Big Data
Big Data – The Genie is out of the Bottle!
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!
Genetics, Genomics, Nanotechnology, and more
Science has interested me for most of my life, but it wasn’t until my first child was born that I shifted from “interested” to “involved.” My eldest daughter was diagnosed with Systemic Onset Juvenile Idiopathic Arthritis (SoJIA – originally called Juvenile Rheumatoid Arthritis, or JRA) when she was 15 months old, which also happened to be about six months into the start of my Consulting company, all while we were in the middle of a very critical Y2K ERP system upgrade and rehosting project. It was definitely a challenging time in my life.
At the time, there was very little research on JRA because it was estimated that only 30,000 children were affected by the disease, and the implication was that funding research would not have a positive ROI. The economics of medical research were eye-opening to me. This was also a few years before major breakthroughs like Enbrel for children.
One of the things that I learned was that this disease could be horribly debilitating. Children often had physical deformities as a result of this disease. Even worse, the systemic type that my daughter has could result in premature death. As a first-time parent, imagining that type of life for your child was extremely difficult.
Luckily, the company I had just started was taking off, so I decided to find ways to make a tangible difference for all children with this disease. We decided to donate 50% of our net profits to fund medical research. Our goal was to fund $1 million in research and find a cure for Juvenile Arthritis within the next 5-7 years.
As someone new to “major gifts” and philanthropy, I quickly learned that some gifting vehicles were more beneficial than others. While most organizations wanted you to start a fund (which we did), the impact tended to be more long-term, supportive, and much less immediate. Later, I met someone passionate, knowledgeable, and successful in her field who showed me a different, better approach (here’s a post that describes it in more detail).
I no longer wanted to blindly give money and hope it was used quickly and properly. Rather, I wanted to treat these donations like investments in a near-term cure. To be successful, I needed to understand research from both medical and scientific perspectives in these areas. That began a new phase in medical research and an independent learning journey in areas where I had limited understanding and expertise.
A lot was happening in Genetics and Genomics at the time (here’s a good explanation of the difference between the two). My interest and efforts in this area led to a position on the Medical and Scientific Advisory Committee with the Arthritis Foundation. Except for me, the other members were talented, successful physicians who were also involved in medical research. We met quarterly, and I asked questions and made suggestions that made a difference. But unlike everyone else on the committee, I needed to study 40+ hours for each call to ensure I understood enough to add value and not be a distraction. Every quarter, I earned my seat at that table, and soon, most of the other members respected me for that (i.e., “not a typical donor”).
A few years later, we did work for a Nanotechnology company (more info here). The Chief Scientist wasn’t interested in explaining what they did until I described some of our research projects on gene expression. He then went into great detail about what they were doing and how he believed it would change what we do in the future. I saw that and agreed. That started my thinking about the potential of leveraging advanced nanotechnology in medicine.
While driving today, I was listening to the “TED Radio Hour” and heard a segment about entrepreneur Richard Resnick. It was exciting because it got me thinking about this again – a topic I haven’t thought about for the past few years (the last time, I was contemplating how new analytics products could be useful in this space).
There are efforts today with custom, personalized medicines that target only specific genes for a specific outcome. The genetic modifications being performed on plants today will likely be performed on humans in the near future (I would guess within 10-15 years as gene editing becomes more mainstream), possibly even performed by some type of robots. Because the body is an incredibly adaptive organism, it may be very challenging to implement anything consequential that is consistently safe and effective long-term. But that day will come.
It’s not a huge leap from genetically modified “treatment cells” to true nanotechnology (not just extremely small particles). Just think, machines that can be designed to work independently within us to do what they are programmed to do and, more importantly, identify and understand adaptations (i.e., artificial intelligence) as they occur and alter their approach and treatment plan accordingly based on findings and changes. This is extremely exciting. From my perspective, being able to do things that positively impact the quality of life for children and their families is a worthy goal.
My advice is to keep learning, stay open-minded, and do what you can to make a difference. You will never know what is possible unless you try. It will also be interesting to see how technologies evolve and work together to create an even greater impact.
Spurious Correlations – What they are and Why they Matter
In an earlier post, I mentioned that one of the big benefits of geospatial technology is its ability to show connections between complex and often disparate data sets. As you work with Big Data, you tend to see the value of these multi-layered, often multi-dimensional perspectives on a trend or event. While that can lead to incredible results, it can also lead to spurious data correlations.
First, I am not a Data Scientist or Statistician, and there are definitely people far more expert on this topic than I am. But, if you are like the majority of companies out there experimenting with geospatial and big data, it is likely that your company doesn’t have these experts on staff. So, a little awareness, understanding, and caution can go a long way in this scenario.
Before we dig into that more, let’s think about what your goal is:
- Do you want to be able to identify and understand a particular trend – reinforcing actions and/or behavior? –OR–
- Do you want to understand what triggers a specific event – initiating a specific behavior?
Both are important, but they’re different. My focus has been identifying trends so that you can leverage or exploit them for commercial gain. While that may sound a bit ominous, it is really what business is all about.
A popular saying goes, “Correlation does not imply causation.” A common example is that you may see many fire trucks for a large fire. There is a correlation, but it does not imply that fire trucks cause fires. Now, extending this analogy, let’s assume that the probability of a fire starting in a multi-tenant building in a major city is relatively high. Since it is a big city, most of those apartments or condos likely have WiFi hotspots. A spurious correlation would be to imply that WiFi hotspots cause fires.
As you can see, there is definitely the potential to misunderstand the results of correlated data. A more logical analysis would lead you to see the relationships between the type of building (multi-tenant residential housing) and technology (WiFi) or income (middle-class or higher). Taking the next step to understand the findings, rather than accepting them at face value, is very important.
Once you have what looks to be an interesting correlation, there are many fun and interesting things you can do to validate, refine, or refute your hypothesis. Even without high-caliber data experts and specialists, you can likely identify correlations and trends that can give you and your company a competitive advantage. Don’t let the potential complexity become an excuse for not getting started. As you can see, gaining insight and creating value with a little effort and simple analysis is possible.
There’s a story in there – I just know it…
I was reading an article from Nancy Duarte about Strengthening Culture with Storytelling, and it made me think about how important a skill storytelling can be in business and how it can be far more effective than just presenting facts and data. These are just a few examples. You probably have many of your own.
One of the best salespeople I’ve ever known wasn’t a salesperson at all. It is Jon Vice, former CEO of the Children’s Hospital of Wisconsin. Jon is very personable and has the ability to make each person feel like they are the most important person in the room (quite a skill in itself). Jon would talk to a room of people and tell a story. Mid-story, you were hooked. You completely bought what he was selling, often without knowing what the “ask” was. It was an amazing thing to experience.
Years ago, when my company was funding medical research projects, my oldest daughter (then only four years old) and I watched a presentation on the mid-term findings of one of the projects. I did this to personalize what they were doing so it would have more impact. The MD/Ph.D. giving the presentation was impressive, but what he showed was slide after slide of data. After 10-15 minutes, my daughter held her Curious George stuffed animal up in front of them (where the shadow would be seen on the screen) and proclaimed, “Boring!”
Six months later, that same person gave his wrap-up presentation. It was short and told an interesting story that explained why these findings were important, laying the groundwork for a follow-on project. A few years later he commented that his initial presentation became a valuable lesson. That was when he realized the story the data told was far more compelling than just the data itself.
A few years ago, the company I work for introduced a high-performance analytics database. We touted that our product was 100 times faster than other products, which happened to be a similar message used by a handful of competitors. In my region, we created a “Why Fast Matters” webinar series and told the stories of our early Proof of Value efforts. This helped my team make the first few sales of this new product and change the approach the rest of the company used to position this product. People understood our value proposition because these success stories made the facts tangible.
I tell my teams to weave the thread of our value proposition into the fabric of a prospect’s story, goals, and aspirations. We become part of their story, and this new story becomes their own (as opposed to our story). This simple approach has been very effective.
What if you not selling anything? Your data tells a story – even more so with big data. Whether you are analyzing data from a single source (such as audit or log data) or correlating data from multiple sources, the data has a story to tell. Whether patterns, trends, or correlated events – the story is there. And once you find it, there is so much you can do with it.
Whether you are selling, managing, teaching, coaching, analyzing, or just hanging out with friends or colleagues, being able to entertain with a story is a valuable skill. It is also a great way to make many things more interesting and memorable in business. So, give it a try.
Getting Started with Big Data
In Sales, I have the opportunity to speak with many customers and prospects about many things. Most are interested in Cloud Computing and Big Data, but they often don’t fully understand how to leverage the technology to maximize the benefits.
Here is a simple three-step process that I use:
1. For Big Data, I explain that there is no single correct definition. Because of this, I recommend that companies focus on what they need rather than what to call it. Results are more important than definitions for these purposes.
2. Relate the technology to something people are likely already familiar with (extending those concepts). For example: Cloud computing is similar to virtualization and has many of the same benefits; Big Data is similar to data warehousing. This helps make new concepts more tangible in any context.
3. Provide a high-level explanation of how “new and old” are different and why new is better using specific examples that they should relate to. For example: Cloud computing often occurs in an external data center – possibly one where you may not even know where it is- so security can be even more complex than in-house systems and applications. It is possible to have both Public and Private Clouds, and a public cloud from a major vendor may be more secure and easier to implement than a similar system using your own hardware;
Big Data is a little bit like my first house. I was newly married, we anticipated having children, and also anticipated moving into a larger house in the future. My wife and I started buying things that fit into our vision of the future and storing them in our basement. We were planning for a future that was not 100% known.
But our vision changed over time, and we did not know exactly what we needed until the end. After 7 years, our basement was very full, and it was difficult to find things. When we moved to a bigger house, we did have a lot of what we needed. But we also had many things that we no longer wanted or needed. And, there were a few things we wished that we had purchased earlier. We did our best, and most of what we did was beneficial, but those purchases were speculative, and in the end, there was some waste.
How many of you would have thought Social Media Sentiment Analysis would be important 5 years ago? How many would have thought that hashtag usage would have become so pervasive in all forms of media? How many understood the importance of location information (and even the timestamp for that location)? I guess it would be less than 50% of all companies.
This ambiguity is both a good and bad thing about big data. In the old data warehouse days, you knew what was important because this was your data about your business, systems, and customers. While IT may have seemed tough in the past, it can be much more challenging now. But the payoff can also be much larger, so it is worth the effort. You often don’t know what you don’t know – and you just need to accept that.
Now we care about unstructured data (website information, blog posts, press releases, tweets, etc.), streaming data (stock ticker data is a common example), sensor data (temperature, altitude, humidity, location, lateral and horizontal forces), temporal data, etc. Data arrives from multiple sources and likely has multiple time-frame references (e.g., constant streaming versus updates with varying granularity), often in unknown or inconsistent formats. Someday soon, data from all sources will be automatically analyzed to identify patterns and correlations and gain other relevant insights.
Robust and flexible data integration, data protection, data governance, and data privacy will all become far more important in the near future! This is just the beginning for Big Data.
- ← Previous
- 1
- 2
- 3
- Next →
