In-Depth

Q&A: "Smart or Lucky" Book Analyzes Tech Successes, Failures

A long-time tech observer delves into why some companies succeed and others fail.

• By Linda L. Briggs
• 03/20/2012

Technology leaders that succeed in today's climate need a combination of both intelligence and luck, says long-time industry observer, author, and pundit Judith Hurwitz. For example, bringing a good product to market before the industry is ready for the technology can lead to failure, she explains, no matter how brilliant the concept.

Hurwitz is president and CEO of Hurwitz & Associates, Inc., a strategy consulting and research firm focused on the business value of emerging computing technologies, and a well-known analyst, author and commentator with decades of experience observing the technology arena.

Along with her team at Hurwitz & Associates, she has authored several books in the For Dummies series, including Cloud Computing for Dummies and Service Oriented Architecture for Dummies. She writes a technology blog and has published hundreds of articles and reports on the computer industry. More recently, Hurwitz is author of the book Smart or Lucky: How Technology Leaders Turn Change into Success. In this interview, the first of two parts, she discusses some of the concepts covered in her book, which analyzes the successes and failures of a number of technology companies.

BI This Week: Regarding your book Smart or Lucky: How Technology Leaders Turn Chance into Success: Which is it? Do you need to be smart or do you need to be lucky to succeed in today's technology world?

Judith Hurwitz: You need to be both. What causes many failures for companies is a technology that is really very good but is too early for the market. That might mean that there's no supporting infrastructure yet in place to make it successful, it might mean that people just don't understand the technology, or they're scared of it because it's so alien to what they know.

There are many examples. Some of the early data mining technologies are good examples. Companies were doing data mining or artificial intelligence back in the 1970s and early 1980s. There was a huge amount of research ... [and] people were really excited about the idea, but at the time, it was a foundational technology and extraordinarily expensive. There wasn't any sort of advanced analytics that let you go and discover the knowledge, and put it into a system, so although the promise was huge, it just wasn't practical. That's an example in which people were extraordinarily smart but their timing was completely off.

Fast forward 25 or 30 years and you have technologies such as IBM's Watson and others. ... All the knowledge [needed] is now on the Web, so you can go out and search for it, search databases and search for patterns, and bring it all together. That's just one example; there are hundreds of them.

One of the examples I talked about in the book is a little company called Patriot Partners from the late 1980s. The goal of the company was to create a marketplace for business services that could be sold on the Web. It never went anywhere because the infrastructure for buying and selling applications in a marketplace just wasn't there. This was 20 years before app stores, but they had it figured out. However, the infrastructure just wasn't there to support the technology.

I suppose Software-as-a-Service (SaaS), which has been around for a long time as ASPs, is another example of a technology idea without an infrastructure.

Yes. Look at the early application service providers from the late 1980s and early 1990s. Same thing. Software-as-a-service and cloud computing -- all of that had its roots going back to the ideas behind utility computing from the 1970s and 1980s. Really smart people were talking about that, but prices were high, it wasn't easy to access that capability, and you didn't have the Web as a commercial platform ... accessible to the ordinary businessperson.

What other specific lessons from "Smart or Lucky" relate back to BI and data warehousing?

If you think about BI, that's an area that has been around for many decades. The very earliest experience was that in order to analyze data, you needed huge machines and you needed very, very knowledgeable individuals. If you think about the early data warehouses, the promise was huge -- that you would put all this data into a warehouse, press a button, and suddenly you would be able to provide your CEO with immediate answers to, say, sales over the last three quarters and projections going forward.

The reality was that these warehouses needed time to evolve, which is really one of the key issues with technology. We get bubble markets in which the capabilities of early technologies are way overblown and overpromised. The reality with these early data warehouses is [that] you could put a lot of information into one of those, but they were so complicated, hard to build, and hard to manage that very few customers actually got the results they wanted. In fact, they sort of became environments where you put things but you couldn't get them back out again, so there was early disappointment with data warehousing products.

What steps to successful companies take to address that early overpromising, which is certainly a pattern we see over and over?

One of the trends you see over time is a focus on the customer. There is a pattern with successful companies that first have the good luck of getting their timing right, then put all their focus not on how cool and sophisticated their technology is but on what business problems they're solving for customers. The more they can create technology that allows customers to solve problems quickly and efficiently, the more customers will stick with them. Successful companies are ones that, after they get lucky, they really get smart. They say, "OK, what are the biggest problems my customers have? How can I now create some solutions that allow them to solve problems so that they can be successful?"

My experience is, unless vendors can turn their customers into heroes armed with a tool or a solution, then technology is just another expensive headache. ...

So first you need to be lucky, then smart about it.

Yes. Although, even the luckiest entrepreneur has to have some understanding of what technology they can build upon and what problems it can solve. If you don't have that amount of smarts, it won't help if you're lucky, but it really is useful to be at the right place at the right time. Luck is a huge element of success.

You said "technology needs time to evolve," but it also sounds like users need time, maybe not to evolve, but to understand how a technology can be used.

Yes, it's a combination of both. If there's a technology, but there's not a market for it. ... Think about something like artificial intelligence, which probably started in the 1950s or '60s. It was an incredible technology, and in the 1980s it was commercialized. A huge number of companies came out with artificial-intelligence-based products to do things such as analyze information and help companies solve business problems. There was some really sophisticated data analysis. The problem was, the technology had not yet been extracted to the point where an ordinary businessperson could make heads or tails of it.

Companies failed because the technology was way too expensive and complex. People said, "Well, artificial intelligence never worked. It was a pipe dream," and that terminology went away. You never heard companies mention "artificial intelligence" again.

Fast forward a while, and you saw companies taking that type of technology and building more sophisticated decision-support and rules-based engines. Those had their foundations in the early artificial intelligence technology, but they had the luxury of learning from earlier mistakes. The technology at the foundation began to mature. Systems got more powerful, so you had the luxury of extracting a lot of that complexity away.

Now, 20 or 30 years later, you see robotics and advanced analytics and predictive analytics. By the way, behind the scenes they're using sophisticated artificial intelligence technology, but no one uses the term, and it took several decades to get over that early disillusionment.

Speaking of customers, you make an interesting point on a recent blog posting that focusing on the customer is a big trend these days, but a lot of companies are either asking the wrong questions of customers or not asking enough questions. Is that part of the luck-versus-smart equation?

What I often see is that companies are getting better about understanding that it's all about the customer, but sometimes they approach it in the wrong way. They focus on the features and functions of their existing product. They'll conduct focus groups and say, "What do you like? What don't you like?" If all you really need to do is to tweak your product, that's fine, but if you're looking into the future and you want to remain competitive, you had better make sure that you're helping your customers to think not about what's there today but what they're going to want tomorrow.

We see this over and over. A company will be so focused on current reality that they miss the new trend -- they miss what it is you can't do. Sometimes when I'm working with a company's customers, we ... ask them, "What can't you do? What [features] would you pay money for?" Then, they're not thinking about version 1.4.2. They're thinking about the business. What's holding my business back? What would be the type of things [they need]?

I always like to hear a customer say, "Wow! If you can do that for me, that would be incredible." If the customer says that and doesn't ask the price, then you know that you're getting to something that is going to solve a really burning problem.

It reminds me of the Steve Jobs quote, that customers don't know what they want until you give it to them.

They really don't know.

Is that one reason some tech companies seem to last and last and reinvent themselves successfully? IBM, which you discuss in your book, often comes to mind as a classic example of a company that has repositioned itself well. Is that because they're asking customers the right questions?

That's one part of it. I think it's a combination. One of the distinguishing factors between companies that last and ones that don't are companies that are not afraid of change, that are not afraid to get rid of their crown jewels.

What happens to companies is they have a product, and even if the customer base is not growing, they still can make a lot of money from it. This is especially true in software because once a customer implements software, they often ... have made a huge investment, customizing it so that it works and so forth. It's not exciting technology anymore and it's sort of long in the tooth, and the vendor is not investing much more in the product. They're getting revenue from it because their investment costs are very low, they get yearly maintenance fees, and they still sell new licenses, maybe not as much as they did five years ago, but they still sell licenses.

The problem is, if they spend all their time on those products, they don't have the energy, time, and money to invest in the future. They get caught focusing on the past; it's very hard to break that pattern.

What did IBM do to break that pattern?

One of the gutsiest things that IBM did was moving away from the PC business. The leadership – it was Lou Gerstner at the time -- was smart enough to understand that they would take a short-term hit. which they did, but the move would allow them to focus on higher-margin products. They did that at a time when the market was in a different place.

HP [recently] looked at doing the same thing because they have the same issue with commodity PCs. However, they weren't able to do so because the supply chain was so entangled in terms of the prices that they get from their suppliers, because they buy so many chips. Now, the same was true of IBM, but at that time, because the market was different, IBM was able to absorb that loss. So, one of the differences is companies that are able to take risks to try things that they have not done before, risks that potentially can help them to change their market.

I have a chapter in the book that's called You're Not Dead Yet that specifically talks about IBM and Apple. Both companies almost went out of business, but they were able to transform themselves by going back to their core, to what made them great companies, and then really build on that -- really, to build on their essential DNA.