Big Data Blog

Big Data Blog

Speed Up Your Data to Slow Down Your Decisions

Mar 4

Written by:
3/4/2013 7:01 AM  RssIcon

Serve Big Data

Mark Twain said, “Never put off till tomorrow what you can do the day after tomorrow.”  Although that quote is typically taken as an oath for procrastination, generally speaking we can’t spend too much time planning before acting.  However, as Leonard Bernstein said, “To achieve great things, two things are needed: a plan, and not quite enough time."

In his excellent book Wait: The Art and Science of Delay, Frank Partnoy examined the data-driven decision making involved in many circumstances, including fast-paced sports such as tennis, where there definitely seems to be not quite enough time to plan before acting since the average window for returning a professional tennis serve is 500 milliseconds, which Partnoy broke down into three phases: See, Prepare, Hit.

The first phase is all about the visual reaction time of when a player first sees the ball leave their opponent’s racket.  Partnoy cited research that found human visual reaction time is surprisingly consistent across all people and all activities — 200 milliseconds, which is roughly half the time it takes for you to blink your eyes.  Professional tennis players need just as much time as everyone else for the See phase, leaving only 300 milliseconds for the Prepare and Hit phases.

As Partnoy explained, the Hit phase “is a serious problem for most of us.  The physical reaction time available to hit a professional tennis serve is barely long enough for us to adjust our racket by a few inches.  Amateurs cannot move to the correct spot and produce a swing with accuracy or power in 300 milliseconds.”

Return Predictive Analytics

By contrast, professionals are so skilled and practiced they can produce near-instantaneous muscle contractions to move their bodies and execute a swing in approximately 100 milliseconds.  Because professional tennis players are so fast, they have more time to gather and process information, albeit not much more time — 200 milliseconds.  But what happens during the Prepare phase can make all the difference.  To paraphrase Twain, “Never return in 100 milliseconds a serve that you can return in 200 milliseconds.”  Or, as Partnoy phrased it, professional tennis players “procrastinate at the speed of light,” enabling them to return serves as slowly as they possibly can.  “They go fast first in order to go slow later.”

Result Great Data-Driven Decisions

As Paige Roberts recently blogged, the real advantage of Hadoop-related technology “aside from the fact that it can handle massive volumes of data without choking and dying, or killing your operating budget, is speed.  We are not a patient society.  The cutthroat world of business has become all about who can get the right answer faster.”  Roberts also cited the recent Harvard Business Review article by Paul Barth and Randy Bean, which explained that “the real quantum leap for companies comes from the ability to accelerate the speed at which they can get to a decision.”

In other words, the faster you can get to the point where you have gathered and processed all of the data needed to support a decision, the more time you will have to leverage that data to make a better decision.  Although it sounds counterintuitive, big data analytics helps you speed up your data to slow down your decisions, thus enabling you to make better decisions faster.



Originally published in Pervasive Big Data & Analytics Blog

Search Big Data Blogs


Big Data (126)
Analytics (66)
Pervasive (50)
DataRush (33)
Hadoop (31)
Industry trends (22)
predictive analytics (20)
Scalability (20)
Multicore (15)
Data Mining (12)
Parallelism (10)
Java (9)
Jim Harris (9)
Cloud (8)
Cyber Security (8)
MapReduce (8)
big data analytics (7)
Data Volumes (7)
Data Warehouse (7)
RushAnalytics (7)
Volumes (7)
Actian (6)
Algorithms (6)
Cost-effective (6)
David Loshin (6)
Decision Support (6)
Julie Hunt (6)
RushAnalyzer (6)
analytics tools (5)
Dataflow (5)
machine learning (5)
Data Science (4)
Forrester (4)
Google (4)
Green IT (4)
Healthcare (4)
Phil Simon (4)
YARN (4)
analytics processes (3)
Big Data Science (3)
BigQuery (3)
Bloor (3)
data centers (3)
data integration (3)
Data Preparation (3)
data tools (3)
data-driven (3)
DataMatcher (3)
machine generated data (3)
Malstone B (3)
Mike Hoskins (3)
Opera Solutions (3)
Retail Analytics (3)
Security (3)
Smart Grid (3)
software (3)
Solutions (3)
telecommunications (3)
transportation analytics (3)
Age of Data (2)
analytics accuracy (2)
architecture (2)
Austin (2)
Bloor Research (2)
Business Intelligence (2)
data management (2)
Data Rush (2)
David Inbar (2)
David Norris (2)
fraud (2)
fraud detection (2)
Gartner (2)
GigaOM (2)
Hadoop Summit (2)
IntegrationWorld (2)
intelligent transportation systems (2)
internet of things (2)
McKinsey (2)
meetup (2)
ParAccel (2)
Pervasive DataRush (2)
Rexer Analytics (2)
smart meters (2)
#FollowFriday (1)
a (1)
Amazon (1)
analytics workflow (1)
Application Development (1)
automation (1)
Benchmarks (1)
best practices (1)
Cloud Analytics Summit (1)
cloud computing (1)
Cloudera (1)
contests (1)
cost (1)
cyber security issues (1)
data flow architecture (1)
Data Integrator - Hadoop Edition (1)
data quality (1)
data visualization (1)
digital marketing (1)
Door64 (1)
easy big data analytics (1)
Ericson (1)
Esri (1)
Facebook (1)
Fuzzy Matching (1)
Goverment (1)
Hadoop User Group (1)
Hadoop World (1)
hardware (1)
HBase (1)
HDFS (1)
industrial internet (1)
Jazoon (1)
Jim Falgout (1)
MalStoneB (1)
Mansour Raad (1)
Neil Raden (1)
Netflix (1)
NetFlow (1)
operational intelligence (1)
Paige Roberts (1)
para (1)
PIG (1)
pilot projects (1)
Predictive Analytics World (1)
psychohistory (1)
Public Sector (1)
Redshift (1)
Robin Bloor (1)
ROI (1)
Rosaria Silipo (1)
RushAccelerator (1)
RushLoader (1)
Sampling (1)
Signal and Noise (1)
SmartDataCollective (1)
spatial analytics (1)
speed (1)
sports (1)
Stephen Swoyer (1)
Steve Shine (1)
Strata (1)
SXSW (1)
Telecom Analytics (1)
Telecommunications Industry Association (1)
TIA (1)
Transportation (1)
TurboRush (1)
VectorWise (1)
Zementis (1)

Latest Posts

Actian Big Data & Analytics Blog has MOVED!
Big Data Phrenology
Big Data, Simpson's Paradox and Sufficient Tools
Data Science and the Art of Data Visualization

Big Data Blog Archives

<October 2014>

Accelerating Big Data 2.0™