Big Data: Finding Actionable Data and Rapid Growth

Glen Rawlins of InfoTrax Technologies shared some key insights with MLM.com on the present state of data and the favorable future of direct selling.

Data has always been a facination for Glen Rawlins. He began his career at Utah State University running reports for university projects During his free time, Glen ran reports for direct sales companies and helped build software and computer systems to calculate commissions. Glen also assisted in a project to improve test score reporting for special needs education facilities, and built reporting systems for accounting.

With all his experience with data, the one constant that Glen dealt with was data storage. Both long term data storage and short term storage was very expensive. When you wanted more space, you had to make a large investment and increase the size of the room you were using to store the computer in.

Fast forward to 2015 and the semantics have changed, technology has improved. Cheaper cloud storage has propelled the idea of gathering multiple sets of data for useful reporting. Now, said Glen, the issue is managing the amount of massive data that’s available to companies. The answer to managing big data is by using technology to organize and present it.

How to manage big data

Data scientists and IT professionals combine data into large data sets. This can make applications run slowly. The solution is to put data into database cubes. These cubes allow for faster dashboard reporting. Dashboards can present the data in an easily-to-digest format where you can spot trends and look at the big picture. The dashboards can then be customized for a specific purpose or group of people. It is not uncommon to have a dashboard for sales, marketing, IT/Web services, or executives.

Big data and the Obama election

During our conversation, Glen referred to the 2008 re-election campaign for President Obama as a prime example on how big data was being used. He saw it as an impressive example of the application possibilities of big data and of effective action being informed by that data. MIT did a scholarly review of how Obama focused his efforts in a Republican dominated congress. Dan Wagner, the Democratic National Committee’s targeting director, was able to improve statistical forecasting data with high accuracy up to 5 months before the election. Mitch Stewart, director of Organizing for America, commented on the 2.5% margin of error calling it “a proof point for a lot of people who don’t understand the math behind [big data] but understand the value of what that math produces” MIT Technology Review,. (2012).

“I don’t think the direct selling industry has figured out how to use big data yet. . .Big data is really about when you take every data point and put it into one big predictive model that is fast, accurate, and actionable.” — Glen Rawlins

You might be asking how the Democratic National Committee actually gathered data and shared it among thousands of volunteers across the country. They launched a campaign asking volunteers to share their personal stories online and in social media. The Obama IT team built a social media campaign field office dashboard. This gave the election campaign team the power to leverage thousands of smaller virtual offices. It also provided near real-time information and communication out into the field without anyone ever having to go to a larger field office. The users could organize their own micro teams and also call people within their area that had been identified prior to the election as needing to be contacted. The IT team used Ushahidi (http://www.ushahidi.com/ written in PHP), to work with Facebook and other social media outlets.
The Obama team also used the Narwhal API that brought together hundreds of applications and data together. Through the API, a person could interact with the dashboard and use the data to get an overall picture of the trends.

One campaign official was quoted in an article from slate.com saying, “It’s not about us trying to leverage the information we have to better predict what people are doing. It’s about us being better listeners. When a million people are talking to you at once it’s hard to listen to everything, and we need text analytics and other tools to make sense of what everyone is saying in a structured way” (Issenberg, S., 2015). This statement about listening is where most companies have trouble starting out in big data. Having the right places to put data and being able to access it quickly with a data cube and/or API integration with user dashboards is essential.

As a result, President Obama focused on key areas to win over the hearts of the people, got the democrats to the polls, and won the 2012 race. Today President Obama still continues to drive the data initiative by opening up vast amounts of government data and imploring academia to use those data to better our society and country (http://youtu.be/dKHz9LbgRmo).

Big data and direct marketing

When I asked Glen about how big data is being used in direct selling and multi-level marketing he said, “I don’t think the direct selling industry has figured out how to use big data yet. Most direct sales companies are using reporting systems both internally and out to distributors. Big data is really about when you take every data point and put it into one big predictive model that is fast, accurate, and actionable.”

Glen subsequently suggested that direct selling companies would have a huge competitive advantage in the marketplace if they could just to get a handle on social data and on listening and responding to that data. Independent distributors and network marketing companies alike know that if they recruit a person with lots of friends on social media, that recruit will be more likely to share products and recruit to the business opportunity.

Both MLM and party plan companies are taking an additional interest in social media and other audience data that is available. It may be not too far in the future that all community based logins and applications to join an opportunity will require some social data with it. Glen said that he thinks direct selling companies should systemize ways to focus attention on and connect with those who have the highest ability to grow the business. This data should influence how you interact with people based on your selected criteria.

What does big data mean to you?

Direct selling companies need to start gathering data—both transactional and informational—then find fast ways to deliver that data and derivative data to dashboards. Companies that learn to use their data will be more agile operationally and grow faster than any direct selling company ever has. Future direct selling companies will have more tools to prospect online, get real time behavioral data and analysis to create meaningful interactions based on data.

References

Wikipedia,. (2015). Big data. Retrieved 19 March 2015, from http://en.wikipedia.org/wiki/Big_data

MIT Technology Review,. (2012). The Definitive Story of How President Obama Mined Voter Data to Win A Second Term | MIT Technology Review. Retrieved 9 March 2015, from http://www.technologyreview.com/featuredstory/509026/how-obamas-team-used-big-data-to-rally-voters/

President Obama. [Whitehouse.gov] (Feb. 25, 2015) President Obama Announces Dr. DJ Patil as the First United States Chief Data Scientist. Retrieved from http://youtu.be/dKHz9LbgRmo

Madrigal, A. (2012). When the Nerds Go Marching In. The Atlantic. Retrieved 9 March 2015, from http://www.theatlantic.com/technology/archive/2012/11/when-the-nerds-go-marching-in/265325/

Issenberg, S. (2015). How Cutting-edge Text Analytics Can Help the Obama Campaign Determine What Voters Want. Slate Magazine. Retrieved 9 March 2015, from http://www.slate.com/articles/news_and_politics/victory_lab/2012/01/project_dreamcatcher_how_cutting_edge_text_analytics_can_help_the_obama_campaign_determine_voters_hopes_and_fears_.2.html