Big Data. Big Decisions
InformationWeek
Special Coverage Series

Commentary

Tony Byrne

Small Data Beat Big Data In Election 2012

The Obama re-election machine was really about small data--and enterprises can learn from that.

Social Studies: Obama vs. Romney
Social Studies: Obama vs. Romney
(click image for larger view and for slideshow)
Even before polls closed Tuesday, some observers were describing the Democrats' vaunted "ground game" -- a.k.a., get-out-the-vote, or GOTV -- as a victory for big data.

Doubtless, the staff of both presidential candidates performed some deep analysis of large datasets. Having participated with my family in GOTV efforts for the past four quadrennial U.S. elections, I have a different take: The Obama re-election machine was really about small data.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

Small Data Writ Large

Compared to previous years, what was fascinating about the Obama effort this time was the intense effort on data curation at the individual record level. In past cycles, registration and voter data seemed like a read-only Excel sheet: You got a well-thumbed, printed list of voters to call or visit with some basic info about them and -- if you were lucky -- some handwritten notes from previous canvassers. This led to a lot of duplicate effort, frustration and miscommunication.

This time, we saw a much more systematic effort to refine and annotate each record iteratively, to match message and approach to where someone lay in their decision cycle. When someone was "touched," their record got updated. If the previous canvasser uncovered a preference for Obama, the next canvasser worked on intent to vote. And if that succeeded, the next canvasser, closer to Election Day, worked to make sure the voter had a practical plan for voting (a key predictor of actual voting, especially among young or distracted voters). A premium seemed to be placed on obtaining mobile phone numbers, updated address information and names of additional potential voters in the household (typically adult children living at home).

[ For more on how the campaigns used big data to chase voters, see Election 2012: Who's Winning Big Data Race? ]

An enterprise data manager would recognize the basic steps:

1. Validate
2. Clean
3. Annotate
4. Enrich
5. Adjust Outreach Message
6. Repeat

To be sure, this was not happening in real time, and it wasn't mobile-enabled, at least from what I saw. A lot of handwritten annotations got updated digitally each evening for reprinting the next day. But the whole process had to be simple and methodical enough that many thousands of undertrained volunteers could master it. I caught a brief glimpse of the packaged software underneath all this, and it was no great shakes, at least from a data-entry standpoint. But with motivated actors, perhaps that didn't matter.

Another notable improvement came in geolocation. For canvassers on the street, more precise maps and better choreography of target lists with a sensible path makes for a more efficient trek. Maybe Google's API will prove another underrated key to this election.

Some Caveats

I should add that this process was by no means foolproof, and every Obama volunteer can tell stories of mistakenly knocking on doors of confirmed Romney supporters, although overall it struck me as a noticeable improvement over what my family saw in 2008.

Also, I'd guess the Romney campaign was similarly taking a more sophisticated, CRM-like approach this cycle as well, although they don't seem to have staffed it as fully. The human element is critical here.

And let's not underestimate content: sophisticated GOTV only works if people are already predisposed to voting for your candidate.

Small Data and Your Enterprise

Still, there are many lessons here. That data quality and relevance matter. That improved execution may compensate for diminished prospects. Perhaps most importantly, that some data is too important to be left just to machines. In the end, the data was big alright, but it took live human beings to realize its value, one record at a time.

As someone who evaluates marketing automation suites -- which try to funnel prospective customers automatically through a similar decision process in the digital world -- it also got me thinking that the current emphasis on sophisticated campaign design over sophisticated data management in that marketplace seems a bit misguided.

Of course, your enterprise probably has to hit targets four times a year, rather than once every four years, and most of you cannot call on passionate volunteers to help. But I wonder if there aren't more lessons to draw here. What do you think?

The business world is changing. Is your company ready? E2 Innovate, formerly Enterprise 2.0, is the only event of its kind, bringing strategic business professionals together with industry influencers and next-gen enterprise technologies. Register for E2 Innovate Conference & Expo today and save $200 on current pricing or get a free expo pass. Nov. 12-15, 2012, at the Santa Clara Convention Center, Silicon Valley.



Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.