Datamation content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.
IBM announced a new initiative this week to use a freemium model to move Watson, their English language AI interface for analytics, into the market more aggressively. But what I find most interesting is that this move mirrors pretty closely what Google did with search – it’s a huge step toward IBM owning the analytics market.
In fact, ironically, I think the only real competitor emerging is Google, which will make for an interesting battle.
What’s Important in Analytics
One of the problems with engineers is that they tend to focus more on the plumbing than the interface. They’ll build incredibly complex tools that even they themselves would have trouble using.
What made companies like Apple, Microsoft, and Google different (at least when they were on the rise) was that they focused much more on the user interface and making technology easier to learn and use. This is why products like IBM PROFs fell off in the face of word processors like the one in Microsoft Office. You didn’t have to learn a brand new way of doing something, you could just launch the tool and in a few minutes you were creating usable results.
Google didn’t launch search, they just made it easy and they found a way to monetize it. Their magic was in looking at the searches others did, learning from them, and then applying what was learned to make subsequent searches easier and more successful. We had indexing long before Google but everyone started from scratch, Google implemented intelligence first, reduced massively the time it took to get meaningful results and now dominates the market. In a way, Google Search was the first example of what could be done with an intelligent product and it made the firm billions.
So what is important with Analytics is similar to what is important in any product: how easily you can become expert enough to get results you can use.
The Problem with Analytics
Much of the difficulty with IT products is that they are created by people who are far removed from the tasks these tools perform. Programmers and software engineers are expert at creating programs but they know virtually nothing about what it takes to run a business, sell a product, or build anything but a program. This is why so often when a customer provides a set of what they think are precise requirements and gets back something that doesn’t look anything like what they asked for.
We often talk about analytics needing data scientists who have a unique skill set, allowing them to get out the answers needed from highly complex data repositories. Since the results of the analysis are supposed to lead to better executive decisions the ideal skill set would have been an MBA Data Scientist, yet I’ve actually never seen one of those. Folks who are good at deep analysis and folks that are good at business tend to be very different folks, and data scientists are in very short supply at the moment.
So a data scientist knows how to talk to an analytics program but doesn’t know business and the executive knows business and doesn’t know how to talk to a data analytics program – and neither of these folks is likely to learn the skills of the other. So what you need is an analytics engine that can learn and then create an interface that a business person can use.
Wrapping Up: The Final Shoe
This takes us full circle back to this latest IBM announcement. How IBM came to dominate the computer space in the first place was with a unique model where they leased but didn’t sell their hardware. The business model was as innovative, perhaps more so, than the hardware and software solution. It lowered the entry risk and up front financial cost of buying a mainframe and they owned the market.
The Freemium model has similar advantages. So if you wrap a product that line executives should prefer with an economic model that removes most of the financial barriers, you should end up with a solution that does for IBM what Search did for Google. And that could do some interesting things to the analytics market, creating a similar set of conditions to those that put IBM on top of technology in the last century.
Photo courtesy of Shutterstock.
