

Increasing interest in more complex analysis

Problem-‐Solving, Learning, and Doing •  There are two facets to the

analyKcs process: –  Discovery –  PresentaKon

•  Big Data AnalyKcs tools must facilitate the ways the different users address opportuniKes for creaKng value


14

IdenKfy the business challenge

Determine the

informaKon you need

Discovery analysis

Present results

Take acKon

Blending Different Sources •  Different types of data

sources: –  Machine-‐generated content –  AutomaKcally-‐generated

content –  Human-‐generated content –  Legacy content


15

Performance & the Extended Enterprise •  The organizaKon’s data

resources expand beyond the administraKve boundaries –  Cloud-‐based applicaKons –  Hosted systems –  Sensors –  Human-‐generated media –  Mobile devices

•  Performance is criKcal to deliver acKonable knowledge within the window of opportunity


16

Questions •  Can you describe some of the enhancements you have undertaken to

improve performance across the extended enterprise? •  Data scienKsts with staKsKcal backgrounds are very different than casual

business users. How does the product saKsfy the needs of the different types of users?

•  What are the disKncKve features of the product that facilitate the “understand” process?

•  How can the product suite train users to become more adept at both the “understand” and “engage” phases of the analyKcs process?

•  The Apple Watch is one of the recently announced wearable devices. How does the “wearable” paradigm impact your plans for big data analyKcs?

•  Speaking of the wearables, what thoughts do you have about adapKng to non-‐convenKonal methods of knowledge delivery? And data acquisiKon?

•  What opportuniKes are there for pushing analyKcs to the middle and edges of the extended enterprise?


17

Stay in Touch •  www.knowledge-‐integrity.com •  www.dataqualitybook.com •  www.decisionworx.com •  If you have quesKons, comments,

or suggesKons, please contact me David Loshin 301-‐754-‐6350 loshin@knowledge-‐integrity.com


18



Upcoming Topics

www.insideanalysis.com

April: BIG DATA

May: CLOUD

June: INNOVATORS


THANK YOU for your

ATTENTION!

Some images provided courtesy of Wikimedia Commons

Technology

Understanding What’s Possible: Getting Business Value from Big Data Quickly