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In August the NYTimes published a piece that reflected on the challenges data science professionals must tackle before they’re even able to look for insights in “big data.” The author does a great job illustrating the reality behind the big data hype and what the data analysis landscape really looks like today. The truth is there is a lot of clean up and data prep work involved before you can even get to the data science of “big data.” Getting the right tools in place isn’t going to fix your data wrangling issues because it comes down to the data being prepared properly and the analysis being done well. Having the right talent helps a lot, but again, their time will be spent doing data prep work more than it will be spent finding actual value in the data.  Furthermore, people with these data science skills are expensive to have on board. It sounds simple that if you buy the right tools, and luckily hire the right talent you’ll start making major discoveries that give you a competitive advantage and lead to new insights. There is more to it than that. It’s not impossible, it’s just not as simple as that.

Soufflés are hard to make

Think of it this way, even with a state-of-the-art kitchen, an untrained person would be helpless if tasked with making a traditional soufflé from scratch. Even if they were equipped with a recipe, all the ingredients, the pots, ramekins and top-of-the-line cooking instruments, an untrained cook would have a hard time getting perfect results.  That’s because it takes much more than all the components being in place to create a delicate dish like a soufflé.  It’s not to say that the tools don’t matter, but more that the combination of tools, knowledge, and experience make all the difference between a flat and tasteless souffle, or a light, airy, and perfectly fluffy one that doesn’t collapse.
  
Now, say you employ an experienced chef to help you at an hourly rate. The actual time necessary to make the soufflé is not much, it’s the prep and cleanup that is time consuming, but do you want to pay the chef to do that at his or her hourly rate, or is it worth considering doing the maintenance with a less expensive person and saving the chef for the actual cooking?
 
What does this have to do with data analysis software?

With traditional on-premise text analytics software or SaaS (Software as a Service), you’re given access to powerful analytics tools and the training and instructions on how to use them for various tasks. What happens when you run into integration setbacks, or time consuming learning curves? Much like having all the right tools and all the right ingredients doesn’t mean you’ll be able to make a soufflé, not having access to additional follow-up services has the potential of causing implementation setbacks and price increases.
 
Solution as a Service (SolaaS)

Consider Solution as a Service (SolaaS) to be that exact combination of experienced and trained talent and state-of-the-art text analytics software allocated for specific tasks to optimize the return on your financial investment. Imagine if the chef you hired worked at your beck and call and used that opportunity to teach you to make the soufflé yourself so you were getting both the finished product you're paying for, plus gaining the knowledge to do it on your own at the same time. Then, when you were ready, they helped you move on to other dishes. On top of that, they had their own crew that was an affordable solution for prep and cleaning. This is what Solution as a Service (SolaaS) is. You get a combination of tools, equipment, and talent delivered to your door, making you fully capable of achieving your goals.
 
Data Science at the Service level

While data science and data analytics can be complicated, all the elements required to benefit from it shouldn’t be a deterrent to making them work for you. The benefits of  text analytics  have been marketed as the next magic elixir to give you a leg up on your competition. There are hidden insights in your data, and getting to them requires effort. Solution as a Service (SolaaS) is one way to get to your data analysis goals in a cost effective way that serves as an excellent, risk-free starting point to a long term strategy.