Big Data – The Genie is out of the Bottle!

Posted on September 29, 2014 Updated on August 15, 2026

Back in early 2011, other members of the Executive team at Ingres and I were betting on the future of our company. We knew we needed to do something big and bold, so we decided to build what we thought the standard data platform would be in 5-7 years. A small minority of team members didn’t believe this was possible and left, while the rest focused on making it happen. We made three strategic acquisitions to fill gaps in our Big Data platform. Today (as Actian), we have nearly achieved our goal. It was a leap of faith back then, but our vision turned out to be spot-on, and our gamble is paying off today.

My mailbox is filled daily with stories, seminars, white papers, etc., about Big Data. While it feels like this is becoming more mainstream, reading and hearing the various comments on the subject is interesting. They range from “It’s not real” and “It’s irrelevant” to “It can be transformational for your business” to “Without big data, there would be no <insert company name here>.”

What I continue to find amazing is hearing comments about big data being optional. It’s not – that genie has already been let out of the bottle. Incredible opportunities await companies that understand and embrace its potential. I like to tell people that big data can be their unfair advantage in business. Is that really the case? Let’s explore that assertion and find out.

We live in the age of the “Internet of Things.” Data about nearly everything is everywhere, and we have tools to correlate it and understand so many things (activities, relationships, likes and dislikes, etc.).  With smart devices that enable mobile computing, we have an extra dimension: location. And, with new technologies such as Graph Databases (based on SPARQL), graphical interfaces to analyze that data (such as Sigma), and identification technology such as Stylometry, it is getting easier to identify and correlate that data. Someday, this will feed into artificial intelligence, becoming a superpower for those who know how to leverage it effectively.

We are generating increasingly large volumes of data about everything we do and everything going on around us, and tools are evolving to make sense of that data better and faster than ever. Organizations that perform the best analysis, get answers fastest, and act on that insight quickly are more likely to win than organizations that look at a smaller slice of the world or adopt a “wait and see” posture. So, that seems like a significant advantage in my book. But is it an unfair advantage?

First, let’s remember that big data is just another tool. Like most tools, it can be misused and abused. Whether a particular application is viewed as “good” or “bad” depends on the goals and perspective of the entity using the tool (which may be the polar opposite of the groups targeted by those people or organizations).  So, I won’t try to judge the various use cases; instead, I’ll present a few and let you decide.

Scenario 1 – Sales Organization: What if you could understand what you were being told a prospect company needs and had a way to validate and refine that understanding? That’s half the battle in sales (budget, integration, and support/politics are other key hurdles). Data that helped you understand not only the actions of that organization (customers and industries, sales and purchases, gains and losses, etc.) but also the stakeholders’ and decision-makers’ goals, interests, and biases. This could provide a holistic view of the environment and allow you to provide a highly targeted offering, with messaging tailored to each individual. That is possible, and I’ll explain soon.

Scenario 2 – Hiring Organization: Many questions cannot be asked by a hiring manager. While I’m not an attorney, I would bet that State and Federal laws have not kept pace with technology. And while those laws vary state by state, there are likely loopholes allowing public records to be used. Moreover, implied data that is not officially considered could color a hiring manager’s or organization’s judgment. For instance, if you wanted to “get a feeling” that a candidate might fit in with the team or the culture of the organization or have interests and views that are aligned with or contrary to your own, you could look for personal internet activity that would provide a more accurate picture of that person’s interests.

Scenario 3 – Teacher / Professor: There are already sites in use to search for plagiarism in written documents, but what if you had a way to make an accurate determination about whether an original work was created by your student? Some people will do the work and write a paper for a student for a fee. So, what if you could not only determine that the paper was not written by your student but also determine who the likely author was?

Do some of these things seem impossible or at least implausible? Personally, I don’t believe so. Let’s start with the typical data our credit card companies, banks, search engines, and social network sites already have about us. Add to that the identified information available for purchase from marketing companies and various government agencies. That alone can provide a pretty comprehensive view of us. But there is so much more that’s available.

Consider the potential of gathering information from intelligent devices accessible through the Internet, your alarm and video monitoring system, etc. These are intended to be private data sources, but history has taught us that anything accessible is subject to unauthorized access and use (just think about the numerous recent credit card hacking incidents).

Even de-identified data (medical/health/prescription/insurance claim data is one major example), which receives much less protection and can often be purchased, could be correlated with a reasonably high degree of confidence to understand other “private” aspects of your life. The key is to look for connections (websites, IP addresses, locations, businesses, people), things that are logically related (such as illnesses/treatments/prescriptions), and then accurately identify (stylometry looks at things like sentence complexity, function words, co-location of words, misspellings and misuse of words, etc. and will likely someday take into consideration things like idea density). It is nearly impossible to remain anonymous in the Age of Big Data.

There has been a paradigm shift in the practical application of data analysis, and companies that understand and embrace it will likely perform better than those that don’t. This technology also raises new ethical considerations and will likely bring new laws and regulations. But for now, the race is on!