Phil Simon is a prolific technology writer and has penned several books in recent years. He’s looked at why IT systems fail, platforms, big data and now, data visualization.
Sometimes I get to connect with Phil in Vegas but this time, I read his new book and shot him some questions about it. Here’s our latest exchange:
(Brian) Your previous book “Too Big to Ignore” looked at the world of big data. Essentially, it was a wakeup call for businesses to get organized and get smart regarding the data avalanche coming their way. Your new book, The Visual Organization, sort of picks up from where the last one ended. Now, you counsel readers about how to make sense of the massive piles of data they’re plowing through. So, data visualization is the key now – correct?
(Phil) It’s important to make sense of vast amounts of structured, semi-structured, and unstructured data. You’ll never hear me say otherwise. I hesitate, though, to classify dataviz as key. I tend to approach things with a holistic perspective. As I write in The Visual Organization, a company with a dysfunctional culture and no sense of innovation cannot save itself via dataviz. In other words, is it a silver bullet? Of course not. All else being equal, though, organizations that encourage data visualization and data discovery will do better than those that fail to recognize their importance.
(Brian) Is visualization important just because of the sheer quantity of information people are parsing or is it because the nuances within the data are often really hard to spot? This seems a lot like panning for a few flecks of gold.
(Phil) There are no guarantees when deploying new dataviz technologies and applications. It’s certainly possible that the squeeze won’t be worth the juice. Fortunately, this isn’t 1998, a time in which millions of dollars were required to deploy enterprise technologies. Thanks to open-source software, cloud computing, SaaS, and other democratic technologies, organizations can begin visualizing their data much quicker than in previous decades.
You’re right, though. This is not a simple journey from point A to point B. As companies like Amazon, Apple, Facebook, Google, and Netflix have shown, though, there’s tremendouspotential value to be gleaned from embracing Big Data. It’s this uncertainty that makes people uncomfortable and, in my view, explains the relative lack of Big-Data adoption.
Brass tacks: Now is precisely the time to act. Waiting for everyone to “get it” squanders today’s opportunities around Big Data and dataviz.
(Brian) Just today, I read an article titled “Ugly secret: Big data can and does lie”. I’ve got to admit, I’m kind of in that camp as many people put a lot of bogus info out there on social media, surveys and other data sources. There’s even a television show dedicated to people who inadvertently fall in love with fake Internet people. So, how are firms to trust what they’re getting in these big data sources?
(Phil) New applications and technologies can certainly help, but I’ve said many times that Big Data does not obviate the new for human intuition. In the book, I urge companies and their employees to exhibit a healthy skepticism. Visualizing errant data can often expeditiously lead to problem resolution.
(Brian) Interpreting the results of big data feeds, triangulated data, etc. can be more art form than a science. I recently saw an article describing how an insurer might not want to insure someone for health insurance who:
- Has no children but drives a minivan
- Has a big screen television
- Doesn’t own a dog.
Why? Because their big data would suggest that this person is probably obese. It seems people with a dog get more exercise. People without big screen televisions are less sedentary and the childless minivan driver uses this mode of transport as it is easy for them to get in/out of. Personally, I was appalled at this data ‘science’ as it confused correlation with causality. It also failed to take into consideration a number of other potential factors (e.g., the dog recently passed away, the minivan was part of a loved one’s estate that a person inherited, and, the TV came with the house). So, how do businesses analyze their big data and make the correct decisions based on a more enlightened understanding of the facts?
(Phil) Great example. I hadn’t heard of that one. You’re talking about two very different things, though. The correct business decision may well not be the “ethical” one. In my previous book, I examined new mobile technologies that allowed for more accurate car-insurance premiums.
Many people will continue to mistake causation with correlation. I wish that I had a simple answer to your question. Just because we analyze and visualize more data doesn’t mean that we will always make correct business decisions, much less ethically justifiable ones. With Big Data, the fleas come with the dog.
(Brian) Where do you stand on the ethical use of big data analyses? The NY Times piece regarding Target’s ability to determine which shoppers are pregnant seems like a particularly good example to explore. Do you have any hard and fast rules that readers should adopt?
(Phil) That’s a great story. I reference it in Too Big to Ignore. Here’s a good general rule of thumb: Just because you can doesn’t mean you should. I can’t say that Target’s behavior was ethical or not. If I ran the company’s marketing department, why wouldn’t I want to use as much information as I could to effectively sell merchandise, especially with Amazon turning my stores into showrooms?
(Brian) Your book makes big data and visualization as part of a quest for firms to find better insights. Why the word “quest”? Do you see the search for insights as a never ending challenge? Is it because the competitive landscape (and the fickle nature of consumers) is increasingly more fluid?
(Phil) I’m glad that you picked up on that. You’re right. It is a quest. The days of “set it and forget it” are rapidly coming to a close. Many of us yearn for a simpler time. As Ray Kurzweil notes, we are living in an era of accelerating technological change. Insights today may no longer be accurate in a month. It’s imperative upon professionals to question long-standing standard reports, KPIs, dashboards, and traditional reporting tools. Yes, they’re still relevant, but they no longer can tell the whole story. What’s more, they don’t allow for true data discovery, especially with respect to vast amounts of unstructured data.
(Brian) What sort of company is not going to become a visual organization? Why? Is there something in their DNA preventing this? What’s their risk?
(Phil) As Drucker once said, culture eats strategy for lunch. Organizations that penalize failure and curiosity will always leave a great deal of innovation and insights on the table. Look at the most successful companies today, as I did researching this book. Netflix is a case in point: Curiosity is not just tolerated, but encouraged.
Jim Barksdale, former CEO of Netscape, famously said “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” No, not everything can be quantified, but we have entered an age of tremendous information, technology, and opportunity. Employees that don’t understand this only hurt themselves and their employers. They inhibit becoming visual organizatons.
(Brian) What topic are you going to research next?
(Phil) I’m honestly not sure. The Internet of Things really intrigues me but don’t hold me to it.
Well, if Phil does the Internet of Things topic for his next book, I’d like to be there when he interviews the refrigerator that sent out 1000’s of spam messages…..
(Cross-posted @ ZDNet | Software and Services Safari Blog)