Four Steps to Ensure Your Big Data Investment Pays Off
Think back to when the Internet was young. Everyone and their brother agreed that a search offering was important, but none were profitable. Google actually arrived late to the game with a fraction of the resources and owned the entire market because it figured out that making search easy was important and how to monetize it. It turns out that making something easy and profitable was the most important part of search.
With analytics, like any tool, if the folks that need to use the tool can’t figure it out, then the tool is worthless. This is why when I first saw IBM’s Watson, I had a “holy crap” moment. IBM was attempting, and had made good progress, to do for analytics what Google did for search. I’m not talking about monetizing, because there are few free analytics tools. I’m talking about making it easy to use. I believed that if IBM, or someone else (Google is working this angle, as well), figured out the right mix, they would own the analytics market. Well, IBM announced a Watson Analytics package that could very well be the beginning of a Google moment, but for analytics. Let me explain.
What’s Important for Product Success
Often the difference between successful companies and those that fail is more about what the firm doesn’t do than what it does. Microsoft originally beat Apple because it was willing to license its technology and listen to partners to gain scale. Apple didn’t. Apple came back largely because Microsoft didn’t want listen to partners and instead tried to beat Apple with an Apple-like offering and failed. IBM lost the PC market because it wasn’t willing to do what was needed to win in the consumer market, and once it lost the consumers, it lost the volumes and user support it needed for corporate. Lenovo did what IBM was unwilling to do and now leads the PC market with a business unit built on IBM’s old PC Company. Google doesn’t like to spend money on marketing and doesn’t like to listen to partners, which is why Android is mostly Samsung and Google+ has never gotten to critical mass.
Companies can fail because they don’t resource an effort based on what is required but on what they are willing to do. In regular life, this is like the guy I knew who wanted to compete in a triathlon but hated swimming (folks who run a lot tend to have no body fat and sink), so he worked extra hard on bicycling and running because he liked to do that stuff. When the race came up, he almost drowned.
What is important in analytics isn’t the analytics engine, it is actually the capability of the user who needs to use it. Using another sports metaphor, you can give a guy who doesn’t know how to play tennis the most expensive tennis racket in the world and he likely won’t be able to play a single volley, but if you start with a great tennis player, he likely could play with a ping pong paddle and still do surprisingly well. If you could create a tennis racket that a novice would instantly be capable with, you’d have a winner.
Why Watson Is Different for Analytics
This is what makes Watson different. It isn’t a great tool that requires an expert to use; it is a great tool that contains the expertise. You still have to be able to articulate what you want but let’s hope that most executives know how to communicate what they want. (I’m sure we’ve all known those who can’t, but that is a problem Watson probably can’t help.)
This is the bridge that other vendors (with the exception of Google, which is working on a solution but hasn’t announced) don’t seem to get. To make analytics truly work, you have to make it easy. Executives aren’t analytics experts and likely will never become analytics experts. A tool that isn’t easy will likely not be used and a tool that isn’t used is a waste of money.
Wrapping Up: The Other Key to Watson Analytics’ Success
The other key for this is the program that IBM has announced, which is based on the fermium model, substantially lowering the costs associated with setting up a cloud-based trial. Part of the issue with all analytics products is that by the time you’ve invested in implementing them, you are generally in too deep, in terms of costs and effort, to pull the plug, which creates too much risk and holds back trials. This latest effort could shorten time to market dramatically and make all the difference in terms of ramping this solution to completion. Granted, there are still likely issues associated with training Watson that have to be addressed but, over time, I expect Watson to improve on this vector as well. If IBM can eliminate this last cost , it could easily own the analytics market by the end of this decade, much like Google owns search today.
Rob Enderle is President and Principal Analyst of the Enderle Group, a forward-looking emerging technology advisory firm. With over 30 years’ experience in emerging technologies, he has provided regional and global companies with guidance in how to better target customer needs; create new business opportunities; anticipate technology changes; select vendors and products; and present their products in the best possible light. Rob covers the technology industry broadly. Before founding the Enderle Group, Rob was the Senior Research Fellow for Forrester Research and the Giga Information Group, and held senior positions at IBM and ROLM. Follow Rob on Twitter @enderle, on Facebook and on Google+
